Social Cost Optimization for Prosumer Community with Two Price-Package Incentives in Two-Settlement Based Electricity Market

In this paper, we consider a future electricity market consisting of aggregated energy prosumers, who are equipped with local wind power plants (WPPs) to support (part of) their energy demands and can also trade energy with day-ahead market (DAM) and energy balancing market (EBM). In addition, an energy aggregator (EA) is established, who can provide the trading gateways between prosumers and the markets. The EA is responsible for making pricing strategies on the prosumers to influence their trading behaviours such that the social benefit of the prosumer community is improved. Specifically, two price packages are provided by the EA: wholesale price (WP) package and lump-sum (LS) package, which can be flexibly selected by prosumers based on their own preferences. Analytical energy-trading strategies will be derived for WP prosumers and LS prosumers based on non-cooperative games and Nash resource allocation strategies, respectively. In this work, a social cost optimization problem will be formulated for the EA, where the detailed WP/LS selection plans are unknown in advance. Consequently, a stochastic Stackelberg game between prosumers and the EA is formulated, and a two-level stochastic convex programming algorithm is proposed to minimize the expectation of the social cost. The performance of the proposed algorithm is demonstrated with a two-settlement based market model in the simulation.