Renewable Energy Management System (REMS): Using optimisation to plan renewable energy infrastructure investment in the Pacific

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1 Renewable Energy Management System (REMS): Using optimisation to plan renewable energy infrastructure investment in the Pacific Abstract: Faisal Wahid PhD Student at the Department of Engineering Science, University of Auckland Volunteer for Engineers Without Borders New Zealand Ula Alaward BE Graduate of the Department of Engineering Science, University of Auckland Dr. Michael O Sullivan Senior Lecturer at the Department of Engineering Science, University of Auckland [email protected] Dr. Cameron Walker Senior Lecturer at the Department of Engineering Science, University of Auckland [email protected] The Islands of the Pacific are heavily dependent on fossil fuels for the majority of their energy needs. This means that their economies are vulnerable to petroleum price spikes. As a consequence, many Island Nations have energy roadmaps that focus on renewable energy generation. However, local generation by heavy consumers (such as schools) is an important part of these roadmaps and the best investment plans for these consumers are complicated to develop. In this paper we present REMS, an optimization tool for developing plans for both investment in renewable energy infrastructure and electricity generation. We describe the optimisation model behind REMS and present a case study that demonstrates REMS in practice. Using REMS, local schools can quickly develop optimal plans for both investment and generation which enables planners to investigate the effect of different factors such as interest rates and demand growth. REMS represents an important contribution to the realisation of renewable energy roadmaps in the Pacific Islands. Keywords: Optimisation, Development Analytics, renewable energy, Off-grid electricity, diesel energy, wind energy 1. INTRODUCTION The Islands of the Pacific vary greatly in terms of size, population and geography. But Pacific Island Nations have one thing in common: they are heavily dependent on fossil fuels for the majority of their energy needs. It is estimated that over 85% of their energy consumption is met by fossil fuels (Niles & Lloyd 2013). Most of the Pacific Island Nations do not produce oil and gas within their own borders, which forces them to import their fuel. Some nations total petroleum imports cost up to 500% of their total export revenue (Jafar 2000). This makes the economies of the Pacific Island Nations vulnerable to price shocks, such as those resulting from the petroleum crises of Pacific Island Nations were the most adversely affected as they could not negotiate discount fuel prices due to their limited demand (Niles & Lloyd 2013). Furthermore due to their geographical isolation from major transportation Page 1 of 11

2 routes, many nations face premiums on the cost of transporting fuel. A great example of such dependency is explained further by (Niles & Lloyd 2013) with a chart illustrating the close correlation of overseas development aid (ODA) per capita and World Oil prices for Small Island Developing States (Figure 1). Figure 1: Net ODA per capita received by Small Island Developing States compared to World Oil Prices The heavy dependence on imported fuel coupled with the increasing fuel prices has prompted policy makers and governments in the South Pacific to look for alternative energy supplies. Particular focus has been directed towards local renewable energy generation. As a consequence many Pacific Island Nations have been working to develop energy policies aimed to reduce their dependence on fossil fuels. These policies include plans to construct local renewable energy generation sources. The Kingdom of Tonga is one of the nations which have established a 20 year plan to meet 50% of their electricity generation through renewable energy sources (TERM 2010). As part of their road map, special attention has been paid towards schools in Tonga, which are primary electricity consumers. They are usually large in size and require a regular supply of electricity, especially during school hours (Wijekoon 2011). Tongan schools are mainly dependant on the government for their financial needs, a large component being electricity bills. Generally, they are connected to a national grid that supplies electricity by the burning of imported diesel fuel. Smart investments into renewable off grid systems would greatly reduce the operational costs of these institutions. The challenge is to develop a long term energy production plan that empowers schools to be energy independent by appropriately investing in micro renewable generation technology and smart energy management. In this paper we propose an optimisation model, the Renewable Energy Management System (REMS), for planning electricity management. REMS can be used by Tongan schools and will include micro renewable generation technology. REMS assesses different technology attributes and determines a long term energy production plan to minimise the total cost of energy consumption. We include a 10 year case study. The paper is divided into 8 sections. Section 2 introduces the decision problem of establishing a renewable energy portfolio and production plan in local schools using REMS. Section 3 describes the high level formulation of REMS leading onto section 4, which presents our case study and discusses the results obtained using REMS. Section 5 presents the conclusions drawn from this case study with Page 2 of 11

3 respect to value added in decision making and discusses some of the limitations of REMS. The remaining appendices supplement sections 1 to 5 with more detailed information and references. 2. PROBLEM DESCRIPTION The Renewable Energy Management System (REMS) model aims to answer two planning questions: 1. How and when should investment in electricity generation infrastructure happen? 2. How should the infrastructure be used for different scenarios of electricity demand? The first question requires strategic decisions about investment in current electricity generation technologies and the second question requires a detailed electricity generation plan for each electricity demand scenario. The optimal strategic decisions depend highly on the costs of the various available technologies, but probably more on the efficiency of each technology in the location under consideration. In the case study presented here (see Section 4), we considered renewable generation via solar and wind technologies, generation using a diesel generator on-site and supply of electricity from the local grid. Note that in Tonga, the local grid also uses diesel to generate electricity, but due to economies of scale and electricity pricing plans the fixed cost of getting electricity from the grid is higher than onsite diesel generation whereas the per kwh cost of electricity from the grid is lower than that of on-site diesel generation. For both solar and wind technologies, the average meteorological conditions were assumed to prevail, i.e., the total possible generation from a solar panel was calculated from mean sunlight hours each month in Tonga and the total possible generation from wind turbine was calculated from the average wind speed each month in Tonga. Once the generating capacity of each technology was determined, the decision in each month of the planning period was the number of units of each technology to add to the infrastructure and the number of units to remove from the infrastructure. For both diesel generation and supply from the grid, at most one unit could be added or removed as this corresponded to running the generator or not and utilising a retail supplier or not respectively. The costs associated with the strategic decisions were the capital expenditure of adding units and the maintenance costs of each unit (e.g., the cost of maintaining a wind turbine or a diesel generator or the monthly fixed cost for the grid). Once the infrastructure development has been planned, then the maximum capacity for electricity generation in each month is known and the actual generation for each technology can be determined. This sub-problem can be solved in a greedy way by using the different technologies in order of least expensive generation per kwh until demand is met. However, the integer programming (IP) model in Section 3 can determine both the (strategic) infrastructure development decisions and the (operation) monthly generation decisions simultaneously, thus taking advantage of cost efficiencies that can be difficult to realise when solving the two problems separately. Furthermore, a single set of infrastructure development decisions can be used in conjunction with multiple demand scenarios, to determine the best expected cost of both infrastructure development and generation planning across the scenarios. This provides a level of robustness for the infrastructure development. The concept of linking infrastructure decisions across multiple scenarios can be extended to enable multi-stage adaptive infrastructure development, but this extended approach is not considered here due to the complexity of the modelling, the difficulty in scenario creation and the computational time required to solve this extended model. Page 3 of 11

4 3. MODEL DEVELOPMENT Given the problem description, the optimal plan for development of electricity generation infrastructure may be formulated as an Integer Programming (IP) model within REMS. In this paper, the objective of the development plan is to meet demand requirements in each month at the lowest cost possible. Note that the cost is discounted according to the risk-free interest rate, i.e., the objective is actually to minimise the net present value of cost of the development plan. The inputs for REMS are: 1) The maximum generation in kwh that can be produced by each unit of a technology type in a month; 2) One or more scenarios of demand for electricity (in kwh per month) over the planning horizon; 3) The installation cost (in $) for each unit of a technology type; 4) The monthly maintenance cost (in $) for each unit of a technology type; 5) The generation cost (in $ per kwh) for each technology type. Given these inputs, REMS considers decisions at two levels: 1) The strategic decisions: the number of units of each type of technology installed and/or decommissioned in each time period; 2) The operational decisions: the kwh generated by each type of technology in each time period for each scenario. Figure 2 shows part of the REMS solution for our case study (presented in Section 4): 1) The local school installs 36 5kW turbines in Jan 09, followed by 24 more in Feb 09; 2) The local school elects to disconnect from the public grid in Jan 09, reconnect in Feb 09 and disconnect again in Apr 09. Added Jan 09 Feb 09 Mar 09 Apr 09 5kW Wind Turbine Grid Removed Jan 09 Feb 09 Mar 09 Apr 09 5kW Wind Turbine Grid Figure 2: Example infrastructure decision from the solution to a development model Page 4 of 11

5 Recall (from Section 2), in this paper, we assume that the same infrastructure decisions must be made for all demand scenarios (there will be more discussion about this assumption in Section 5). This means that the same generation capacity is available in each scenario, but different generation decisions can be made in each scenario in order to meet the demand of the specific scenario. Error! Reference source not found. shows the generation for scenarios that define the baseline demand (0% growth), 10% growth, 20% growth and 30% growth of the baseline demand, where the probability for each of these 4 scenarios is 0.25, 0.5, 0.15 and 0.01 respectively. Actual Generation (Demand scenario growth = 0%, P = 25%) Jan-09 Feb-09 Mar-09 Apr-09 5kW Wind Turbine 44,907 59,198 59,198 31,733 Grid 0 18,253 1,806 0 Actual Generation kw s (Demand scenario growth = 10%, P = 50%) Jan-09 Feb-09 Mar-09 Apr-09 5kW Wind Turbine 45,281 59,198 59,198 32,791 Grid 0 19,544 3,331 0 Actual Generation in kw s (Demand scenario growth = 20%, P = 15%) Jan-09 Feb-09 Mar-09 Apr-09 5kW Wind Turbine 45,655 2,582 3,050 33,849 Grid 0 77,451 61,004 0 Actual Generation in kw s (Demand scenario growth = 30%, P = 10%) Jan-09 Feb-09 Mar-09 Apr-09 5kW Wind Turbine 46,029 3,873 4,575 34,906 Grid 0 77,451 61,004 0 Figure 3: Example generation decision from the solution to a development model Page 5 of 11

6 The decisions need to meet two constraints: 1) Demand in each month of each scenario must be met by electricity generation from the technology types; 2) The monthly generation from a specific technology type in each scenario must be less than the maximum generation possible from that technology type. Note that this upper bound is determined by the number of installed units of that technology type, i.e., it is given by the infrastructure variables (that are the same for all scenarios). There may be further constraints necessary to implement other constraints that are particular to the technology types or the school under consideration, but they will not be considered in this paper. 4. CASE STUDY AND RESULTS Recall (from Section 3), the inputs for REMS are: 1) The maximum generation in kwh that can be produced by each unit of a technology type in a month; 2) One or more scenarios of demand for electricity (in kwh per month) over the planning horizon; 3) The installation cost (in $) for each unit of a technology type; 4) The monthly maintenance cost (in $) for each unit of a technology type; 5) The generation cost (in $ per kwh) for each technology type. A single case study was used to validate and analyse model. Background information for the case study was provided by Engineers Without Borders New Zealand (EWBNZ) from their work in Tongan schools. The case study involves a mixture of real and representative data. Regrettably there was a lack of historical electricity consumption data from the Tongan schools for use in REMS. Hence the data associated with the demand for electricity (input 2) are monthly consumption from a local New Zealand school. It is as an approximation to demand from a Tongan school. We have not enforced any spatial constraints on the number of renewable technologies that can be place on location. However it is not difficult to supplement the model with additional constraints that restrict the number of feasible installations of infrastructure. The generation costs for wind is zero as wind is a free renewable energy source. The technology specifications, associated with inputs such as the installation costs; monthly maintenance cost and maximum generation for the different wind turbines were provide by First Wind Turbine Manufacturing Company. As for diesel generators; data for inputs such as electricity generation cost ($/kw s) was gathered from the United States Energy Information Authority (USEIA 2012). A constant maintenance cost was introduced for diesel generators. Most institutions in Tonga already have diesel generators, including the Tongan schools that EWBNZ work with. Therefore installation costs (input 3) are zero for diesel generators because this is already a sunk cost and is irrelevant for the problem data. Initially solar Photo Voltaic (PV) technology was added to the portfolio. However with closer inspection of the amount generated it was decided to remove it from the portfolio because of its small generation capacity. For example a 4.8kW PV cell array would on average generate 500kWh s of electricity per month, whereas a similar capacity wind turbine (5kW s) would generate nearly three times as much (1286 kwh s) per month with similar installation costs. Recall the two key decision questions stated section 2 which were: Page 6 of 11

7 1. How and when should investment in electricity generation infrastructure happen? 2. How should the infrastructure be used for different scenarios of electricity demand? In the remainder of this section we discuss the quality of the solutions produced by REMS with regards to the plan of investing in renewable electricity generation infrastructure and electricity generation. The results show that including a Net Present Value (NPV) value in REMS is significant, as it enables smarter technology purchases that reduce the overall cost. Realistically the future value of the technology decreases with time and not all generation assets needs to be purchased and installed to meet the entire forecasted demand scenario. Additional generation assets can be purchased and installed closer to the periods when there is demand peaks. This provides an opportunity for schools to better time their technology purchases and instalments in order to meet future demand with minimal cost. This is highlighted by comparing two solutions of REMS; one with 5% NPV with the other with 0% NPV value as illustrated by Figure 4 and Figure 5 respectively. Figure 4 illustrates that sixty 5kW wind turbine units were initially purchased and installed over the first two months (January and February 09). Additional units are purchased and installed periodically each year during November and January. Overall 106 wind turbines were purchased and installed when including the 5% NPV. When not including a NPV a total of one hundred and eleven 5kW wind turbine units were purchased at the beginning of the planning horizon. Under the baseline demand scenario and with the scenario of 10% increase in demand (see Figure 6 and Figure 7) almost all the demand is met by the installed wind turbines. This is because the generation cost of consuming electricity from the grid and diesel is significantly more expensive than the cost of installing and maintaining wind turbines. It is only when the demand scenario is inflated by 20% and above (see Figure 9 and Figure 10) that the next economical option is drawing power from the grid. However drawing power from the grid is only justifiable in those periods when wind generation capacity is exhausted for long durations. Diesel generators are only used on one occasion (in March 2011, see Figure 10) in the most extreme demand scenario (30% increase in demand). This is because REMS is able to make the trade-off between the marginal cost of running diesel generators for shorter periods as a more economical option than connecting to the grid. Connecting to the grid requires paying for hefty connection fees and thus in order to benefit from cheap power schools have to draw from the grid for a long period time. Figure 4: Change in infrastructure with 5% net present value Page 7 of 11

8 Figure 5: Changes in infrastructure without zero net present value Figure 6: Operation plan with baseline demand scenario Figure 7: Operation plan with 10% increase in demand scenario Page 8 of 11

9 Figure 8: Operation plan with 20% increase in demand scenario Figure 9: Operation plan with 30% increase in demand in scenario Figure 10: Instance of diesel generator usage for 30% increase in demand scenario Page 9 of 11

10 5. CONCLUSION Our proposed optimisation model, termed REMS, for renewable energy management can be used to develop robust renewable energy generation technology portfolios and flexible generation plans at minimum net present cost. Through REMS we are able to show that optimisation adds value to the decision process by illustrating the effect of various factors, such as the risk-free interest rate and differing levels of electricity demand, with respect to both investment in renewable generation technology and monthly electricity generation. We also illustrate, through REMS, the trade-off in marginal cost of running renewable generation with respect to grid supply and diesel generation. REMS highlights that grid supply is only economically justifiable under extreme increases in demand over long periods of time. Our modelling framework enables consideration of uncertainty by including multiple scenarios of possible future levels of demand, and assigning probabilities to each of these. Our model could also be used with a larger collection of scenarios generated via Monte Carlo techniques that utilise appropriate distributions. In cases where these distributions are difficult to determine it would be easy to modify our approach to include very pessimistic realisations of demand, and then have the solution meet a certain level of service (for example insisting that at any point in time at least 95% of the scenarios demands are met). One limitation of our current model is the lack of recourse for infrastructure investment decisions. For example, if low growth of demand was observed, the purchase of additional turbines in November 2015 could be delayed with a subsequent reduction in net present cost. As stated earlier (in Section 2), a more complex model could enable these recourse decisions, but this is the focus of future research. However, the grid supply is only used in later months (e.g., from 2017) and is an easy decision to reverse, hence some recourse is possible in the current solution, but it is not directly included in the model. We believe that optimisation models such as REMS can empower Tongan schools and other institutions in the Pacific to take the step to becoming energy independent. Long term it offers cost savings which frees up capital that can be better spent on other areas in need. REMS can help schools and institutions to justify subsidies from the government or other donor partners for the investment in renewable energy technologies. Schools can also use REMS to develop financial plans for the future with regards to the purchase of new technologies and maintenance or sale of existing assets. Furthermore schools can negotiate with their power suppliers on cheaper future contracts as they are able to better forecast the effect of these contracts on future consumption of electricity from the suppliers. The overarching issue of energy independence of South Pacific Island Nations from imported fossil fuels is not just an economic problem. (Woodruff 2007) have stressed the need for developing effective models for managing renewable energy projects to ensure long term sustainability. Others such as (Yu & Gilmour 1996) and (Niles & Lloyd 2013) have identified the need for greater emphasis on improving the capability of institutions and developing local human resources as means to build the local capacity in energy planning, system management and project organisation. The challenge for any tools like REMS is to empower the local sectors to be more self-sufficient by providing them with the insights into smart decision making and effective management. Page 10 of 11

11 APPENDIX A. MATHEMATICAL PROGRAMMING FORMULATION 1 ( ) ( ) ( ) ( ) 6. ACKNOWLEDGEMENTS Acknowledgements can be made before the references if pertinent to the project. Use Level 2 Heading and Body text style. 7. REFERENCES Jafar, M., Renewable energy in the South Pacific - Options and Constraints. Renewable Energy, 19, pp Niles, K. & Lloyd, B., Small Island Developing States (SIDS) & energy aid: Impacts on the energy sector in the Caribbean and Pacific. Energy for Sustainable Development, 17(5), pp Available at: [Accessed January 10, 2014]. TERM, TONGA ENERGY ROAD MAP, Wijekoon, D., Energy Awareness in Schools M1 Basics. Woodruff, A., An Economic Assessment of Renewable Energy Options for Rural Electrification in Pacific Island Countries., (February). Yu, X. & Gilmour, A., Current Limitations on further introductions of renewable energy systesm in the South Pacific. Energy Policy, 24(8), pp U.S. Energy Information Administration, n.d. Gasoline and Diesel Fuel Update 2012.[Online] Available at: [Accessed 2012]. 1 Note, this means the decision on when to implement technology options is independent of scenario. More sophisticated modelling would have decisions for individual scenarios, i.e.,, but these decisions would have to be the same for scenarios and periods where the histories of the scenarios up to that period were identical. However, this requires bundling of identical scenarios up to some period t in which the scenarios then differ. This is the focus of future modelling work. Page 11 of 11