Optimal Virtual Power Plant Management for Multiple Grid Support Services

被引：16

作者：

Bolzoni, Alberto ^{[1
]}

Parisio, Alessandra ^{[1
]}

Todd, Rebecca ^{[1
]}

Forsyth, Andrew J. ^{[1
]}

机构：

[1] Univ Manchester, Dept Elect & Elect Engn, Manchester M13 9PL, Lancs, England

来源：

IEEE TRANSACTIONS ON ENERGY CONVERSION | 2021年 / 36卷 / 02期

基金：

“创新英国”项目; 英国工程与自然科学研究理事会;

关键词：

Frequency control; Cogeneration; Buildings; Real-time systems; Optimization; Batteries; Thermal management; Model predictive control; multiple service provision; virtual power plants; power system dynamics; PREDICTIVE CONTROL; ENERGY; MICROGRIDS; STORAGE;

D O I：

10.1109/TEC.2020.3044421

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

A hierarchical control architecture is proposed for the optimal day-ahead commitment of multiple grid support services within a virtual power plant (VPP). The day-ahead optimization considers pricing and cost data to determine the commitment schedule, and a robust Model Predictive Control (MPC) approach is included to minimize the unbalance fees during real-time operations. The multi-level control has been demonstrated experimentally using a hybrid test system, where the VPP is formed of a commercial 240 kW, 180 kWh battery energy storage system (BESS), while the additional assets are modelled in a real-time digital simulator (RTDS). Two case studies are analyzed: the first assumes a purely-electrical VPP, with a single connection to the public network; the second involves a multi-energy approach, with the introduction of a gas-supplied Combined Heat and Power unit (CHP). Both winter and summer price scenarios are tested. The results show the superiority of the multiple-service operation compared to providing a single grid support service. For example, the net revenue is increased by 30% (winter) and 7% (summer) when compared to just frequency regulation, and by +99% (winter) and 30% (summer) when compared to only energy arbitrage.

引用

页码：1479 / 1490

页数：12

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