Cost-benefit analysis of the roll-out of smart electricity metering grid in Lithuania. Cost-benefit analysis of the smart metering roll-out scenarios

Transcription

1 Cost-benefit analysis of the roll-out of smart electricity metering grid in Lithuania Cost-benefit analysis of the smart metering roll-out scenarios September 2012

2 Table of Contents 1 Abbreviations and terms Summary Introduction Scenarios of the smart metering roll-out SCENARIO SETTING PARAMETERS ALTERNATIVE SCENARIOS BASE CASE SCENARIO ADVANCED FUNCTIONALITY SCENARIO MULTI-METERING SCENARIO 15 5 Cost-benefit analysis GENERAL INFORMATION COST-BENEFIT ANALYSIS GUIDELINES 17 6 Financial analysis PROJECT INVESTMENT REPORTING PERIOD AND ITS JUSTIFICATION GENERAL PREMISES OF THE COST-BENEFIT ANALYSIS EVALUATION OF INVESTMENT COSTS SURVEY OF THE SMART METERING COMPONENT MANUFACTURERS SMART METERS DATA CONCENTRATORS BALANCING METERS DATA COLLECTION SYSTEM MDM SYSTEM MULTI-METERING CONTROLLER IN-HOUSE DISPLAY ROLL-OUT OF THE SMART METERING SYSTEM STAFF TRAINING PROJECT MANAGEMENT COSTS PROJECT PUBLICITY SUMMARY OF INVESTMENT COSTS FORECAST OPERATING COSTS DATA TRANSMISSION COSTS IS MAINTENANCE COSTS TROUBLESHOOTING COSTS OF SMART METERS AND OTHER EQUIPMENT SMART METER AND OTHER EQUIPMENT ELECTRICITY EXPENSES SUMMARY OF OPERATING COSTS SMART METERING SYSTEM CONSUMER BENEFITS STANDARD METER REPLACEMENT PROGRAM SAVINGS 52 2

3 6.5.2 STANDARD METER ROLL-OUT COST SAVINGS SAVINGS ON TAKING THE METER READINGS IMPROVED CASH FLOW MANAGEMENT CALL CENTRE COST SAVINGS ABSENCE OF STANDARD METER ELECTRICITY COSTS SUMMARY OF BENEFITS FOR THE SMART METERING SYSTEM IMPLEMENTER CALCULATION OF FINANCIAL RATIOS FOR INVESTMENTS CALCULATION OF FINANCIAL RATIOS FOR THE CAPITAL 65 7 Economic analysis ECONOMIC BENEFITS FOR SOCIETY AND THE STATE REDUCTION OF ELECTRICITY CONSUMPTION TRANSFER OF CUSTOMER LOAD FROM PEAK HOURS TO OFF-PEAK HOURS BENEFIT FROM REDUCED CO2 EMISSIONS REDUCTION OF COMMERCIAL LOSSES SAVINGS OF POWER DISCONNECTION/ RESTRICTION / RECONNECTION SUMMARY OF ECONOMIC BENEFITS CALCULATION OF ECONOMIC RATIOS FOR INVESTMENTS FACTORS THAT MAY AFFECT THE RESULTS OF COST BENEFIT ANALYSIS SENSITIVITY ANALYSIS DETERMINATION OF VARIABLES BASIC CASE SCENARIO SENSITIVITY ANALYSIS ADVANCED FUNCTIONALITY SCENARIO SENSITIVITY ANALYSIS MULTI-METERING SCENARIO SENSITIVITY ANALYSIS SENSITIVITY ANALYSIS CONCLUSIONS BREAK-EVEN POINT RISK EVALUATION 86 8 Summary Description of the Basic case scenario implementation BASIC CASE SCENARIO DEFINITION IMPLEMENTATION PLAN POSSIBLE SOURCES OF FINANCING 100 3

4 List of Tables Table 1. Terms and abbreviations used in the report... 7 Table 2. Results of financial analysis... 9 Table 3. Results of economic analysis Table 4. Parameters of the scenarios of the smart metering roll-out Table 5. General premises of the cost-benefit analysis Table 6. List of manufacturers survey participants Table 7. Average prices of the main smart metering system components Table 8. Number of meters without remote data transmission option and distribution by the user's meter type in Table 9. Forecasted number of meters in Table 10. Scope of smart meter roll-out per year Table 11. Investments in smart meters under the Base case scenario Table 12. Investments in smart meters under the Advanced Functionality scenario Table 13. Investments in smart meters under the Multi-metering scenario Table 14. Investments in data concentrators under the Base case scenario Table 15. Investments in data concentrators under the Advanced Functionality scenario Table 16. Investments in data concentrators under the Multi-metering scenario Table 17. Investments in balancing meters under the Base case, Advanced Functionality and Multi-metering scenarios Table 18. Data collection system costs under different installation scenarios Table 19. MDM system costs under different installation scenarios Table 20. Investments in multi-metering controllers under the Multi-metering scenario Table 21. Investments in the in-house display under the Advanced Functionality scenario Table 22. Investments in the in-house display under the Multi-metering scenario Table 23. Costs of the smart metering roll-out under the Base case scenario Table 24. Costs of smart meter roll-out under the Advanced Functionality scenario Table 25. Costs of smart meter roll-out under the Multi-metering scenario Table 26. Project management costs under the Base case scenario Table 27. Project management costs under the Advanced Functionality scenario Table 28. Project management costs under the Multi-metering scenario Table 29. Project publicity costs Table 30. Investment costs under the Base case scenario Table 31. Investment costs under the Advanced Functionality scenario Table 32. Investment costs under the Multi-metering scenario Table 33. Base case scenario operating costs Table 34. Operating costs under the Advanced Functionality scenario Table 35. Operating costs under the Multi-metering scenario Table 36. DSO meter replacement program up to

5 Table 37. Savings resulting from replacing standard meters not required in the Basic case, Advanced Functionality and Multi-metering scenarios Table 38. Savings resulting from the elimination of replacement (work) of standard meters in the Basic case, Advanced Functionality and Multi-metering scenarios Table 39. Benefits for the smart metering system implementer in the Basic case scenario Table 40. Benefits for the smart metering system implementer in the Advanced Functionality scenario Table 41. Benefits for the smart metering system implementer in the Multi-metering scenario Table 42. Components of financial indicator estimates Table 43. Results of financial analysis for investment based on each consumer group Table 44. Results of the financial analysis for capital Table 45. Economic benefits of Basic case scenario Table 46. Economic benefits of Advanced Functionality scenario Table 47. Economic benefits of Multi-metering scenario Table 48. Results of economic analysis by consumer groups Table 49. Basic case scenario sensitivity analysis results Table 50. Advanced Functionality scenario sensitivity analysis results Table 51. Multi-metering scenario sensitivity analysis results Table 52. Legal and political risks Table 53. Economic and political risks Table 54. Technical risks Table 55. Social risks Table 56. Financial analysis results Table 57. Economic analysis results

6 List of Figures Figure 1. General cost-benefit analysis guidelines Figure 2. Forecasts of electricity consumption growth and inflation in Figure 3. Forecasts of electricity consumption with and without smart metering system Figure 4. Meter distribution by communication type Figure 5. The place of the data collection system in the smart metering infrastructure Figure 6. Typical architecture of the data collection system Figure 7. Chart of MDM system with current IS integration and new applications Figure 8. Example of in-house display manufactured by GEO Figure 9. Summary of investment costs Figure 10. Data transmission costs per year, LTL Figure 11. Annual IS maintenance costs, LTL Figure 12. Annual smart meters troubleshooting costs, LTL Figure 13. Annual smart meter electricity costs, LTL Figure 14. Operating costs under every scenario, LTL Figure 15. Savings on taking the meter readings per year Figure 16. Interest cost decrement benefit resulting from improved cash flow management Figure 17. Average annual call centre cost savings ( ) Figure 18. Average annual savings of electricity consumption in standard meters Figure 19. Smart metering system implementer s benefits Figure 20. Calculation of financial ratios for investments Figure 21. Basic case scenario investments, operating costs and created benefits Figure 22. Advanced Functionality scenario investments, operating costs and created benefits Figure 23. Multi-metering scenario investments, operating costs and created benefits Figure 24. Average annual electricity consumption savings ( ) Figure 25. Schedule of electricity consumption in Lithuania, June Figure 26. Average annual decrement of electricity costs due to reduced consumption in peak hours Figure 27. Average annual benefit of CO2 emission decrement Figure 28. Savings resulting from decreased commercial losses Figure 29. Average annual savings of power supply disconnection/ restriction / reconnection Figure 30. Summary of economic benefits ( ), LTL Figure 31. Economic result in the Basic case scenario Figure 32. Economic result in the Advanced Functionality scenario Figure 33. Economic result in the Multi-metering scenario Figure 34. Assumptions for smart metering system development in Lithuania Figure 35. Basic case scenario layout Figure 36. Project implementation plan

7 1 Abbreviations and terms Key terms and abbreviations used in this document are presented in the table below. Table 1. Terms and abbreviations used in the report Term, abbreviation Definition Contract Contract No Object of the contract Project Client of the services, the contracting authority, TIC Service provider, EY The service provider undertakes to provide the preparation service of the costbenefit analysis of the roll-out of the smart electricity metering grid in Lithuania to the service recipient "Cost-benefit analysis of the roll-out of the smart electricity metering grid in Lithuania" JSC Technologijų ir Inovacijų Centras JSC Ernst &Young Baltic EU EC VKEKK MDM system Multi-metering Multi-metering controller HAN WAN Data concentrator PLC technology GPRS technology Wi-Fi technology TOU / Time-of-use pricing DSO NPV IRR European Union European Commission National Control Commission for Prices and Energy A system for controlling and metering the meters' measurement data System combining the electricity, gas, water and heating metering systems by using smart grids Multi-metering controller operates as a central controller, combining different types of energy metering systems Smart grid communication tool to transfer information from/to the smart meter to/from any operating appliance in the house of the consumer Global network Data concentrator is a device which acts as an intermediary between the smart meter and final data processing and collection system Communication technology, which enables data transfer through the energy distribution network cables Mobile communication technology for data transfer within the GSM network Wireless Internet technology enabling to transfer data to the data concentrator. Wi- Fi technology sends data using radio waves and computer network Wi-Fi devices are often defined as WLAN technology products based on the standards of the Institute of Electrical and Electronic Engineers. Pricing of the differential tariff component of electricity distribution depending on the electricity consumption time. Distribution system operator. Net present value Internal rate of return 7

8 2 Summary The interim project reports established three scenarios of the smart metering roll-out. The establishment of scenarios was based on: Technical task of the project; Results of the alternatives analysis of smart metering system parameters; Results of stakeholder expectations analysis and evaluation of parameters; Recommendations of the EU cost-benefit analysis on the smart metering roll-out. The scenarios of the smart metering roll-out are defined by these main parameters: Market model what data transfer market model will be applied in installing smart metering systems. This parameter is important in establishing responsibilities and ownership rights of market participants. The data transfer market model is also important in determining the possibility of combining the metering systems of other utility service providers (gas, water and heating); Functionality of smart meters availability of functions in the meters used in the smart metering system. This parameter is important, because it directly influences the cost and generated benefits of the project; Communication technologies modes of communication and communication devices to be used in the smart metering system. This parameter has direct influence on the project capital and operating costs. Communication technologies also determine the level of utilization of the current infrastructure; Applicable pricing models new distribution tariff pricing models to be used in the roll-out of the smart metering system. Application of pricing models directly influences the generated benefits of the smart metering roll-out. Foreign pilot projects indicate that the application of pricing models affects the final result of the cost-benefit analysis. Scope of the smart metering roll-out the number of current meters to be replaced with smart meters. This parameter has a direct impact on the project implementation capital expenditure and generated benefits. Speeds of the smart metering roll-out how long will the process of smart meter roll-out take. The speed of installation affects the cash flow of the project throughout its implementation period. Tree scenarios of the smart metering roll-out were prepared based on these parameters (see section 4 for more information): Base case scenario; Advanced Functionality scenario; Multi-metering scenario. This report includes a detailed cost-benefit analysis of every scenario, based on the European Commission's (hereafter the EC) recommended cost-benefit analysis guidelines. The cost-benefit analysis is comprised of two parts: financial analysis; economic analysis. The financial analysis includes the costs and benefits to the project operator, who, in Lithuania, as provided by the Law on Energy, would be the distribution system operator (hereafter the DSO). Meanwhile, the economic analysis includes total benefits to both the project operator (financial analysis) and other indirect beneficiaries such as consumers, the State (economic analysis). EC guidelines for cost-benefit analysis recommend to rate projects by the strength of indicators of net present value (NPV) and the internal rate of return (IRR). 8

9 Financial analysis The financial analysis was based on: Equipment manufacturers poll 1 ; the data from the DSO; Forecasts of international analyst firms; Results of foreign pilot projects; Information in the public domain. Cost-benefit analysis does not include generated benefits and costs incurred by the Smart Grid. The analysis also excludes the installation of additional power-saving systems or electricity sub-metering which could increase opportunities for electricity savings for commercial users. The financial analysis of the project scenarios demonstrated that overall investment costs amount to (see section 6.3 for more): Under the Base case scenario 869 million LTL in nominal value or 659 million LTL in discounted value; Under the Advanced Functionality scenario million LTL in nominal value or 994 million LTL in discounted value; Under the Multi-metering scenario million LTL in nominal value or 836 million LTL in discounted value. Investment costs per one meter (see more in section 6.3): Under the Base case scenario LTL in nominal value or LTL in discounted value; Under the Advanced Functionality scenario LTL in nominal value or LTL in discounted value; Under the Multi-metering scenario LTL in nominal value or in discounted value. The financial analysis was conducted for the period from 2014 to Project investments would be made from 2015 to In addition, the calculations include one-year period of preparation for the project in It should be noted that during the period from 2012 to 2029, the DSO plans to install new meters every year. Connection of new meters was taken into the consideration when conducting the cost-benefit analysis, so the total number of electricity accounts in Lithuania would be around 1.95 million meters in Results of the financial analysis for investments demonstrated that none of the scenarios result in a positive return for the project operator, i.e. the DSO. The least negative result is under the Base case scenario and the main reasons for that are: Smart metering roll-out takes place to the lesser extent (80%) smaller numbers of meters are required. Smart meters with minimal functional requirements are installed (without the HAN communications and in-house display), which means that the price of meters is lower. Due to the smaller scale, the costs of smart meter roll-out are lower. Table 2. Results of financial analysis Net present value (in ), LTL Base case scenario Advanced Functionality scenario Multi-metering scenario Note: The project does not generate positive cash flow in any of the years, therefore the internal rate of return is not calculated 1 Due to confidentiality obligations prices of specific manufacturers are not provided 9

10 Economic analysis The economic analysis of the scenarios has also demonstrated that none of the scenarios is economically viable, so it should be concluded that the smart metering roll-out in Lithuania is not beneficial under any scenario. The main reasons that led to the negative results of the cost-benefit analysis are: The average bill for electricity per household in Lithuania is one of the lowest in the EU; Transmission and distribution networks have significant spare capacity, so more efficient consumption will not affect them; Electricity producers in Lithuania have a lot of spare capacity, so more efficient consumption will not have an influence on them; Profile of electricity consumption shows that the peaks in Lithuania are minimal. Table 3. Results of economic analysis Net present value (in ), LTL Base case scenario Advanced Functionality scenario Multi-metering scenario Internal rate of return (years ), % Base case scenario Advanced Functionality scenario Multi-metering scenario % % % Upon the receipt of results of all economic scenarios of the project, a sensitivity analysis was conducted to demonstrate which variables have the most influence on the project results. These variables are: Electricity price; Changes in electricity consumption habits; Price of smart metering equipment; Forecasts of electricity demand. Although the cost-benefit analysis of the smart metering roll-out demonstrated that under current assumptions none of the scenarios is economically profitable, yet, the results can change dramatically in case of economic, political or social changes. The cost-benefit analysis can be significantly affected by the following changes: Rapid development of smart metering equipment technologies, increase in its demand and the influence of these factors' over the prices; Results of pilot projects; Increase in electricity consumption (e.g. rapid growth in electromobile demand); Integration to the Europe's electricity markets after the introduction of LitPol Link and NordBalt link; Changes in electricity consumption habits (rising consumption in peak times); Principles of determining the distribution and transmission tariffs; Rapid development of micro-generation; Individualization of heating metering. 10

11 3 Introduction Technology and Innovation Centre (hereafter the TIC) coordinating the project for smart electricity grid development in Lithuania announced the procurement for drafting the cost-benefit analysis (hereafter the Analysis) of the roll-out of smart metering system in Lithuania, and the contract was awarded to JSC Ernst &Young Baltic The following main tasks were carried out in the previous interim reports of the project: Review of regulatory documents of the smart metering system for electricity; Analysis of best practices of five foreign countries in the smart metering roll-out or implementation of pilot projects; Evaluation of the current situation; Analysis of stakeholder expectations and a user survey; Establishment of three scenarios of the smart metering roll-out. The main targets of this report are: To prepare a comparison of the scenarios of the smart metering roll-out; To carry out the financial and economic analysis; To assess the risks of scenario implementation; To carry out sensitivity analysis; To prepare the recommendations for the roll-out; To prepare a schedule for the roll-out; To present the guidelines for the investment plan; 11

12 4 Scenarios of the smart metering roll-out 4.1 Scenario setting parameters Smart electricity metering combines several general parameters which determine different roll-out scenarios. Three comparative alternative scenarios have to be established and assessed in the context of this project for which a detailed cost-benefit analysis will be carried out. The scenarios of the smart metering roll-out were established based on six general parameters. Those were identified based on foreign practice and the assessment of smart metering projects implemented by the service provider in other countries. Market model what data transfer market model will be applied in installing smart metering systems. This parameter is important in establishing responsibilities and ownership rights of market participants. The data transfer market model is also important in determining the possibility of combining the metering systems of other utility service providers (gas, water and heating); Functionality of smart meters - availability of functions in the meters used in smart metering system. This parameter is important, because it directly influences the cost and generated benefits of the project; Communication technologies modes of communication and communication devices to be used in smart metering system. This parameter has direct influence on the project capital and operating costs. Communication technologies also determine the level of utilization of the current infrastructure; Applicable pricing models new distribution tariff pricing models to be used in the roll-out of the smart metering system. Application of pricing models directly influences the generated benefits of the smart metering roll-out. Foreign pilot projects indicate that the application of pricing models affect the final result of the cost-benefit analysis. Scope of the smart metering roll-out the number of current meters to be replaced with smart meters. This parameter has a direct impact on the project implementation capital expenditure and generated benefits. Speed of the smart metering roll-out how long will the process of the smart metering roll-out take. The speed of the roll-out affects the cash flow of the project throughout its implementation period. 4.2 Alternative scenarios Analysis of each of the above alternatives of parameters helped to determine the most preferred and viable alternatives. Given the results of the most likely alternatives analysis, the stakeholders expectations analysis was carried out, which demonstrated how market participants envision the scenarios of the smart metering roll-out. The stakeholder analysis covered: Electricity market participants; Regulatory institutions; Electricity consumers; Solution providers (manufacturers, network providers, IT system manufacturers); Other utility service providers; Additional-potential service providers. In total, more than 20 meetings with various stakeholders were organised. However, it should be noted that the opinion of other utility service providers about the smart metering roll-out is not based on the cost-benefit analysis of that sector, but only on the initial opinion about the possibilities of such alternative. Therefore, in order to know the exact interest of other utility service providers to participate in the project, it is necessary to carry out the financial analysis of such project's 12

13 payback in those sectors. Detailed stakeholder expectations and analysis is presented in the "Stakeholders expectations analysis" interim report. Further, based on: Technical task of the project; Results of the alternatives analysis of smart metering parameters; Results of stakeholder expectations analysis and evaluation of parameters; Recommendations of the EU cost-benefit analysis on the smart metering roll-out; three roll-out scenarios were established for which a detailed cost-benefit analysis will be carried out. The scenarios and their parameters are illustrated in the table below. Table 4. Parameters of the scenarios of the smart metering roll-out Main parameters of the Advanced Functionality Base case scenario scenarios scenario Multi-metering scenario Market model Distribution system Distribution system Data management operator s model operator s model company model Functionality of meters Basic functionality Basic functionality with Basic functionality with HAN support, in-house HAN support and inhouse display display and a Multimetering option Communication technologies "Last mile" PLC and GPRS From data concentrator: GPRS "Last mile" PLC and GPRS From data concentrator: GPRS "Last mile" PLC and GPRS From data concentrator: GPRS Roll-out time by the year 2020 by the year 2020 by the year 2020 Scope of roll-out 80 % of consumers 100 % of consumers 80 % of consumers Models of pricing Obligatory time of use Obligatory time of use Obligatory time of use pricing pricing pricing Base case scenario On 9 March 2012 the EC issued a recommendation C(2012) 1342 "On preparations for the roll-out of smart metering systems". The recommendation stipulates that one of the scenarios must comply with the requirement of the EC Directive 2009/72/EC to install 80% of smart metering systems to users in the districts with positive results of cost-benefit analysis by the year Meters must also fulfil the basic requirements of functionality. The basic scenario of the smart metering roll-out was developed with these recommendations taken into account. More detailed premises for establishing the scenario are presented in the "Scenarios of the roll-out of smart metering system" interim report. Market model distribution network operator s model. The interim report "Scenarios of the rollout of smart metering system" determinant that in order to implement this alternative, the easiest and fastest data market model to implement would be the DSO model, where all responsibility for installing the smart meters, data collection, transmission, processing and transmission to other stakeholders would rest with the DSO. Meter functionality basic. The parameters for meters functionality under the Base case scenario were chosen with compliance to the EC 2 and CENELEC 3 recommendations (see section 3.2 in "Scenarios of the roll-out of smart metering system" interim report). Communication technologies PLC and GPRS. The choice of communication technologies was determined by the meter manufacturers opinion that fibre optics is not a suitable solution for 2 Commission Recommendation of on preparations for the roll-out of smart metering systems, Standardization mandate to CEN, CENELEC and ETSI in the field of measuring instruments for the development of an open architecture for utility meters involving communication protocols enabling interoperability M/441,

14 smart metering communications. Moreover, most of the manufacturers of smart meters do not even offer such a means of communications. Potentially a more suitable alternative would be to use PLC technology in the "last mile" communication and otherwise to use GPRS communication. Meanwhile, GPRS technology can be used from data concentrators to data collection centres. It should also be noted that the meters' communication measures might ensure competitive conditions in the delivery of communication and equipment provides opportunities to use new communication transmission means in the future. Besides, a cheaper technology (if any) could be used for data transmission and without worsening the results of cost-benefits analysis. Roll-out volumes 80% of users. The scope of the roll-out volumes was determined by the EC smart grid cost-benefit recommendations. Speed of roll-out by the year The time of the roll-out of smart metering system was determined according to both the EC recommendations and the stakeholder expectations. The main argument for longer installation period is the risk not to be able to adequately prepare for the roll-out by the year Pricing obligatory time of use pricing, when peak-use times subject to the highest tariffs are distinguished. Inclusion of this pricing model into the scenario was determined by the stakeholder assessment and the results of foreign pilot projects Advanced Functionality scenario The Advanced Functionality scenario was established according to the results of the comparative analysis, analysis of stakeholder expectations and evaluation of smart metering parameters. Below are the main parameters of the scenario. Market model distribution network operator s model. It was determined in the interim report "Scenarios of the roll-out of smart metering system" that the most effective alternative when installing smart metering systems under this scenario is the application of the DSO model. Meter functionality basic, with HAN option and the in-house display. Parameters of meter functionality were chosen after analysing the results of pilot smart metering roll-out tests from various EU countries, which demonstrated that most electricity is saved after the installation of inhouse displays for users. Moreover, analysis of stakeholder expectations also showed the priority for additional meter functionality. Communication technologies PLC and GPRS. The choice of communication technologies was determined by the meter manufacturers opinion that fibre optics is not a suitable solution for smart metering communications because of additional installation of fibre to the meter. In addition to this, most of the manufacturers of smart meters do not even offer such means of communications. Potentially a more suitable alternative would be to use the PLC technology in the "last mile" communication, otherwise to use GPRS or radio communication. Meanwhile, to use GPRS technology from data concentrators to data collection centres. It should also be noted that the meters' communication measures ensure competitive conditions in the supply of communications, and equipment provides opportunities to use new communication transmission means in the future. Besides, a cheaper technology (if any) could be used for data transmission without worsening the results of the cost-benefits analysis. Roll-out volumes 100 % of users. This scenario assesses the possibility to install smart metering system for the maximum number of users. Moreover, 100 % roll-out of electricity metering is anticipated in the technical task of the project. Speed of roll-out by the year The time of the smart metering roll-out was determined according to both the EC recommendations and the stakeholder expectations. The main argument for longer installation period is the risk not to be able to adequately prepare for the roll-out by the year Pricing obligatory time of use pricing, when peak-use times subject to the highest tariffs are distinguished. Inclusion of this pricing model into the scenario was determined by the stakeholder assessment and results of foreign pilot projects. 14

15 The main difference of this scenario from the basic case scenario is a higher meter functionality and larger scope of meter roll-out. In case of the implementation of the Advanced Functionality scenario, the meter functionality is extended by the parameter enabling the HAN functions and adding the in-house display showing real time information about electricity consumption and other relevant information. From the point of view of stakeholders, such meter functionality ensures more efficient exploitation of smart metering systems. Moreover, foreign experience demonstrates that such display is the most effective information transmission device most affecting electricity consumption changes. This scenario oversees 100 % scope of smart meter roll-out. All remaining parameters do not differ from the Base case scenario, because they have been identified as potentially the most suitable for smart meter roll-out in Lithuania Multi-metering scenario According to the technical task of the project, one of the scenarios of the smart metering roll-out must provide options to combine electricity, gas, water and heating metering systems. Given this requirement, this scenario will have to provide the option of combined Multi-metering of gas, heating and water 4 Market model data management company s model. The interim report "Scenarios of the roll-out of smart metering system" determined that most efficient roll-out of smart metering systems under this scenario would by applying the data management company s model, by establishing a company responsible for collecting, processing and transmission of data recorded by the meters. This data control model would allow for the market participants to dramatically reduce the share of the general data control costs. Meter functionality Basic functionality with HAN support, the in-house display and the Multimetering option. Parameters of meter functionality were chosen to ensure the efficiency of energy consumption and enable the combination of different utility metering systems. Communication technologies PLC and GPRS. The selection of communication technologies was determined by the meter manufacturers opinion that fibre-optic is not a suitable solution for smart metering communications because of additional installation of fibre up to the meter. In addition to this, most of the manufacturers of smart meters do not even offer such means of communications. Potentially a more suitable alternative would be to use the PLC technology in the "last mile" communication otherwise to use GPRS or radio communication. Meanwhile, to use GPRS technology from data concentrators to data collection centres. It should also be noted that the meters' communication measures ensure competitive conditions in the supply of communications and equipment provides opportunities to use new communication transmission means in the future. Besides, a cheaper technology (if any) could be used for data transmission and without worsening the results of the cost-benefits analysis. Roll-out volumes 80% of users. The scope of the roll-out was determined according to the EC smart grid cost-benefit recommendations. Speed of the roll-out by the year The time of the roll-out of smart metering system was determined according to both the EC recommendations and the stakeholder expectations. The main argument for longer installation period is the risk not to be able to adequately prepare for the roll-out by the year Pricing obligatory time of use pricing, when peak-use times subject to the highest tariffs are distinguished. Inclusion of this pricing model into the scenario was determined by the stakeholder assessment and results of foreign pilot projects. The main differences of this scenario from the Base case and the Advanced Functionality scenarios are the option of Multi-metering, which should be a characteristic of the installed smart metering system and the data market model applied to this scenario. The distribution network model was chosen in the alternative scenarios described above, however the roll-out of the Multi-metering system it would be more effective 4 Analysis does not include the evaluation of other utility providers costs and benefits 15

16 to apply the alternative of establishing a data management company market model which could be responsible for collecting, processing and transmitting the metering data of different utility providers and allow to reduce the operating costs and increase the operating efficiency due to more effective data collection process. Moreover, it is likely that utility companies would not agree for the distribution network operator to store data about gas, hot water and heating consumption, however, would not object if it is done by an independent accounting company. In case of Multi-metering scenario implementation, the Basic meter functionality would be used with HAN function, the in-house display and the option to combine separate heating, hot and cold water and gas metering systems. As in case of previous scenarios, PLC and GPRS communication means would be used in addition to the obligatory distribution tariff time of use pricing. Smart metering devices should be installed to 80 % of users during the roll-out period of smart metering system by the year

17 5 Cost-benefit analysis 5.1 General information This report contains the cost-benefit analysis of three scenarios of the roll-out of smart metering system. The analysis is described in the following sections. The cost-benefit analysis is carried out based on: EC Guide to Cost Benefit Analysis of Investment Projects 5 ; EC recommendations for preparations for the roll-out of smart metering systems 6 Costs and benefits were assessed for the following project implementation scenarios: "Do nothing" scenario the project is not implemented. Base case scenario ("do minimum") the project implemented according to the parameters described in section Advanced Functionality scenario ("do something") the project implemented according to the parameters described in section Multi-metering scenario ("do something differently") the project implemented according to the parameters described in section Because the "do nothing" scenario means the absence of smart metering and lack of any changes, its result is equal to zero. Meanwhile, the costs and benefits of other three scenarios are calculated as an incremental change from the current situation, i.e. the scenarios are compared to the "do nothing" scenario. Based on cost-benefit analysis guidelines, the cost-benefit analysis is comprised of the two main parts: Financial analysis, which includes costs and benefits to the project operator, i.e. the DSO; Economic analysis, where the social and financial parts of the project are assessed together generated benefits to the project operator and to both the state and society. All alternatives are analysed and compared to each other in order to determine the most effective alternative from the economic perspective; 5.2 Cost-benefit analysis guidelines General cost-benefit analysis guidelines are illustrated in the figure below. 5 Guide to Cost Benefit Analysis of Investment Projects, European Commission, Commission Recommendation of on preparations for the roll-out of smart metering systems, European Commission,

18 Figure 1. General cost-benefit analysis guidelines Base case scenario Alternatives selection Advanced functionality scenario Multi-metering scenario Required investment Financial analysis Operational expences Benefits Investment returns Social benefits determination Economic analysis Social benefits quantification Economical project evaluation (sum of financial results and social benefits) The cost-benefit analysis under each and every scenario is carried out to all users collectively, singling out the costs and benefits to separate user groups. User groups were identified in discussions with the client and other stakeholders. These groups of users are: Household urban users; Household rural users; Commercial users with under 30 kw of permissible power limit (hereafter the commercial users under 30 kw); Commercial users with over 30 kw of permissible power limit (hereafter the commercial users over 30 kw); All household consumers; All commercial consumers. It should be noted that splitting the results of analysis according to groups of users demonstrates the relative distribution of costs and benefits. However, the results of separate user group analysis do not show the financial or economic result if smart metering system was to be installed to only a specific group of users. Installation for a specific user group would be a separate scenario, because the investment costs of such scenario can differ dramatically from the roll-out of smart metering systems for 80 % or 100 % of users (e.g. cost of the meter, data collection centre or the MDM system). 18

19 6 Financial analysis 6.1 Project investment reporting period and its justification It is estimated that the first year of the proposed project reporting period is 2015, the last year is Only the preparation work will be carried out during the first year, meanwhile the installation of information systems (hereafter the IS) commences in Installation of meters and other equipment starts in Investment period justification: Period of financial analysis recommended for energy projects in the guidelines of cost-benefit analysis is no longer than 25 years. Period of financial recommended for other 7 projects in the guidelines of cost-benefit analysis is no longer than 15 years. Medium service time of smart metering infrastructure is no longer than 15 years 8. Since the lifetime of the smart meters is significantly shorter than that of the usual infrastructure of the energy sector (power plants, essential network elements), the average smart metering infrastructure lifetime, plus preparation and IS installation periods constitute the reporting period of the project. 6.2 General premises of the cost-benefit analysis General premises of the cost-benefit analysis and their justification are presented in the table below. Table 5. General premises of the cost-benefit analysis Premise Size Justification Source: Discount rate of the financial analysis Discount rate of the economic analysis 5 % 5.5 % Currency rate LTL/EUR The average annual growth in electricity consumption under the "do nothing" scenario (when smart metering system is not installed) Average annual inflation during the reporting period The projected decline in losses (not implementing the project of the roll-out of smart metering system) 2.1 % 2.29 % %; %; %; %. Based on the EC costbenefit analysis guidelines Based on the EC costbenefit analysis guidelines According to the currency exchange-rate set by the Bank of Lithuania. Based on the forecasts of the transmission system operator Based on the forecasts of the Global Insight international analytical company icy/sources/docgener/guides/co st/guide2008_en.pdf, p icy/sources/docgener/guides/co st/guide2008_en.pdf, p Information is not publicly available - LESTO forecasts New meters addition units - LESTO forecasts - 7 The average lifetime of smart metering infrastructure for energy, water, railroad, road, port, telecommunications, industry 8 Based on the results of smart metering manufacturers survey. 19

20 Premise Size Justification Source: throughout the period Final electricity price for users excluding VAT 0.35 LTL/kwh 31 % of transmitted electricity is acquired from medium-voltage grid, therefore the final price for users is 2 ct/kwh lower than the average price for customers getting electricity from the lowvoltage grid. LESTO information Electricity purchase price LTL/kWh - LESTO information Commercial customers expenses for 1 kwh of electricity (excluding VAT) LTL/kWh - LESTO information DSO-purchased electricity quantity, kwh DSO-transmitted electricity quantity, kwh Based on the DSO financial report Based on the DSO financial report icy/sources/docgener/guides/co st/guide2008_en.pdf, p. 5 icy/sources/docgener/guides/co st/guide2008_en.pdf, p. 5 Annual growth in electricity consumption demand and the inflation under the "do nothing" scenario are displayed in the figure below: It should be noted that electricity prices are indexed by the inflation rate. Meanwhile, the purchase and consumption volumes of electricity are indexed by the forecasted growth in the consumption demand of electricity. Figure 2. Forecasts of electricity consumption growth and inflation in ,50% 2,00% 2,20% 2,31% 2,29% 2,34% 2,37% 2,36% 2,35% 2,34% 2,32% 2,31% 2,30% 2,30% 2,30% 2,30% 2,30% 2,30% 2,30% 1,97% 2,10% 2,10% 2,10% 2,10% 2,10% 2,10% 2,10% 2,10% 2,10% 2,10% 2,10% 2,10% 2,10% 2,10% 2,10% 2,10% 2,10% 2,10% 1,50% 1,00% 0,50% 0,00% Demand growth Infliation Schedule of the forecasted growth in electricity consumption under the "do nothing" scenario (considering only the expected growth of demand) and after the smart metering roll-out (including the projected growth in consumption and electricity consumption efficiency) is displayed in the figure below for comparison. 20

21 Figure 3. Forecasts of electricity consumption with and without smart metering system Electricity consumption considering demand growth, kwh Electricity consumptio after implementation of smart meters, kwh Based on the DSO data, electricity consumption between different groups of users in 2011 accounted to: Household urban consumers 19.8 %; Household rural consumers 10.6 %; Commercial consumers under 30 kw 8.7 %; Commercial consumers over 30 kw 60.9 %. Further calculations are made assuming that such distribution of electricity users will not change in the future. 6.3 Evaluation of investment costs The main costs of investment in the project of the smart metering roll-out contain these components: Smart meters (see section 6.3.2); Data concentrators (see section 6.3.3); Balancing meters (see section 6.3.4); Data collection system (see section 6.3.5); Metering data control and accounting system (hereafter the MDM system) (see section 6.3.6); Multi-metering controller (only for the Multi-metering scenario) see section 6.3.7); In-house display (see section 6.3.8) Smart metering roll-out 9 (see section 6.3.9); Staff training 2 (see section ); Project management 2 (see section ); Project publicity 2 (see section ); Survey of the smart metering component manufacturers The costs of smart metering components were determined according to the international meter manufacturers survey. The survey included the highest global and Baltic manufacturers, producing smart meters, data concentrators and data collection or MDM systems. The table below contains the list of manufacturers who were requested to submit proposals, and the manufacturers response to the survey. 9 Since these costs would be capitalized they are integrated into the investment costs 21

22 Table 6. List of manufacturers survey participants Manufacturer Annual income Responded Itron 2.4 billion USD Yes Elster 1.9 billion USD Yes Landis+Gyr 1.5 billion USD Yes Elgama Elektronika - Yes Sagemcom 2.2 billion USD No Apator 500 million USD No Iskraemeco - No It should be noted that all foreign manufactures belong to one or several international smart metering alliances: Prime, IDIS and Meters and More, providing standardized products. A survey carried out by Ernst & Young Poland in 2012 of manufactures installing similar number of smart meters was additionally used to improve the data representativeness. Data from manufacturers such as ADD, JM, Tronic, Echelon and Kamstrup were used in the analysis. Foreign countries experience and expert evaluation was also used when assessing the smart metering roll-out costs. Average prices of main smart metering components are presented in the table below: Table 7. Average prices of the main smart metering system components Smart meter Three phase, price, Single-phase, price, LTL LTL With HAN With HAN Basic Basic communication communication Smart meter with GPRS communication Smart meter with radio communication Smart meter with PLC technology Smart meter with 3G technology Other constituents Price, LTL Balancing meter 911 Data concentrator Multi-metering controller (integrated into the meter) 59 Data collection system ~3 million LTL MDM system ~17 million LTL Controller for commercial consumers over 30 kw LTL Smart meters Average costs of smart meters were determined based on the international survey of manufacturers (see section 6.3.1). Smart meters have to meet the following general specifications: Measurement functions: Registration of hourly active electricity consumption profiles; Registration of total electricity consumption amounts; Power peak measurements in 1 h periods; Reactive energy measurement 11 ; 10 Based on information from LESTO 22

23 Data storage for at least 63 days; Remote control option; Remote electricity transmission disconnection/renewal; Remote control of maximum permissible power; Pre-determined event registration; Configuration functions: Remote clock synchronization; Remote tariff and calendar configuration; Remote software update; Communication transmission functions; Transmitted data encryption according to the AES-128 protocol. Smart meters also have to meet the parameters established for each scenario functionality in the interim report "Smart metering roll-out scenarios". Meters under the Advanced Functionality and the Multimetering scenarios must have the option of HAN communication using Zigbee, M-Bus, Wi-Fi 12 or Z-wave protocol. More accurate smart meter specification demand has to be determined after implementing pilot projects. Average smart meter prices according to manufacturer survey data are presented in Table 7. Distribution of smart meters by phases was determined based on the current proportions. Table 8 presents the distribution of meters according to phases in Lithuania in The calculations also assume that new meters are connected to the distribution network every year. It should be noted that commercial consumers over 30 kw already have the remote data transmission function, therefore there is no need to replace them. Also the meters should not be replaced for the rest of commercial consumers with over 30 kw permissible power, where the number of meters is They would only require installing remote transmission data controllers that would ensure the remote transmission of the meter readings. Table 8. Number of meters without remote data transmission option and distribution by the user's meter type 13 in 2012 Type of consumers Single phase (units) Three-phase (units) Total (units) % Household urban users Household rural users Commercial consumers under kw Commercial consumers over kw Total: Note: Every year newly installed meters are distributed in proportion to the current meter type and the user groups. Source: LESTO 11 It should be noted that reactive power measurement for household users is not a necessary smart meter option; however, this functionality is included in all smart meters and does not influence the costs. 12 Less acceptable due to relatively high electricity costs. 13 Based on the distribution network operator s data 23

24 The number of meters in is presented in the table below considering that new meters are connected every year. Table 9. Forecasted number of meters in Type of consumers/ year Household urban consumers Household rural consumers Commercial consumers under 30 kw Commercial consumers over 30 kw Total Type of consumers/ year Household urban consumers Household rural consumers Commercial consumers under 30 kw Commercial consumers over 30 kw Total Distribution of meters by the type of modem was determined based on these assumptions: Foreign practice. (99 % of meters in Italy support PLC communication technology. 20 % of meters in Sweden use GPRS technology and the rest are divided between PLC and radio frequency (RF)); Application of PLT technology in pilot projects and the planned massive roll-out (the pilot project in Spain (1 million meters), the pilot project in France (300 thousand meters) is based on PLC technology. Number of apartment houses in Lithuania (based on the data of Statistics Lithuania, 65 % of the Lithuanian population lives in apartment houses); LESTO expert insights concerning the potential distribution of network communications in Lithuania. Figure 4. Meter distribution by communication type Household Buitiniai vartotojai urban mieste users 10% Household Buitiniai vartotojai rural users kaime 20% 90% 80% PLC GPRS PLC GPRS Commercial Komerciniai vartotojai users under iki 30 kw kw Commercial Komerciniai vartotojai users over virš kw 50% 50% 100% PLC GPRS GPRS Smart meter roll-out under all scenarios would take 5 years (from 2016 to 2020). This speed of project implementation was determined based on the EC recommendations to install the smart metering system before the year 2020 (if the cost-benefit analysis demonstrates a positive result) and the period required for proper preparation for the smart metering roll-out (including pilot projects, preparation of technical specifications, procurements etc.). In calculating the scope of the roll-out, it was assumed that the speed of the meter roll-out for four years would be the same, meanwhile, the roll-out in the first year would be 30 % slower, because usually unexpected obstacles arise in projects of such scale and smart meter 24

25 installers do not yet have experience and skills. Pilot projects should be carried out to evaluate these challenges and properly prepare for the massive smart metering system roll-out. The number of meters replaced per year was calculated using the formula: Number of meters in the user group in the corresponding year (1) x scope of installation (2) x ratio of the network communication type in the user group (3) (1) see Table 9 (2) see Table 10 (line "Replaced per annum") (3) see Figure 4 After summing up results of all user groups a total number of replaced meters per year were obtained. The speed of smart meter roll-out and the number of replaced meters per year, and total numbers are presented in the table below. Table 10. Scope of smart meter roll-out per year. Base case scenario Advanced Functionalit y scenario Multimetering scenario* Replaced per annum ** Total: 11 % % % % % 80 % 80 % PLC meters GPRS meters Replaced per annum % 21.5 % 21.5 % 21.5 % 21.5 % 100 % 100 % PLC meters GPRS meters Replaced per annum % % % % % 80 % 80 % PLC meters GPRS meters *Total of 80 % of metering is replaced according to the parameters of scenario implementation. **Annual number of installed meters Given the calculated number of meters replaced per year, the investments in smart meters under every scenario were calculated. These investments include only the purchase of smart meters and were calculated using this formula: Meter price (1) x number of meters installed per year (2) x share of meters in the user group (3) (1) see Table 7 (2) see Table 10 (3) see Table 8 Based on the premises mentioned-above, the smart meter installation costs were calculated for each scenario. They are presented in the tables below: 25

26 Table 11. Investments in smart meters under the Base case scenario Type of consumers Household urban, LTL Household rural, LTL Commercial under 30 kw, LTL Commercial over 30 kw, LTL Total, LTL: * Total: %, from total 50 % 40 % 8 % 2 % 100 % *Costs per annum Table 12. Investments in smart meters under the Advanced Functionality scenario Type of consumers Household urban, LTL Household rural, LTL Commercial under 30 kw, LTL Commercial over 30 kw, LTL Total, LTL: * Total: %, from total 50 % 40 % 8 % 2 % 100 % * Costs per annum Table 13. Investments in smart meters under the Multi-metering scenario Type of consumers Household urban, LTL * Total: %, from total 50 % Household rural, LTL % Commercial under 30 kw, LTL Commercial over 30 kw, LTL Total, LTL: % 2 % 100 % 26

27 * Costs per annum Costs of investments in smart meters are highest under the Advanced Functionality scenario, because meters with HAN communication and 100 % of accounts are installed. Meanwhile, under the Base case scenario, 80 % of accounts without HAN communication are installed and under the Multi-metering scenario 80 % of accounts with HAN communication are installed. Detailed investment costs by user groups per year are presented in Appendix Data concentrators Average costs of data concentrator purchase were determined based on the international survey of manufacturers (see section 6.3.1). On the basis of expert evaluation, data concentrators have to satisfy the specifications listed above: 27 Safe and reliable communication ensured with at least 300 meters (up to 1000 meters under special circumstances); Safe and reliable communication ensured with central data control system; Installed clock with remote synchronization option; Option to perform local software configuration; Option to remotely upgrade and install the concentrator s software; Captured hourly and other data transferred to the MDM system, after transmission to the concentrator, should be stored for more than 60 days. It is important to note that data in the concentrator is simply stored, but not processed or analysed; Option to transfer the information to the collection system on attempts to alter the data, broken connection with the meter, voltage or signal failures. It is estimated that the average data concentrator will cost LTL. Since PLC data concentrators are almost always installed in 10/0.4 kv transformer stations, it is assumed that the number of PLC data concentrators should be approximately equal to the number of transformers set up in transformer stations, i.e units. Also, it is planned to install 850 additional data concentrators every year in the newly set up transformer stations. The scope of data concentrator installation under every scenario coincides with the volumes of smart meter roll-out. Total investments in data concentrators under every scenario are calculated by using the formulas presented and are presented in the tables below. Base case and Advanced Functionality scenarios Number of data concentrators (1) x scope of installation (2) x data concentrator price (3) (1) (calculated according to the formula (850 x 4)) (2) see Table 10 (3) see Table 7 Multi-metering scenario Number of data concentrators (1) /scope of installation (2) x data concentrator price (3) x share of data reading times of the distribution network operator (4) (1) (calculated using the formula (850 x 4)) (2) see Table 10 (3) see Table 7 (4) 68.9 % Calculation is presented below. It should be noted that under the Multi-metering scenario data the concentrator costs are divided to other utility service providers. In the survey on the analysis of stakeholder expectations, the providers of heating, water and gas indicated their interest in using a joint communication channel for remote data

28 transmission if such data transmission is economically profitable for them. The Multi-metering assessment assumes that other utility service providers will join the smart metering system to 100 %, and their installation will happen at the same time as the roll-out of electricity metering. Connection of other smaller utility service providers to the project is assessed in the sensitivity analysis. Distribution of costs is based on the ratio of the projected data reading times between different service providers. It was calculated by using the assumptions presented below: Based on the data of the Statistics Lithuania, 79 % of households in Lithuania use cold water from the municipal supply. It is also assumed that 30 % of households have two water meters. The overall number of water meters used in the calculations is meters; Based on the data of the Statistics Lithuania, 50.2 % of households in Lithuania use district hot water supply. It is also assumed that 30 % of households have two hot water meters. The overall number of hot water meters used in the calculations amounts to meters. Based on the data of Lithuanian District Heating Providers Association, the number of heating meters installed in apartment houses is ; Based on AB Lietuvos Dujos data, the average number of gas meters in Lithuania is ; Cold water, heating, hot water and gas meter readings are taken once a month. Data of household electricity user meters is taken once a month and later of commercial users meters is taken times a month (365/12 = 30.42); Total number of data readings per month: ( ) x 1 + ( ) x ( ) x 1 = Of them, number of DSO data readings 14 : ( ) x 1 + ( ) x / = 68.9 % Number of other utility service provider data readings 14 : ( ) x 1 / = 31.1 % Distribution of data concentrations costs under the Multi-metering scenario were calculated using this proportion, i.e. the DSO covers 68.9 % of all data concentrator costs. Table 14. Investments in data concentrators under the Base case scenario. Type of consumers Household urban, LTL Household rural, LTL Commercial under 30 kw, LTL Commercial over 30 kw, LTL Total, LTL: * Total: %, from total % % 37 % 4 % 100 % 14 Note: Approval of regulatory institutions is required for the terms of data transmission used in the analysis. 28

29 *Costs per annum Table 15. Investments in data concentrators under the Advanced Functionality scenario. Type of consumers Household urban, LTL Household rural, LTL Commercial under 30 kw, LTL Commercial over 30 kw, LTL Total, LTL: * Total: %, from total % % 37 % 4 % 100 % * Costs per annum Table 16. Investments in data concentrators under the Multi-metering scenario. Type of consumers Household urban, LTL Household rural, LTL Commercial under 30 kw, LTL Commercial over 30 kw, LTL Total, LTL: * Total: %, from total % % 34 % 12 % 100 % * Costs per annum From the data presented we see that the highest purchase costs of data concentrators are under the Advanced Functionality scenario due to 100 % of accounts to be installed. Costs are lower under the Base case scenario due to 80 % of accounts being installed, meanwhile, under the Multi-metering scenario, the costs are the lowest because the costs of installing 80 % of accounts are partly covered (31.1 %) by other utility service providers. Detailed investment costs by user groups annually are presented in Appendix Balancing meters Balancing meters are intended to assess the balances and imbalances of transmitted electricity. A balancing meter records the quantity of electricity being transmitted to the corresponding transformer station. After the amount of consumed electricity is captured by the smart meters and compared with the data of the balancing meter, inconsistencies can be noticed and responded accordingly. A balancing meter would be installed in every 10/0.4 kv transformer substation. There are transformers built in transformer stations in total in Lithuania, according to the DSO data, and it is estimated to have transformers before commencing the project implementation. Therefore, 44 29

30 500 balancing meters (or technical accounts) would be installed during the project implementation. Also, an installation of 850 additional balancing meters every year (from 2013) is projected in the newly set up transformer stations. Since balancing meters would be installed in transformer stations, their data would be transmitted to the PLC data concentrator, situated in the very same transformer station and then transferred to the data collection centre using GPRS communication. It should be noted that in order to receive accurate balances, it is necessary for all users to connect to the respective transformer station and have smart metering. Only in this case it is possible to accurately capture the level of electricity consumption and compare it with the readings of the balancing meter. Average costs of balancing meter purchase were determined based on an international survey of manufacturers (see section 6.3.1). It is estimated that the average price of one balancing meter (without installation) is 911 LTL. The balancing meter purchase costs were calculated using the formula below: All demand of balancing meters (1) x total number of meters per user group (2) x scope of installation (3) x balancing meter price (4) (1) (calculated using the formula (850 x 4)) (2) see Table 8; (3) see Table 10. Since balancing meters have to be installed in all transformer stations regardless of the scope of meter roll-out, the scope of meter roll-out is equal to the scope of roll-out under the Advanced Functionality scenario. (4) see Table 7. Balancing meter costs are presented in the tables below. Table 17. Investments in balancing meters under the Base case, Advanced Functionality and Multi-metering scenarios. Type of consumers Household urban, LTL Household rural, LTL Commercial under 30 kw, LTL Commercial over 30 kw, LTL Total, LTL: * Total: %, from total 55 % 38 % 6 % 1 % 100 % *Represented are costs per annum Under all scenarios the balancing meter purchase costs are the same because, in order to properly compare the amounts of transmitted and consumed electricity, balancing meters have to be installed in all transformer stations regardless of the scope of meter roll-out. Detailed investment costs by user groups annually are presented in Appendix 1. 30

31 6.3.5 Data collection system The task of the data collection system (or head end system) is to collect the unprocessed data from the smart meters and transmit them to the MDM system. The place of the data collection system in the smart metering infrastructure is demonstrated in the illustration below. Figure 5. The place of the data collection system in the smart metering infrastructure GPRS PLC Data concentrator Data collecting system (head end system) MDM system GPRS Main functions of the data collection system are: To collect the unprocessed data from the smart meters and transmit them to the MDM system; To collect the information on the condition of meters and the network and transmit it to the MDM system; To store detailed information on the condition of the network to be used for analytical purposes and statistics; To perform the remote meter control function (remote configuration, firmware upgrade, changes tariff settings, clock synchronization and so on. Since there are many data collection service manufacturers on the market, software interaction is an important factor. It should be noted that major smart metering manufacturers offer a full package of solutions covering meters, data collection and MDM systems. Typical architecture of the data collection system is presented in the figure below. 31

32 WEB Server Data storage Figure 6. Typical architecture of the data collection system. System operator Consultations Data reading (if necessary) Administration Links (web services) User profiles Meters addresses Consumption history Other meters information Alarms/ incidents Internet Intranet Remote meters configuration Reports System administrator System operator System configuration Reading frequency configuration Links with communication servers Typically technical equipment of the data collection system is comprised of these components: One database server; One or several application servers; Several communication servers; With the increasing number of remote-readable meters the scope of data collection system is ensured through the use of more application and communication servers increasing the speed of server data processing (increasing the number of RAM or kernels) and the database memory size. Costs of data collection system were determined based on the international survey of manufacturers (see section 6.3.1). It should also be noted that data collection costs are indicative because the price is highly related to the functionality and specifications of the system. It is estimated that the average data collection system serving about 1.5 million metering points might cost around LTL 15. Data collection system installation costs would amount to LTL. It should be noted that in case of the Multi-metering scenario data would be collected from ~4.3 million meters, however readings of other utility providers would be firstly collected in the controller in the electricity meter and only then transmitted from the electricity meter to the data collection system. Therefore, the total number of points from which data will travel to the data collection system is about 1.5 million. However, according to expert estimates, data collection system costs under the Multi-metering scenario would be twice as high, since the system would have to process and document the data from 4.3 million meters and different data reading specifics would be used. Data collection system would be installed before installing the smart meters, i.e. in System installation, along with the MDM system installation and the integration with current information systems (hereafter the IS) would last no more than 2 years. Distribution of data collection system costs by user groups is presented in the table below. 15 Costs of servers for data collection and MDM systems are included into the MDM system costs 32

33 Table 18. Data collection system costs under different installation scenarios Data collection system and installation costs, LTL 33 Base case scenario Advanced Functionality scenario Multi-metering scenario Data collection system costs under the Base case and the Advanced Functionality scenarios are the same because the scope of the system is calculated for the maximum possible number of new smart meters, so that in the future under the Base case scenario it is possible to add the rest of the metering. Under the Multi-metering scenario, the data collection system costs would be shared with other utility service providers. The costs sharing principle is the same as when calculating the costs of data concentrators, i.e. the DSO covers 68.9 % of all costs (for more, see section 6.3.3). It should also be noted that calculation of all project costs and benefits by different user groups for comparison showed that the data collection system costs were divided proportion to the number of meters. However, if smart metering system was to be installed only to one chosen user group, data collection costs would not be proportionally lower to the number of meters. Roll-out of the smart metering system for only one user group would constitute a separate scenario, which is not analysed in this analysis. Detailed investment costs by user groups annually are presented in Appendix MDM system MDM system is a smart metering data storage place, ensuring that metering data is safe, verified and easily accessible by the DSO, independent electricity providers and other stakeholders. The MDM system consolidates information from several million electricity users and is highly complex, requiring a large data storage application. The main functionality requirements of the MDM system are: Interface to the data collection system; Metering data verification and analysis; Data transmission to other applications and stakeholders; Error identification and management; Data audit and analysis; Mutual communication; With these options, the MDM system can ensure the processes of the main DSO and other stakeholders, such as invoicing, revenue management, asset management, network management, customer profiles and information management. The MDM should also ensure such functions: The MDM system must have access to the user's meter and an option to configure it. The MDM system must have an option for manual data input by the system operator. Such information should be marked with corresponding tags showing that the data was inputted manually. The MDM system must have an option of reporting on the technical condition of meters and information on metrological verifications. It is recommended for the MDM system to have functionality to automatically generate reports on identified network failures. The MDM system must enable data transmission to other stakeholders only with an authorized login to the system. Data from the MDM system to authorized users should be transferred in two ways: o Regularly;

34 o On request. Maximum data safety and confidentiality must be assured. Access to the MDM system data should be ensured on two levels: o o Control of external access to the data by with login by authorised stakeholders; Control of internal access to the data by identifying every user. Also some levels of consumption should be determined to ensure access only to specific information. The MDM system chart with integration of current DSO information systems and new application adaptation is presented in the figure below. Figure 7. Chart of MDM system with current IS integration and new applications User Flexible tarrifs Product bundles On-line/mobile payments Loyalty programs Demand response Prepaid energy Guaranteed power quality Monitoring & forecasting Loss localization TEVIS exploitation register Bilingas - Accounting GIS- Geographical system SCALA - finance Prepayments Demand response UVIS - requests Mano elektra - website PnP disconnection register Website DB Demand response Trouble management Links with other stakeholders IT systems Data management Links with other applications MDM system Error fixing Data transmission to other applications Forecasting Meters data collection Forecasting Managemen t analysis Balances calculation Current systems New systems Data storage Head end system MDM system costs were determined based on the experience of foreign pilot projects (pilot project in Poland) and the international survey of manufacturers 16. It should also be noted that the MDM system costs are indicative because the price is strongly related to the system functionality. It is estimated that the average MDM system with technical equipment serving 1.5 million metering points might cost around LTL. The MDM system installation costs including the integration to the current IS would amount to 2.07 million LTL. As in case of data collection system, the MDM system costs would be double under the Multi-metering scenario. General MDM system costs include: MDM system license costs; 16 It should be noted that the IS prices provided by manufacturers are lower than the costs incurred in pilot projects. Therefore more conservative assumptions of pilot projects were used for the analysis. It is likely, however, that the IS costs could be lower. 34

35 The MDM system with full graphical environment; MDM system installation costs; Hardware; The MDM system would be installed before installing the meters, i.e. in The system installation would take less than 2 years. The data collection system costs are presented in the table below. Table 19. MDM system costs under different installation scenarios Data collection system and installation, LTL Base case scenario Advanced Functionality scenario Multi-metering scenario The MDM system costs under the Base case and the Advanced Functionality scenarios are the same, because the scope of the system is calculated for the maximum possible number of new smart meters installed, so that in the future under the Base case scenario it is possible to add the rest of the metering. Under the Multi-metering scenario, the MDM system costs would be shared with other utility services providers. The cost sharing principle is the same as when calculating the costs of data concentrators, i.e. the DSO covers 68.9 % of all costs (for more, see section 6.3.3). It should also be noted that the calculation of all project costs and benefits by different user groups for comparison showed that the MDM system costs were divided proportionally to the number of meters. However, if smart metering system was to be installed only to one of the user groups, the MDM system costs would not be proportionally lower to the number of meters. Smart metering roll-out for only one user group would constitute a separate scenario, which is not analysed in this study. Detailed investment costs by user groups per year are presented in Appendix Multi-metering controller A multi-metering controller is required to collect data from water, gas, heating and electricity meters and transmit the data to the concentrator or directly to the data collection system (depending on the network communication). Average costs of multi-metering controllers were determined based on the international survey of manufacturers (see section 6.3.1). It was estimated that the average price of the multi-metering controller is 59 LTL. A multi-metering controller can operate as a separate device which collects the data from all meters and transmits them to the data concentrator. However, the multi-metering controller is usually integrated into the electricity meter, which uses M-Bus protocol to collect data from other meters and transmit them to the data concentrator. Smart metering manufacturers who participated in the survey also propose such meters with a Multi-metering option. Since the multi-metering controller is a necessary tool to collect data from the metering devices of other utility service providers, the number of multi-metering controllers and the distribution of costs were calculated on the assumption that 50 % of costs will depend on the ratio of the number of meters of other utility service providers with the number of electricity meters, and 50 % on the data transmission proportions (it is assumed that 100 % of other utility service providers will join the project and the roll-out in other volumes is assessed in the sensitivity analysis). 50 % of costs dependent on the ratio of meters were calculated using this methodology: Costs of multi-metering controller would be shared between the four utility providers (heating, gas, water, electricity), because it is the lowest number of those having heating and hot water meters. Costs of multi-metering controllers would be shared between the three utility providers (gas, water, electricity). ( = ) 35

36 Costs of multi-metering controller would be shared between the two utility providers (water, electricity). ( = ) Fixed part of costs was calculated using the formula: Multi-metering controller price / 4 * *scope of the smart metering roll-out + multi-metering controller price / 3 * * scope of the smart metering roll-out + multi-metering controller price / 2 * * scope of the smart metering roll-out 50 % of costs dependent on data transmission volumes were calculated using the same methodology as the data concentrator purchase costs (see section 6.3.3), i.e. the DSO covers 68.9 % of all costs. The distribution of multi-metering controller costs attributed to electricity meters by user groups is presented in the table below: Table 20. Investments in multi-metering controllers under the Multi-metering scenario. Type of consumers Household urban, LTL Household rural, LTL Commercial under 30 kw, LTL Commercial over 30 kw, LTL Total, LTL: *Costs per annum * Total: %, from total % Detailed investment costs by user groups per year are presented in Appendix In-house display % 38 % 6 % 100 % In-house displays are special screens displaying the real time or near real time information on household electricity consumption. The displays can also display various other information, for example, the daily budget set by the user for electricity consumption, current electricity price and monthly electricity expenses. Even though the main function of the in-house display is showing the real time and historic data on electricity consumption and prices, information on average household energy consumption for comparison or electricity consumption per specific appliance can also be displayed. According to foreign practice, the minimum functionality parameters of the in-house display should include: Colour display; Near real time readings of electricity consumption in kw (with deviation of several seconds or minutes); Near real time readings of time costs in litas (with deviation of several seconds or minutes); Display of daily, weekly, monthly electricity costs or costs per payment period; Option of comparison with the previous period; 36

37 Electricity price of the current and coming period in LTL/kWh (final consumer price). If the electricity provider is not a DSO, the independent power provider must have an option to display the final electricity price on the in-house display); Time and date display; Communication with an electricity meter (or a multi-metering controller) using Zigbee, M-Bus, Bluetooth, Wi-Fi or Z-Wave protocols; Warning information in case of deviation from average consumption or after reaching the set consumption limit; Display of additional information (air temperature, CO2 costs) (optional). Figure 8. Example of in-house display manufactured by GEO. Source: GEO Since most smart meter manufacturers do not produce in-house displays, but buy products externally, the price of in-house displays was determined based on expert EY assessment and smart metering equipment prices provided by KEMA international consulting company 17. It was estimated that the average price of in-house display is 87 LTL. Under the Multi-metering scenario, in-house display costs are shared in the same proportions as for the multi-metering controllers (see section 6.3.7). 50 % of in-house display purchase costs will depend on the ratio of the number of metres of other utility providers with the number of electricity meters, and other 50 % on the data transmission proportions. Where multi-metering controllers are not installed at home, the costs of in-house display purchase will be covered 100 % by the DSO. Investments in in-house displays were calculated using the formula: Number of meters to be installed for a respective user group (1) x in-house display price (2) (1) see Table 10 (2) 87 LTL Distribution of in-house displays costs based on user groups is presented in the tables below: 17 Development of Best Practice Recommendations for Smart Meters Rollout in the Energy Community, KEMA International B.V.,

38 Table 21. Investments in the in-house display under the Advanced Functionality scenario. Type of consumers Household urban, LTL Household rural, LTL Commercial under 30 kw, LTL Commercial over 30 kw, LTL Total, LTL: * Total: %, from total % % 39 % 6 % 100 % *Costs per annum Table 22. Investments in the in-house display under the Multi-metering scenario. Type of consumers Household urban, LTL Household rural, LTL Commercial under 30 kw, LTL Commercial over 30 kw, LTL Total, LTL: * Total: %, from total % % 38 % 8 % 100 % * Costs per annum It should be noted that in-house displays are not calculated for commercial users over 30 kw, because: 38 Metres of commercial users are usually vary remote from main offices, so data transmission becomes complicated; Power specialists working in large commercial objects can take the meter readings if needed; Commercial users can have more than one meter per object. Detailed investment costs by user groups per year are presented in Appendix Roll-out of the smart metering system The costs of smart meter roll-out were determined according to the current meter roll-out costs in Lithuania. Meter roll-out costs include the smart meter parameterization and the preparation for the Plug & Play installation. Data concentrator installation costs were determined based on the smart meter manufacturer survey results (see section 6.3.1), which demonstrate that data concentrator installation is on average 4.87 times more costly than the smart meter roll-out. Considering meter roll-out costs in Lithuania, the data concentrator installation costs were calculated (28*4.87 = 136 LTL).

39 Balancing meter roll-out costs were calculated based on average prices provided by technical metering manufacturers and installers. Installation costs of smart metering components are: Meter installation in the city 28 LTL; Meter installation in rural area 55 LTL; Metering roll-out for commercial users 47 LTL; Data concentrator installation costs 136 LTL; Balancing meter installation (including the technical metering installation with metering panels in a 10/0.4 kv transformer substation) LTL; Also, based on foreign practice and expert estimates, it was calculated that the roll-out of 5 % of smart metering system would be unsuccessful on the first attempt and it would require the repeated installer s service. Meter and other equipment installation costs were calculated using the formula: Number of meters to be installed for a respective user group (1) x installation costs (2) + number of data concentrators to be installed (3) x concentrator installation costs (2) + number of balancing meters to be installed (4) x balancing meter installation costs (2) (1) see Table 10 (2) see section above (3) see (4) see Smart meter system roll-out costs are presented in the tables below: Table 23. Costs of the smart metering roll-out under the Base case scenario. Type of consumers Household urban, LTL * Total: %, from total 55 % Household rural, LTL % Commercial under 30 kw, LTL Commercial over 30 kw, LTL Total, LTL: % 1 % 100 % * Costs per annum Table 24. Costs of smart meter roll-out under the Advanced Functionality scenario. Type of consumers Household urban, LTL Household rural, LTL * Total: %, from total 55 % 38 % 39

40 Type of consumers Commercial under 30 kw, LTL Commercial over 30 kw, LTL Total, LTL: * Total: %, from total 6 % 1 % 100 % * Costs per annum Table 25. Costs of smart meter roll-out under the Multi-metering scenario. Type of consume rs Househol d urban, LTL Househol d rural, LTL Commerc ial under 30 kw, LTL Commerc ial over 30 kw, LTL Total, LTL: * Total: %, from total 55 % 38 % 6 % 1 % 100 % * Costs per annum The lowest smart meter roll-out costs are under the Multi-metering scenario since 80 % of accounts are installed, while the data concentrator roll-out costs are shared with other utility service providers. The highest costs are under the Advanced Functionality scenario since 100 % of electricity meters are installed including the highest number of data concentrators. Detailed investment costs by user groups per year are presented in Appendix Staff training Specialized training of installers is necessary for successful smart meter roll-out. Staff training costs (when installing 80 % of accounts) were calculated based on these assumptions: Meter replacement for household urban users takes 0.5 h; Meter replacement for commercial urban users takes 1 h; Meter replacement for household rural users takes 1.2 h; Meter replacement for commercial rural users takes 1.3 h; Average number of effective working hours is h (6 h per day, 5 days per week, 52 weeks per year) (6 x 5 x 52 = 1 560); One worker can replace urban meters per year (1 560 x 0.5 = 3 120) 40

41 Considering the number of meters replaced per year (see section 6.3.2), the required number of workers to replace urban meters is 55 ( / = 55). One worker can replace rural and commercial users meters per year (1 560 x = 1 337); Considering the number of meters replaced per year (see section 6.3.2) the required number of workers to replace rural and commercial users meters is 110 ( / = 108). In total, 163 workers will have to be trained for the smart metering roll-out; Training duration 40 hours; According to the data of the Statistics Lithuania, the added value created by one worker in the energy sector is 93.4 LTL per hour; Staff training costs amount to litas under the Base case and the Multi-metering scenarios (163 x 40 x 93.4 = ). Staff training costs under the Advanced Functionality scenario amount to LTL, since more smart meter installers have to be trained. Additionally, training centre facilities and experts will be necessary for staff training. These costs were calculated based on these premises: One expert can train a group of 20 people; Hourly training expert service fee is 200 LTL/h.; Number of training rooms: 8 rooms under the Base case and the Multi-metering scenarios (165 / 20 = 8 ) and 10 rooms under the Advanced Functionality scenario (206 / 20 = 10); Training room rent cost per day is 200 LTL; Training duration: 5 days (40 / 8 = 5); Expert costs under the Base case and the Multi-metering scenarios amount to 163 / 20 x 40 x 200 = ; Expert costs under the Advanced Functionality scenario amount to 204 / 20 x 40 x 200 = ; Training room costs under the Base case and the Multi-metering scenarios amount to 8 x 200 x 5 = 8 000; Training room costs under the Advanced Functionality scenario amount to 10 x 200 x 5 = ; Total staff training costs amount to: Under the Base case scenario: LTL ( = ); Under the Advanced Functionality scenario: LTL ( = ); Under the Multi-metering scenario: LTL ( = ); Detailed investment costs by user groups per year are presented in Appendix Project management costs Typically, external consultants are used to manage projects of such scale. Responsibilities of external consultants include: Project planning (scheduling, cash flows, control, etc.); Arrangement of procurements, contract drafting and preparation for approvals; Ongoing project control; Expert recommendations concerning specific areas of the project (technical, financial, environmental, public relations etc.); Assistance in preparing applications for funding and funding administration; Reporting to company management and external reports; 41

42 Assistance in the issues of contract performance; Preparation and coordination of external communications; Project accounting; Other. According to expert estimates and experience in other projects, the project management costs on average amount to 1-2 % of all project capital investments. The estimates were made on the assumption that project management costs amount to 1 % of capital investments. Under the Base case scenario, investment costs amount to LTL in Under the Advanced Functionality scenario, investment costs amount to LTL in Under the Multi-metering scenario, investment costs amount to LTL in The summary of capital investments for the project alternatives is presented in Appendix 1. Given the estimated project capital investments, the annual project management costs are presented in the tables below. Table 26. Project management costs under the Base case scenario. Type of consumers Household urban, LTL Household rural, LTL Commercia l under 30 kw, LTL Commercia l over 30 kw, LTL Total, LTL Total: Table 27. Project management costs under the Advanced Functionality scenario %, from total 53 % 39 % 7 % 1 % 100 % Type of consumers Household urban, LTL Household rural, LTL Commercia l under 30 kw, LTL Commercia l over 30 kw, LTL Total: Total, LTL %, from total 53 % 39 % 7 % 1 % 100 % 42

43 Table 28. Project management costs under the Multi-metering scenario. Type of consumers Household urban, LTL Household rural, LTL Commercia l under 30 kw, LTL Commercia l over 30 kw, LTL Total: Total, LTL %, from total 53 % 39 % 7 % 1 % 100 % The differences in the project management costs are caused by capital investments of different scenarios, which depend on the scope of the roll-out, meter type and so on. Detailed investment costs by user groups per year are presented in Appendix Project publicity Project publicity costs were calculated based on the publicity costs in the projects of similar volume and penetration (the project Skaitmeninė TV 2012). Project publicity costs are presented in the table below. Costs by customer groups are divided in proportion to the number of meters. Table 29. Project publicity costs Household urban, LTL Household rural, LTL Commercial under 30 kw, LTL Commercial over 30 kw, LTL Total, LTL: Project publicity costs are the same under all scenarios, regardless of 80 % or 100 % of accounts will be installed. Detailed investment costs by user groups per year are presented in Appendix Summary of investment costs Overall investment costs for each component and investment costs per meter are presented in the table below: Table 30. Investment costs under the Base case scenario Investment cost component Total costs (discounted), LTL: Costs per one meter (discounted), LTL: Smart meters Data concentrators Balancing meters Data collection system MDM system

44 Smart metering roll-out Repeated smart metering roll-out Staff training Project management Project publicity Total: Note: 5% discount rate is applied Table 31. Investment costs under the Advanced Functionality scenario. Investment costs component Total costs (discounted), LTL: Costs per one meter (discounted), LTL: Smart meters Data concentrators Balancing meters In-house display Data collection system MDM system Smart metering roll-out Repeated smart metering roll-out Staff training Project management Project publicity Total: Note: 5 % discount rate is applied Table 32. Investment costs under the Multi-metering scenario. Investment costs component Total costs (discounted), LTL: Costs per one meter (discounted), LTL: Smart meters Data concentrators Balancing meters In-house display multi-metering controllers Data collection system MDM system Smart metering roll-out Repeated smart metering roll-out Staff training Project management

45 Project publicity Total: Note: 5% discount rate is applied Total investment costs described in the sections above are presented in the chart below: Figure 9. Summary of investment costs , Base case scenario Advanced Funcionality scenario Multi-metering scenario Households urban Households rural Commercial users under 30 kw Commercial users over 30 kw Meter costs 500,0 400,0 300,0 200,0 Note: 5% discount rate is applied Highest investment costs are in case of the Multi-metering scenario. Main reasons of that are: The scenario includes in-house display installation; Additional roll-out of multi-metering controllers, with major part of the costs (calculation is presented in section 6.3.7) to be covered by the DSO. Detailed annual capital investments by different user groups are presented in Appendix Forecast operating costs Forecast operating costs related to the smart metering roll-out would be comprised of: Data transmission costs (see section 6.4.1); IS maintenance costs (see section 6.4.2); Smart meter troubleshooting costs (see section 6.4.3); Smart meter electricity costs (see section 6.4.4); Data transmission costs Data transmission costs are comprised of information transmission from the concentrator to the data collection system (WAN level) using GPRS communication or from the smart meter to the data collection system (the "last mile" and the WAN level). Data transmission costs were determined based on the current DSO data transmission costs and potential discount due to economies of scale 18. These costs would be: 3 LTL/month for the data transmission from the smart meter to the data collection system. Estimated based on the potential data transmission price anticipated by the DSO after implementing the massive smart metering roll-out. 18 Since the data transmission price depends on various service level deals, service providers are currently not able to give exact prices. 45

46 6 LTL/month for data transmission from the data concentrator to the data collection system (the price would be higher since the volume of data transmitted from concentrator has to be significantly bigger and dependent on the number of meters connected to the concentrator). Estimated based on the current DSO data transmission costs from one meter with the option of remote data reading. Data transmission costs are calculated to both smart meters which communicate through the GPRS communication channel and data concentrators, to which data from the meters is delivered using PLC technology and further transmitted using GPRS communication. Under the Base case scenario the data would be transmitted using GPRS communication from meters (see Table 10) and data concentrators in 2029 (see section 6.3.3) Under the Advanced Functionality scenario the data would be transmitted using GPRS communication from meters (see Table 10) and data concentrators in 2029 (see section 6.3.3) Under the Multi-metering scenario the data would be transmitted using GPRS communication from meters (see Table 10) and data concentrators in 2029 (see section 6.3.3) Data transmission costs were calculated using the formula: Number of GPRS meters in the corresponding year (1) x data transmitter price (2) x 12 (3) + number of data concentrators in the corresponding year (4) x data transmitter price (2) x 12 (3) (1) see Table 10; (2) 3 LTL or 6 LTL (3) 12 months (4) see section 6.3.3; Since the data transmission costs depend on the number of smart meters, these expenses differ every year. Data transmission costs per year under every scenario are illustrated in the figure below: Figure 10. Data transmission costs per year, LTL Base Bazinis case scenarijus scenario Advanced Išplėstinio funkcionalumo functionality scenarijus scenario Multi-metering Jungtinės apskaitos scenario scenarijus Average annual data transmission costs amount to 19 : Under the Base case scenario 12.3 million LTL. Under the Advanced Functionality scenario 15.4 million LTL. Under the Multi-metering scenario 8.5 million LTL. Data transmission costs differ due to different smart metering roll-out volumes. Detailed data transmission costs by different user groups per are presented in Appendix Average annual costs were calculated by summing up all corresponding expenses in the period and dividing those by the project lifetime 14 years. 46

47 6.4.2 IS maintenance costs IS maintenance costs include data collection and MDM system maintenance. Data collection and MDM system maintenance costs cover: Annual license fee; Correction of errors; Update installations; Helpdesk; Based on the data provided by the IS systems manufacturers, the annual maintenance costs of data collection systems and MDM systems would amount to 23 % of the system purchase price. Annual data collection and MDM system maintenance costs were calculated using the formula: Purchase and installation costs of the data collection system and MDM system (1) x 23 % (2) (1) See sections and (2) Value of annual maintenance costs from investments in the IS Annual IS maintenance costs are displayed in the figure below: Figure 11. Annual IS maintenance costs, LTL Bazinis scenarijus Išplėstinio funkcionalumo scenarijus Jungtinės apskaitos scenarijus Base Bazinis case scenarijus scenario Advanced Išplėstinio functionality funkcionalumo scenario scenarijus Multi-metering Jungtinės apskaitos scenario scenarijus Under the Base case and the Advanced Functionality scenarios, the IS maintenance costs are similar, because their purchase and installation costs are equal. Meanwhile, under the Multi-metering scenario the IS maintenance costs are the highest since the IS investment costs are the highest. It should be noted that under the Multi-metering scenario, the maintenance costs are shared between other utility providers. The share of IS maintenance costs attributable to the DSO amounts to 68.9 % (see section 6.3.3). Detailed IS maintenance costs by different user groups per are presented in Appendix Troubleshooting costs of smart meters and other equipment Smart meter repair costs were calculated based on the current DSO meter repair costs and potential growth in the number of meter repairs. Repairs of meters and other equipment covers the troubleshooting of all faults. Based on the DSO data, the current average number of meter malfunctions amounts to 1.45 % per year. It must be assumed that the smart metering roll-out can increase the number of malfunctions since current malfunctions are identified only when reported by the user or incidentally. Besides, some of the 47

48 malfunctions are not identified at all, since they are not reported by anyone. Given the above factors and the foreign experience, the number of malfunction could increase by up to 2 %. Since the repair of simple meters will be unnecessary after the smart metering roll-out, the extent of troubleshooting was calculated by subtracting the projected number of repairs from the current average number of repairs per year, and that amounts to 0.55 %. Based on the data from the DSO, the meter troubleshooting costs are comprised of: Household meter repair 45 LTL; Commercial meter repair 104 LTL; Since the meter troubleshooting costs are comprised of 71 % of wage costs and 21 % of transportation costs, it is assumed that smart meter and other equipment troubleshooting costs would be the same. Additionally, malfunctions in data concentrators and balancing meters would have to be repaired every year. If assumed that the malfunction volumes of these devices also amount to 2 %,their repair costs are then equal to the commercial meter repair costs. The multi-metering controller repair costs are not included into the operating costs since they would be integrated into the smart meter price and included in the meter repair volumes. Repair costs of smart meters, data concentrators and balancing meters were calculated using the formula. Number of installed meters in the corresponding year (1) x 0,55 % (2) x troubleshooting costs (3) + number of installed data concentrators in the corresponding year (4) x 2 % (5) x troubleshooting costs (3) + number of installed balancing meters in the corresponding year (6) x 2 % (5) x troubleshooting costs (3) (1) see section 6.3.2; (2) 2 % 1.45 % = 0.55 %; (3) 45 LTL or 105 LTL; (4) see section 6.3.3; (5) Number of data concentrator malfunctions; (6) See section Annual smart meter repair costs are displayed in the figure below: Figure 12. Annual smart meters troubleshooting costs, LTL Base Bazinis case scenarijus scenario Advanced Išplėstinio funkcionalumo functionality scenarijus scenario Multi-metering Jungtinės apskaitos scenario scenarijus Average annual smart meter troubleshooting costs amount to: 48 Under the Base case scenario thousand LTL; Under the Advanced Functionality scenario thousand LTL; Under the Multi-metering scenario thousand LTL; Troubleshooting costs are highest under the Advanced Functionality scenario because of the highest number of smart metering to be installed. Meanwhile, the troubleshooting costs are the lowest under the

49 Multi-metering scenario because in case of the concentrator malfunction its costs are shared with other utility service providers according to the proportion of data transmission (see section 6.3.3). Detailed annual smart meter and other equipment troubleshooting costs by user groups are presented in Appendix Smart meter and other equipment electricity expenses Smart electricity meters also use electricity. Based on the information provided by the smart metering manufacturers, the average power consumed by the meter power is: Smart meters with PLC communication 1.6 W; Smart meters with GPRS communication 3 W; Data concentrators 7.5 W; Multi-metering controllers 1 W. It should be noted that under the Multi-metering scenario the costs of electricity used by data concentrators and multi-metering controllers are shared with the DSO and other utility service providers according to the data transmission proportions (see section 6.3.3) Smart meter electricity costs were calculated based on the DSO electricity purchase price, electricity transmission price and the VIAP (see section 6.2). Smart meter and other equipment electricity expenses were calculated using the formula: Number of meters installed in the corresponding year (1) x meter s power (2) x 365 x 24 / number of data concentrators installed in the corresponding year (3) x data concentrator s power (3) x 365 x 24 / number of multi-metering controllers installed in the corresponding year (4) x multimetering controller s power (3) x 365 x 24 / (1) see Table 10 (2) see section above (3) see section (4) see section Figure 13. Annual smart meter electricity costs, LTL Base Bazinis case scenarijus scenario Advanced Išplėstinio funkcionalumo functionality scenarijus scenario Multi-metering Jungtinės apskaitos scenario scenarijus It should be noted that under the Multi-metering scenario the costs of electricity used by data concentrators and multi-metering controllers are shared with the DSO and other utility service providers according to the data transmission volumes (see section 6.3.3). Average annual smart meter electricity costs amount to: Under the Base case scenario 7.7 million LTL. Under the Advanced Functionality scenario 9.7 million LTL.

50 Under the Multi-metering scenario 9.3 million LTL. Note: Since the DSO will no longer incur electricity costs with former meters, they are attributed to the benefits generated by smart metering system. Detailed annual smart meter and other equipment electricity costs by user groups are presented in Appendix Summary of operating costs Total operating costs for each constituent and operating cost per meter are presented in the tables below: Table 33. Base case scenario operating costs Cost component Total costs (discounted), LTL: Costs per one meter (discounted), LTL: Data transmission from the meters Data transmission from data concentrators Data collection system maintenance MDM system maintenance Smart meter troubleshooting costs Data concentrators, balancing meters troubleshooting costs Smart meters, data concentrators, balancing meters electricity expenses Total: Note: 5% discount rate is applied Table 34. Operating costs under the Advanced Functionality scenario. Cost component Total costs (discounted), LTL Costs per one meter (discounted), LTL Data transmission from the meters Data transmission from data concentrators Data collection system maintenance MDM system maintenance Smart meter troubleshooting costs Data concentrators, balancing meters troubleshooting costs Smart meters, data concentrators, balancing meters electricity expenses Total: Note: 5% discount rate is applied 50

51 Table 35. Operating costs under the Multi-metering scenario. Cost component Total costs (discounted), LTL: Costs per one meter (discounted), LTL: Data transmission from the meters Data transmission from data concentrators Data collection system maintenance MDM system maintenance Smart meter troubleshooting costs Data concentrators, balancing meters troubleshooting costs Smart meters, data concentrators, balancing meters electricity expenses Total: Note: 5% discount rate is applied Operating costs include the costs described in the sections above. Annual overall operating costs are displayed in the figure below. Figure 14. Operating costs under every scenario, LTL Base Bazinis case scenarijus scenario Advanced Išplėstinio funkcionalumo functionality scenarijus scenario Multi-metering Jungtinės apskaitos scenario scenarijus Average annual operating costs amount to 20 : Under the Base case scenario 25.8 million LTL. Under the Advanced Functionality scenario 30.9 million LTL. Under the Multi-metering scenario 25.4 million LTL. Average annual costs per meter: Under the Base case scenario 16.6 LTL; Under the Advanced Functionality scenario 15.9 LTL; Under the Multi-metering scenario 16.3 LTL. Costs per meter were calculated using the formula: 20 Average annual costs were calculated by summing up corresponding expenses in the period and dividing those by project lifetime 14 years. 51

52 Average annual operating costs (1) / number of installed meters (2) (1) see section above (2) see Table 10 Under the Base case scenario, the operating costs per meter are the highest because in this scenario 80 % of accounts are installed, however the number of balancing meters is the same as in the Advanced Functionality scenario. Therefore, the operating costs associated with balancing meters are shared between more meters, and the costs per meter are lower. Meanwhile, in the Advanced Functionality scenario, the operation costs are the lowest since they are shared between the highest number of meters. In the Multi-metering scenario the costs are shared with other utility service providers, however, under this scenario the IS maintenance costs are double that of other scenarios costs. It should be noted that the operating costs include metrological meter verification, however this does not incur any additional costs or expenses, as the current meters must also be verified metrologically. Also, according to the preliminary estimates of the State Metrological Service, metrological verification of smart meters should not differ from current procedures, and which means that costs should also remain the same. Detailed annual operating costs by different user groups are presented in Appendix Smart metering system consumer benefits Whereas the smart metering roll-out would be executed by the DSO, the basic financial benefits of the smart metering roll-out were identified. The basic DSO benefits are as follows: No need for replacement of outdated meters with new standard meters 21 (see chapter 6.5.1); No need for standard meter installation costs (see chapter 6.5.2); No need for standard meter scheduled inspection (see chapter 6.5.3); Improved cash flow management (see chapter 6.5.4); Reduced call centre costs (see chapter 6.5.5); Reduced standard meter power supply rate (see chapter 6.5.6); Also, in addition to tangible financial benefits, the smart metering roll-out would engender the consumer contentment with project implementer's with services supplied by the DSO as well as improve the company's image Standard meter replacement program savings The DSO has scheduled a replacement program for meters currently being used to take place until 2020 and replace the old electricity meters with new electronic meters. Since old meters are replaced with smart meters when implementing the smart metering programme, there is no need to install new standard meters instead of the old ones. The volumes of the DSO meter replacement program are presented in the following figure. Table 36. DSO meter replacement program up to Total: Urbanhousehold, units Standard metres mean the currently used inductive and electronic meters 52

53 Total: Ruralhousehold, units Commercial, up to 30 kw, units Commercial, over 30 kw, units Singlephase, units Threephase, units Source: LESTO Based on the DSO-supplied data, weighted average costs of standard meters are as follows: Standard single-phase meter 65.8 LTL; Standard three-phase meter LTL; Based on the quantity of replaceable meters and the meter prices, the savings that the DSO would accrue by not installing the standard meters were calculated. The savings were calculated according to the following formula: Replaceable single-phase meter quantity (1) x part of single-phase meters in a respective consumer group (2) x standard meter price (3) + Replaceable three-phase meter quantity (1) x part of single-phase meters in a respective consumer group (2) x standard meter price (3) (1) See The DSO has scheduled a replacement program for meters currently being used to take place until 2020 and replace the old electricity meters with new electronic meters. Since old meters are replaced with smart meters when implementing the smart metering programme, there is no need to install new standard meters instead of the old ones. The volumes of the DSO meter replacement program are presented in the following figure. (2) Household urban consumers: single-phase meters %; three-phase meters 9.59 % Household rural consumers: single-phase meters %; three-phase meters % Commercial consumers, up to 30 kw: single-phase meters %; three-phase meters % Commercial consumers, over 30 kw: single-phase meters %; three-phase meters % (excluding meters with remote accounting) (3) See chapter above The following table shows annual savings based on consumer groups. Table 37. Savings resulting from replacing standard meters not required in the Basic case, Advanced Functionality and Multi-metering scenarios. Consumer type Household urban, LTL excl. VAT Household rural, LTL excl. VAT Total: %, of total 52 % 40 %

54 Consumer type Commercial, up to 30 kw, LTL excl. VAT. Commercial, over 30 kw, LTL excl. VAT. Total, LTL excl. VAT: Total: %, of total % % 100 % In all cases savings are equal, because the same meter replacement requirement (lesser than the implementation volumes of each scenario) is not required in all scenarios. Detailed annual meter replacement program savings based on consumer groups are provided in Appendix Standard meter roll-out cost savings As mentioned in chapter 0, roll-out of smart meters does not require to execute the scheduled program of old meters' replacement. This way the standard meter roll-out costs are also saved, since the new smart meters are installed. Meter installation costs are as follows: Meter installation in the city 28 LTL; Meter installation in rural area 55 LTL; Meter installation for commercial consumers 47 LTL; The savings were calculated according to the following formula: Number of meters replaced (1) x meter replacement cost (2) (1) See Table 36 (2) See chapter above The following table shows annual savings based on consumer groups. Table 38. Savings resulting from the elimination of replacement (work) of standard meters in the Basic case, Advanced Functionality and Multi-metering scenarios. Consumer type Household urban, LTL Household rural, LTL Commercial, up to 30 kw, LTL Commercial, over 30 kw, LTL Total: %, of total % Total, LTL % 59 % 7 % 100 % In all cases savings are equal, because the same meter replacement requirement (lesser than the implementation volumes of each scenario) is not required in all scenarios. 54

55 Lt Detailed annual standard meter installation savings based on consumer groups are provided in Appendix Savings on taking the meter readings After the roll-out of smart metering system there will be no need to take meter readings physically, because all meter data will be transmitted to the DSO remotely. The DSO is currently conducting visits on the adjustment of direct readings (for household consumers) and risky consumers (for household and commercial consumers). Readings are taken once a year (for household consumers). Up to visits are conducted annually for taking the readings of commercial consumers. The highest savings are achieved by eliminating the visits to risky consumers (circa visits annually) and direct debit consumers (circa consumers at the end of the year and growing). Therefore about visits for taking readings can be reduced annually. It is possible to reduce reading visits annually for commercial consumers. Based on the data provided by DSO, the costs for taking the readings are currently 2.83 LTL per meter. The costs for taking the readings are linked to the annual inflation value. The savings were calculated according to the following formula: Number of replaced household meters (1) x 0.47 (2) x reading cost + Number of replaced commercial meters(1) x (3) x readings cost (1) See Table 10; (2) The ratio of all household meters and inspected meters per year is / ; (3) The ratio of all commercial meters and inspected meters per year is / ; Note: If the product of the number of replaced meters and the ratio is greater than for household consumers, only the savings for household user meters are calculated. If the product of the number of replaced meters and the ratio is greater than for commercial consumers, only the savings for 10,000 commercial consumer meters are calculated. The following figure displays the savings on taking the meter readings per year. Figure 15. Savings on taking the meter readings per year Total: Total: Total: Base Bazinis case scenarijus scenario Advanced Išplėstinio functionality funkcionalumo scenario scenarijus Jungtinės Multi-metering apskaitos scenario scenarijus Household Household Buitiniai mieste Buitiniai kaime Commercial Komerciniai iki under 30 kw Commercial Komerciniai virš over 30 kw urban rural 30 kw 30 kw Savings on taking the meter readings in the Advanced Functionality scenario are the highest, since a greater number of meters are replaced during the first year of the project than it is in the Basic case or the Multi-metering scenarios. It means that during the first year there is a lesser need for taking the mechanical meter readings. 55

56 Detailed annual savings for taking the meter readings based on consumer groups are presented in Appendix Improved cash flow management By rolling-out the smart metering system, the DSO will see the exact amount of energy consumed by consumers every month. It would allow to compare the amount of electricity consumed against the bill payment. Moreover, smart metering system also allows to remotely turn off or restrict electricity supply to consumers. Therefore, the DSO can manage cash flows more effectively, by providing accurate bills to consumers and having a possibility to restrict the supply of electricity to insolvent consumers. With reduced number of overdue household users or consumers who pay inaccurately, the DSO might gain financial benefits. These consumers do not usually receive bills from the DSO, therefore their payments are often inaccurate or paid less frequently than once in each month. Based on the data provided by the DSO, 28.9 % of consumer payments are overdue, whereas the rolling-out of the smart metering system would enable the DSO to provide accurate bills for electricity consumed, thus accelerate revenue collection and cash flow management. In turn, it would increase revenue received by the DSO and accordingly decrease interest costs, since the company's liabilities would be reduced by part of receivables. The interest cost was calculated based on average interest rate for non-banking sector companies announced by the Bank of Lithuania, which is 5.7 % 22. Note that this benefit is calculated for the household consumer groups only. Benefit created due to better cash flow management is calculated based on the following formula: Amount of energy transferred via a power grid for a respective consumer group (1) x price of electricity for household consumers (2) x number of overdue bills (3) x average payment overdue duration (4) x average annual interest norm for non-banking sector companies (5) (1) See chapter 6.2; (2) See chapter 6.2; (3) 28.9 %; (4) 15 days or 15/365 = years; (5) 5.7 %. The following figure displays the benefit of decreased receivables in every scenario. Figure 16. Interest cost decrement benefit resulting from improved cash flow management Bazinis scenarijus Išplėstinio funkcionalumo scenarijus Jungtinės apskaitos scenarijus Average annual savings resulting from improved cash flow management are: Basic case scenario thousand LTL;

57 Advanced Functionality scenario thousand LTL; Multi-metering scenario thousand LTL. The savings in the Advanced Functionality scenario are the greatest, since a greater number of accounts is implemented compared to the Basic case or the Multi-metering scenarios. Detailed savings resulting from improved annual cash flow management based on consumer groups are provided in Appendix Call centre cost savings Rolling-out the smart metering system allows the DSO operator to detect grid failures more promptly and accurately and to respond to failure repair faster. Moreover, the DSO could also provide failure recording information to an in-house display via an SMS message. In this case the DSO call centre costs should also decrease, since consumers have to call and report failures less often. Current DSO call centre expenditure is LTL annually. Electricity transmission failure-related calls comprise 41.5 % of the total flow while metering device failure-related calls comprise approximately 2.5 %. By properly educating consumers and informing them about DSO-recorded failures, the call centre costs related to failures could be reduced by 90 %. While calculating call centre cost savings, consideration was given to the fact that in instances of cable break, the information relating to failure from areas with PLC meters will not reach the DSO and call centre services will be used. According to the DSO financial reports for 2011, the SAIDI index (incl. force majeure ) was 302 minutes. This implies that the DSO will not receive failure information for this period and call centre service will be required. Call centre cost savings were calculated according to the following formula: Share of installed meters (1) x Annual call centre costs (2) x failure-related costs (3) x failure-related savings (4) (1) See Table 10; (2) LTL; (3) 41.5 %; (4) 89.9 %. Depending on the electricity outages per year. 57

58 Lt The following figure displays average call centre cost savings annually. Figure 17. Average annual call centre cost savings ( ) Total: Total: Total: Base Bazinis case scenarijus scenario Advanced Išplėstinio functionality funkcionalumo scenario Jungtinės Multi-metering apskaitos scenario scenarijus scenarijus Buitiniai Household mieste urban Household Buitiniai kaime rural Commercial Komerciniai under iki kw kw Commercial Komerciniai over virš 30 kw Detailed annual call centre cost savings based on consumer groups are presented in Appendix Absence of standard meter electricity costs By rolling-out the smart metering system, standard meter electricity costs will be eliminated. Based on the DSO-supplied data, weighted average costs of electricity in standard meters are as follows: Annual costs of electricity in a single-phase meter is 11 kwh; Annual costs of electricity in a three-phase meter is 20 kwh; Savings due to a loss of electricity consumption in standard meters are calculated according to the following formula: Number of installed single-phase smart electricity meters (1) x standard meter electricity costs x (electricity purchase price + transfer price + VIAP) (2) + Number of installed three-phase smart electricity meters (1) x standard meter electricity costs x (electricity purchase price + transfer price + VIAP) (2) (1) See Table 10 (2) See chapter 6.2 The following figure displays average electricity cost savings in standard meters per year. 58

59 Figure 18. Average annual savings of electricity consumption in standard meters Total: Total: Total: Base Bazinis case scenarijus scenario Advanced Išplėstinio functionality funkcionalumo scenario scenarijus Jungtinės Multi-metering apskaitos scenario scenarijus Household Buitiniai mieste urban Household Buitiniai kaime rural Commercial Komerciniai under iki kw kw Commercial Komerciniai over virš 30 kw The average annual savings in the Advanced Functionality scenario are the greatest, since the maximum number of electricity accounts is replaced compared to the Basic case or Multi-metering scenarios. Detailed savings due to annual electricity cost reduction in standard meters based on consumer groups are presented in Appendix Summary of benefits for the smart metering system implementer Preceding chapters describe and estimate the benefits and savings that a smart metering system implementer (i.e. the DSO) would accrue. These benefits are as follows: Elimination of the need for replacement of outdated meters with new standard meters; No need for standard meter installation costs; No need for standard meter scheduled inspections; Improved cash flow management; Reduced call centre costs; Absence of standard meter electricity costs; The following tables provide all operating costs according to each component and operating costs for one meter. Table 39. Benefits for the smart metering system implementer in the Basic case scenario. Benefits Total benefits (discounted), LTL: Benefits for one meter (discounted), LTL: No need to install standard meters No replacement of standard meters No need for standard meter scheduled inspections Improved cash flow management Reduced call centre costs

60 Benefits Absence of standard meter electricity costs Total benefits (discounted), LTL: Benefits for one meter (discounted), LTL: Total: Note: 5 % discount rate applied Table 40. Benefits for the smart metering system implementer in the Advanced Functionality scenario. Benefits Total benefits (discounted), LTL: Benefits for one meter (discounted), LTL: No need to install standard meters No replacement of standard meters No need for standard meter scheduled inspections Improved cash flow management Reduced call centre costs Absence of standard meter electricity costs Total: Note: 5 % discount rate applied Table 41. Benefits for the smart metering system implementer in the Multi-metering scenario. Benefits Total benefits (discounted): Benefits for one meter (discounted): No need to install standard meters No replacement of standard meters No need for standard meter scheduled inspections Improved cash flow management Reduced call centre costs Absence of standard meter electricity costs Total: Note: 5 % discount rate applied Total annual benefits for the smart metering system implementer are provided in the following figure. They are calculated by summing up all benefits annually. 60

61 Lt Figure 19. Smart metering system implementer s benefits Bazinis scenarijus Išplėstinio funkcionalumos scenarijus Jungtinės apskaitos scenarijus Average annual benefit for a smart metering system implementer consisting of the above listed parameters is 23 : In the Basic case scenario 16.1 million LTL; In the Advanced Functionality scenario 17.9 million LTL; In the Multi-metering scenario 16.1 million LTL; Average annual benefits for one meter are: In the Basic case scenario 10.3 LTL; In the Advanced Functionality scenario 9.22 LTL; In the Multi-metering scenario 10.3 LTL. Detailed annual savings for a smart metering system implementer based on consumer groups are provided in Appendix Calculation of financial ratios for investments To estimate a benefit of the planned investments today, the Project financial analysis for investments was executed and the financial net present value (FNPV) was calculated. Figure 20. Calculation of financial ratios for investments. Total capital investment Financial investment profitability Costs and benefits 23 Average annual costs calculated by summing up all respective benefits for a period of and by dividing it by a project activity period (14 years). 61