TY - JOUR
T1 - Optimal Virtual Power Plant Management for Multiple Grid Support Services
AU - Bolzoni, Alberto
AU - Parisio, Alessandra
AU - Todd, Rebecca
AU - Forsyth, Andrew
N1 - Funding Information:
December 7, 2020. Date of publication December 14, 2020; date of current version May 21, 2021. This work was supported in part the UK Engineering andPhysicalSciencesResearchCouncil(EPSRC)under\MANIFESTProject OW carbon technologies are increasingly deployed in BuildingEnergyManagementUtilizingEnergyStorage(133463).Paperno. LEP/N032888/1,andinpartbyInnovateUKunderProjectQ-PLUSIntelligentpower systems to achieve net-zero carbon emissions by TEC-00292-2020. (Corresponding author: Alberto Bolzoni.) 2050; these include distributed energy sources, storage systems The authors are with the Department of Electrical and Electronic Engi- and controllable loads, and are often grouped to form a virtual [email protected];[email protected]; re-neering,TheUniversityofManchester,M139PLManchester,U.K.(e-mail: power plant (VPP) [1]. The diversified nature of the VPP assets [email protected]; [email protected]). allow frequency regulation services to be provided along with DedicatedtothememoryofourlatecolleagueRebeccaTodd energy arbitrage, to maximize revenues. https://doi.org/10.1109/TEC.2020.3044421.Colorversionsofoneormoreofthefiguresinthisarticleareavailableat Among the different VPP energy management control tech- Digital Object Identifier 10.1109/TEC.2020.3044421 niques, Model Predictive Control (MPC) is recognized as a 0885-8969 © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
Publisher Copyright:
© 1986-2012 IEEE.
PY - 2021/6/1
Y1 - 2021/6/1
N2 - 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.
AB - 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.
KW - Model predictive control
KW - multiple service provision
KW - power system dynamics
KW - virtual power plants
U2 - 10.1109/TEC.2020.3044421
DO - 10.1109/TEC.2020.3044421
M3 - Article
SN - 0885-8969
VL - 36
SP - 1479
EP - 1490
JO - IEEE Transactions on Energy Conversion
JF - IEEE Transactions on Energy Conversion
IS - 2
M1 - 9292995
ER -