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In this paper, the efficient salp swarm algorithm (ESSA) is proposed to solve the energy management (EM) with emission problem of renewable micro-grid (MG) including storage devices. Because of the uncertainties in the renewable energy sources (RESs), load demand and market prices, the probabilistic approach based on (2m + 1) point estimate method and ESSA is employed to solve the probabilistic EM problem. The proposed ESSA can be derived by introducing two modifications on the conventional salp swarm algorithm (SSA) to improve the balance between exploration and exploitation, speed up the convergence and avoiding the stuck in local optima of the SSA. The ESSA is employed to solve the deterministic and probabilistic EM with emission problem. Where the multi-objective optimization problem of cost and emission functions is transferred into a single objective function to minimize the total operating cost of the MG. The proposed ESSA is evaluated using a typical grid-connected MG with energy storage devices and compared with other methods. The results verify the superiority of the ESSA to solve the EM problem of the MG over other methods. • The probabilistic approach based on ESSA and point estimate method is proposed. • The proposed method is used to solve the deterministic and probabilistic EM of MG. • The EM problem considers minimization of cost and emission as a single problem. • The proposed method has been evaluated using a typical MG with different scenarios. • Results of all cases show the superiority of the proposed method over other methods. [ABSTRACT FROM AUTHOR]

This paper presents a new version of JAYA algorithm to solve the problem of optimal power flow (OPF) incorporating renewable energy sources (RES) with four different objective functions that reflect fuel cost minimization, emission minimization, transmission power loss minimization, and voltage profile improvement. The proposed modified JAYA (MJAYA) algorithm can be derived by modifying the equation that used to update the solutions based on best and worst solutions. The formulation of the OPF problem incorporating RES with four different objective functions is introduced in this paper. Then, the proposed MJAYA is employed to solve this problem by converting it into a single objective function using price and weighting parameters. In addition, the scalability of the proposed MJAYA is tested. To prove the effectiveness of the proposed MJAYA algorithm, it is applied to two different test systems (IEEE 30-bus system and IEEE 118-bus system). The obtained results of different scenarios using the proposed MJAYA algorithm are compared with those obtained using other published methods in the literature. The comparisons with other published methods show the superiority of the proposed MJAYA algorithm over other methods with different complexities. • The modified JAYA algorithm (MJAYA) is proposed in this paper. • The proposed method is used to solve the OPF problem with renewable energy sources. • The OPF considers minimization of cost, emission, power loss and voltage deviation. • The proposed method has been evaluated using two test systems with different cases. • Results of all cases show the superiority of the proposed method over other methods. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a new version of shuffle frog leaping algorithm (SFLA) which called modified SFLA (MSFLA) is proposed. To drive the proposed method both local and global search mechanisms in the original SFLA are modified. The local search mechanism is modified by introducing the movement inertia equation of particle swarm optimization (PSO). Furthermore, the global search mechanism is modified using the crossover and mutation operators of genetic algorithm (GA). Also, the formulation of the combined heat, emission and economic dispatch (CHEED) problem considering the availability of wind and solar power is presented where the objective functions of the CHEED problem are converted into a single objective function using price penalty function. Then, the proposed MSFLA is employed to solve the problem of CHEED with wind and solar power. To show the effectiveness of the proposed MSFLA, different test systems with different scenarios are used. In addition, the scalability of the proposed MSFLA is tested using large-scale test systems. The results of the proposed MSFLA are compared with those obtained by other heuristic and non-heuristic published methods. The comparison with other methods proves the efficiency and the superiority of the proposed MSFLA over other methods in all scenarios. Highlights • A new version of SFLA is proposed in this paper. • This version is derived by modifying local and global search mechanisms in SFLA. • The proposed method is used to solve the CHEED problem with renewable sources. • The proposed method has been evaluated using different test systems and cases. • Results of all cases show the superiority of the proposed method over other methods. [ABSTRACT FROM AUTHOR]

Nowadays, there is a growing interest in the microgrid systems with a high penetration of renewable sources. In this paper, the modified harmony search (MHS) algorithm is proposed to solve the combined economic emission dispatch (CEED) problem of the microgrid taking into account the solar and wind power cost functions. The proposed algorithm can be derived by not only adjusting the parameters but also improving the structure and operation of the original harmony search (HS) algorithm. The solution of the CEED problem of the microgrid taking into account the solar and wind power cost functions is obtained for different scenarios using the MHS algorithm and some recently published algorithms. The results of all scenarios show the effectiveness of the MHS algorithm over other published algorithms employing same data. [ABSTRACT FROM AUTHOR]

Dynamic economic dispatch (DED) is one of the most significant non-linear complicated problems showing non-convex characteristic in power systems. This is due to the effect of valve-points in the generating units’ cost functions, the ramp-rate limits and transmission losses. Hence, proposing an effective solution method for this optimization problem is of great interest. The original bacterial foraging (BF) optimization algorithm suffers from poor convergence characteristics for larger constrained problems. To overcome this drawback, a hybrid genetic algorithm and bacterial foraging (HGABF) approach is presented in this paper to solve the dynamic economic dispatch problem considering valve-point effects, ramp-rate limits and transmission losses. The HGABF approach can be derived by integrating BF algorithm and genetic algorithm (GA), so that the BF’s drawback can be treated before employing it to solve the complex and high dimensioned search space of the DED problem. To illustrate the effectiveness of the HGABF approach, several test systems with different numbers of generating units are used. The results of HGABF approach are compared with those obtained by other published methods employing same test systems. These results show the effectiveness and the superiority of the introduced method over other published methods. [ABSTRACT FROM AUTHOR]

This article investigates the optimal management of multi-carrier water and energy system (MCWES) considering the high penetration of renewable energy sources as non-dispatchable units and the seawater desalinization mechanism for serving water demand in the target area. The proposed model encompasses several demand layers including power energy, natural gas and water layer which supplies the electricity, thermal and drinkable water demands in the smart island. In order to capture the uncertainty effects in the technical decisions of optimal scheduling, a stochastic approach based on unscented transform (UT) is developed to handle the forecast error in the electrical and thermal energy demands, market energy prices related to the different energy layers and the output power forecast error in the renewable energy sources. Solving the proposed coordinated scheduling problem in an hourly timescale requires heavy calculations that make it impractical. Therefore, a novel reinforcement learning (RL) based approach is devised for finding a near optimal solution and facilitates the searching process with a trivial computational burden. The simulation results indicate that the proposed cooperation approach minimizes both the operation and investment costs substantially with an efficient computational burden based on the advanced features coming out of the proposed RL approach. Last but not least, the simulation results on a practical smart island advocate the effectiveness and high efficacy of the proposed model. Also it was seen that the RL approach could properly solve the optimization model and the selection of the sizes of the components was highly linked to the hourly values of demands and prices of the energy. [ABSTRACT FROM AUTHOR]

Economic Dispatch in Cogeneration Systems (EDCS) provides the optimal scheduling of heat and power of generation units. This can be achieved by minimizing the total cost of fuel (TCF) of the cogeneration units taking into consideration their operational limits. A manta ray foraging MRF optimizer, in this paper, is developed to solve the EDCS problem including the valve point impacts, and wind power. MRF optimizer is designed with adaptive penalty functions for acquiring the most feasible and best operational points for the EDCS problem. Infeasible solutions are handled with various degrees and penalized depending on their remoteness from the closest possible point. The overall power and heat loading are completely achieved by the equality constraints. Also, the cogeneration units' dynamic operating limits are not adversely affected since its concerning limitations of heat-only and power-only units are fulfilled. Two test systems of small 5 and large 96-units, are analyzed. In addition to this, an assessment of the recent optimization techniques, which are applied on to EDCS, has been developed and discussed. The applications are carried out for two scenarios at peak and daily variation in the power and heat loading condition. The wind power inclusion is assessed for each scenario in terms of the overall reduction in the total fuel costs. It was proven also; the inclusion of wind power achieves more economical solution at different scenarios with reduction up to 8%. It is crystal clear that the outputs obtained illustrate MRF optimizer efficiency, feasibility, and capability to obtain better solutions in minimizing the fuel cost compared to other optimization techniques at acceptable convergence rates. Moreover, the solutions demonstrate the ability of MRF optimizer application on the large-scale 96-unit systems. • A manta ray foraging (MRF) optimizer is developed to solve the EDCS problem. • The EDCS problem involves the valve point impacts, and wind power inclusion. • MRFO is designed with adaptive penalty for acquiring the best EDCS points. • Two scenarios at peak and daily variation in the power and heat loads. • Inclusion of wind power achieves economical solutions for different scenarios. [ABSTRACT FROM AUTHOR]