MOPSO based day-ahead optimal self-scheduling of generators under electricity price forecast uncertainty

In competitive electricity markets, self-scheduling for power producer is a conflicting bi-objective mixed-integer nonlinear optimization problem, where a producer tries to maximize his profit and at the same time, minimizes the risk associated with price forecast uncertainty, while satisfying all the operational constraints. This paper proposes a multi-objective particle swarm optimization (MOPSO) based meta-heuristic technique to provide Pareto optimal solution for thermal power producers to schedule their generators in a day-ahead electricity market. The locational margin price forecast uncertainty in PJM market is considered to implicitly include the uncertainty related to congestions. The achieved Pareto presents the optimal possible trade-off between expected profit and risk of the generator.