Optimal grid-forming control of battery energy storage systems providing multiple services: Modeling and experimental validation

被引:0
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作者
Gerini, Francesco [1 ]
Zuo, Yihui [1 ]
Gupta, Rahul [1 ]
Zecchino, Antonio [1 ]
Yuan, Zhao [1 ]
Vagnoni, Elena [2 ]
Cherkaoui, Rachid [1 ]
Paolone, Mario [1 ]
机构
[1] Distributed Electrical Systems Laboratory (DESL), Ecole Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
[2] Technology Platform for Hydraulic Machines (PTMH), Ecole Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
基金
欧盟地平线“2020”;
关键词
Battery energy storage systems - Distribution feeders - Frequency containment reserve - Grid-forming converter - Model validation - Multiple service models - Optimal scheduling - Phase A - Power set - Setpoints;
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摘要
This paper proposes and experimentally validates a joint control and scheduling framework for a grid-forming converter-interfaced Battery Energy Storage Systems (BESSs) providing multiple services to the electrical grid. The framework is designed to dispatch the operation of a distribution feeder hosting heterogeneous prosumers according to a dispatch plan and to provide frequency containment reserve and voltage control as additional services. The framework consists of three phases. In the day-ahead scheduling phase, a robust optimization problem is solved to compute the optimal dispatch plan and frequency droop coefficient, accounting for the uncertainty of the aggregated prosumption. In the intra-day phase, a model predictive control algorithm is used to compute the power set-point for the BESS to achieve the tracking of the dispatch plan. Finally, in a real-time stage, the power set-point originated by the dispatch tracking is converted into a feasible frequency set-point for the grid forming converter by means of a convex optimization problem accounting for the capability curve of the power converter. The proposed framework is experimentally validated by using a grid-scale 720 kVA/560 kWh BESS connected to a 20 kV distribution feeder in the EPFL campus hosting stochastic prosumption and PV generation. © 2022 The Author(s)
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