Demand response strategy management with active and reactive power incentive in the smart grid: a two-level optimization approach

Ryuto Shigenobu; Oludamilare Bode Adewuyi; Atsushi Yona; Tomonobu Senjyu; Ryuto Shigenobu; Oludamilare Bode Adewuyi; Atsushi Yona; Tomonobu Senjyu

doi:10.3934/energy.2017.3.482

AIMS Energy

2017, Volume 5, Issue 3: 482-505. doi: 10.3934/energy.2017.3.482

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Research article Topical Sections

Demand response strategy management with active and reactive power incentive in the smart grid: a two-level optimization approach

Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan

Received: 28 March 2017 Accepted: 09 May 2017 Published: 17 May 2017

High penetration of distributed generators (DGs) using renewable energy sources (RESs) is raising some important issues in the operation of modern power system. The output power of RESs fluctuates very steeply, and that include uncertainty with weather conditions. This situation causes voltage deviation and reverse power flow. Several methods have been proposed for solving these problems. Fundamentally, these methods involve reactive power control for voltage deviation and/or the installation of large battery energy storage system (BESS) at the interconnection point for reverse power flow. In order to reduce the installation cost of static var compensator (SVC), Distribution Company (DisCo) gives reactive power incentive to the cooperating customers. On the other hand, photovoltaic (PV) generator, energy storage and electric vehicle (EV) are introduced in customer side with the aim of achieving zero net energy homes (ZEHs). This paper proposes not only reactive power control but also active power flow control using house BESS and EV. Moreover, incentive method is proposed to promote participation of customers in the control operation. Demand response (DR) system is verified with several DR menu. To create profit for both side of DisCo and customer, two level optimization approach is executed in this research. Mathematical modeling of price elasticity and detailed simulations are executed by case study. The effectiveness of the proposed incentive menu is demonstrated by using heuristic optimization method.
- Voltage control,
- Electric vehicle,
- Battery energy storage system,
- Demand response,
- Load Flexibility,
- Price elasticity
Citation: Ryuto Shigenobu, Oludamilare Bode Adewuyi, Atsushi Yona, Tomonobu Senjyu. Demand response strategy management with active and reactive power incentive in the smart grid: a two-level optimization approach[J]. AIMS Energy, 2017, 5(3): 482-505. doi: 10.3934/energy.2017.3.482

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Abstract

High penetration of distributed generators (DGs) using renewable energy sources (RESs) is raising some important issues in the operation of modern power system. The output power of RESs fluctuates very steeply, and that include uncertainty with weather conditions. This situation causes voltage deviation and reverse power flow. Several methods have been proposed for solving these problems. Fundamentally, these methods involve reactive power control for voltage deviation and/or the installation of large battery energy storage system (BESS) at the interconnection point for reverse power flow. In order to reduce the installation cost of static var compensator (SVC), Distribution Company (DisCo) gives reactive power incentive to the cooperating customers. On the other hand, photovoltaic (PV) generator, energy storage and electric vehicle (EV) are introduced in customer side with the aim of achieving zero net energy homes (ZEHs). This paper proposes not only reactive power control but also active power flow control using house BESS and EV. Moreover, incentive method is proposed to promote participation of customers in the control operation. Demand response (DR) system is verified with several DR menu. To create profit for both side of DisCo and customer, two level optimization approach is executed in this research. Mathematical modeling of price elasticity and detailed simulations are executed by case study. The effectiveness of the proposed incentive menu is demonstrated by using heuristic optimization method.

References

[1]	Woyte A, Thong VV, Belmans R, et al. (2006) Voltage fluctuations on distribution level introduced by photovoltaic systems. IEEE T Energy Convers 21: 202–209. doi: 10.1109/TEC.2005.845454
[2]	Thatte A, Ilic M (2006) An assessment of reactive power/voltage control devices in distribution networks. IEEE Power Engineering Society General Meeting, 8.
[3]	Mazhari S, Monsef H, Romero R (2015) A multi-objective distribution system expansion planning incorporating customer choices on reliability. IEEE T Power Syst 31: 1–11.
[4]	Bukhsh W, Zhang C, Pinson P (2015) An integrated multiperiod OPF model with demand response and renewable generation uncertainty. IEEE T Smart Grid 7: 1495–1503.
[5]	Senjyu T, Miyazato Y, Yona A, et al. (2008) Optimal distribution voltage control and coordination with distributed generation. IEEE T PowerDeliver 23: 1236–1242. doi: 10.1109/TPWRD.2007.908816
[6]	Wang J, Fu C, Zhang Y (2008) SVC control system based on instantaneous reactive power theory and fuzzy PID. IEEE T Ind Electron 55: 1658–1665. doi: 10.1109/TIE.2007.911933
[7]	Viawan F, Karlsson D (2008) Voltage and reactive power control in systems with synchronous machine-based distributed generation. IEEE T PowerDeliver 23: 1079–1087. doi: 10.1109/TPWRD.2007.915870
[8]	Rabelo BC, Hofmann W, da Silva J, et al. (2009) Reactive power control design in doubly fed induction generators for wind turbines. IEEE T Ind Electron 56: 4154–4162. doi: 10.1109/TIE.2009.2028355
[9]	Radman G, Raje RS (2008) Dynamic model for power systems with multiple FACTS controllers. Electr Pow Syst Res 78: 361–371. doi: 10.1016/j.epsr.2007.03.004
[10]	Chang YC (2012) Multi-objective optimal SVC installation for power system loading margin improvement. IEEE T Power Syst 27: 984–992. doi: 10.1109/TPWRS.2011.2176517
[11]	Oshiro M, Yoza A, Senjyu T, et al. (2012) Optimal operation strategy with using BESS and DGs in distribution system. JICEE 2: 20–27.
[12]	Ziadi Z, Taira S, Oshiro M, et al. (2014) Optimal power scheduling for smart grids considering controllable loads and high penetration of photovoltaic generation. IEEE T Smart Grid 5: 2350–2359. doi: 10.1109/TSG.2014.2323969
[13]	Choudar A, Boukhetala D, Barkat S, et al. (2015) A local energy management of a hybrid PV-storage based distributed generation for microgrids. Energ Convers Manage 90: 21–33. doi: 10.1016/j.enconman.2014.10.067
[14]	Luo Y, Shi L, Tu G (2014) Optimal sizing and control strategy of isolated grid with wind power and energy storage system. Energ Convers Manage 80: 407–415. doi: 10.1016/j.enconman.2014.01.061
[15]	Smith J, Sunderman W, Dugan R, et al. (2011) Smart inverter volt/var control functions for high penetration of PV on distribution systems. IEEE Power Systems Conference and Exposition, 1–6.
[16]	Xin H, Qu Z, Seuss J, et al. (2011) A self-organizing strategy for power flow control of photovoltaic generators in a distribution network. IEEE T Power Syst 26: 1462–1473. doi: 10.1109/TPWRS.2010.2080292
[17]	Reddy SS, Abhyankar AR, Bijwe PR (2015) Co-optimization of energy and demand-side reserves in day-ahead electricity markets. IJEEPS 16: 195206.
[18]	Roozbehani M, Dahleh MA, Mitter SK (2012) Volatility of power grids under real-time pricing. IEEE T Power Syst 27: 1926–1940.
[19]	Khederzadeh M, Khalili M (2014) High penetration of electrical vehicles in microgrids: threats and opportunities. IJEEPS 15: 457–469.
[20]	Morais H, Sousa T, Soares J, et al. (2015) Distributed energy resources management using plug-in hybrid electric vehicles as a fuel-shifting demand response resource. Energ Convers Manage 97: 78–93. doi: 10.1016/j.enconman.2015.03.018
[21]	Price elasticities for energy use in buildings of the United States, Tech. rep., U.S. Energy information Administration (EIA) 2014. Available form:
[22]	https://www.eia.gov/analysis/studies/buildings/energyuse/
[23]	22. Shimoji T, Tahara H, Matayoshi H, et al. (2015) Optimal scheduling method of controllable loads in dc smart apartment building. IJEEPS 16: –244.
[24]	23. Zakariazadeh A, Jadid S, Siano P (2014) Multi-objective scheduling of electric vehicles in smart distribution system. Energ Convers Manage 79: 43–53. doi: 10.1016/j.enconman.2013.11.042
[25]	24. Tian H, Yuan X, Ji B, et al. (2014) Multi-objective optimization of short-term hydrothermal scheduling using non-dominated sorting gravitational search algorithm with chaotic mutation. Energ Convers Manage 81: 504–519. doi: 10.1016/j.enconman.2014.02.053
[26]	25. Moghaddam MP, Abdollahi A, Rashidinejad M (2011) Flexible demand response programs modeling in competitive electricity markets. Appl Energ 88: 3257–3. doi: 10.1016/j.apenergy.2011.02.039
[27]	26. Liu G, Tomsovic K (2014) A full demand response model in co-optimized energy and reserve market. Electr Pow Syst Res 111: 62–70. doi: 10.1016/j.epsr.2014.02.006
[28]	27. Aalami H, Moghaddam MP, Yousefi G (2010) Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl Energ 87: 243–250. doi: 10.1016/j.apenergy.2009.05.041
[29]	28. Venkatesan N, Solanki J, Solanki SK (2012) Residential demand response model and impact on voltage profile and losses of an electric distribution network. Appl Energ 96: 84–91. doi: 10.1016/j.apenergy.2011.12.076
[30]	29. Huang Q, Kang JZ, Jiang B, et al. (2011) Dynamic residential demand response and distributed generation management in smart microgrid with hierarchical agents. Energ Procedia 12: 76–90. doi: 10.1016/j.egypro.2011.10.012
[31]	30. Mostafa HE, El-Sharkawy MA, Emary AA, et al. (2012) Design and allocation of power system stabilizers using the particle swarm optimization technique for an interconnected power system. Int J Elec Power 34: 57–65. doi: 10.1016/j.ijepes.2011.09.005
[32]	31. Al-Saedi W, Lachowicz SW, Habibi D, et al. (2012) Power quality enhancement in autonomous microgrid operation using particle swarm optimization. Int J Elec Power 42: 139–149. doi: 10.1016/j.ijepes.2012.04.007
[33]	32. Nickabadi A, Ebadzadeh MM, Safabakhsh R (2011) A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput 11: 3658 –3670. doi: 10.1016/j.asoc.2011.01.037
[34]	33. Zhang L, Tang Y, Hua C, et al. (2015) A new particle swarm optimization algorithm with adaptive inertia weight based on bayesian techniques. Appl Soft Comput 28: 138–149. doi: 10.1016/j.asoc.2014.11.018
[35]	34. Bansal JC, Singh PK, Saraswat M, et al. (2011) Inertia weight strategies in particle swarm optimization. World Congress on Nature & Biologically Inspired Computing, Nabic 2011, Salamanca, Spain, 633–640.
[36]	35. Shigenobu R, Noorzad AS, Muarapaz C, et al. (2016) Optimal operation and management for smart grid subsumed high penetration of renewable energy, electric vehicle, and battery energy storage system. IJEEPS 17: 173–189.

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