Offering strategy of a price-maker virtual power plant in the day-ahead market

Nhung Nguyen-Hong (Department of Electrical Engineering, School of Electrical and Electronics Engineering, Hanoi University of Science and Technology, Hanoi)
Khai Bui Quang (Department of Electrical Engineering, School of Electrical and Electronics Engineering, Hanoi University of Science and Technology, Hanoi)
Long Phan Vo Thanh (Department of Electrical Engineering, School of Electrical and Electronics Engineering, Hanoi University of Science and Technology, Hanoi)
Duc Bui Huynh (Department of Electrical Engineering, School of Electrical and Electronics Engineering, Hanoi University of Science and Technology, Hanoi)

International Journal of Renewable Energy Development
Vol 12, No 4 (2023): July 2023

With the rapid increase of renewable energy sources (RESs), the virtual power plant model (VPP) has been developed to integrate RESs, energy storage systems (ESSs), and local customers to overcome the RESs’ disadvantages. When the VPP’s capacity is large enough, it can participate in the electricity market as a price-maker instead of a price-taker to obtain a higher profit. This study proposes a bi-level optimization model to determine the optimal trading strategies of a price-maker VPP in the day-ahead (DA) market. The operation schedule of the components in the VPP is also optimized to achieve the highest profit for the VPP. In the bi-level optimization problem, the upper-level model is maximizing the VPP’s profit while the lower-level model is the DA market-clearing problem. The bi-level optimization problem is formulated as a Mathematical Problem with Equilibrium Constraints (MPEC), reformulated to a Mixed Integer Linear Problem (MILP), then solved by GAMS and CPLEX. This study applies the bi-level optimization model to a test VPP system, including wind plants (WP), solar plants (PV), biogas energy plants (BG), ESSs, and several customers. The maximum power outputs of WP and PV are 100MW and 90MW, respectively. The total installed capacity of BG is 70MW, while the ESS’ rated capacity is 100MWh. The local customers have the highest total consumption of 100MW. In addition to the VPP, four GENCOs and three retailers participate in the DA market. The results show that the market-clearing price varies depending on the participants’ production/consumption quantity and offering/bidding price. However, based on the optimization model, the VPP can take full advantage of WP and PV available power output, choose the right time to operate BG, then obtain the highest profit. The results also show that with the ESS’ rated capacity of 100MWh, the ESS’ rated discharging/charging power increased from 10MW to 50MW will increase VPP’s profit from 45987$ to 49464$. The obtained results show that the proposed model has practical significance