Electricity Projection with Peak Load Shifting Strategy in Wuxi Sino-Swedish Eco-City. Chang Su

Transcription

1 Electricity Projection with Peak Load Shifting Strategy in Wuxi Sino-Swedish Eco-City Chang Su Master of Science Thesis KTH School of Industrial Engineering and Management Energy Technology EGI MSC Division of Applied Thermodynamics and Refrigeration SE STOCKHOLM

2 Master of Science Thesis EGI MSC Electricity Projection with Peak Load Shifting Strategy in Wuxi Sino-Swedish Eco-City Chang Su Approved Examiner Supervisor 18th Oct, 2013 Professor Per Lundqvist Commissioner Mr. Omar Shafqat Contact person Omar Shafqat Abstract Wuxi Sino-Swedish Eco-City, a pilot city region with an area of 2.4 km 2, is a demonstration project for innovation in energy technology and integrated smart city solutions in China. After the 1st phase of the project, general outlines of the city s energy system were drawn and applicable technologies are provided. However, no work has been performed on building electricity load projection and load analysis. This thesis will therefore firstly focus on establishing the building electricity load projection model, using simulation software STELLA. Then the model is scaled up for the whole city region. The simulation results show that there is foreseen to be electricity peak in summer and winter, due to the cooling and heating demand. Based on simulation results, an electricity DSM (demand side management) strategy should be implemented in order to balance the load. Peak load shifting strategy is thus chosen to be investigated. Two technology options (ice-storage system and thermal storage system), which could be implemented to balance the electricity peak, is analyzed by scenarios. Also, commercial feasibility of implementing such technologies is discussed. Keywords: Eco-City, simulation, electricity DSM, peak load shifting, heating and cooling demand, ice-storage, heat pump 2

3 TABLE OF CONTENTS ABSTRACT CHAPTER ONE: INTRODUCTION INTERNATIONAL BACKGROUND CHINA S NEW TARGET IN 2020 AND WUXI SINO-SWEDISH ECO-CITY PROJECT RESEARCH QUESTION CHAPTER TWO: LITERATURE STUDY REVIEW OF OTHER ECO-CITY PROJECTS HAMMARBY SJÖSTAD SINO-SINGAPORE ECO-CITY SYSTEMS THINKING METHOD GENERAL PRINCIPLES DEMAND SIDE MANAGEMENT CHAPTER THREE: ENERGY SYSTEM ANALYSIS OF WUXI SINO-SWEDISH ECO-CITY SYSTEM BOUNDARY SIMPLIFICATION OF THE ENERGY SYSTEM FOUR STEP CALCULATION DEFINING REFERENCE ENERGY DEMANDS DEFINING THE REFERENCE ENERGY SUPPLY SYSTEM DEFINING THE REGULATION OF THE ENERGY SUPPLY SYSTEM DEFINING ALTERNATIVES CHAPTER FOUR: THE MODEL ASSUMPTIONS AND SIMPLIFICATIONS CONCEPTUAL MODEL MODULE FUNCTION EXPLANATION INTERFACE RESIDENTIAL SECTOR COMMERCIAL SECTOR COST SECTOR CHAPTER FIVE: RESULTS AND SCENARIOS BASELINE (REFERENCE) SCENARIO SCENARIO 1, ICE-STORAGE SYSTEM IMPLEMENTED RESIDENTIAL SECTOR COMMERCIAL SECTOR SCENARIO 2, THERMAL STORAGE SYSTEM IMPLEMENTED RESIDENTIAL SECTOR COMMERCIAL SECTOR CHAPTER SIX: DISCUSSION COST ANALYSIS BASELINE SCENARIO SCENARIO 1, ICE-STORAGE SYSTEM IMPLEMENTED SCENARIO 2, THERMAL STORAGE SYSTEM IMPLEMENTED COMPARISON OF ELECTRICITY COSTS SENSITIVITY ANALYSIS

4 7. CHAPTER SEVEN: CONCLUSION CHAPTER EIGHT: FUTURE WORK BIBLIOGRAPHY

5 List of figures and tables FIGURE 1 GREEN HOUSE GAS CONCENTRATION INCREASE (SOLOMON, ET AL., 2007)... 7 FIGURE 2 HISTORICAL CO2 EMISSION OF CHINA (THE WORLD BANK, 2013)... 7 FIGURE 3 CHINA S URBANIZATION SPEED AND ENERGY INTENSITY (THE WORLD BANK, 2013)... 8 FIGURE 4 GEOGRAPHICAL LOCATION OF WUXI, FIGURE 5 THE SINO-SWEDISH ECO-CITY VISUAL EFFECT (WUXI INSTITUTE OF URBAN PLANNING AND DESIGN, 2010)... 9 FIGURE 6 THE WELL-KNOWN HAMMARBY PROJECT IN STOCKHOLM, 2013 (GLASHUSETT, 2007) FIGURE 7 BUILDING DESIGN OUTLOOK OF SINO-SINGAPORE ECO-CITY (SINO-SINGAPORE TIANJIN ECO-CITY ADAMINISTRATIVE COMMITTEE, 2008) FIGURE 8 DEMAND SIDE MANAGEMENT LOAD SHAPE CHANGE OBJECTIVES (CHENG, 2005) FIGURE 9 LOCATION OF WUXI SINO-SWEDISH ECO-CITY, FIGURE 10 URBAN PLANNING STRUCTURE (SHAFQAT, 2012) FIGURE 11 HOURLY RESIDENTIAL ACTIVITY SURVEY OF QINGDAO (EASTERN ONLINE CO.,LTD, 2010) FIGURE 12 HOME OCCUPANCY RATE FIGURE 13 FRIDGE OPERATION RATE FIGURE 14 LIGHTS OPERATION RATE FIGURE 15 TV OPERATION RATE FIGURE 16 COMPUTER OPERATION RATE FIGURE 17 MICROWAVE OVEN OPERATION RATE FIGURE 18 LAUNDRY MACHINE OPERATION RATE FIGURE 19 COOLING AND HEATING LOAD CALCULATION METHOD (ENGDAHL & WALLGREN, 2013) FIGURE 20 ASSUMED RESIDENTIAL BUILDING FROM DESIGN LAYOUT (ENGDAHL & WALLGREN, 2013) FIGURE 21 HOURLY COOLING DEMAND OF RESIDENTIAL BUILDING BLOCK 1 ON AUG 1ST FIGURE 22 HOURLY HEATING DEMAND OF RESIDENTIAL BUILDING BLOCK 1 ON JAN 1ST FIGURE 23 SCALED UP YEARLY SEASONAL DEMAND FOR RESIDENTIAL BUILDING BLOCK FIGURE 24 OFFICE ELECTRICITY BASELOAD FOR A NORMAL WORKING DAY FIGURE 25 HOURLY COOLING LOAD OF COMMERCIAL BUILDING BLOCK 14 ON AUG 1ST FIGURE 26 HOURLY HEATING LOAD OF COMMERCIAL BUILDING BLOCK 14 ON JAN 1ST FIGURE 27 SCALED UP HEATING AND COOLING DEMAND OF BUILDING BLOCK 14 IN A WHOLE YEAR FIGURE 28 PV ELECTRICITY GENERATION IN A WHOLE YEAR IN WUXI FIGURE 29 INTERFACE OF THE MODEL FIGURE 30 RESIDENTIAL SECTOR FIGURE 31 COMMERCIAL SECTOR FIGURE 32 COST SECTOR FIGURE 33 RESIDENTIAL ELECTRICITY LOAD FIGURE 34 COMMERCIAL ELECTRICITY LOAD FIGURE 35 TOTAL ELECTRICITY LOAD FIGURE 36 COOLING DEMAND OF RESIDENTIAL BUILDING BLOCK 1, AUG 1ST FIGURE 37 SIMULATION RESULT OF SCENARIO1, RESIDENTIAL BUILDINGS FIGURE 38 SIMULATION RESULT OF BASELINE SCENARIO, RESIDENTIAL BUILDINGS FIGURE 39 COOLING DEMAND OF COMMERCIAL BUILDING BLOCK 14, AUG 1ST FIGURE 40 SIMULATION RESULT OF SCENARIO1, COMMERCIAL BUILDINGS FIGURE 41 SIMULATION RESULT OF BASELINE SCENARIO, COMMERCIAL BUILDINGS

6 FIGURE 42 HEATING DEMAND OF RESIDENTIAL BUILDING BLOCK 1, FEB 1ST FIGURE 43 SIMULATION RESULT OF SCENARIO2, RESIDENTIAL BUILDINGS FIGURE 44 SIMULATION RESULT OF SCENARIO1, RESIDENTIAL BUILDINGS FIGURE 45 HEATING DEMAND OF COMMERCIAL BUILDING BLOCK 14, FEB 1ST FIGURE 46 SIMULATION RESULT OF SCENARIO2, COMMERCIAL BUILDINGS FIGURE 47 SIMULATION RESULT OF CENARIO1, COMMERCIAL BUILDINGS FIGURE 48 RESIDENTIAL ELECTRICITY COST, BASELINE FIGURE 49 COMMERCIAL ELECTRICITY COST, BASELINE FIGURE 50 RESIDENTIAL ELECTRICITY COST, SCENARIO FIGURE 51 COMMERCIAL ELECTRICITY COST, SCENARIO FIGURE 52 RESIDENTIAL ELECTRICITY COST, SCENARIO FIGURE 53 COMMERCIAL ELECTRICITY COST, SCENARIO FIGURE 54 COMPARISON OF RESIDENTIAL TOTAL ELECTRICITY COST IN DIFFERENT SCENARIOS FIGURE 55 COMPARISON OF COMMERCIAL TOTAL ELECTRICITY COST IN DIFFERENT SCENARIOS FIGURE 56 ELECTRICITY DEMAND OF RESIDENTIAL BUILDINGS FIGURE 57 ELECTRICITY DEMAND OF COMMERCIAL BUILDINGS TABLE 1 AREA ALLOCATION DATA (SHAFQAT, 2012) TABLE 2 SIMPLIFIED AREA ALLOCATION DATA TABLE 3 SIMPLIFIED LIST OF APPLICABLE TECHNOLOGIES TABLE 4 POSSESSION OF GOODS PER 100 HOUSEHOLDS IN WUXI CITY TABLE 5 COMPARISON OF QINGDAO AND WUXI TABLE 6 SUNRISE AND SUNSET TIME OF WUXI (TIME AND DATE, 1995) TABLE 7 ASSUMED SUNRISE AND SUNSET TIME TABLE 8 PARAMETERS INPUT TO DEISGNBUILDER (ENGDAHL & WALLGREN, 2013) TABLE 9 REFERENCE ELECTRICITY CONSUMPTION INDICATOR OF BEIJING LARGE-SCALE COMMERCIAL BUILDING (KWH/M 2 YEAR) (TSINGHUA UNIVERSITY DEST GROUP, 1995) TABLE 10 PEAK LOAD AND OFF-PEAK LOAD ELECTRICITY PRICE OF HEAT BOILER (ICE-STORAGE) IN JIANGSU PROVINCE (COMMODITY PRICE BUREAU OF WUXI CITY, 2006) TABLE 11 ICE-STORAGE SYSTEM OPERATION STRATEGY FOR RESIDENTIAL BUILDING BLOCK TABLE 12 SIZING OF ICE-STORAGE SYSTEM FOR RESIDENTIAL BUILDING BLOCKS TABLE 13 ICE-STORAGE SYSTEM OPERATION STRATEGY FOR COMMERCIAL BUILDING BLOCKS TABLE 14 SIZING OF ICE-STORAGE SYSTEM FOR COMMERCIAL BUILDINGS TABLE 15 THERMAL STORAGE SYSTEM OPERATION STRATEGY FOR RESIDENTIAL BUILDING BLOCKS TABLE 16 THERMAL STORAGE SYSTEM OPERATION STRATEGY FOR COMMERCIAL BUILDING BLOCKS TABLE 17 COMPARISON OF ELECTRICITY COSTS

7 1. Chapter one: Introduction 1.1. International background According to the report of IPCC, green house gases (especially CO 2 ) concentration is increasing rapidly due to anthropogenic influence dating from industrial evolution (Mets, et al., 2007). Figure 1 green house gas concentration increase (Solomon, et al., 2007) In the past decade, emerging countries such as China and India are gradually become major CO 2 emission economic bodies. 10,000 8,000 6,000 4,000 2,000 0 CO 2 Emission (Mt) 12,000 East Asia & Pacific (all income levels) EAS China CHN Year Figure 2 historical CO2 emission of China (The World Bank, 2013) After Copenhagen climate change conference in Demark, Dec 2012, though neither international protocol nor agreement was signed, declarations were made. Many countries are still making their own efforts towards a more efficient and sustainable development (UNFCCC, Conference of the Parties (COP), 2009). For instance, the European Union has set its own target of , which means by the year 2020, the EU member countries will have a 20% reduction of greenhouse gas emissions compared to 1990; a 20% increase in energy efficiency; and a 20% share of renewables including a 10% share for transport (European Comission, 2012). China is currently going under fast industrialization and urbanization. Pollution issues are severe and energy intensity is so high that obviously it is unsustainable. Together with huge population, cities are expected to play a vital role in future energy sector. 7

8 Energy intensity (kg oil equivalent per capita) Energy intensity Urbanization rate 60% 50% 40% 30% 20% 10% 0% Urbanization rate (% urban population) Year Figure 3 China s urbanization speed and energy intensity (The World Bank, 2013) 1.2. China s New Target in 2020 and Wuxi Sino-Swedish Eco-City Project With the improvement of life standard, the Chinese are asking more and more of a clear sky and clean air. Under such requirement, the central government of China clearly stated in the 12 th five-year plan that by the year 2020, the country s total CO 2 emission should reduce 40%~45% compared with the reference year of 2005 (Information Office of the State Council, 2012). The plan was aimed at developing the ecological civilization, as well as having an efficient use of natural resources under the current legislation frame. Many solutions will be implemented in order to fulfill the plan, especially in urban planning, civil construction as well as sustainable development in energy sectors. In response to the China s 12 th five-year plan, local governments started to upgrade their development model. Wuxi Sino-Swedish Eco-City is just such a project. With a district area of 2.4 km 2, the project will serve as a pilot case for different technological solutions in energy and sustainability. And it can be used as an experimental field or give inspirations for future innovation and large-scale implementation. Wuxi, locating in the northwest of Shanghai, is expanding its urban territory. With accelerating development of industrialization and urbanization, Wuxi is facing great environmental pressure under the fact that its ecological system is getting more and more fragile. In Aug 2009, former Premier Wen Jiabao paid a work inspection to Wuxi and required the local government to achieve a higher goal in developing the city to be eco-friendly, high technology, livable as well as compatible with modern service industry. Under such requirement, Wuxi local government started up the eco-city project. 8

9 Figure 4 geographical location of Wuxi, 2013 The project includes a broad aspect of innovation. Many new concepts and technologies will be either tested or implemented in sectors such as energy, water resource, waste management, transportation etc. This thesis work will focus on energy sector. Figure 5 the Sino-Swedish Eco-City visual effect (Wuxi Institute of Urban Planning and Design, 2010) 1.3. Research question Various reports have been done regarding energy sector of the eco-city project. However, no work has been performed on building electricity load projection and load analysis. This thesis will therefore firstly focus on establishing the building electricity load projection using simulation software STELLA. Then, what electricity demand side management strategy can be applied into the eco-city? What is the commercial and environmental potential of implementing the particular strategy? This thesis will secondly answer these questions based on simulation results. By applying demand side management strategy, which shifts the electricity peak to fill the valleys, both commercial and environmental values will be created (Strengers, 2012). 9

10 2. Chapter two: Literature Study 2.1. Review of other eco-city projects In the first part of literature study, some other eco-city projects are reviewed. After that, the general principles used in analyzing the energy system of this very project are introduced Hammarby Sjöstad Figure 6 the well-known Hammarby project in Stockholm, 2013 (GlashusEtt, 2007) As Stockholm s biggest environment project at the time, Hammarby locates in the south of the city. New requirements on land usage, building technologies, materials and other various goals are supposed to be achieved. The total environmental evaluation should be two times better as in the early 1990s (Iverot & Brandt, 2011). The energy sector requires the renewable technology plays an important role. Solar energy, biogas and reuse of waste heat are coupled in buildings to be efficient. Combined heat and power (CHP), heat plant, waste water treatment plant and solar energy will also serve in the area. (Iverot & Brandt, 2011) (Nakamura & Kondo, 2009). However, when Professor Nils Brandt performed an evaluation study on Hammarby after its completion, the study found that a number of the goals, especially on energy sector, had not been realized. Some of them were not clear and unrealistic, some goals lacked values to refer, some goals didn t have baseline to compare with (Iverot & Brandt, 2011). But one has to notice that the reason Hammarby didn t live up to its goal is because the environmental targets and technological solutions only came into serious consideration after Hammarby had started construction in the year If planned from the beginning since year 1990, Hammarby still got the chance to achieve its goal (Engberg & Svane, 2007). 10

11 Sino-Singapore Eco-City In November, 2007, China central government and Singapore government signed an official cooperation on developing an eco-city in the coastal area of southeast Tianjin city, in order to provide a sustainable development model for other cities in China or even the world. The development period is 10 to 15 years with expected population of 350,000. In this project, the energy goal is set to reach a renewable energy share of at least 20% by the year Photovoltaic, wind energy, ground source heap pump etc should be used. Also, smart grids should be implemented from the beginning. Towards the end of the year 2008, the general urban planning is completed and the local government is now trying to appeal as much investment as possible. Currently several Japan and Korea companies have participated in the project. Figure 7 building design outlook of Sino-Singapore Eco-city (Sino-Singapore Tianjin Eco-city Adaministrative Committee, 2008) 2.2. Systems thinking method General principles Under the situation that a number of renewable energy technologies have gradually been implemented in the existing energy system, modeling and simulation of the new energy system is an effective method to monitor and identify optimal operation strategies in energy system (Lund, 2009). Generally speaking, an energy system analysis model should fulfill the following 4 significant criteria (Lund, 2009): The energy system model should be able to make a consistent and comparative analysis of all alternative scenarios and the reference scenario in the question. This criteria point out that it is important to evaluate and calculate all the alternative scenarios as well as the reference equally. In this way, a proper basis for a consistent comparison can be made. The reference can be a provided proposal that one wish to compare with alternatives, or it may be an official plan that has already been made by local authorities. 11

12 The model should be able to analyze radical technological changes. This criteria show that the model should be able to analyze not only the existing system, but also other systems that are very different both technically and institutionally. The design of the model should not rely too heavily on the existing energy system. It should give flexibilities to imply future change. The model should be able to provide suitable information for feasibility studies and the design of public regulation measures based on concrete institutional economics. This means that except focusing on technological feasibility, the model should also analyze some relevant parameters such as external costs, job creation, industrial innovation etc. Market values, business and socioeconomic feasibility studies should also be carried out. The model including methodology and results should be communicative. Therefore, the model should be both transparent and coherent with it theme. This means that the model should have a documentation that is easily accessible, user-friendly as well as publicly available. Moreover, good references will further improve the model. In addition to the 4 important criteria mentioned above, the model should include the intermittencies of renewable energy. Thus the time step should be in hour or day which is dynamic. Besides, the fluctuation factor of renewable energy should also be considered. Based on the above principles, a dynamic electricity load simulation model is built in this thesis Demand side management Demand side management (DMS) is nowadays part of integrated resource planning (IRP). It is a series of methods to help utilities maintain the balance between electricity supply and demand under uncertain conditions (Gellings & Chamberlin, 1993). There are six traditional activities taken by utilities to alter the shape of the load, in order to balance the demand and supply: peak clipping, valley filling, load shifting, strategic conservation, strategic load growth and flexible load shape (Gellings & Chamberlin, 1993). Figure 8 demand side management load shape change objectives (Cheng, 2005) 12

13 In this paper, the main demand side management strategy investigated is load shifting. 13

14 3. Chapter three: Energy System Analysis of Wuxi Sino-Swedish Eco-City 3.1. System boundary As mentioned in chapter 1.2, Wuxi Sino-Swedish Eco-City is the 2.4km 2 demonstration area in the south of Wuxi city. The geographical location is showed as the following figure: Figure 9 location of Wuxi Sino-Swedish Eco-City, 2013 Therefore, the system boundary is chosen as the geographical boundary (the black square in the above picture). The energy system will only consider the part of the electricity demand and renewable energy supply within the geographical boundary. Since no heating boiler is allowed in this area (China Academy of Building Research, Shanghai Institute, 2010), cooling and heating load is covered by HVAC system which consumes electricity. Therefore cooling and heating demand is converted to electricity demand as well Simplification of the energy system There are different types of buildings such as residential buildings, commercial buildings, public buildings etc. According to the Implementation Guidelines on the Eco-city Indicator System of Taihu New City, Wuxi, building energy standard and installation requirements are not the same. In the model, we need to subcategorize the buildings and analyze them individually. The site allocation in the urban planning is as follow: 14

15 Data is given as: Figure 10 urban planning structure (Shafqat, 2012) Table 1 area allocation data (Shafqat, 2012) Block No. Land use Building type Site area (m 2 ) Plot ratio 1 Residential Res apartments Residential Res apartments Residential Res apartments Municipal Vacuum waste collection Education School Residential Res apartments Residential Res apartments Residential Res apartments Commercial Community centre/parks Residential Res apartments Commercial Community centre/parks Residential Res apartments Municipal Public square Commercial Low rise shops Commercial Low rise shops Commercial Low rise shops Commercial Low rise shops Commercial Shops and low carbon hotel Municipal Low carbon exhibition center Sports Sports stadium Total Area km2 Due to the lack of information, it is very hard to include the energy analysis of all types of buildings. Since municipal and education buildings only account for 3% of the total eco-city area, they can be neglected in electricity load analysis. Moreover, the electricity load is foreseen to be dominated by residential and commercial buildings, so that the sport stadium is omitted, too. With the above simplification, the new table excluding the omitted parts is: Table 2 Simplified area allocation data Block No. Land use Building type Site area (m 2 ) Plot ratio 15

16 1 Residential Res apartments Residential Res apartments Residential Res apartments Residential Res apartments Residential Res apartments Residential Res apartments Commercial Community centre/parks Residential Res apartments Commercial Community centre/parks Residential Res apartments Commercial Low rise shops Commercial Low rise shops Commercial Low rise shops Commercial Low rise shops Commercial Shops and low carbon hotel And the applicable technologies are: Table 3 Simplified list of applicable technologies Building Type Plot ratio Applicable Technology Residential 1 3 PV, heat pump, ice-storage Residential 2 2 PV, heat pump, ice-storage Commercial 1 (community center) 1, 2 PV, heat pump, ice-storage Commercial 2 (low rise shops) 0.5 PV, heat pump, ice-storage Commercial 3 (low carbon hotel) 0.5 PV, heat pump, ice-storage Therefore, only residential building and commercial building is considered and only the technologies listed in the table will be analyzed. Also, as shown in the figure of urban planning structure (figure 13), the site area is divided into several building blocks. Since at this stage of the project there are neither buildings nor residents, it is impossible to evaluate the electricity demand in the unit of one household, nor one building. Thus, the electricity demand is calculated and scaled up to one building block. Further explanation will be provided in chapter Four step calculation A four step calculation method is adopted in this paper (Lund, 2009) Defining reference energy demands In order to study the building electricity load in the future, projection needs to be made. Since no boiler is allowed in this area (China Academy of Building Research, Shanghai Institute, 2010), cooling and heating load is covered by HVAC system which consumes electricity. Therefore cooling and heating demand is converted to electricity demand. In this situation, the reference energy demand (electricity demand) is estimated from two building 16

17 types: residential building and commercial building. When conducting a demand side management study, many resources and efforts need to be put into a survey in order to provide a subsequent projection of load profile. The eco-city is still under construction, so it is impossible to get the user s real time electricity load profile. Since the goal is to give projections of electricity load, as long as the simulations are based on the same assumptions and approximation, the absolute magnitude of the load has no significant meaning. The total electricity demand for one building block unit is composed of baseload and seasonal load. Elect Demd tot = Baseload + Seasonal load Estimation of residential building electricity load a) Building baseload By studying the residents life-style, together with statistical data of general home appliances power, the estimated electricity baseload profile can be built by using a bottom-up methodology. Major home appliances and nominal power Major home appliances that account for electricity consumption are listed in the following table: Table 4 Possession of goods per 100 households in Wuxi city (Wuxi Statistical Bureau, 2012), (China electricity information open website, 2011) Commodity name Year 2011 (unit/100 households) 17 Average power (kw) Laundry machine ~0.38 Refrigerator ~0.13 Microwave oven ~1.5 Color TV set ~0.1 Computer Air conditioner ~1.3 Water heater Lighting ~0.1 It is assumed that after the construction is completed, each household will possess home appliances following the above statistical number. Life-style survey Since there is actually no residence in Wuxi Eco-City, it is impossible to make a real survey on people s life-style. Thus the result of a life-style survey conducted by Isurvey.com.tw is used to substitute Wuxi Eco-City. The survey is done in Qingdao, a coastal city in the north of Jiangsu Province. Since Wuxi and Qingdao are both rich and industrialized city, the life-style is assumed to be similar. Table 5 comparison of Qingdao and Wuxi Name Wuxi Qingdao GDP (billion Yuan) Urban citizen income (Yuan/a) Although the study was done in the year 2000, people s lives have changed greatly ever since, it is reasonable to assume that they still follow a similar life schedule. Except for one thing: people in

18 Wuxi (south China schedule) do not go home from work at noon whereas people in Qingdao (north China schedule) go home for lunch as they have a longer lunch break. The life-style pattern is adjusted and estimated according to the schedule. E.g. home occupancy rate will remain low at noon since most of the people stay at office to have lunch. When people go back home at night, lights and TVs are on. Laundry operation rate is evaluated from the section housekeeping. Microwave oven is used when people are eating. Figure 11 hourly residential activity survey of Qingdao (Eastern Online Co.,Ltd, 2010) Baseload estimation method From the picture above, it is possible to give the appliances hourly probability of operation. The home occupancy rate is given by reading the blue square dots on the up-left picture (e.g. for 100 households, if at a particular time there are 50 households with at least 1 person at home each, the home occupancy rate is 50%), where at 12:00 the upheaval is adjusted to remain flat since people will stay at office to have lunch rather than going home to cook. To indicate the difference between workdays and weekend, the weekend home appliance rate is adjusted accordingly. Home Occupancy Rate % 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Home Occupancy Rate Weekday Weekend Figure 12 home occupancy rate Fridge is assumed to be running all day long 18

19 Fridge 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Figure 13 fridge operation rate Lighting will have seasonal affect. Since daylight hours are different in the four seasons, it will lead to the different lighting probability. Wuxi is 31 N, so the sunrise and sunset time is: Table 6 Sunrise and sunset time of Wuxi (Time and Date, 1995) Season Date Sunrise Sunset Winter 1-Dec 6:38 16:54 1-Jan 6:56 17:06 1-Feb 6:50 17:32 Spring 1-Mar 6:25 17:56 1-Apr 5:47 18:17 1-May 5:13 18:37 Summer 1-Jun 4:54 18:57 1-Jul 4:57 19:06 1-Aug 5:14 18:53 Autumn 1-Sep 5:33 18:21 1-Oct 5:51 17:43 1-Nov 6:13 17:09 So the sunrise and sunset time is assumed to be: Table 7 Assumed sunrise and sunset time Season Month Sunrise Sunset Winter Dec, Jan, Feb 7:00am 5:00pm Spring Mar, Apr, May 6:00am 6:00pm Summer Jun, Jul, Aug 5:00am 7:00pm Autumn Sep, Oct, Nov 6:00am 6:00pm Therefore the lighting is adjusted based on the sunrise and sunset time 19

20 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Lighting summer spring, autumn winter 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Figure 14 lights operation rate The hourly operation rate of TV is directly read from the life-style survey graph. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% TV weekday TV weekend TV 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Figure 15 TV operation rate Computer is assumed to be the same as TV since they are both regarded as entertainment home appliances in this study. 20

21 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% computer weekday computer weekend Computer 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Figure 16 computer operation rate The hourly operation rate of microwave oven is read by cooking percentage from life-style survey. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Microwave oven Figure 17 microwave oven operation rate Laundry is read from housekeeping rate. 21

22 Laundry 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 Figure 18 laundry machine operation rate The load of one kind of appliance in one residential building can thus be determined by the following equation: Load i = (Number of households i Occupancy rate) (Operation percentage i Power of a appliance i ) The total load of one building block is simply the sum up of all the appliances. Load tot = Load i However, it has to be claimed that in a particular building block, the number of building and number of household are unknown yet, which means they have to be assumed. Then, the baseload profile of one building block can be generated. b) Building seasonal load Various projects have been done regarding the eco-city s energy system. Thus the cooling and heating load data is imported from another report which focused solely on residential cooling and heating demand. The data is obtained from DesignBuilder. The load calculation method is shown as following (Engdahl & Wallgren, 2013). Figure 19 cooling and heating load calculation method (Engdahl & Wallgren, 2013) 22

23 Figure 20 assumed residential building from design layout (Engdahl & Wallgren, 2013) Since there is no building on sight, inspired by a model picture, with the assumed parameters, the hourly heating and cooling load for one building can be obtained (Engdahl & Wallgren, 2013). Table 8 Parameters input to DeisgnBuilder (Engdahl & Wallgren, 2013) Parameters Value Unit Comments source Temperature Heating degree days for 1000<HDD18<2000 Heating degree Hot summer cold winter region in (Huang & 18 degrees (HDD) days National Building Standard for Deringer, 2007) Residential Buildings Cooling degree days for 150<CDD26<300 Cooling degree (Huang & 26 degrees (CDD) days Deringer, 2007) Building Structure Data Heavy construction - - Assumption (Huang & Deringer, 2007) Length 30 m Assumption Width 12 m Assumption Height 16.8 m Assumption Stories 6 stories Assumption Basement Yes Assumption Total floor area 2331 m 2 Assumption Glazing % Assumption Window frame Wooden - Assumption Apartment sizes for each floor Entrance, stairs and elevator for each floor 60/86 m 2 Assumption 41.6 m 2 Assumption HVAC system Constant volume, COP 1 - Assumption DesignBuilder standard Indoor height 2.5 m Assumption Exterior window shading Yes - Assumption Fresh air rate 7.5 1/(s person) (Sherman, 2012) Air changes per hour 0.7 1/(s m) (Abel & Elmroth, 2007) 23

24 Indoor lighting 2 W/(m 2 100lux) (Hanselaer, et al., 2007) Household Life-Style Family size 2.70 persons Tianchi Heat generation from DesignBuilder W/m 2 Different values depending on DesignBuilder occupants standard activity level in different rooms Standard Schemes Electrical apparatuses DesignBuilder W/m 2 Different values depending on DesignBuilder electricity consumption standard activity level in different rooms Standard Schemes Domestic water usage 120 1/(person day) Lin, Feng, 2011 Domestic hot water usage 1583 kwh/year In average for a 80m 2 apartment in Changsha Taichi Comfortable indoor temperature in winter Comfortable indoor temperature in summer Temperature in common areas winter Temperature in common areas summer 20 Garde Garde Garde Garde.2013 Supply air temperature 17 Operation ratio for heating and cooling Heating: 1 st Nov~31 st May Cooling: 1 st Jun~30 th Sep - Taichi Weekend operation of heating and cooling 7am~1am - Assumption taichi Weekday operation of heating and cooling Building U-values Exterior wall 1.0 W/(m 2 K) Walls between unheated and heated spaces 2.0 W/(m 2 K) Entrance door 3.0 Exterior window 20%<WWR<30% Shading coefficient ESW/N Skylight window<4% of roof area Shading for skylight window 3.2 W/(m 2 K) 0.7/0.8 W/(m 2 K) 3.2 W/(m 2 K) 0.5 W/(m 2 K) Roof 0.8 W/(m 2 K) 24

25 Floors between unheated and heated spaces 2.0 W/(m 2 K) Floors exposed to outdoor air 1.5 W/(m 2 K) However, it has to be claimed that in a particular building block, the number of building and number of household are unknown yet, which means they have to be assumed. But for the cooling and heating load, one method could be applied to scale up the load for a particular building block. As the sample building total floor area is given (2331m 2,see table 10 above), kw/m 2 can be calculated. With site area and plot ratio given in allocation data (in table 4), the total floor area of a certain residential building block can be calculated. Total loor area = Site area Plot ratio Thus the hourly seasonal load of a certain commercial building can be calculated. Hourly load of a particular residential building block = kw Total loor area m2 Cooling load From the dada sheet, for a single building, the cooling load for a typical summer day is shown as a representative. The COP of an air-conditioner can range from 2.6 to 2.8 according to national standard (General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China & Standardization Administration of the People's Republic of China, 2010) cooling load Figure 21 hourly cooling demand of residential building block 1 on Aug 1st Heating load From the data sheet, for a single building, the heating load for a typical winter day is shown as a representative. The COP value of the heat pump can range from 3 to 4 according to national standard. 25

26 heating load Figure 22 hourly heating demand of residential building block 1 on Jan 1st heating demand Figure 23 scaled up yearly seasonal demand for residential building block Estimation of commercial electricity load a) Building baseload Since the commercial buildings can still be subcategorized into office building, shopping mall, hotel etc, and office equipments are various, the bottom-up method can no longer be used. Thus the load projection can be built based on total annual electricity consumption and its distribution. Due to the lack of data, a research data conducted in Beijing is used in this model. Table 9 Reference electricity consumption indicator of Beijing large-scale commercial building (kwh/m 2 year) (Tsinghua University DeST Group, 1995) Sector Public Building Office Building Hotel Commercial building Lighting Office Equipment Lift Drinking Fountain

27 and Electric Boiler Hot Water Pump As can be seen from the data, lighting, office equipment and lift compose the major electricity baseload consumption. For baseload, it doesn t fluctuate greatly in workdays during the whole year compared with seasonal load. So given the kwh/m 2 a, divided by 365 days, the electricity consumption per square meter for a whole day can be acquired (kwh/m 2 ). Multiply this number by the total building floor area, the total baseload electricity consumption for the whole building can be calculated. Then the dynamic hourly load can be calculated by multiply the baseload electricity consumption for the whole building with the weight factor of office occupancy rate. hourly load = kwh/m2 a total building loor area 365 weight factor of of ice occupancy rate For a normal workday, the office occupancy rate is adjusted accordingly. The hourly electricity base load can be: Figure 24 office electricity baseload for a normal working day Scaled up for a whole year, the commercial electricity baseload can be acquired. b) Building seasonal load There is currently no real commercial building in the eco-city; the data is obtained from DesignBuilder. The parameters are chosen from the database of DesignBuilder. Cooling load From the dada sheet, the cooling load for a typical summer day is shown as a representative figure. 27

28 1000 commercial cooling load Figure 25 hourly cooling load of commercial building block 14 on Aug 1st Heating load commerical heating load 2500 Figure 26 hourly heating load of commercial building block 14 on Jan 1st 2000 heating load cooling load Figure 27 scaled up heating and cooling demand of building block 14 in a whole year 28

29 Defining the reference energy supply system Electricity grid In the eco-city electricity system, buildings will mainly consume electricity from the grid. Renewables at this stage can only serve as a supplement. For cooling supply, air-conditioning devices are installed. For heating supply, heat pumps are installed. Both heating and cooling demand is converted to electricity demand, which is covered by local grid Photovoltaic panels The data of PV system is imported from another project which estimated the PV potential of the eco-city (Zhang & Abuammuna, 2012) PV electricity generation Figure 28 PV electricity generation in a whole year in Wuxi Wind power Since Wuxi locates in poor wind energy potential area, wind power is not considered in the system Heat pump The heating load is covered by heat pumps whenever it is needed. The heating supply is converted to electricity supply. If COP of a heat pump is chosen, the electricity demand is: W h = COP h COP h : coefficient of performance of heat pump 29 Q h

30 Q h : nominal delivered heat by heat pump W h : nominal work input into heat pump Air-conditioning The cooling load is covered by air-conditioning whenever it is needed. The cooling supply is converted to electricity supply. If COP of an air-conditioner is chosen, the electricity demand is: W c = COP c COP c : coefficient of performance of air-conditioner Q c : nominal delivered cooling power W c : nominal work input into air-conditioner Q c Defining the regulation of the energy supply system Electricity supply strategy The regulation strategy can be chosen either as a technical optimization or from market point of view. This analysis will choose technical optimization as the regulation strategy, which means all kinds of technology required will be installed in the system. Electricity demand is always covered whenever it is needed. With integration of renewable energy system in the eco-city, the net electricity demand required from the grid (this part of electricity is supplied by power plants which burn fossil fuels, mainly coal) is: Net Demd grid = Elect Demd tot E PV Peak-load shifting strategy In demand side management, shifting or balancing the peak load is an important strategy. Wuxi locates in the hot summer, cold winter area of China. In a hot summer day, commercial buildings will consume a lot of electricity to provide indoor cooling. The electricity load is extremely high especially in the afternoon. It is defined as the peak load (Sadineni & Boehm, 2012). Peak load demand in commercial buildings can contribute up to 50% of its total electricity bill (JE, 1995). It will also bring unsteady effects to the electricity load (Newsham & Bowker, 2010). In order to shift the peak to valley area, various peak load shifting strategies has been reviewed (Sun, et al., 2013). In electricity price vary market such as China, it is profitable for commercial buildings to shed their peak load to off-peak period. This model will apply ice-storage device as the technical solution for peak load shifting strategy. Similarly, in winter time, thermal storage device could be applied to shift electricity peak. 30

31 Table 10 Peak load and off-peak load electricity price of heat boiler (ice-storage) in Jiangsu Province (Commodity Price Bureau of Wuxi City, 2006) Price (Yuan/kWh) Category Residential boiler or ice-storage Non-industry, normal business Time Off-peak 8:00~24:00 Valley 0:00~8:00 <1kV ~10kV <1kV ~10kV ~11kV Note: non-industry, normal business includes hotel, restaurant, office building, hospital etc. As shown in the table, during valley time when the electricity price is cheap, the ice-storage device will operate to make ice in order to store cooling energy. During the day time, the device will release the cooling energy by melting the ice and transport it to the building Defining alternatives The system is open, so whenever there is new and feasible technology, it can be applied into the system without too much barrier. Besides, if there can be better electricity load management strategy, it should be used. 31

32 4. Chapter four: the Model The model is built by using STELLA. The function of the model is to provide building electricity simulation and projections Assumptions and simplifications Firstly, some basic assumptions and parameters of energy portfolio are based on Wuxi Statistical Year book and the Construction Guidelines of Sino-Swedish Eco-City. The electricity demand projection is given by historical statistics and available data. Secondly, since it is hard to make a detailed energy simulation of each room, the simulation is based on one building block. Each building block will be assumed to have one central heat pump system and one thermal storage tank. Similarly, each building block will be assumed to have one central AC system and one ice-storage device. When sizing the thermal storage and ice-storage device, the energy conversion factor is assumed to be 100%, which means no losses will be considered. Thirdly, electricity has to be consumed when it is generated. It is every expensive to store in a large scale with current technologies. So in this model, electricity storage system is not considered. Thus the model is a real time dynamic electricity simulation model Conceptual model There are three types of values in the model, the input parameters, calculation parameters and the output parameters. The input parameters are values needed to get the results. They are data that obtained from statistics, survey or database. The calculation parameters are used to process the simulation. The output parameters are model results. They are the residential electricity load profile, commercial electricity load profile, total CO 2 emission etc. System Boundary Input parameter Room occupancy rate; Number of household; Home appliances power; Appliances operation rate; Cooling demand; Heating demand; Electricity cost; Calculation Output parameter Electricity load profile; Total electricity consumption; Total primary fuel consumption; Total electricity cost; Total CO2 emission; Budget saving 32

33 4.3. Module function explanation In STELLA, the model contains 4 parts, the interface, the residential sector, the commercial sector, and the cost analysis sector Interface Interface is for user friendly purpose. It shows the results of the simulation and the user can change the parameters to simulate different scenarios. The switch represents a policy decision. If the switch is on, it means the stake holder will invest a set of ice-storage or thermal storage system. If the switch is off, it means the stake holder will not invest in such a system. Figure 29 interface of the model Residential sector Building baseload is described by 6 major home appliances as explained in They are refrigerator, lighting, TV, computer, microwave oven and laundry machine. With the aggregation of all the home appliances, the building baseload is acquired. The net demand from the grid is then the building load plus the seasonal load minus the renewable electricity generation. 33

34 Figure 30 residential sector Commercial sector The commercial sector is also described by baseload and seasonal load. The data is generated by statistical report and DesignBuilder. Figure 31 commercial sector Cost sector Electricity cost is hourly demand multiplies hourly price. 34

35 Figure 32 cost sector 35

36 5. Chapter five: Results and Scenarios 5.1. Baseline (reference) scenario In baseline scenario, the simulation is run without any peak load shifting technology. It only reflects the projection of normal electricity load of Wuxi Sino-Swedish Eco-City. Figure 33 residential electricity load The total residential electricity demand is 25,797,130kW; the electricity cost is 11,698,301Yuan. Figure 34 commercial electricity load The total commercial electricity demand is 13,312,468kW; the electricity cost is 9,036,487Yuan. Figure 35 total electricity load From the above figure, as expected, there exist two major peaks in electricity load profile, one in 36

37 summer time and one in winter time. Thus peak load shifting strategy is for sure to be applied in the eco-city Scenario 1, ice-storage system implemented In scenario 1, ice-storage system is implemented in the eco-city Residential sector By applying ice-storage system into the residential sector of eco-city, the summer peak load is balanced. Two problems need to be solved in this situation: the operation strategy of the ice-storage system and the sizing of the ice-storage system. 7.1~7.16, 50% working load 7.17~8.18, 100% working load 8.19~8.31, 50% working load By analyzing a typical summer day, e.g. Aug 1 st, the hourly cooling demand can be with a shape of the following figure: Figure 36 cooling demand of residential building block 1, Aug 1st From the above figure, it is obvious to see that electricity peak load demand appears in the noon (13:00~14:00) and in the evening (19:00~0:00). Thus, ice-storage device should work to store cooling power in valley hours when electricity price is cheap (2:00~7:00), and melt ice to provide cooling power in peak hours (13:00~14:00 and 19:00~24:00) in order to balance the peak. Sizing of the ice-storage system is determined by investigating the load profile of a certain building block. For residential building block 1, an ice-storage system with a power of 3000kW is installed. Considering both the operation strategy and sizing issue, conclusion can be reflected in the following table: Table 11 ice-storage system operation strategy for residential building block1 Time Cooling demand (kw) Ice supplied (kw) 37 Ice stored (kw) 0: (Valley) 0 0 1:00 0 (Valley) :00 0 (Valley)

38 With 3:00 0 (Valley) :00 0 (Valley) :00 0 (Valley) :00 0 (Valley) : (Valley) : (Valley) : (Peak) :00 0 (Peak) :00 0 (Peak) : (Peak) : (Peak) :00 0 (Peak) :00 0 (Peak) :00 0 (Peak) : (Peak) : (Peak) : (Peak) : (Peak) : (Peak) : (Peak) : (Peak) Stored cooling power = Supplied storage During 1:00~6:00 when electricity is at valley price, the ice-storage system is operated at nominal power, which means in each hour, 2878kWh cooling energy is stored. The accumulation stops at 6:00. So a total of 17,268kWh cooling energy is stored. Then during 12:00~13:00, when the first peak appears, the ice-storage system will provide 2158kWh cooling energy in each hour to help balance the peak. Then from 18:00 to 23:00 when the second peak appears, the ice-storage system will again provide cooling energy per hour to help balance the peak, until all cooling energy is consumed. Then the next day it starts the operation loop again. By investigating the cooling demand profile of all residential building blocks, the sizing is chosen as: Table 12 sizing of ice-storage system for residential building blocks No. of building block Sizing of ice-storage system (kw)