- Email: [email protected]
Application opportunity of vehicles-to-grid in Indonesian electrical grid
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect
Energy Procedia 00 (2018) 000–000 Available online www.sciencedirect.com Available online atatwww.sciencedirect.com Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2019) 000–000 621–626 EnergyProcedia Procedia160 00 (2017) www.elsevier.com/locate/procedia
2nd International Conference on Energy and Power, ICEP2018, 13–15 December 2018, 2nd International Conference on Energy and Power, ICEP2018, 13–15 December 2018, Sydney, Australia Sydney, Australia
Application opportunity of vehicles-to-grid in Indonesian electrical 15th International on District in Heating and Coolingelectrical ApplicationTheopportunity of Symposium vehicles-to-grid Indonesian grid grid Assessing theMuhammad feasibilityAziz ofa,*, using the heat demand-outdoor b Muhammad Huda a, b Muhammad Aziz *, Muhammad Huda temperature function for a long-term district heat demand forecast Institute of Innovative Research, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan a
a Department of Transdisciplinary ScienceTokyo and Engineering, Tokyo Institute of Technology, Nagatsuta-cho, Midor-ku, Yokohama, Institute of Innovative Research, Institute of Technology, 2-12-1 Ookayama, 4259 Meguro-ku, Tokyo 152-8550, Japan a,b,c a a 226-8503, Japanb c c b Kanagawa Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midor-ku, Yokohama, Kanagawa 226-8503, Japan a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Abstract Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract b
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
Electric vehicles (EVs) have gained a massive and global interest, including in Indonesia, due to their beneficial characteristics. However,vehicles the increase EVs gained also causes severaland problems, especially the stressintoIndonesia, the electrical because of theircharacteristics. high charging Electric (EVs)ofhave a massive global interest, including duegrid to their beneficial demand. Inthe addition, ofseveral the gridproblems, to balanceespecially its supplythe andstress demand in Indonesia additionally worsen the condition. However, increasethe oflack EVscapability also causes to the electrical grid because of their high charging Abstract Indonesia has the no sufficient storage electricity which can dispatched (releaseadditionally and absorb) worsen to balance demand. Inalmost addition, lack capability of (buffer) the grid for to balance its supply andbe demand in Indonesia theeffectively condition. electrical grid. The controllable charging discharging behavior of EVs led to the idea to utilize EVs fortosupporting the grid. Indonesia almost has no sufficient storageand (buffer) for electricity which can has be dispatched (release and absorb) balance effectively District heating are charging commonly addressed inelectrical the literature oneled oftothe most effective solutions for decreasing the The feasibility of networks vehicle-to-grid (V2G) and in Indonesian grid isasevaluated inthe this study. The evaluated ancillary services electrical grid. The controllable discharging behavior of EVs has idea to utilize EVs for supporting the grid. greenhouse gas from the building sector. These systems high investments which returned throughservices the heat include load levelling and frequency regulation. The feasibility ofemissions vehicle-to-grid (V2G) in Indonesian electrical gridrequire is evaluated in this study. The are evaluated ancillary sales. Due to the changed climateregulation. conditions and building renovation policies, heat demand in the future could decrease, include load levelling and frequency prolonging the investment return period. © 2018 The Authors. Published by Elsevier Ltd. © 2019 The Authors. Published Ltd. The scope of this paperunder isby toElsevier assess feasibility license of using(https://creativecommons.org/licenses/by-nc-nd/4.0/) the heat demand – outdoor temperature function for heat demand This ismain an open access article the CCthe BY-NC-ND © 2018 The Authors. Published by Elsevier Ltd. This is an open access under thelocated CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) forecast. Thepeer-review districtarticle of Alvalade, in of Lisbon (Portugal), was used as a2nd caseInternational study. The Conference district is consisted 665 Selection and under responsibility the scientific committee of the on Energyofand This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and under responsibility the typology. scientific committee of the 2nd International Conference on Energy and buildings thatpeer-review vary in both construction periodof and Three weather scenarios (low, medium, high) and three district Power, ICEP2018. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Energy and Power, ICEP2018. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Power, ICEP2018. comparedElectric with results a dynamic model, previously Keywords: vehicle;from ancillary service;heat load demand leveling; frequency regulation developed and validated by the authors. The results showed thatancillary when only weather change frequency is considered, the margin of error could be acceptable for some applications Keywords: Electric vehicle; service; load leveling; regulation (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.scenarios, Introduction The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1. Introduction decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and Environmentally clean energy and transportation systems have become trending issues recently because they deal renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the Environmentally clean energy and transportation systems have become trending including issues recently because they deal with the quality of human life [1]. In the transportation technologies efficient coupled scenarios). The values suggested could be used tosector, modifyseveral the function parameters for the hybrid, scenarioshighly considered, and with the quality of human life [1]. In the transportation sector, several technologies including hybrid, highly efficient improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +81-3-5734-3809; fax: +81-3-5734-3559. Cooling. address:author. [email protected] * E-mail Corresponding Tel.: +81-3-5734-3809; fax: +81-3-5734-3559.
E-mail address: [email protected] Keywords:©Heat Forecast; Climatebychange 1876-6102 2018demand; The Authors. Published Elsevier Ltd. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 1876-6102 © 2018 Thearticle Authors. Published by Elsevier Ltd. Selection under responsibility of the scientific of the 2nd International Conference on Energy and Power, ICEP2018. This is an and openpeer-review access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Energy and Power, ICEP2018. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Energy and Power, ICEP2018. 10.1016/j.egypro.2019.02.214
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Muhammad Aziz et al. / Energy Procedia 160 (2019) 621–626 Aziz M and Huda M / Energy Procedia 00 (2018) 000–000
diesel engine, hydrogen-based fuel cell, and electric vehicles (EVs) have been developed. Among them, EVs have received an intensive global attention in the last decade because of their superior characteristics compared to conventional vehicles adopting an internal combustion engine. These characteristics include high total energy efficiency [2], lower environmental impact [3], and high driving performance. In addition, EVs have potential characteristic of controllable charging and discharging which can be utilized to release and absorb the electricity [4]. The International Energy Agency predicted that to achieve a greenhouse gas concentration at 450 ppm, the share of EVs, including plug-in hybrid and battery EVs, should make up about 40% of the global new vehicles sales [5]. Therefore, if these EVs are connected and controlled massively to the electrical grid, they can be considered as huge amount of battery. On the other hand, if charging of EVs is not controlled, a massive charging of EVs causes serious grid stress due to high electricity demand and a gap between the peak and non-peak loads. Therefore, coordination of EVs becomes very important in the future as the number of EVs increases. Focusing on Indonesia, the share of renewable energy, including geothermal [6], wind [7], biomass [8], and solar [9], is increasing gradually. Although the adoption of renewable energy to the grid has a positive impact, especially related to environmental issue, high fluctuation of renewable energy leads to several problems in electrical grid [10]. These include the imbalance between supply and demand, and high demand for fossil fuels-based power generator to cover this imbalance. Utilization of EVs to support the grid is considered potential in terms of technology, economy, and environment. New business opportunities are also predicted to appear, in addition to the new prosumer (producer and consumer) concept in liberalized electricity market. Following the global trend of EVs adoption, it is also believed that in Indonesia, the number of EVs is also increasing in the near future. Combining these two phenomena of EV adoption and increase of renewable energy share, the concept of vehicle to grid (V2G) is predicted to become more popular. V2G leads to possible synergetic advantages for both vehicles and grid. For the grid utilities and operator, EVs are expected to be able to provide back-up electricity, shift the electricity load, and response quickly to balance the grid. According to Statistics Indonesia (BPS), the number of passenger vehicles in 2016 was about 14.5 million with a growth of about 8% [11]. This number is about double than one in 2008, which was accelerated mainly due to high economic growth and the increase of income and number of the middle class [12]. To the best of authors’ knowledge, there is no study available in the literature discussion about the feasibility and opportunity of V2G implementation in Indonesia. Therefore, in this study, the feasibility of EVs adoption to provide ancillary service in Indonesian grid is analyzed. First, the condition of Indonesian grid, especially in Java-MaduraBali (Jamali) grid, is briefly analyzed in the context of possibility of EVs for load leveling. Furthermore, the analysis related to frequency condition of Indonesian grid is also analyzed in order to measure the opportunity of EVs to participate in frequency regulation service to the grid. All analyses are performed based on the data received from Indonesia state-owned electricity company PLN (load data) and measured data (frequency). 2. Ancillary services through V2G The basic concept of V2G is presented in Fig. 1. EVs, which are not in a motion, are parked and connected to bidirectional charger. As charger is connected directly to the grid and bi-directional, charger can receive (EV charging mode) and deliver (EV discharging mode) the electricity from and to the grid, respectively. Actually, not only bidirectional charger can participate in V2G, unidirectional charger is also able to perform V2G service, which is absorbing the electricity from the grid. The EV owners also have a contract with the aggregator. In addition, massively distributed EVs are controlled by the aggregator, which also participate in electricity market by monitoring the electricity price and available V2G service, and bidding the service. Once aggregator won the bidding for V2G service, the aggregator communicates with the EVs and control them according to the service policy. Several possible ancillary services in V2G have been developed, including frequency regulation [13], voltage regulation, load levelling [14], congestion mitigation, and power storage [15,16]. Although EVs charging and discharging are influenced by several factors, including temperature [17], both of them can be performed in very fast duration leading to very responsive energy buffer and balancer (storage). In this study, the feasibility of EVs for services of load levelling and frequency regulation is analyzed.
Muhammad Aziz et al. / Energy Procedia 160 (2019) 621–626 Aziz M and Huda M / Energy Procedia 00 (2018) 000–000
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Fig. 1. Basic concept of V2G, including EVs, bi-directional charger, and aggregator.
3. Analysis of load levelling utilizing EVs in Indonesian grid Grid load can be categorized into base and fluctuating load. The base load can be considered as the basic consumption, which does not change mostly throughout the time. On the other hand, the fluctuating load relates to the fluctuating demand due to activities in certain time. When the fluctuating load creates a highest point (peak), it is usually called as a peak load. Generally, the peak load occurs following high consumption due to several human activities, including industrial and residential activities. It can occur during the noon as high demand for industry, or during the evening until the night because of residential demand. As the load fluctuation must be covered by the supply (generation) to achieve the balance, this fluctuation leads to several technical and economic problems, including low energy efficiency, high operating cost, and high environmental impacts. To cover this fluctuation, energy storage, including the coordinated EVs via V2G, can be utilized and dispatched to supply the electricity to the grid when the demand is high (noon and evening), and absorb the electricity when the demand is low (during mid night until the night). Fig. 2 shows the electricity demand (capacity, MW) pattern in Indonesia, especially in Jamali grid network, for one week and one day during December 2017.
Fig. 2. Load pattern in Indonesia Jamali grid network (a) Electricity load for a representative week; (b) Electricity load for a representative weekday
The load pattern during weekdays is almost similar having three main peaks: before noon, afternoon, and evening peaks. The highest and longest peak is achieved in the evening, which was about 24 GW. However, the lowest demand occurs after midnight (2–3 AM) with capacity of about 18 GW. In addition, during weekend, the peak load decreases significantly, especially in Sunday. The gap between the highest peak and lowest valley is very high, which is about 25% of the highest peak value. This large gap is potential to cause several grid problems, and conventionally covered through ramping up/down of generator operation rate and the generator start/stop (unit commitment). Fig. 3 shows the load duration curve of the generated electricity amount (MWh) during a weekday for each 30 min duration, based on the electricity load in Fig. 2. Load duration curve is generally used to figure out the relationship between the required generating capacity and its consumption capacity. Load levelling in terms of both capacity (MW) and amount (MWh) can be conducted using EVs. For the load levelling of electricity capacity (MW), highly responsive EVs can discharge their electricity during the peak time. Therefore, the peak can be cut, and peaking power generator, which is usually operated by fossil fuels, can be avoided. Moreover, regarding the load levelling in terms of electricity amount (MWh), the peak load amount can be shifted also by using the electricity discharged from EVs.
Muhammad Aziz et al. / Energy Procedia 160 (2019) 621–626 Aziz M and Huda M / Energy Procedia 00 (2018) 000–000
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Hence, the peak power covered by generator can be reduced. On the other hand, EVs can be charged during midnight when the electricity load is low. Therefore, the load during night is getting higher (valley filling).
Fig. 3. Load duration curve for a representative weekday in Jamali grid network (December 13, 2017). Table 1. Calculation of the available capacity (MW) and electricity amount (MWh) from EVs, and peak cut threshold based on available capacity and electricity amount from EVs.. Case
(A) EVs available capacity (B) EVs available (MW) electricity amount (MWh)
(C) Maximum load levelling amount based on A and B (MWh)
(D) Peak cut threshold based on A and B (MW)
A5%-P5%
106.9
427.5
97.3
23,805
A5%-P20%
427.5
1,710.0
800.3
23,484
A5%-P35%
748.1
2,992.5
2,341.4
23,163
A5%-P50%
1,068.8
4,275.0
4,275.0
22,930
A20%-P5%
427.5
1,710.0
800.3
23,484
A20%-P20%
1,710.0
6,840.0
6,840.0
22,683
A20%-P35%
2,992.5
11,970.0
11,970.0
22,247
A20%-P50%
4,275.0
17,100.0
17,100.0
21,858
Based on statistical data [11], the number of passenger cars in Java, Madura, and Bali in 2016 was about 9 million. To measure the potential of load levelling in the near future, several assumptions are made: 1) the number of vehicles increases to 15 million in 2030, 2) the average charger capacity is 3 kW, 3) charger loss per pass is 5%, 4) average EVs battery capacity is 24 kWh, and 5) average charging and discharging capacity for V2G service is 50% of the battery capacity. In addition, the EV adoption percentage is set to 5 and 20%, considering early and developed adoption, respectively. In addition, the V2G participation is also subdivided into 5, 20, 35, and 50%. Table 1 shows the calculation of the available capacity (MW) and electricity amount (MWh) from EVs, maximum load levelling amount, and peak cut threshold based on available capacity and electricity amount from EVs. The available capacity of EVs (MW) for V2G is calculated based on the available capacity for charging and discharging. Therefore, it is strongly influenced by the charger capacity and its performance. In addition, the available electricity amount for load levelling (peak cutting and valley filling, MWh) is significantly affected by the EVs battery capacity and depth of charge and discharge during V2G. The maximum load levelling amount and peak cut threshold are limited by both charging/discharging capacity and available electricity amount from EVs. High EV adoption and V2G participation lead to larger load levelling amount and lower peak cut threshold. Fig. 4 shows the results of load levelling amount under different EV adoption and V2G participation conditions. In early adoption period (EV adoption of 5% and V2G participation of 20%), the maximum load levelling amount in the representative weekday is about 800 MWh. In addition, the peak can be reduced to about 23,500 MW, which is equivalent to 98% of the highest peak load, especially occurred during evening. As each EV adoption and V2G participation rates increase to 20%, larger amount of load levelling and lower peak cut threshold can be obtained, which are about 6,800 MWh and 22,700 MW, respectively. In this case, three peak loads occurred during before noon, afternoon, and evening can be levelled and moved to low load time (midnight).
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a) EV adoption of 5% and V2G participation of 20% (b) EV adoption of 20% and V2G participation of 20%. Fig. 4. Load levelling amount under different EV adoption and V2G participation
4. Analysis of frequency regulation through V2G in Indonesian grid Frequency regulation is performed in order to maintain the grid frequency within the defined limits by balancing both supply and demand in a given control area and defined time basis [18]. In general, there are three classifications of reserves (frequency regulations): primary, secondary, and tertiary reserves, which are categorized based on response time, frequency deviation, duration, and capacity. Primary frequency regulation is generally defined as unit (both up and down) which can respond fast within a few seconds as it deals with sudden imbalances. Each grid network has different policy for primary frequency regulation. The primary regulation is performed in order to balance between the supply and demand and stabilize the grid frequency to close to the target/reference frequency, which is 50 Hz. Therefore, it is conducted in both directions of up and down by production (supply) and consumption (demand) units. If the primary frequency regulation fails to restore the frequency to the designated original value, secondary frequency regulation is dispatched. It is also similar when the secondary regulation cannot satisfy to reach the standard value, the tertiary frequency regulation will be issued. Fig. 5 shows the frequency fluctuation for one day during representative weekday (December 13, 2017). The frequency fluctuates in the range of 49.6–50.4 Hz (fluctuation of ±0.4 Hz). This fluctuation is considered very large which is potential to cause several damages to the appliances and machines. Combining Figs. 2 and 5, it can be observed that largest fluctuations occur when there is any big and sudden change (both increase and drop) in the electricity load, especially during early morning (around 2 AM, lowest load), morning (8-10 AM, load increase), and evening (7-9 PM, highest load). The change of load must be followed with the change of supply to achieve the balanced electricity in the grid. However, the response of supply cannot be sufficiently fast compared to the change of the load.
Fig. 5. Frequency fluctuation during a representative weekday in Jamali grid network (December 13, 2017).
As the response of supply must be sufficiently fast to balance well the grid, a responsive electricity supplier and absorber is demanded. In the future, as the adoption of renewable energy increases, the demand for very responsive regulator is urgently required. EVs are categorized as very responsive electricity supplier and absorber due to the battery they own [19]. The utilization of EVs in frequency regulation is expected able to improve the quality of the
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electrical grid. EVs are considered potential to acts as regulator providing primary and secondary frequency regulation services. Although the frequency fluctuation occurs frequently, the amount of electricity supplied to and absorbed from the grid is considered low. By controlling the charging and discharging behavior of EVs, the frequency regulation by EVs can be performed. 5. Conclusion The increasing attention to EVs as convenient transportation tool due to their advantageous characteristics has led a massive and accelerated adoption of EVs. In addition, as both charging and discharging behaviors of EVs are controllable, the utilization of EVs are not limited only to transportation sector, but also include the energy sector. Therefore, the economic performance of EVs can be improved. As developing country, the demand of electricity in Indonesia increases leading to several problems, including low quality of electricity. Indonesia has no large scale of energy storage, which can buffer sufficiently the load fluctuation and a responsive time. The utilization of EVs for load levelling and frequency regulation in Indonesia are briefly described in this study. A large gap between highest and lowest loads is very high, leading to inefficient generator operation and large frequency fluctuation. Massive EV adoption and their participation to V2G programs are potential way to achieve high quality of electricity in the country. References [1] D. F. Dominkovi, I. Ba, A. S. Pedersen, and G. Kraja. “The future of transportation in sustainable energy systems: Opportunities and barriers in a clean energy transition.” Renewable and Sustainable Energy Reviews 82 (2018): 1823–1838. [2] M. Aziz and T. Oda. “Simultaneous quick-charging system for electric vehicle.” Energy Procedia 142 (2017): 1811–1816. [3] T. Oda, M. Aziz, T. Mitani, Y. Watanabe, and T. Kashiwagi. “Actual Congestion and Effect of Charger Addition in the Quick Charger Station: Case Study Based on the Records of Expressway.” Electrical Engineering Japan 198(2) (2017): 11–18. [4] M. Aziz, T. Oda, T. Mitani, Y. Watanabe, and T. Kashiwagi. “Utilization of electric vehicles and their used batteries for peak-load shifting.” Energies 8(5) (2015): 3720–3738. [5] IEA, “Energy Climate and Change World Energy Outlook Special Report.” [6] L. A. Prananto, F. B. Juangsa, R. M. Iqbal, M. Aziz, and T. A. F. Soelaiman. “Dry steam cycle application for excess steam utilization: Kamojang geothermal power plant case study.” Renewable Energy 117 (2018): 157–165. [7] F. B. Juangsa, B. A. Budiman, M. Aziz, and T. A. F. Soelaiman, “Design of an airborne vertical axis wind turbine for low electrical power demands,” Int. J. Energy Environ. Eng., vol. 8, no. 4, pp. 293–301, 2017. [8] M. Aziz, T. Kurniawan, T. Oda, and T. Kashiwagi. “Advanced power generation using biomass wastes from palm oil mills.” Applied Thermal Engineering 114 (2017): 1378–1386. [9] M. Rumbayan, A. Abudureyimu, and K. Nagasaka. “Mapping of solar energy potential in Indonesia using artificial neural network and geographical information system.” Renewable and Sustainable Energy Reviews 16(3) (2012): 1437–1449. [10] T. Mitani, M. Aziz, T. Oda, A. Uetsuji, Y. Watanabe, and T. Kashiwagi. “Annual assessment of large-scale introduction of renewable energy: Modeling of unit commitment schedule for thermal power generators and pumped storages.” Energies 10(6) (2017): 738. [11] Statistics Indonesia. “Development of motor vehicles by type, 1949-2016,” 2016. [Online]. Available: https://www.bps.go.id/linkTableDinamis/view/id/1133. [12] M. Shahbaz, Q. M. A. Hye, A. K. Tiwari, and N. C. Leitão. “Economic growth, energy consumption, financial development, international trade and CO2emissions in Indonesia.” Renewable and Sustainable Energy Reviews 25 (2013): 109–121. [13] C. Peng, J. Zou, and L. Lian. “Dispatching strategies of electric vehicles participating in frequency regulation on power grid: A review.” Renewable and Sustainable Energy Reviews 68 (2017): 147–152. [14] M. Aziz, T. Oda, and T. Kashiwagi. “Extended utilization of electric vehicles and their re-used batteries to support the building energy management system.” Energy Procedia 75 (2015): 1938–1943. [15] B. Tarroja, L. Zhang, V. Wifvat, B. Shaffer, and S. Samuelsen. “Assessing the stationary energy storage equivalency of vehicle-to-grid charging battery electric vehicles.” Energy 106 (2016): 673–690. [16] M. Aziz and T. Oda. “Advanced battery-assisted quick charger for electric vehicles,” in G. Pistoia and B. Liaw (eds) in Behaviour of LithiumIon Batteries in Electric Vehicles, (2018) Amsterdam, Springer. [17] M. Aziz, T. Oda, and M. Ito. “Battery-assisted charging system for simultaneous charging of electric vehicles.” Energy 100 (2016): 82–90. [18] S. Zhang, Y. Mishra, and M. Shahidehpour. “Utilizing distributed energy resources to support frequency regulation services.” Applied Energy 206 (2017): 1484–1494. [19] T. Oda, M. Aziz, T. Mitani, Y. Watanabe, and T. Kashiwagi. “Mitigation of congestion related to quick charging of electric vehicles based on waiting time and cost–benefit analyses: A japanese case study.” Sustainable Cities and Society 36 (2018): 99–106.
ScienceDirect ScienceDirect
Energy Procedia 00 (2018) 000–000 Available online www.sciencedirect.com Available online atatwww.sciencedirect.com Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2019) 000–000 621–626 EnergyProcedia Procedia160 00 (2017) www.elsevier.com/locate/procedia
2nd International Conference on Energy and Power, ICEP2018, 13–15 December 2018, 2nd International Conference on Energy and Power, ICEP2018, 13–15 December 2018, Sydney, Australia Sydney, Australia
Application opportunity of vehicles-to-grid in Indonesian electrical 15th International on District in Heating and Coolingelectrical ApplicationTheopportunity of Symposium vehicles-to-grid Indonesian grid grid Assessing theMuhammad feasibilityAziz ofa,*, using the heat demand-outdoor b Muhammad Huda a, b Muhammad Aziz *, Muhammad Huda temperature function for a long-term district heat demand forecast Institute of Innovative Research, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan a
a Department of Transdisciplinary ScienceTokyo and Engineering, Tokyo Institute of Technology, Nagatsuta-cho, Midor-ku, Yokohama, Institute of Innovative Research, Institute of Technology, 2-12-1 Ookayama, 4259 Meguro-ku, Tokyo 152-8550, Japan a,b,c a a 226-8503, Japanb c c b Kanagawa Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midor-ku, Yokohama, Kanagawa 226-8503, Japan a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Abstract Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract b
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
Electric vehicles (EVs) have gained a massive and global interest, including in Indonesia, due to their beneficial characteristics. However,vehicles the increase EVs gained also causes severaland problems, especially the stressintoIndonesia, the electrical because of theircharacteristics. high charging Electric (EVs)ofhave a massive global interest, including duegrid to their beneficial demand. Inthe addition, ofseveral the gridproblems, to balanceespecially its supplythe andstress demand in Indonesia additionally worsen the condition. However, increasethe oflack EVscapability also causes to the electrical grid because of their high charging Abstract Indonesia has the no sufficient storage electricity which can dispatched (releaseadditionally and absorb) worsen to balance demand. Inalmost addition, lack capability of (buffer) the grid for to balance its supply andbe demand in Indonesia theeffectively condition. electrical grid. The controllable charging discharging behavior of EVs led to the idea to utilize EVs fortosupporting the grid. Indonesia almost has no sufficient storageand (buffer) for electricity which can has be dispatched (release and absorb) balance effectively District heating are charging commonly addressed inelectrical the literature oneled oftothe most effective solutions for decreasing the The feasibility of networks vehicle-to-grid (V2G) and in Indonesian grid isasevaluated inthe this study. The evaluated ancillary services electrical grid. The controllable discharging behavior of EVs has idea to utilize EVs for supporting the grid. greenhouse gas from the building sector. These systems high investments which returned throughservices the heat include load levelling and frequency regulation. The feasibility ofemissions vehicle-to-grid (V2G) in Indonesian electrical gridrequire is evaluated in this study. The are evaluated ancillary sales. Due to the changed climateregulation. conditions and building renovation policies, heat demand in the future could decrease, include load levelling and frequency prolonging the investment return period. © 2018 The Authors. Published by Elsevier Ltd. © 2019 The Authors. Published Ltd. The scope of this paperunder isby toElsevier assess feasibility license of using(https://creativecommons.org/licenses/by-nc-nd/4.0/) the heat demand – outdoor temperature function for heat demand This ismain an open access article the CCthe BY-NC-ND © 2018 The Authors. Published by Elsevier Ltd. This is an open access under thelocated CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) forecast. Thepeer-review districtarticle of Alvalade, in of Lisbon (Portugal), was used as a2nd caseInternational study. The Conference district is consisted 665 Selection and under responsibility the scientific committee of the on Energyofand This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and under responsibility the typology. scientific committee of the 2nd International Conference on Energy and buildings thatpeer-review vary in both construction periodof and Three weather scenarios (low, medium, high) and three district Power, ICEP2018. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Energy and Power, ICEP2018. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Power, ICEP2018. comparedElectric with results a dynamic model, previously Keywords: vehicle;from ancillary service;heat load demand leveling; frequency regulation developed and validated by the authors. The results showed thatancillary when only weather change frequency is considered, the margin of error could be acceptable for some applications Keywords: Electric vehicle; service; load leveling; regulation (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.scenarios, Introduction The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1. Introduction decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and Environmentally clean energy and transportation systems have become trending issues recently because they deal renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the Environmentally clean energy and transportation systems have become trending including issues recently because they deal with the quality of human life [1]. In the transportation technologies efficient coupled scenarios). The values suggested could be used tosector, modifyseveral the function parameters for the hybrid, scenarioshighly considered, and with the quality of human life [1]. In the transportation sector, several technologies including hybrid, highly efficient improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +81-3-5734-3809; fax: +81-3-5734-3559. Cooling. address:author. [email protected] * E-mail Corresponding Tel.: +81-3-5734-3809; fax: +81-3-5734-3559.
E-mail address: [email protected] Keywords:©Heat Forecast; Climatebychange 1876-6102 2018demand; The Authors. Published Elsevier Ltd. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 1876-6102 © 2018 Thearticle Authors. Published by Elsevier Ltd. Selection under responsibility of the scientific of the 2nd International Conference on Energy and Power, ICEP2018. This is an and openpeer-review access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Energy and Power, ICEP2018. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Energy and Power, ICEP2018. 10.1016/j.egypro.2019.02.214
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diesel engine, hydrogen-based fuel cell, and electric vehicles (EVs) have been developed. Among them, EVs have received an intensive global attention in the last decade because of their superior characteristics compared to conventional vehicles adopting an internal combustion engine. These characteristics include high total energy efficiency [2], lower environmental impact [3], and high driving performance. In addition, EVs have potential characteristic of controllable charging and discharging which can be utilized to release and absorb the electricity [4]. The International Energy Agency predicted that to achieve a greenhouse gas concentration at 450 ppm, the share of EVs, including plug-in hybrid and battery EVs, should make up about 40% of the global new vehicles sales [5]. Therefore, if these EVs are connected and controlled massively to the electrical grid, they can be considered as huge amount of battery. On the other hand, if charging of EVs is not controlled, a massive charging of EVs causes serious grid stress due to high electricity demand and a gap between the peak and non-peak loads. Therefore, coordination of EVs becomes very important in the future as the number of EVs increases. Focusing on Indonesia, the share of renewable energy, including geothermal [6], wind [7], biomass [8], and solar [9], is increasing gradually. Although the adoption of renewable energy to the grid has a positive impact, especially related to environmental issue, high fluctuation of renewable energy leads to several problems in electrical grid [10]. These include the imbalance between supply and demand, and high demand for fossil fuels-based power generator to cover this imbalance. Utilization of EVs to support the grid is considered potential in terms of technology, economy, and environment. New business opportunities are also predicted to appear, in addition to the new prosumer (producer and consumer) concept in liberalized electricity market. Following the global trend of EVs adoption, it is also believed that in Indonesia, the number of EVs is also increasing in the near future. Combining these two phenomena of EV adoption and increase of renewable energy share, the concept of vehicle to grid (V2G) is predicted to become more popular. V2G leads to possible synergetic advantages for both vehicles and grid. For the grid utilities and operator, EVs are expected to be able to provide back-up electricity, shift the electricity load, and response quickly to balance the grid. According to Statistics Indonesia (BPS), the number of passenger vehicles in 2016 was about 14.5 million with a growth of about 8% [11]. This number is about double than one in 2008, which was accelerated mainly due to high economic growth and the increase of income and number of the middle class [12]. To the best of authors’ knowledge, there is no study available in the literature discussion about the feasibility and opportunity of V2G implementation in Indonesia. Therefore, in this study, the feasibility of EVs adoption to provide ancillary service in Indonesian grid is analyzed. First, the condition of Indonesian grid, especially in Java-MaduraBali (Jamali) grid, is briefly analyzed in the context of possibility of EVs for load leveling. Furthermore, the analysis related to frequency condition of Indonesian grid is also analyzed in order to measure the opportunity of EVs to participate in frequency regulation service to the grid. All analyses are performed based on the data received from Indonesia state-owned electricity company PLN (load data) and measured data (frequency). 2. Ancillary services through V2G The basic concept of V2G is presented in Fig. 1. EVs, which are not in a motion, are parked and connected to bidirectional charger. As charger is connected directly to the grid and bi-directional, charger can receive (EV charging mode) and deliver (EV discharging mode) the electricity from and to the grid, respectively. Actually, not only bidirectional charger can participate in V2G, unidirectional charger is also able to perform V2G service, which is absorbing the electricity from the grid. The EV owners also have a contract with the aggregator. In addition, massively distributed EVs are controlled by the aggregator, which also participate in electricity market by monitoring the electricity price and available V2G service, and bidding the service. Once aggregator won the bidding for V2G service, the aggregator communicates with the EVs and control them according to the service policy. Several possible ancillary services in V2G have been developed, including frequency regulation [13], voltage regulation, load levelling [14], congestion mitigation, and power storage [15,16]. Although EVs charging and discharging are influenced by several factors, including temperature [17], both of them can be performed in very fast duration leading to very responsive energy buffer and balancer (storage). In this study, the feasibility of EVs for services of load levelling and frequency regulation is analyzed.
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Fig. 1. Basic concept of V2G, including EVs, bi-directional charger, and aggregator.
3. Analysis of load levelling utilizing EVs in Indonesian grid Grid load can be categorized into base and fluctuating load. The base load can be considered as the basic consumption, which does not change mostly throughout the time. On the other hand, the fluctuating load relates to the fluctuating demand due to activities in certain time. When the fluctuating load creates a highest point (peak), it is usually called as a peak load. Generally, the peak load occurs following high consumption due to several human activities, including industrial and residential activities. It can occur during the noon as high demand for industry, or during the evening until the night because of residential demand. As the load fluctuation must be covered by the supply (generation) to achieve the balance, this fluctuation leads to several technical and economic problems, including low energy efficiency, high operating cost, and high environmental impacts. To cover this fluctuation, energy storage, including the coordinated EVs via V2G, can be utilized and dispatched to supply the electricity to the grid when the demand is high (noon and evening), and absorb the electricity when the demand is low (during mid night until the night). Fig. 2 shows the electricity demand (capacity, MW) pattern in Indonesia, especially in Jamali grid network, for one week and one day during December 2017.
Fig. 2. Load pattern in Indonesia Jamali grid network (a) Electricity load for a representative week; (b) Electricity load for a representative weekday
The load pattern during weekdays is almost similar having three main peaks: before noon, afternoon, and evening peaks. The highest and longest peak is achieved in the evening, which was about 24 GW. However, the lowest demand occurs after midnight (2–3 AM) with capacity of about 18 GW. In addition, during weekend, the peak load decreases significantly, especially in Sunday. The gap between the highest peak and lowest valley is very high, which is about 25% of the highest peak value. This large gap is potential to cause several grid problems, and conventionally covered through ramping up/down of generator operation rate and the generator start/stop (unit commitment). Fig. 3 shows the load duration curve of the generated electricity amount (MWh) during a weekday for each 30 min duration, based on the electricity load in Fig. 2. Load duration curve is generally used to figure out the relationship between the required generating capacity and its consumption capacity. Load levelling in terms of both capacity (MW) and amount (MWh) can be conducted using EVs. For the load levelling of electricity capacity (MW), highly responsive EVs can discharge their electricity during the peak time. Therefore, the peak can be cut, and peaking power generator, which is usually operated by fossil fuels, can be avoided. Moreover, regarding the load levelling in terms of electricity amount (MWh), the peak load amount can be shifted also by using the electricity discharged from EVs.
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Hence, the peak power covered by generator can be reduced. On the other hand, EVs can be charged during midnight when the electricity load is low. Therefore, the load during night is getting higher (valley filling).
Fig. 3. Load duration curve for a representative weekday in Jamali grid network (December 13, 2017). Table 1. Calculation of the available capacity (MW) and electricity amount (MWh) from EVs, and peak cut threshold based on available capacity and electricity amount from EVs.. Case
(A) EVs available capacity (B) EVs available (MW) electricity amount (MWh)
(C) Maximum load levelling amount based on A and B (MWh)
(D) Peak cut threshold based on A and B (MW)
A5%-P5%
106.9
427.5
97.3
23,805
A5%-P20%
427.5
1,710.0
800.3
23,484
A5%-P35%
748.1
2,992.5
2,341.4
23,163
A5%-P50%
1,068.8
4,275.0
4,275.0
22,930
A20%-P5%
427.5
1,710.0
800.3
23,484
A20%-P20%
1,710.0
6,840.0
6,840.0
22,683
A20%-P35%
2,992.5
11,970.0
11,970.0
22,247
A20%-P50%
4,275.0
17,100.0
17,100.0
21,858
Based on statistical data [11], the number of passenger cars in Java, Madura, and Bali in 2016 was about 9 million. To measure the potential of load levelling in the near future, several assumptions are made: 1) the number of vehicles increases to 15 million in 2030, 2) the average charger capacity is 3 kW, 3) charger loss per pass is 5%, 4) average EVs battery capacity is 24 kWh, and 5) average charging and discharging capacity for V2G service is 50% of the battery capacity. In addition, the EV adoption percentage is set to 5 and 20%, considering early and developed adoption, respectively. In addition, the V2G participation is also subdivided into 5, 20, 35, and 50%. Table 1 shows the calculation of the available capacity (MW) and electricity amount (MWh) from EVs, maximum load levelling amount, and peak cut threshold based on available capacity and electricity amount from EVs. The available capacity of EVs (MW) for V2G is calculated based on the available capacity for charging and discharging. Therefore, it is strongly influenced by the charger capacity and its performance. In addition, the available electricity amount for load levelling (peak cutting and valley filling, MWh) is significantly affected by the EVs battery capacity and depth of charge and discharge during V2G. The maximum load levelling amount and peak cut threshold are limited by both charging/discharging capacity and available electricity amount from EVs. High EV adoption and V2G participation lead to larger load levelling amount and lower peak cut threshold. Fig. 4 shows the results of load levelling amount under different EV adoption and V2G participation conditions. In early adoption period (EV adoption of 5% and V2G participation of 20%), the maximum load levelling amount in the representative weekday is about 800 MWh. In addition, the peak can be reduced to about 23,500 MW, which is equivalent to 98% of the highest peak load, especially occurred during evening. As each EV adoption and V2G participation rates increase to 20%, larger amount of load levelling and lower peak cut threshold can be obtained, which are about 6,800 MWh and 22,700 MW, respectively. In this case, three peak loads occurred during before noon, afternoon, and evening can be levelled and moved to low load time (midnight).
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a) EV adoption of 5% and V2G participation of 20% (b) EV adoption of 20% and V2G participation of 20%. Fig. 4. Load levelling amount under different EV adoption and V2G participation
4. Analysis of frequency regulation through V2G in Indonesian grid Frequency regulation is performed in order to maintain the grid frequency within the defined limits by balancing both supply and demand in a given control area and defined time basis [18]. In general, there are three classifications of reserves (frequency regulations): primary, secondary, and tertiary reserves, which are categorized based on response time, frequency deviation, duration, and capacity. Primary frequency regulation is generally defined as unit (both up and down) which can respond fast within a few seconds as it deals with sudden imbalances. Each grid network has different policy for primary frequency regulation. The primary regulation is performed in order to balance between the supply and demand and stabilize the grid frequency to close to the target/reference frequency, which is 50 Hz. Therefore, it is conducted in both directions of up and down by production (supply) and consumption (demand) units. If the primary frequency regulation fails to restore the frequency to the designated original value, secondary frequency regulation is dispatched. It is also similar when the secondary regulation cannot satisfy to reach the standard value, the tertiary frequency regulation will be issued. Fig. 5 shows the frequency fluctuation for one day during representative weekday (December 13, 2017). The frequency fluctuates in the range of 49.6–50.4 Hz (fluctuation of ±0.4 Hz). This fluctuation is considered very large which is potential to cause several damages to the appliances and machines. Combining Figs. 2 and 5, it can be observed that largest fluctuations occur when there is any big and sudden change (both increase and drop) in the electricity load, especially during early morning (around 2 AM, lowest load), morning (8-10 AM, load increase), and evening (7-9 PM, highest load). The change of load must be followed with the change of supply to achieve the balanced electricity in the grid. However, the response of supply cannot be sufficiently fast compared to the change of the load.
Fig. 5. Frequency fluctuation during a representative weekday in Jamali grid network (December 13, 2017).
As the response of supply must be sufficiently fast to balance well the grid, a responsive electricity supplier and absorber is demanded. In the future, as the adoption of renewable energy increases, the demand for very responsive regulator is urgently required. EVs are categorized as very responsive electricity supplier and absorber due to the battery they own [19]. The utilization of EVs in frequency regulation is expected able to improve the quality of the
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electrical grid. EVs are considered potential to acts as regulator providing primary and secondary frequency regulation services. Although the frequency fluctuation occurs frequently, the amount of electricity supplied to and absorbed from the grid is considered low. By controlling the charging and discharging behavior of EVs, the frequency regulation by EVs can be performed. 5. Conclusion The increasing attention to EVs as convenient transportation tool due to their advantageous characteristics has led a massive and accelerated adoption of EVs. In addition, as both charging and discharging behaviors of EVs are controllable, the utilization of EVs are not limited only to transportation sector, but also include the energy sector. Therefore, the economic performance of EVs can be improved. As developing country, the demand of electricity in Indonesia increases leading to several problems, including low quality of electricity. Indonesia has no large scale of energy storage, which can buffer sufficiently the load fluctuation and a responsive time. The utilization of EVs for load levelling and frequency regulation in Indonesia are briefly described in this study. A large gap between highest and lowest loads is very high, leading to inefficient generator operation and large frequency fluctuation. Massive EV adoption and their participation to V2G programs are potential way to achieve high quality of electricity in the country. References [1] D. F. Dominkovi, I. Ba, A. S. Pedersen, and G. Kraja. “The future of transportation in sustainable energy systems: Opportunities and barriers in a clean energy transition.” Renewable and Sustainable Energy Reviews 82 (2018): 1823–1838. [2] M. Aziz and T. Oda. “Simultaneous quick-charging system for electric vehicle.” Energy Procedia 142 (2017): 1811–1816. [3] T. Oda, M. Aziz, T. Mitani, Y. Watanabe, and T. 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