基于分布式可交易能源机制的光伏与储能联动产消者的市场运营策略

Peng Hou, Guangya Yang, Junjie Hu, Philip J. Douglass, Yusheng Xue

工程(英文) ›› 2022, Vol. 12 ›› Issue (5) : 171-182.

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工程(英文) ›› 2022, Vol. 12 ›› Issue (5) : 171-182. DOI: 10.1016/j.eng.2022.03.001
研究论文
Article

基于分布式可交易能源机制的光伏与储能联动产消者的市场运营策略

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A Distributed Transactive Energy Mechanism for Integrating PV and Storage Prosumers in Market Operation

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摘要

太阳能光伏(PV)和电池存储系统成本的下降正在推动其在住宅配电系统中的应用。在住宅配电系统中,越来越多的消费者正在成为产消者。伴随这一趋势的是家庭能源管理系统(HEMS)的潜在推广,它为生产者提供了一种应对能源价格、天气和能源需求等外部因素的手段。然而,产消者的经济运行会影响电网安全,尤其是在能源价格极低或极高的情况下。因此,设计一个能够满足配电系统中关键利益相关者(即网络运营商、产消者和集电商)利益的框架至关重要。本文提出了一种新的基于交易能量(TE)的操作框架。在此框架下,集电商通过协商过程与配电网运营商交互以确保网络安全;而在较低级别,产消者通过HEMS将其调度提交给集电商。如果网络安全面临风险,集电商将向产消者发送代表安全成本(CoS)的额外价格成分,以刺激进一步的响应。仿真结果表明,所提出的框架能够有效保证配电系统中集电商和产消者的经济运行,同时保持电网安全。

Abstract

The decreasing cost of solar photovoltaics (PVs) and battery storage systems is driving their adoption in the residential distribution system, where more consumers are becoming prosumers. Accompanying this trend is the potential roll-out of home energy management systems (HEMSs), which provide a means for prosumers to respond to externalities such as energy price, weather, and energy demands. However, the economic operation of prosumers can affect grid security, especially when energy prices are extremely low or high. Therefore, it is paramount to design a framework that can accommodate the interests of the key stakeholders in distribution systems—namely, the network operator, prosumer, and aggregator. In this paper, a novel transactive energy (TE)-based operational framework is proposed. Under this framework, aggregators interact with the distribution grid operator through a negotiation process to ensure network security, while at the lower level, prosumers submit their schedule to the aggregator through the HEMS. If network security is at risk, aggregators will send an additional price component representing the cost of security (CoS) to the prosumer to stimulate further response. The simulation results show that the proposed framework can effectively ensure the economic operation of aggregators and prosumers in distribution systems while maintaining grid security.

关键词

需求侧管理 / 能源产销者 / 可交易能源 / 集电商 / 电网安全性

Keywords

Demand management / Prosumer / Transactive energy / Aggregator / Grid security

引用本文

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Peng Hou, Guangya Yang, Junjie Hu. 基于分布式可交易能源机制的光伏与储能联动产消者的市场运营策略. Engineering. 2022, 12(5): 171-182 https://doi.org/10.1016/j.eng.2022.03.001

参考文献

[1]
Yang G, Hou P, Sera D, Martins JPR, Douglass PJ, Martens S, et al. Integration of PV+storage—technical and economic evaluation at distribution grids. In: Proceedings of the 8th International Workshop on the Integration of Solar Power into Power Systems; 2018 Oct 16–17; Sweden, Stockholm. 2018. p. 1–5.
[2]
Ding Y, Pineda S, Nyeng P, Østergaard J, Larsen EM, Wu Q. Real-time market concept architecture for EcoGrid EU—a prototype for European smart grids. IEEE Trans Smart Grid 2013;4(4):2006–16.
[3]
Han X, Sossan F, Bindner HW, You S, Hansen H, Cajar PD. Load kick-back effects due to activation of demand response in view of distribution grid operation. In: IEEE PES Innovative Smart Grid Technologies; 2014 Oct 12–15; Istanbul, Turkey. Piscataway: IEEE Press; 2014.
[4]
Wu H, Pratt A, Chakraborty S. Stochastic optimal scheduling of residential appliances with renewable energy sources. IEEE Power & Energy Society General Meeting; 2015 Jul 26–30; Denver, CO, USA. Piscataway: IEEE Press; 2015.
[5]
Hao H, Corbin CD, Kalsi K, Pratt RG. Transactive control of commercial buildings for demand response. IEEE Trans Power Syst 2017;32(1):774–83.
[6]
Xiao Y, Wang X, Pinson P, Wang X. Transactive energy based aggregation of prosumers as a retailer. IEEE Trans Smart Grid 2020;11(4):3302–12.
[7]
Hu J, Yang G, Bindner HW, Xue Y. Application of network-constrained transactive control to electric vehicle charging for secure grid operation. IEEE Trans Sustain Energy 2017;8(2):505–15.
[8]
Hu J, Yang G, Ziras C, Kok K. Aggregator operation in the balancing market through network-constrained transactive energy. IEEE Trans Power Syst 2019;34(5):4071–80.
[9]
Dolatabadi M, Siano P. A scalable privacy preserving distributed parallel optimization for a large-scale aggregation of prosumers with residential PVbattery systems. IEEE Access 2020;8:210950–60.
[10]
Nizami MSH, Hossain MJ, Fernandez E. Multiagent-based transactive energy management systems for residential buildings with distributed energy resources. IEEE Trans Industr Inform 2020;16(3):1836–47.
[11]
Mohammad A, Zamora R, Lie TT. Transactive energy management of PV-based EV integrated parking lots. IEEE Syst J 2020;15(4):5674–82.
[12]
Wu Y, Shi J, Lim GJ, Fan L, Molavi A. Optimal management of transactive distribution electricity markets with co-optimized bidirectional energy and ancillary service exchanges. IEEE Trans Smart Grid 2020;11(6):4650–61.
[13]
Zeng L, Li C, Li Z, Shahidehpour M, Zhou B, Zhou Q. Hierarchical bipartite graph matching method for transactive V2V power exchange in distribution power system. IEEE Trans Smart Grid 2020;12(1):301–11.
[14]
Nunna HSVSK, Sesetti A, Rathore AK, Doolla S. Multiagent-based energy trading platform for energy storage systems in distribution systems with interconnected microgrids. IEEE Trans Ind Appl 2020;56(3):3207–17.
[15]
Bedoya JC, Ostadijafari M, Liu CC, Dubey A. Decentralized transactive energy for flexible resources in distribution systems. IEEE Trans Sustain Energy 2020;12(2):1009–19.
[16]
Nizami MSH, Hossain MJ, Mahmud K. A nested transactive energy market model to trade demand-side flexibility of residential consumers. IEEE Trans Smart Grid 2021;12(1):479–90.
[17]
Divshali PH, Choi BJ, Liang H. Multi-agent transactive energy management system considering high levels of renewable energy source and electric vehicles. IET Gener Transm Distrib 2017;11(15):3713–21.
[18]
Huang S, Wu Q, Shahidehpour M, Liu Z. Dynamic power tariff for congestion management in distribution networks. IEEE Trans Smart Grid 2019;10 (2):2148–57.
[19]
Parizy ES, Bahrami HR, Loparo KA. A decentralized three-level optimization scheme for optimal planning of a prosumer nano-grid. IEEE Trans Power Syst 2020;35(5):3421–32.
[20]
Yan M, Shahidehpour M, Paaso A, Zhang L, Alabdulwahab A, Abusorra A. Distribution network-constrained optimization of peer-to-peer transactive energy trading among multi-microgrids. IEEE Trans Smart Grid 2020;12 (2):1033–47.
[21]
Ullah MH, Park JD. Peer-to-peer energy trading in transactive markets considering physical network constraints. IEEE Trans Smart Grid 2021;12 (4):3390–403.
[22]
Li J, Zhang C, Xu Z, Wang J, Zhao J, Zhang YA. Distributed transactive energy trading framework in distribution networks. IEEE Trans Power Syst 2018;33 (6):7215–27.
[23]
Hogan WW. Electricity scarcity pricing through operating reserves. Econ Energy Env Pol 2013;2(2):65–86.
[24]
Papavasiliou A, Smeers Y. Remuneration of flexibility using operating reserve demand curves: a case study of Belgium. Energy J 2017;38(6):105–35.
[25]
Ghamkhari M. Transactive energy pricing in power distribution systems. In: 2019 IEEE Green Technologies Conference (GreenTech); 2019 Apr 3–6; Lafayette, LA, USA. Piscataway: IEEE Press; 2019.
[26]
Tsaousoglou G, Pinson P, Paterakis NG. Transactive energy for flexible prosumers using algorithmic game theory. IEEE Trans Sustain Energy 2021;12(3):1571–81.
[27]
Zia MF, Benbouzid M, Elbouchikhi E, Muyeen SM, Techato K, Guerrero JM. Microgrid transactive energy: review, architectures, distributed ledger technologies, and market analysis. IEEE Access 2020;8:19410–32.
[28]
Aaberg L. Country specific issues related to DSO tariffs [Internet]. Copenhagen: Danish Energy Regulatory Authority; [cited 2020 Oct 20]. Available from: http://www.nordicenergyregulators.org/wp-content/uploads/2017/02/DSOtariffs-in-Denmark.pdf.
[29]
De Sa FR, Barroso LA, Lino PR, Carvalho MM, Valenzuela P. Time-of-use tariff design under uncertainty in price-elasticities of electricity demand: a stochastic optimization approach. IEEE Trans Smart Grid 2013;4(4): 2285–95.
[30]
Hou P, Douglass PJ, Yang G, Hielsen AH. Optimal scheduling of PV and battery storage at distribution network considering grid tariffs. In: 11th IET International Conference on Advances in Power System Control, Operation and Management; 2018 Nov 11–15; Hongkong, China. London: IET; 2018.
[31]
Siano P, De Marco G, Rolán A, Loia V. A survey and evaluation of the potentials of distributed ledger technology for peer-to-peer transactive energy exchanges in local energy markets. IEEE Syst J 2019;13(3):3454–66.
[32]
Sunny Home Manager 2.0 [Internet]. Niestetal: SMA Solar Technology AG; [cited 2020 Oct 20]. Available from: https://www.sma.de/en/ products/monitoring-control/sunny-home-manager-20.html.
[33]
Faqiry MN, Wang L, Wu H. HEMS-enabled transactive flexibility in real-time operation of three-phase unbalanced distribution systems. J Mod Power Syst Clean Energy 2019;7(6):1434–49.
[34]
Mignone D. The REALLY BIG collection of logic propositions and linear inequalities. Technical report. Switzerland: ETH Zurich; 2002 Feb 27. Report No.: AUT01-11.
[35]
Hou P, Hu J, Yang G. Convex optimization of virtual storage system scheduling in market environment. J Mod Power Syst Clean Energy 2019;7(6):1744–8.
[36]
Leou RC. Optimal charging/discharging control for electric vehicles considering power system constraints and operation costs. IEEE Trans Power Syst 2016;31 (3):1854–60.
[37]
Boyd S, Parikh N, Chu E, Peleato B, Eckstein J. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 2010;3(1):1–122.
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