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This article proposes an enhanced quasi-reflection jellyfish optimization (QRJFO) algorithm for solving the optimal power flow (OPF) problem. The multi-dimension objective functions are the fuel costs, transmission losses and pollutant emissions. Despite the simple structure of the jellyfish optimization algorithm, it requires significant exploitation and exploration control characteristics to support its capability. In the proposed QRJFO, a cluster is chosen randomly for every jellyfish from the population to reflect the social group that shares information in it. It varies from one to the next. The exploration phase is supported by introducing quasi-opposition-based learning. The performance of the proposed QRJFO algorithm is evaluated on the IEEE 57-bus, practical West Delta Region system and large-scale IEEE 118 bus. The simulation results demonstrate the quality of the solution and resilience of QRJFO. It is very significant for operating power systems from economic, technical and environmental perspectives. [ABSTRACT FROM AUTHOR]

One of the critical optimization issues in the economic management of power and heat systems is the Combined heat and power economic dispatch (CHPED). The valve-point effects of thermal units as well as the interdependency of CHP outputs make the nonlinearity and non-convexity in optimization and dispatch models. A novel Amalgamated Heap-based and JellyFish Optimizer (AHJFO) is proposed, in this paper, to improve the efficiency of two newly developed techniques, Heap-based Optimizer (HO) and Jellyfish Optimizer (JFO). The proposed AHJFO incorporates an adjustment strategy function (ASF) to increase the explorative characteristic at the beginning of iterations by upgrading the produced solutions using HO. Further, as iterations go, it improves the exploitative characteristic by expanding the created solutions using JFO. The proposed AHJFO provides higher effectiveness compared to HO and JFO to obtain the solution of CHPED problem for medium 24 and large 96-unit systems. The simulation results show that the proposed AHJFO based on ASF aids in the avoidance of premature convergence and improves solution accuracy. Besides, the proposed AHJFO is successfully applied to the CHPED issue involving (N-1) unit outages. A re-dispatch strategy based on AHJFO is presented and the impact of outages of all units is analyzed after (N-1) unit outages. Also, further applications of the proposed AHJFO for ED problem are carried out with/without additional constraint models of power flow considering the IEEE 30-bus system. The simulation results show the superiority of the proposed AHJFO compared to HO, JFO and other algorithms for solving the CHPED and ED issues. Moreover, feasibility study is demonstrated of the solutions obtained for the CHPED and ED issues with additional constraint models. • Combined heat and power economic dispatch problem is solved by AHJFO Optimizer. • The AHJFO provides higher effectiveness for medium 24 and large 96-unit systems. • The simulation results are applied to the CHPED issue involving (N-1) outages. • Applications of the AHJFO for ED problem are carried out with/without additional constraint models. • Feasibility study is demonstrated of the solutions obtained for the CHPED and ED issues. [ABSTRACT FROM AUTHOR]

An enhanced multi-objective Quasi-Reflected Jellyfish Search Optimizer (MOQRJFS) is presented in this article for solving multi-dimensional Optimal Power Flow (MDOPF) issue with diverse objectives which display the minimization of economic fuel cost, total emissions, and the active power loss with satisfying operational constraints. Despite the simple structure of JFS with control of exploitation and exploration, searching capability of the JFS requires more support. Hence, two modifications are performed on the standard JFS algorithm. The first modification is that a cluster with a random size has been proposed which illustrates the social community that can share the data in the cluster and are dissimilar from one to another. The second modification is that a quasi-opposition-based learning is emerged in JFS to support the exploration phase. As selection criteria for the best solutions, a fuzzy decision-making strategy is joint into MOQRJFS optimizer. Additionally, the Pareto optimality concept is added to extract the non-dominated solutions. The superiority of the MOQRJFS is proved throughout application on IEEE 30-bus system, IEEE 57-bus system, the West Delta Region System of 52 bus (WDRS-52) in Egypt, and a large scale 118-bus system. Thirteen cases with economic, environmental, and technical objectives of MDOPF are included in this study. The outcomes of the proposed MOQRJFS have been compared with the conventional MOJFS and the reported techniques in the literature. It is clearly observed that the MOQRJFS give the minimum values compared with these techniques which reveals its robustness, effectiveness, and superiority when handling MDOPF among other techniques. • An enhanced multi-objective Quasi-Reflected Jellyfish Search Optimizer is presented. • Two modifications are performed on the standard JFS algorithm. • The superiority is proved on four test systems. • Thirteen cases with economic, environmental, and technical objectives are considered. • The outcomes of the proposed optimizer have been compared with others. [ABSTRACT FROM AUTHOR]