Joint optimization of energy and reserve in deregulated power markets: Alternative approach using Mean Variance Mapping Optimization

Abstract

This paper presents an optimization methodology to perform simultaneous energy and reserve scheduling, considering the transmission network and forced outages of generating units and transmission lines. The strategy is structured by linking a set of mathematical programming tools to the Mean Variance Mapping Optimization algorithm, an emergent evolutionary strategy. A distinctive feature of the proposal is the election of required nodal reserve as a decision variable in the meta heuristic algorithm, which improves the speed at which the method approaches an optimal configuration. The cost of contingencies during the reserve deployment stage is considered in the formulation. Results indicate that a near optimal cost is reached with much less computational effort than that exhibited by existing proposals. An example is presented to illustrate and test the proposed scheme.