Optimal Virtual Power Plant Management for Multiple Grid Support Services

Alberto Bolzoni, Alessandra Parisio, Rebecca Todd, Andrew Forsyth

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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.

Original languageEnglish
Article number9292995
Pages (from-to)1479-1490
Number of pages12
JournalIEEE Transactions on Energy Conversion
Issue number2
Early online date14 Dec 2020
Publication statusPublished - 1 Jun 2021


  • Model predictive control
  • multiple service provision
  • power system dynamics
  • virtual power plants


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