Multiobjective optimization for smart grid system design
This chapter proposes a framework for designing smart grid systems that considers multiple objectives. A grid is considered a combination of an electric grid and a network of transmission lines, substations, and transformers that delivers electricity from a power plant to consumers. A smart grid renders the grid efficient, provides a friendly environment for active grid participants, and improves the energy efficiency of the underlying power system. In a smart grid, it is desirable to optimize various objectives, such as minimizing the power consumption, maximizing the quality of service, and optimizing a stored energy level for emergency operations. To achieve these objectives, a multiobjective approach is investigated. First, objectives of the smart grid system are formulated as a multiobjective optimization problem (MOP) Multiobjective evolutionary algorithms are then employed to solve the MOP, yielding a set of approximate Pareto optimal solutions and an approximate Pareto front (APF) Based on the preference of a decision maker, the final solution is selected from among the obtained solutions according to the associated performance represented by the APF. A multiobjective approach to smart grid system designs is thus provided.