This page has only limited features, please log in for full access.
This paper proposes the concept “active energy agent (AEA)” to characterize the autonomous and interactive entities of power system. The future distribution network is a peer-to-peer (P2P) community based on numbers of AEAs. A two-stage “P2P Plus” mechanism is developed to address the electricity transaction within AEA community. In the first “P2P” stage, electricity is directly traded among AEAs via P2P price bidding. The model of P2P transaction is established, and the method of multi-dimensional willingness is adopted in price bidding. In the second “Plus” stage, the centralized coordination by distribution company (DisCo) is formulated as a constrained optimization problem, in which the objective is to maximize profit and the constraints are the basic rights of AEAs and line ratings of distribution network. A 30-bus test system including 29 AEAs and main grid is investigated. Numeric simulation results verify the effectiveness of the proposed models and methods regarding flow constraint. Comparative study reveals the economic motivations of AEAs to participate in P2P transaction, the efficiency of combined search, and the benefit of DisCo from pricing control.
Min Fu; Zhiyu Xu; Ning Wang; Xiaoyu Lyu; Weisheng Xu. “Peer-to-Peer Plus” Electricity Transaction within Community of Active Energy Agents Regarding Distribution Network Constraints. Energies 2020, 13, 2408 .
AMA StyleMin Fu, Zhiyu Xu, Ning Wang, Xiaoyu Lyu, Weisheng Xu. “Peer-to-Peer Plus” Electricity Transaction within Community of Active Energy Agents Regarding Distribution Network Constraints. Energies. 2020; 13 (9):2408.
Chicago/Turabian StyleMin Fu; Zhiyu Xu; Ning Wang; Xiaoyu Lyu; Weisheng Xu. 2020. "“Peer-to-Peer Plus” Electricity Transaction within Community of Active Energy Agents Regarding Distribution Network Constraints." Energies 13, no. 9: 2408.
This paper addresses decentralized energy trading among virtual power plants (VPPs) and proposes a peer-to-peer (P2P) mechanism, including two interactive layers: on the bottom layer, each VPP schedules/reschedules its internal distributed energy resources (DERs); and on the top layer, VPPs negotiate with each other on the trade price and quantity. The bottom-layer scheduling provides initial conditions for the top-layer negotiation, and the feedback of top-layer negotiation affects the bottom-layer rescheduling. The local scheduling/rescheduling of a VPP is formulated as a stochastic optimization problem, which takes into account the uncertainties of wind and photovoltaic power by using the scenarios-based method. In order to describe the capability of a seller VPP to generate more energy than the scheduled result, the concept of power generation potential is introduced and then considered during order initialization. The multidimensional willingness bidding strategy (MWBS) is modified and applied to the price bidding process of P2P negotiation. A 14-VPP case is studied by performing numerous computational experiments. The optimal scheduling model is effective and flexible to deal with VPPs with various configurations of DERs. The parallel price bidding with MWBS is adaptive to market situations and efficient due to its rapid convergence. It is revealed that VPPs can obtain higher profit by participating in P2P energy trading than from traditional centralized trading, and the proposed mechanism of two-layer “interactivity” can further increase VPPs’ benefits compared to its “forward” counterpart. The impacts of VPP configuration and VPP number are also studied. It is demonstrated that the proposed mechanism is applicable to most cases where VPPs manage some controllable DERs.
Xiaoyu Lyu; Xu; Ning Wang; Min Fu; Lyu; Wang; Fu; Zhiyu Xu; Weisheng Xu. A Two-Layer Interactive Mechanism for Peer-to-Peer Energy Trading Among Virtual Power Plants. Energies 2019, 12, 3628 .
AMA StyleXiaoyu Lyu, Xu, Ning Wang, Min Fu, Lyu, Wang, Fu, Zhiyu Xu, Weisheng Xu. A Two-Layer Interactive Mechanism for Peer-to-Peer Energy Trading Among Virtual Power Plants. Energies. 2019; 12 (19):3628.
Chicago/Turabian StyleXiaoyu Lyu; Xu; Ning Wang; Min Fu; Lyu; Wang; Fu; Zhiyu Xu; Weisheng Xu. 2019. "A Two-Layer Interactive Mechanism for Peer-to-Peer Energy Trading Among Virtual Power Plants." Energies 12, no. 19: 3628.
This paper addresses the coordinative operation problem of multi-energy virtual power plant (ME-VPP) in the context of energy internet. A bi-objective dispatch model is established to optimize the performance of ME-VPP in terms of economic cost (EC) and power quality (PQ). Various realistic factors are considered, which include environmental governance, transmission ratings, output limits, etc. Long short-term memory (LSTM), a deep learning method, is applied to the promotion of the accuracy of wind prediction. An improved multi-objective particle swarm optimization (MOPSO) is utilized as the solving algorithm. A practical case study is performed on Hongfeng Eco-town in Southwestern China. Simulation results of three scenarios verify the advantages of bi-objective optimization over solely saving EC and enhancing PQ. The Pareto frontier also provides a visible and flexible way for decision-making of ME-VPP operator. Two strategies, “improvisational” and “foresighted”, are compared by testing on the Institute of Electrical and Electronic Engineers (IEEE) 118-bus benchmark system. It is revealed that “foresighted” strategy, which incorporates LSTM prediction and bi-objective optimization over a 5-h receding horizon, takes 10 Pareto dominances in 24 h.
Jiahui Zhang; Zhiyu Xu; Weisheng Xu; Feiyu Zhu; Xiaoyu Lyu; Min Fu. Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization. Applied Sciences 2019, 9, 292 .
AMA StyleJiahui Zhang, Zhiyu Xu, Weisheng Xu, Feiyu Zhu, Xiaoyu Lyu, Min Fu. Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization. Applied Sciences. 2019; 9 (2):292.
Chicago/Turabian StyleJiahui Zhang; Zhiyu Xu; Weisheng Xu; Feiyu Zhu; Xiaoyu Lyu; Min Fu. 2019. "Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning-Based Prediction and Particle Swarm Optimization." Applied Sciences 9, no. 2: 292.