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Conventional transmission expansion planning (TEP) has evolved with the increasing influence of distributed generations (DGs) and electric vehicles (EVs). This increases uncertainty in power systems. On the other hand, reactive power plays a key role in voltage stability, so modeling reactive power planning in TEP is very important. The amount of reactive power is a determining factor regarding the stability of the network voltage and in case of shortage, occurrence of some events may endanger the voltage stability. This research aims at proposing a hybrid TEP and reactive power planning for the first time. Furthermore, the impact of wind, PV power plants, and EVs, as the common sources of uncertainties, are included. The ultimate goal of HTEP‐RPP is to find the transmission line candidates that meet the reactive power requirements. The simulation case studies on IEEE 24‐ and 118‐ buses confirm that the TEP based on DCPF may lead to some unreliable operating conditions due to violating the voltage constraints. In this case, the HTEP‐RPP results, by applying the imperialist competitive algorithm (ICA), are more comprehensive and effective than the results obtained by gravitational search algorithm (GSA), for the studied problem or even the popular TEP solutions. [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 paper proposes a novel optimization algorithm, namely hybrid Time Varying Acceleration Coefficients-Gravitational Search Algorithm-Particle Swarm Optimization (hybrid TVAC-GSA-PSO), to solve the large-scale, highly nonlinear, non-convex, non-smooth, non-differential, non-continuous, and complex Combined Heat and Power Economic Dispatch (CHPED) problems. The suggested algorithm is based on the Newtonian laws of gravitation and motion as well as swarm behaviors. The proposed technique combines the best performances of three optimization methods (i.e. GSA, TVAC-GSA, and TVAC-PSO) in terms of their specific strategies in particle movements through a self-adoptive learning strategy. The effectiveness and robustness of the suggested algorithm are tested on a set of five benchmark functions and two large-scale CHPED problems (considering valve-point loading effect and prohibited zones of conventional power units, transmission losses, as well as special characteristic of CHP units). The obtained results by the suggested algorithm in terms of quality solution, computational performance, and convergence characteristic are compared with various algorithms to show the ability of the proposed approach and its robustness in finding a better fuel cost with a less expensive solution. [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]

This paper describes a novel modified optimization algorithm based on a new heuristic method, namely Time Varying Acceleration Coefficient Gravitational Search Algorithm (TVAC-GSA), to solve both single- and multi-objective Optimal Power Flow (OPF) problems in hybrid systems especially focusing on electricity-gas network. The suggested method is based on the Newtonian laws of gravitation and motion. Sum of the complexity of both electrical and gas-based networks in terms of the valve-point loading effect of generator units, energy hub structure, energy flow equations, and different related equality and inequality constraints make the optimization problem highly nonlinear, non-convex, non-smooth, non-differential, and high-dimensional. The effectiveness of the proposed algorithm to solve such a complex problem is verified on a new introduced hybrid system based on a modified version of IEEE 14-bus network. Comparison of results obtained by the presented method with those obtained by GSA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) shows the better accuracy and fast convergence of the new method in finding an operating point with lower objective function value. [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]

This paper presents the application of a novel optimization algorithm, namely gravitational search algorithm (GSA) to solve the non-convex combined heat and power economic dispatch (CHPED) problems. The proposed approach is based on the gravitational law and the law of particles motion. The effectiveness of the suggested algorithm is tested on study-cases which include modeling of valve-point loading effect and transmission losses. Results of GSA-based CHPED problem in terms of quality solution and computational performance are compared with various algorithms to show the ability of the introduced algorithm in finding an operating point with lower fuel cost. [ABSTRACT FROM AUTHOR]