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Combined bidding strategies can reduce the imbalance penalty caused by wind power uncertainty. Power generation stakeholders can benefit from the combined bidding with a suitable coalition. However, the coalition formation method for wind farms with other resources is less reported. In this paper, a sequential coalition formation method is studied. The relationship constraint of wind-thermal combined bidding in a day-ahead market is described by the star graph. Technique for order preference by similarity to ideal solution (TOPSIS) is used to rank the thermal power plants considering multiple evaluation criteria. On the basis of the star graph and the TOPSIS, the coalition structure graph (CSG) is simplified. An actual wind farm and five thermal power plants in northeast China are utilized to verify the effectiveness and practicability of the proposed method. The results show that the proposed method can reduce the computational complexity of coalition formation. When wind power deviations are 0.1, 0.2, 0.3, and 0.4, the retrieval coalition structures are only 4.93%, 4.93%, 6.40%, and 7.39% of those in the basic CSG. The utilities of the formed coalitions have increment rates of 1.30%, 2.79%, 4.13%, and 5.45% compared with those under separated bidding in different wind power scenarios.

Particle swarm optimization (PSO) is a widely used intelligent optimization algorithm. The premature convergence and the constraint handling, however, remain problems for the global optimization searching. In this paper, a dual fitness value chaotic PSO algorithm with equality constraint handling (EDFC-PSO) is proposed. The particle mutation process based on the chaotic Cat mapping is introduced to increase the diversity of particles. Equality constraints are solved by parametric equations, and inequality constraints are considered using the dual fitness value. The proposed EDFC-PSO algorithm is tested by several test functions and then compared with other algorithms. Further, the performance of the proposed algorithm is validated by a combined bidding model of a power system. Our EDFC-PSO algorithm has equality constraints satisfied and enhances the global searching capability in both the test functions and the engineering model.