Abstract：At present, there are four problems in the actual operation of the virtual power plant(VPP): (1) The centralized, centralized-decentralized and fully decentralized control modes of the VPP currently used cannot achieve common optimization in flexibility, compatibility and scalability, which will affect the overall operation effect of the VPP. (2) Because the VPP uses the network for two-way communication, the power generation and load data of each unit in the system are vulnerable to malicious attacks and tampering by hackers, which makes the operation process of the VPP wrong, resulting in increased system costs or equipment failures. (3) Wind, solar and other renewable energy power generation has uncertainty and randomness. If the VPP cannot quickly adjust the operation state when the power generation situation changes dramatically, it will lead to the increase of penalty costs and affect the overall benefits of the system. (4) The distributed characteristics of VPP make it difficult to integrate and coordinate the internal units. In the existing research, intelligent optimization algorithm is usually used to calculate the scheduling strategy of VPP, but such algorithm does not adapt to the distributed characteristics of VPP, and there are still deficiencies in calculation time and accuracy, which will lead to the rise of system operation cost and the decline of operation efficiency. To solve these problems, this paper proposes a VPP model and scheduling strategy based on optimized computing block-chain system to improve the information security level and operation efficiency of VPP. Firstly, the basic structure of the block-chain system and the feasibility of the combination of block-chain technology and VPP are studied, and then on this basis, block-chain based distributed particle swarm optimization algorithm (BD-PSO) and proof of optimization calculation workload (POCW) consensus algorithm suitable for VPP operation environment are proposed, improve the computing speed of scheduling optimization problems. Thirdly, the improved BD-PSO algorithm and POCW algorithm are used to improve the traditional block-chain, and an optimized computational block-chains (OCB) system is proposed to improve the adaptability of the combination of block-chain and VPP and improve the operation efficiency of the system. Finally, the OCB virtual power plant (OCB-VPP) structure model and scheduling strategy are established based on OCB. Through the actual data of a certain region, the differences between OCB-VPP and traditional VPP in coping with the fluctuation of renewable energy output, economic benefits of the system and carbon emissions are compared and analyzed. The advantages of BD-PSO algorithm in calculation speed and accuracy are analyzed, and the effectiveness and economy of the proposed model are verified. The simulation results of actual data in a certain area show that: (1) BD-PSO algorithm produces more calculation results based on Lin-WPSO algorithm, which are compared in a larger range, optimizes the disadvantage that the Lin-WPSO algorithm is easy to fall into local optimization, and improves the calculation speed and accuracy by 50.19% and 9.86% respectively. (2) Compared with the traditional VPP system, OCB-VPP system benefits from the improvement of system architecture and algorithm performance, and has advantages in scheduling efficiency and accuracy, which significantly reduces the operation cost of the system. The typical daily cost of each season is reduced by 13.29%, the carbon emission is reduced by 14.53%, the wind / light consumption of the system is increased by 9.95%, and the economic benefits and energy utilization of the system have been significantly improved. Finally, the following conclusions are drawn through analysis: (1) compared with Lin-wPSO, WOA and LRO algorithms, the improved BD-PSO optimization algorithm in this paper has significant advantages in calculation speed and accuracy. Therefore, BD-PSO optimization algorithm is very suitable for the VPP environment with complex and changeable operating conditions. (2) OCB-VPP structure is more suitable for VPP, which can improve the information security level and operation efficiency of VPP. (3) Compared with traditional centralized VPP, OCB-VPP structure can reduce system operation costs, reduce carbon emissions and improve renewable energy utilization.
刘雨佳, 樊艳芳, 白雪岩, 宋雨露. 基于优化计算型区块链系统的虚拟电厂模型与调度策略[J]. 电工技术学报, 2023, 38(15): 4178-4191.
Liu Yujia, Fan Yanfang, Bai Xueyan, Song Yulu. Virtual Power Plant Model and Scheduling Strategy Based on Optimized Computing Block-Chain System. Transactions of China Electrotechnical Society, 2023, 38(15): 4178-4191.
[1] 李翔宇, 赵冬梅. 基于模糊-概率策略实时反馈的虚拟电厂多时间尺度优化调度[J]. 电工技术学报, 2021, 36(7): 1446-1455. Li Xiangyu, Zhao Dongmei.Research on multi-time scale optimal scheduling of virtual power plant based on real-time feedback of fuzzy-probability strategy[J]. Transactions of China Electrotechnical Society, 2021, 36(7): 1446-1455. [2] 麻秀范, 余思雨, 朱思嘉, 等. 基于多因素改进Shapley的虚拟电厂利润分配[J]. 电工技术学报, 2020, 35(S2): 585-595. Ma Xiufan, Yu Siyu, Zhu Sijia, et al.Profit allocation to virtual power plant members based on improved multifactor dhapley value method[J]. Transactions of China Electrotechnical Society, 2020, 35(S2): 585-595. [3] 赵昊天, 王彬, 潘昭光, 等. 支撑云-群-端协同调度的多能园区虚拟电厂:研发与应用[J]. 电力系统自动化, 2021, 45(5): 111-121. Zhao Haotian, Wang Bin, Pan Zhaoguang, et al.Research and application of park-level multi-energy virtual power plants supporting cloud-cluster-end multi-level synergetic dispatch[J]. Automation of Electric Power Systems, 2021, 45(5): 111-121. [4] 周玮, 蓝嘉豪, 麦瑞坤, 等. 无线充电电动汽车V2G模式下光储直流微电网能量管理策略[J]. 电工技术学报, 2022, 37(1): 82-91. Zhou Wei, Lan Jiahao, Mai Ruikun, et al.Research on power management strategy of DC microgrid with photovoltaic, energy storage and EV-wireless power transfer in V2G mode[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 82-91. [5] 郑重, 苗世洪, 李超, 等. 面向微型能源互联网接入的交直流配电网协同优化调度策略[J]. 电工技术学报, 2022, 37(1): 192-207. Zheng Zhong, Miao Shihong, Li Chao, et al.Coordinated optimal dispatching strategy of AC/DC distribution network for the integration of micro energy Internet[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 192-207. [6] 陈张宇, 刘东, 刘浩文, 等. 基于精细化需求响应的虚拟电厂优化调度[J]. 电网技术, 2021, 45(7): 2542-2550. Chen Zhangyu, Liu Dong, Liu Haowen, et al.Optimal dispatching of virtual power plant based on refined demand response[J]. Power System Technology, 2021, 45(7): 2542-2550. [7] 赵东元, 胡楠, 傅靖, 等. 提升新能源电力系统灵活性的中国实践及发展路径研究[J]. 电力系统保护与控制, 2020, 48(24): 1-8. Zhao Dongyuan, Hu Nan, Fu Jing, et al.Research on the practice and road map of enhancing the flexibility of a new generation power system in China[J]. Power System Protection and Control, 2020, 48(24): 1-8. [8] 佘维, 胡跃, 杨晓宇, 等. 基于能源区块链网络的虚拟电厂运行与调度模型[J]. 中国电机工程学报, 2017, 37(13): 3729-3736. She Wei, Hu Yue, Yang Xiaoyu, et al.Virtual power plant operation and scheduling model based on energy blockchain network[J]. Proceedings of the CSEE, 2017, 37(13): 3729-3736. [9] 周国亮, 李刚. 区块链背景下虚拟电厂分布式调度策略研究[J]. 计算机工程与应用, 2020, 56(15): 268-273. Zhou Guoliang, Li Gang.Research on distributed scheduling strategy of virtual power plant considering blockchain[J]. Computer Engineering and Applications, 2020, 56(15): 268-273. [10] 任建文, 张青青. 基于能源区块链的虚拟电厂两阶段鲁棒优化调度[J]. 电力自动化设备, 2020, 40(8): 23-33. Ren Jianwen, Zhang Qingqing.Two-stage robust optimal scheduling of virtual power plant based on energy blockchain[J]. Electric Power Automation Equipment, 2020, 40(8): 23-33. [11] Lu Jing, Wu Shihong, Cheng Hanlei, et al.Smart contract for distributed energy trading in virtual power plants based on blockchain[J]. Computational Intelligence, 2021, 37(3): 1445-1455. [12] 周洪益, 余爽, 柏晶晶, 等. 基于区块链技术的虚拟电厂交易管理模式研究[J]. 电力需求侧管理, 2021, 23(1): 72-76. Zhou Hongyi, Yu Shuang, Bai Jingjing, et al.Research on trading pattern of virtual power plant based on blockchain technology[J]. Power Demand Side Management, 2021, 23(1): 72-76. [13] Kavousi-Fard A, Almutairib A, Al-Sumaitic A, et al.An effective secured peer-to-peer energy market based on blockchain architecture for the interconnected microgrid and smart grid[J]. International Journal of Electrical Power & Energy Systems, 2021, 132: 107171. [14] Regueiro C, Seco I, Diego S, et al.Privacy-enhancing distributed protocol for data aggregation based on blockchain and homomorphic encryption[J]. Information Processing & Management, 2021, 58(6): 102745. [15] 黄伟, 左欣雅, 刘弋铭. 基于多区块链结构的综合能源系统调度构架[J]. 电力系统自动化, 2021, 45(23): 12-20. Huang Wei, Zuo Xinya, Liu Yiming.Multiple blockchains based dispatching architecture for integrated energy system[J]. Automation of Electric Power Systems, 2021, 45(23): 12-20. [16] 邵奇峰, 金澈清, 张召, 等. 区块链技术: 架构及进展[J]. 计算机学报, 2018, 41(5): 969-988. Shao Qifeng, Jin Cheqing, Zhang Zhao, et al.Blockchain: architecture and research progress[J]. Chinese Journal of Computers, 2018, 41(5): 969-988. [17] Dzobo O, Malila B, Sithole L.Proposed framework for blockchain technology in a decentralised energy network[J]. Protection and Control of Modern Power Systems, 2021, 6(1): 31. [18] Wang Lei, Ma Yichao, Zhu Liuzhu, et al.Design of integrated energy market cloud service platform based on blockchain smart contract[J]. International Journal of Electrical Power & Energy Systems, 2022, 135: 107515. [19] Li Wei, Wang Lixia, Wang Dawei.Power energy supply and demand scheduling method based on blockchain technology[J]. Journal of Physics: Conference Series, 2021, 1994(1): 012001. [20] 杨锡运, 陶准, 黄欣欣, 等. 基于异构能源区块链的综合能源系统交易模型[J]. 电力自动化设备, 2021, 41(9): 224-231. Yang Xiyun, Tao Zhun, Huang Xinxin, et al.Transaction model of integrated energy system based on heterogeneous energy blockchain[J]. Electric Power Automation Equipment, 2021, 41(9): 224-231. [21] 任洪波, 吴琼, 任建兴. 基于需求侧视角的天然气分布式热电联产系统节能效益研究[J]. 中国电机工程学报, 2015, 35(17): 4430-4438. Ren Hongbo, Wu Qiong, Ren Jianxing.Energy performance assessment of combined heat and power system based on demand-side viewpoint[J]. Proceedings of the CSEE, 2015, 35(17): 4430-4438. [22] 熊宇峰, 司杨, 郑天文, 等. 考虑热电综合利用的光伏储氢独立供能系统容量优化配置[J]. 中国电力, 2020, 53(10): 66-73. Xiong Yufeng, Si Yang, Zheng Tianwen, et al.Optimal capacity configuration of solar-hydrogen independent power-supply system considering electricity-heat comprehensive utilization[J]. Electric Power, 2020, 53(10): 66-73. [23] 方仍存, 杨洁, 周奎等. 计及全生命周期碳成本的园区综合能源系统优化规划方法[J]. 中国电力, 2022, 55(12): 135-146. Fang Rengcun, Yang Jie, Zhou Kui, et al.Optimal planning method of IES considering carbon cost from the perspective of the whole life cycle[J]. Electric Power, 2022, 55(12): 135-146. [24] 孙益文. 低碳经济下我国天然气产业投资战略研究[D]. 东营: 中国石油大学(华东), 2016. [25] 李嘉琪, 李柏姝. 面向生命周期的燃料电池低碳优化设计[J]. 时代汽车, 2020(21): 124-125. Li Jiaqi, Li Boshu.Low-carbon fuel cell optimization design for life cycle[J]. Auto Time, 2020(21): 124-125.
2023, Vol. 38 
