Generation rescheduling using multiobjective bilevel optimization

Abstract

This paper presents a new multiobjective optimization method that can be used for generation rescheduling in power systems. Generation rescheduling in restructured power systems is performed by the system operator for different operations like congestion management, day-ahead scheduling, and preventive maintenance. The nonlinear nature of the equations involved and the constraints on decision variables pose a challenge to find the global optimum. In order to find the global optimum using a genetic algorithm, a bilevel optimization method is proposed. In the proposed multiobjective optimization method, the objectives are classified as primary and secondary based on their relative importance. The best solution is found using the secondary objective from the acceptable solutions of the Pareto-optimal front in the primary objective plane. As the financial feasibility and adherence to emission limits are of higher importance, the primary objectives considered are minimization of generation cost and emission. The secondary objective considered is reliability, to find the most reliable solution from the set satisfying the primary objectives. The proposed technique is validated on the IEEE 30-bus system and the results are presented.

Multiobjective generation scheduling using multicore processing-based continuous genetic algorithm

“…This is obtained from the final pareto-optimal set of both the cores and it lies within the limits of both cost and emission. The GC and emission obtained using single core processing and a single crossover rate of genetic algorithm are 22,586.02 $/h and 1673.89 lb/h, respectively (Vakkapatla & Pinni, 2019). As the cost and emission are higher when compared with the results of the proposed method, multicore processing using different parameters of genetic algorithm can be concluded to be superior in performance.…”

“…The secondary objective is used to filter the pareto-optimal set of solutions obtained by simultaneous optimization of GC and emission. Aggregate Forced Outage Rate (AFOR) (Vakkapatla & Pinni, 2019) is used to find the most reliable solution from the pareto-optimal set of solutions. It is defined as…”

“…The increase in penetration of renewables has an impact on the reliable operation of power system due to the inherent uncertainty of renewable energy. To improve the reliability of power system operation, reliability indices are also considered in the algorithms used for unit commitment and generation scheduling (Alsac & Stott, 1974;Vakkapatla & Pinni, 2019).…”

Dynamic optimal management of a hybrid microgrid based on weather forecasts

“…In general, power operation problems are strongly nonlinear. Thus, meta-heuristic and population-based algorithms [6] could be trapped in local optima and solution quality is very dependent on parameters' choice. Moreover, hybrid microgrids generally incorporate renewable sources that are intermittent.…”