Genetic Algorithm Based Cost-emission Optimization of Unit Commitment Integrating with Gridable Vehicles

Abstract

This paper first proposes a multilayer framework of vehicle-to-grid (V2G) system based on the concept of gridable vehicles (GVs). GVs can draw and store energy from the power grid as loads, as well as feed energy back to the grid as resources. Then, unit commitment integrating with GVs is analyzed using the proposed framework. The objective is to minimize the total operating cost and emissions of the V2G system by intelligently scheduling the generating units and GVs based on the use of genetic algorithm. The results illustrate that the operating cost and emissions can be reduced and the system reserves can be enhanced by applying V2G.