Self-adaptive hybrid algorithm based bi-level approach for virtual power plant bidding in multiple retail markets

Abstract

Virtual power plant (VPP) has become a promising technique to facilitate distributed energy resources (DERs) to participate in the power markets. Considering the respective interests of the VPP agent and distribution system operator (DSO), a bi-level optimisation model for VPP bidding in multiple retail markets (including active power, reactive power and spinning reserve market) as price-maker is formulated. In the upper layer, taking into account various DERs, the VPP agent aims to develop hourly bidding prices and quantities of multiple market commodities to maximise its operation profits. In the lower layer, DSO conducts the retail market clearing to minimise the system operation cost considering the network constraints and bidding plans of the market participants. Moreover, the quadratic coupling constraints among different market commodities due to capacity limitation of distributed generators and branches are formulated explicitly in the proposed model. The hybrid simulated annealing-genetic algorithm with self-adaptive parameters is adopted to cope with the non-linearity and compute the economic bidding plans for VPP. The effectiveness of the proposed approach is verified under different scenarios through case studies, which indicate its superiority and great potential for implementation.