考虑精细化碳交易的共享储能多微网系统经济调度

艾欣; 张瑛楠; 潘玺安

doi:10.19912/j.0254-0096.tynxb.2024-1014

PDF(1386 KB)

太阳能学报 ›› 2025, Vol. 46 ›› Issue (10) : 65-76. DOI: 10.19912/j.0254-0096.tynxb.2024-1014

考虑精细化碳交易的共享储能多微网系统经济调度

艾欣, 张瑛楠, 潘玺安

作者信息 +

ECONOMIC DISPATCH OF SHARED ENERGY STORAGE MULTI-MICROGRID SYSTEMS CONSIDERING REFINED CARBON TRADING

Ai Xin, Zhang Yingnan, Pan Xi’an

Author information +

文章历史 +

摘要

为提升配电系统的低碳经济性,首先构建多微网共享储能系统协同优化调度框架,同时为在充分考虑碳交易市场供需关系的基础上实现减排激励的灵活调整与精细刻画,提出基于动态碳价和碳配额供求关系的精细化碳交易成本计算方法;然后,为在提升多微网共享储能系统在实时阶段的功率决策时效性的同时增强决策模型对含不确定性因素的风光出力时序信息的感知能力,提出一种结合长短期记忆网络与多智能体近端策略优化方法的强化学习算法;最后,在IEEE 33节点系统中对所提模型与方法在系统低碳运行能力、运行经济性与决策时效性等方面的提升作用进行仿真验证。

Abstract

In order to improve the low-carbon economy of the distribution system, this paper first constructs the collaborative optimization scheduling framework of multi microgrid shared energy storage system. At the same time, in order to realize the flexible adjustment and fine characterization of emission reduction incentives on the basis of fully considering the supply-demand relationship of the carbon trading market, a refined carbon trading cost calculation method based on the dynamic carbon price and carbon quota supply-demand relationship is proposed; Then, in order to improve the timeliness of power decision-making in the real-time phase of the multi microgrid shared energy storage system and enhance the ability of the decision-making model to perceive the time-series information of wind and solar output with uncertain factors, a reinforcement learning algorithm combining short-term and long-term memory network and multi-agent near end strategy optimization method is proposed; Finally, in IEEE 33 bus system, the improvement effect of the proposed model and method on the low-carbon operation ability, operation economy and decision timeliness of the system is verified by simulation.

导出引用

艾欣, 张瑛楠, 潘玺安. 考虑精细化碳交易的共享储能多微网系统经济调度[J]. 太阳能学报. 2025, 46(10): 65-76 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1014

Ai Xin, Zhang Yingnan, Pan Xi’an. ECONOMIC DISPATCH OF SHARED ENERGY STORAGE MULTI-MICROGRID SYSTEMS CONSIDERING REFINED CARBON TRADING[J]. Acta Energiae Solaris Sinica. 2025, 46(10): 65-76 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1014

中图分类号： TM715

参考文献

[1] 栗峰, 丁杰, 周才期, 等. 新型电力系统下分布式光伏规模化并网运行关键技术探讨[J]. 电网技术, 2024, 48(1): 184-199.
LI F, DING J, ZHOU C Q, et al.Key technologies of large-scale grid-connected operation of distributed photovoltaic under new-type power system[J]. Power system technology, 2024, 48(1): 184-199.
[2] DAI R, ESMAEILBEIGI R, CHARKHGARD H.The utilization of shared energy storage in energy systems: a comprehensive review[J]. IEEE transactions on smart grid, 2021, 12(4): 3163-3174.
[3] ZHU H, LI H, LIU G J, et al.Energy storage in high variable renewable energy penetration power systems: technologies and applications[J]. CSEE journal of power and energy systems, 2023, 9(6): 2099-2108.
[4] 刘畅, 卓建坤, 赵东明, 等. 利用储能系统实现可再生能源微电网灵活安全运行的研究综述[J]. 中国电机工程学报, 2020, 40(1): 1-18, 369.
LIU C, ZHUO J K, ZHAO D M, et al.A review on the utilization of energy storage system for the flexible and safe operation of renewable energy microgrids[J]. Proceedings of the CSEE, 2020, 40(1): 1-18, 369.
[5] LI L X, CAO X L, ZHANG S.Shared energy storage system for prosumers in a community: investment decision, economic operation, and benefits allocation under a cost-effective way[J]. Journal of energy storage, 2022, 50: 104710.
[6] LU J G, ZHENG W J, YU Z W, et al.Optimizing grid-connected multi-microgrid systems with shared energy storage for enhanced local energy consumption[J]. IEEE access, 2024, 12: 13663-13677.
[7] 王继东, 许秋铭, 黄婷, 等. 含共享储能的数据中心微网群分布式优化调度[J]. 电网技术, 2024, 48(8): 3238-3247.
WANG J D, XU Q M, HUANG T, et al.Distributed optimal scheduling of data center microgrids cluster with shared energy storage[J]. Power system technology, 2024, 48(8): 3238-3247.
[8] CHEN Y H, ZHU Z Y, LIN H J, et al.Shared energy storage optimization considering electricity price and PV output uncertainty[C]//2023 5th Asia Energy and Electrical Engineering Symposium (AEEES), Chengdu, China, 2023: 1696-1703.
[9] ZHANG L, LIU D Y, CAI G W, et al.An optimal dispatch model for virtual power plant that incorporates carbon trading and green certificate trading[J]. International journal of electrical power & energy systems, 2023, 144: 108558.
[10] 崔杨, 沈卓, 王铮, 等. 考虑绿证-碳排等价交互机制的区域综合能源系统绿色调度[J]. 中国电机工程学报, 2023, 43(12): 4508-4517.
CUI Y, SHEN Z, WANG Z, et al.Green dispatch of regional integrated energy system considering green certificate-carbon emission equivalent interaction mechanism[J]. Proceedings of the CSEE, 2023, 43(12): 4508-4517.
[11] YAN N, LI X J, WU Z L, et al.Low-carbon economic scheduling with Demand-Side response uncertainty in regional integrated energy system[J]. International journal of electrical power & energy systems, 2024, 156: 109691.
[12] 骆钊, 黎博文, 毕贵红, 等. 含CCUS和P2G的综合能源系统分布式鲁棒优化调度[J]. 高电压技术, 2024, 50(8): 3486-3499.
LUO Z, LI B W, BI G H, et al.Distributed robust optimal scheduling of integrated energy system with CCUS and P2G[J]. High voltage engineering, 2024, 50(8): 3486-3499.
[13] WANG R H, BI X W, BU S Q.Real-time coordination of dynamic network reconfiguration and volt-VAR control in active distribution network: a graph-aware deep reinforcement learning approach[J]. IEEE transactions on smart grid, 2024, 15(3): 3288-3302.
[14] CAO D, HU W H, ZHAO J B, et al.Reinforcement learning and its applications in modern power and energy systems: a review[J]. Journal of modern power systems and clean energy, 2020, 8(6): 1029-1042.
[15] HE X T, GE S Y, LIU H, et al.Frequency regulation of multi-microgrid with shared energy storage based on deep reinforcement learning[J]. Electric power systems research, 2023, 214: 108962.
[16] DING L F, CUI Y K, YAN G F, et al.Distributed energy management of multi-area integrated energy system based on multi-agent deep reinforcement learning[J]. International journal of electrical power & energy systems, 2024, 157: 109867.
[17] 陈泽宇, 方志远, 杨瑞鑫, 等. 基于深度强化学习的混合动力汽车能量管理策略[J]. 电工技术学报, 2022, 37(23): 6157-6168.
CHEN Z Y, FANG Z Y, YANG R X, et al.Energy management strategy for hybrid electric vehicle based on the deep reinforcement learning method[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 6157-6168.
[18] 余兴兴, 李元诚, 王庆乐, 等. 基于联邦强化学习的社区共享储能日前调度[J]. 中国电机工程学报, 2024, 44(20): 8103-8113.
YU X X, LI Y C, WANG Q L, et al.Day-ahead scheduling of community shared energy storage based on federated reinforcement learning[J]. Proceedings of the CSEE, 2024, 44(20): 8103-8113.
[19] LEE S, XIE L, CHOI D H.Privacy-preserving energy management of a shared energy storage system for smart buildings: a federated deep reinforcement learning approach[J]. Sensors, 2021, 21(14): 4898.
[20] 陈金富, 朱乔木, 石东源, 等. 利用时空相关性的多位置多步风速预测模型[J]. 中国电机工程学报, 2019, 39(7): 2093-2106.
CHEN J F, ZHU Q M, SHI D Y, et al.A multi-step wind speed prediction model for multiple sites leveraging spatio-temporal correlation[J]. Proceedings of the CSEE, 2019, 39(7): 2093-2106.
[21] 张晓英, 陈宝奇, 马志程, 等. 基于主从博弈理论的热电联供型太阳能热发电站冬季优化运行研究[J]. 太阳能学报, 2023, 44(11): 155-165.
ZHANG X Y, CHEN B Q, MA Z C, et al.Study on optimal operation of solar thermal power plant based on leader-follower game theory in winter[J]. Acta energiae solaris sinica, 2023, 44(11): 155-165.
[22] 李亚峰, 王维庆, 寇洋, 等. 考虑绿证-碳联合交易与需求响应综合能源系统经济运行[J]. 太阳能学报, 2023, 44(11): 538-546.
LI Y F, WANG W Q, KOU Y, et al.Considering green certificate-carbon joint trading and demand response integrated energy system economic operation[J]. Acta energiae solaris sinica, 2023, 44(11): 538-546.
[23] SHEN J R.Low-carbon optimal dispatch of campus integrated energy system considering flexible interactions between supply and demand[J]. International journal of electrical power & energy systems, 2024, 156: 109776.
[24] 杨秀, 傅广努, 刘方, 等. 考虑多重因素的空调负荷聚合响应潜力评估及控制策略研究[J]. 电网技术, 2022, 46(2): 699-714.
YANG X, FU G N, LIU F, et al.Potential evaluation and control strategy of air conditioning load aggregation response considering multiple factors[J]. Power system technology, 2022, 46(2): 699-714.
[25] TOSTADO-VÉLIZ M, REZAEE JORDEHI A, HASANIEN H M, et al. A novel stochastic home energy management system considering negawatt trading[J]. Sustainable Cities and Society, 2023, 97: 104757.
[26] ZHAO J, YANG Z L, SHI L Y, et al.Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads[J]. Applied energy, 2024, 356: 122430.
[27] WU Z Y, WANG J X, ZHONG H W, et al.Sharing economy in local energy markets[J]. Journal of modern power systems and clean energy, 2023, 11(3): 714-726.
[28] YU C, VELU A, VINITSKY E, et al.The surprising effectiveness of PPO in cooperative, multi-agent games[EB/OL]. https://arxiv.org/abs/2103.01955v4.
[29] 谭海旺, 杨启亮, 邢建春, 等. 基于XGBoost-LSTM组合模型的光伏发电功率预测[J]. 太阳能学报, 2022, 43(8): 75-81.
TAN H W, YANG Q L, XING J C, et al.Photovoltaic power prediction based on combined XGBoost-LSTM model[J]. Acta energiae solaris sinica, 2022, 43(8): 75-81.
[30] WIESE F, SCHLECHT I, BUNKE W D, et al.Open power system data-frictionless data for electricity system modelling[J]. Applied energy, 2019, 236: 401-409.
[31] 段新会, 黄嵘, 齐传杰, 等. 计及碳交易与需求响应的微能源网双层优化模型[J]. 太阳能学报, 2024, 45(3): 310-318.
DUAN X H, HUANG R, QI C J, et al.Bi-level optimization model for micro energy grid considering carbon trading and demand response[J]. Acta energiae solaris sinica, 2024, 45(3): 310-318.