Abstract
Today, in various leading power utilities in developing countries, achieving optimal operational energy management and planning, taking into account the costs reduction of generation, transmission and distribution of electricity, and also reducing the emission of an environmental pollutant becomes more and more important. Optimal use of renewable energy sources (RESs) is an effective way to achieve these goals. In this regard, in this research article, an improved multi-objective differential evolutionary (IMODE) optimization algorithm is suggested and performed to dispatch electricity generations in a smart microgrid (MG) system, taking into account economy and emission as competitive issues. In this paper, a nonlinear equation of multi-objective optimization issue with various equality and inequality limitations is formulated in order to lower the total operational costs of the MG considering environmental pollution effects simultaneously. In order to address the issue of optimal operation of the MG in single-objective and multi-objective forms, an intelligent method according to the improved differential evolutionary (IDE) optimization is utilized and performed and the proposed algorithm is implemented on different problems. First, it is assumed that there is no limit to the exchange of power overhead, and secondly, the limitation of power exchange with the upstream grid is considered. In multi-objective mode, these two modes are also considered. In order to show the impact of renewable energy on the cost, in the third part of the simulations, the operation is solved with maximum participation of renewable energy sources. In the final section, the sensitivity analysis on the number of populations in this problem is performed. The obtained results of the simulation are compared to differential evolutionary (DE) and particle swarm optimization (PSO) techniques. The effectiveness of the suggested multi-operational energy management method is confirmed by applying a study case system.
Highlights
IntroductionFor consumers, the potential of lower costs, higher reliability of services and power quality, increasing in power efficiency and power independency can be the reasons for higher interests in distributed generation (DG). Using renewable energy sources (RESs) and green powers, including wind and photovoltaics, can offer notable environmental advantages [1,2,3,4]
In order to illustrate the impact of RE generations on the cost, in the third part of the simulations, the operation is solved with maximum participation of renewable energy sources
In order to demonstrate the high efficiency of the improved multiobjective differential evolutionary (IMODE) algorithm in solving the problem of optimal operation of the MG in both single-objective and multi-objective modes, this algorithm has been carried out on different problems
Summary
For consumers, the potential of lower costs, higher reliability of services and power quality, increasing in power efficiency and power independency can be the reasons for higher interests in DGs. Using RESs and green powers, including wind and photovoltaics, can offer notable environmental advantages [1,2,3,4]. Using RESs and green powers, including wind and photovoltaics, can offer notable environmental advantages [1,2,3,4] In this way, MG can act as a concept that gives an effective solution for integrating renewable DGs into the major power and energy network and offers the requirements of generation power systems [5,6,7,8]. A MG network system consists of a number of homes, industrial, and commercial consumers, as well as DGs (essentially RESs), with a central control system, which typically uses one or more battery energy storage (ES) systems as backup [9,10]
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