An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes

Abstract

In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and uncertainty. In this framework, microgrids constitute an effective solution to deal with the coordination and operation of these distributed energy resources. This paper proposes a Genetic Algorithm (GA) to address the combined problem of Unit Commitment (UC) and Economic Dispatch (ED). With this end, a model of a microgrid is introduced together with all the control variables and physical constraints. To optimally operate the microgrid, three operation modes are introduced. The first two attend to optimize economical and environmental factors, while the last operation mode considers the errors induced by the uncertainties in the demand forecasting. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm was applied to an example scenario to illustrate its performance. The achieved simulation results demonstrate the validity of the proposed approach.

A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization

“…This work is a step forward with respect to previous works [38], where a 24-h offline planner was designed using an evolutionary approach. Thus, a novel MPC strategy is used to manage the uncertainty and errors made in power demand forecasting.…”

“…In addition, MGs can interconnect to each other to create a new grid with an improved performance. Thus, let us consider the MG that is depicted in Figure 1 and that has been previously studied in [38]. This MG is composed by two dispatchable units, two renewable energy resources, and an ESS.…”

“…This subsection describes the particular parameters used in the MG under study. Although other values can be considered, the values employed describe a representative scenario [15,38].…”

“…Normally, after a number of generations, the best solution for the problem is included in the final population. Although there are many variants of genetic algorithms, in this work a mupluslambda implementation is used, which has been proven to achieve significant results in similar problems [38].…”

A Novel way out to Unit Commitment Problem utilizing Evolutionary Particle Swarm Optimization

“…It is assumed that the creation cost, for unit ‗j' in a given time interim is a quadratic capacity of the measure of delivered intensity of the generator, . [6] (1) Where , , are the consequent cost coefficients of the units.…”