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Combined heat and power economic dispatch (CHPED) problem is one of the challenging issues in power system control, and operation, that searches for the optimal values of power and heat generation of different units to minimize the total operation cost. The interdependency of heat and power outputs of CHP units and valve point loading effects of power-only units make this problem very nonlinear and non-convex. So far, a wide variety of optimization algorithms based on simulation of various natural phenomena have been proposed and applied to solve this complex problem, claiming that the proposed algorithm is better than the similar optimization algorithms in terms of accuracy and run-time. In this paper, the CHPED problem is investigated in different case studies by using the imperialist competitive Harris hawks optimization (ICHHO) algorithm, as a combination of the imperialist competitive algorithm and Harris hawks optimization. The effectiveness of the proposed algorithm is first evaluated by testing it on the standard cases of 5, and 7 units as small-scale systems and 48-unit as the large-scale test system, considering the electrical losses. Also for the first time, the multi-zone CHPED (MZCHPED) problem is solved by a metaheuristic algorithm. The obtained results show that ICHHO algorithm reduces the operating costs in the range of 0.02–1.1%, compared to the best-reported results, in different single-zone CHPED (SZCHPED) problems. Also, the MZCHPED results, prove the effective performance of the algorithm compared to the similar SZCHPED problem. [ABSTRACT FROM AUTHOR]

Power system optimization, especially in the field of operation, has experienced essential changes in recent years regarding the introduction of the concept of the hybrid energy system (HES). As a result, the optimal power flow (OPF) problem, as the cornerstone of power system operation studies, has faced new challenges regarding the modeling of these complicated systems. In this article, the OPF in a HES is proposed using the hybrid particle swarm optimization-genetic algorithm (HPSO-GA) approach considering the fuel cost as the main objective and the emission cost as the primary objective function. As the mentioned objectives are counterintuitive, to obtain the optimal point, they should be considered simultaneously. To overcome this problem, the Pareto optimal front (POF) is applied and the multi-criteria technique for order performance by similarity to ideal solution (TOPSIS) is used simultaneously. The results of the simulation case study on an improved IEEE 14-bus standard system confirmed the performance of the proposed method against the mathematical method by applying MATLAB optimization. [ABSTRACT FROM AUTHOR]

The economic dispatch (ED) is one of the most important short-term problems in power systems, and solving it quickly is essential. However, classical optimization tools are often too computationally demanding to be considered satisfactory. This has motivated the application of metaheuristic methods, which offer a good compromise in terms of solution quality and computation time. However, these methods have been applied in an isolated way and on different problem definitions and case studies, so that there were no clear insights on how they compared to each other. This paper fills this gap by performing an objective comparison of six metaheuristics solving the ED in several case studies under different conditions. Although mixed-integer programming performs best for small case studies, our results confirm that metaheuristics are able to efficiently solve the ED problem. Genetic algorithms emerge as the best performers in terms of solution quality and computation time, followed by PSO and TLBO. [ABSTRACT FROM AUTHOR]

Highlights • Applying a novel evolutionary optimization algorithm namely MCSA to ELD problem. • Proposing two modification methods for improving the CSA performance. • Applying the MCSA to five well-known ELD test systems. • Employing four well-known benchmark functions to verify the MCSA. • Addressing the MCSA as a highly competitive with some previous algorithms. Abstract This paper presents a novel evolutionary optimization algorithm namely the modified crow search algorithm (MCSA) for solving the non-convex economic load dispatch (ELD) problem which improves the crow search algorithm (CSA) by an innovative selection of the crows and adaptive adjustment of the flight length. MCSA is a population-based technique based on the intelligent behavior of the crows in finding food sources. In MCSA, each crow saves its food in hiding-places for the time it needs. Also, each crow searches environment to find the better foods by stealthily following other crows to discover their hiding-places. The proposed MCSA develops the search capability of crows in the original CSA and introduces a new way by which a destination is selected by a crow to follow. To indicate the applicability of MCSA in the ELD problem, it is applied on five different well-known test systems. The results are compared in terms of the solution quality, robustness, and computing time with other methods implying that the proposed method has a superior performance than the other techniques. [ABSTRACT FROM AUTHOR]

This research work proposes the hybrid meta-heuristic algorithm to solve the multi-area economic dispatch problem to operate the power systems economically. The proposed hybrid algorithm combines the exchange market and grasshopper optimization algorithms. The grasshopper optimization algorithm sometimes suffers from premature convergence. To overcome this drawback, a hybrid algorithm for the better balance between exploration and exploitation and thus finding optimal solutions with high quality and robustness, is proposed in this paper. The main target of this paper is to investigate the application of the hybrid algorithm for optimizing the multi-area economic dispatch problem, satisfying different constraints of the valve point loading effect, multiple fuels, prohibited operation zones, ramp-rate limit, the tie-line constraints, and transmission loses. In this regard, after validation of the proposed hybrid algorithm by applying it on some benchmark functions of diverse nature, the effectiveness and robustness of the proposed algorithm are evaluated in three case studies of the multi-area economic dispatch problem. The comparative results confirmed the effectiveness of the proposed method in terms of robustness and quality solutions. Also, the applied method reduces the fuel cost in the range of 0.0229%–1.1622%. • Introducing a new meta-heuristic algorithm based on the combination of EMA and GOA. • Using the EMA-GOA to optimize some well-known benchmark functions. • Applying the algorithm for the first time to different kinds of multi-area economic dispatch problems. • Investigating the proposed method performance on different types of the mentioned problems. [ABSTRACT FROM AUTHOR]

The term smart grid refers to a modernization of the electrical network consisting in the integration of various technologies such as dispersed generation, dispatchable loads, communication systems and storage devices which operates in grid-connected and islanded modes. As a result, traditional optimization techniques in new power systems have been seriously influenced during the last decade. One of the most important technical and economical tools in this regard is the Optimal Power Flow (OPF). As a fundamental optimization tool in the operation and planning fields, OPF has an undeniable role in the power system. This paper reviews and compares the OPF approaches mainly related to smart distribution grids. In this work, the main OPF approaches are compared in terms of their objective functions, constraints, and methodologies. Furthermore, computational performances, case study networks and the publication date of these methods are reported. Finally, some basic challenges arising from the new OPF methodologies in smart grids are addressed. [ABSTRACT FROM AUTHOR]

This paper proposes a new optimization algorithm, namely Self-Adoptive Learning with Time Varying Acceleration Coefficient-Gravitational Search Algorithm (SAL-TVAC-GSA), to solve highly nonlinear, non-convex, non-smooth, non-differential, and high-dimension single- and multi-objective Energy Hub Economic Dispatch (EHED) problems. The presented algorithm is based on GSA considering three fundamental modifications to improve the quality solution and performance of original GSA. Moreover, a new optimization framework for economic dispatch is adapted to a system of energy hubs considering different hub structures, various energy carriers (electricity, gas, heat, cool, and compressed air), valve-point loading effect and prohibited zones of electric-only units, as well as the different equality and inequality constraints. To show the effectiveness of the suggested method, a high-complex energy hub system consisting of 39 hubs with 29 structures and 76 energy (electricity, gas, and heat) production units is proposed. Two individual objectives including energy cost and hub losses are minimized separately as two single-objective EHED problems. These objectives are simultaneously minimized in the multi-objective optimization. Results obtained by SAL-TVAC-GSA in terms of quality solution and computational performance are compared with Enhanced GSA (EGSA), GSA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to demonstrate the ability of the proposed algorithm in finding an operating point with lower objective function. [ABSTRACT FROM AUTHOR]

• Proposing a new MES model for IOPGF problem, including different electrical and heat equipment. • Applying an improved crow search algorithm (ICSA) to solve the proposed model. • Reporting the obtained results of simulation on the modified IEEE-30 bus network, and some benchmark functions. This paper solves the integrated optimal power and gas flow (IOPGF) problem in a multicarrier energy system (MES), to find the operating variables of electrical and natural gas networks satisfying some practical constraints. The proposed problem models optimal power flow (OPF), optimal gas flow (OGF), and economic heat dispatch (EHD) equations which make it a very complex optimization problem that cannot be easily solved by the classical optimization techniques. In addition, a population-based evolutionary algorithm (PEA), namely the improved crow search algorithm (ICSA) is introduced and applied to solve the OPF and the IOPGF problems. The results are compared with other well-known PEAs to ensure the higher performance of the ICSA. Also, the superior performance of the ICSA is confirmed by applying it to solve five well-known benchmark functions (BFs). [ABSTRACT FROM AUTHOR]