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With the increase in population and the growth of technology, the load demand has increased and major changes in spinning reserve are unavoidable. Short‐term forecasting to hourly predict the required load and spinning reserve is of great importance. All of the power system studies in planning and operation fields are depend on short‐term hourly load forecasting. In this work, the problem of load forecasting and spinning reserve based on deep learning (DL) algorithms and traditional methods is investigated with the help of the proposed information combination system. The proposed method tries to reduce the weaknesses of the stated methods and increase the accuracy of the predicted signal. First, short‐term predicting of load and spinning reserve is performed using a combination of adaptive network‐based fuzzy inference system (ANFIS) and meta‐heuristic algorithms including differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The ANFIS‐PSO is selected as the best ANFIS combination in load and spinning reserve prediction with a lower error criterion than other methods. Also, the long short‐term memory (LSTM) network can provide good accuracy for load and spinning reserve forecasting. Therefore, the combination of ANFIS‐PSO and LSTM is used to reduce the average error and error variance. [ABSTRACT FROM AUTHOR]

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]

Energy hubs can provide high flexibility in the operation of the integrated systems. This paper presents Time Varying Acceleration Coefficients Particle Swarm Optimization (TVAC-PSO) algorithm to optimize the Multiple Energy Carriers Economic Dispatch (MECED) problem for hybrid electrical and natural gas networks. The simulation results on two case studies are reported. The first one verifies an introduced gas loss formula and another one is devoted to a modified version of IEEE 14-bus test system. The results are compared with PSO, Genetic Algorithm (GA), and Differential Evolution (DE) technique. They show that a hybrid system can operate at a lower cost than independently-operated systems; CHP units can supply more electrical demand than the electrical network while their contribution in supplying the heat demand may be smaller than that of gas furnaces. In a hybrid system, energy efficiency can be reached, producing electrical power and heat locally; CHP units have a great role in reduction of operational cost; In the coupled mode, electrical power losses are decreased, while the gas losses are increased; The simple proposed gas loss formula can provide acceptable results; Cost reductions due to employing CHPs and applying the proposed optimization technique are greater than the other methods. [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 presents a novel Long-Term Load Forecasting (LTLF) technique based on the new heuristic method, namely Gravitational Search Algorithm (GSA). The objective of the suggested approach is establishing a more accurate LTLF model to minimize the average error of modeling. In order to estimate different fitting functions based on the proposed algorithm, two different case studies include Egyptian and Kuwaiti grids are selected. Also, the results are compared with a conventional approach, namely Least Squares (LS) method, and Particle Swarm Optimization (PSO) as a heuristic algorithm, to select the best LF model. Finally, based on the average and maximum errors arise from the estimations as a decision condition; the best function is selected for the LTLF problem. [ABSTRACT FROM AUTHOR]