Improved Ant Colony Algorithm(ACA) and Game Theory for Economic Efficiency Evaluation of Electrical Power Market

The economic efficiency evaluation of electrical power market modeled by game theory, stated as a mixed nonlinear optimization problem, is solved using the ant colony algorithm (ACA). As a heuristic approach, ACA has proven to be robust when applied to global optimization problems of a combinatorial nature. In this work, generation companies (GenCo) join the power market in the form of grand coalition and maximize their profit by choosing different bidding strategy in day-ahead market. Then the bidding strategy of different GenCos was modeled as the path choosing of state transition space in ACA and the GenCos choose a set of path combination to maximize their profit. Based on improved ACA, the actual bidding price and ideal price is calculated through the bidding energy portfolio and the market efficiency could be thus evaluated by the price-cost marginal index (PCMI) which is obtained by the bidding and ideal price. The proposed approach has been tested on IEEE 30-bus test system through day load data. Test results demonstrated the feasibility and effectiveness of the method for the application considered