• Energy Research

Wind power can be considered one of the most important green sources of electric power. The prediction of wind power is necessary to boost the power grid operations’ efficiency and increase power market competitiveness. Artificial neural networks (ANNs) are widely used in prediction applications, including wind power. The Random Vector Functional Link (RVFL) is an efficient ANN model that can be employed in time-series forecasting applications. However, the configuration process of the RVFL needs to be improved. Thus, in this paper, we presented an optimized RVFL network using a new naturally inspired technique called the Capuchin search algorithm (CapSA). The main function of the CapSA is to boost the configuration of the traditional RVFL and enhance its prediction capability. We implement extensive evaluation experiments using public datasets from four wind turbines located in France, using several evaluation measures called RMSE, MAE, MAPE, and R2. The evaluation outcomes reveal that the CapSA-RVFL obtained the best prediction accuracy compared to the original RVFL and several variants of the RVFL model, which verifies that the application of CapSA has a significant contribution to improving the prediction capability of the RVFL.

Wind power can be considered one of the most important green sources of electric power. The prediction of wind power is necessary to boost the power grid operations’ efficiency and increase power market competitiveness. Artificial neural networks (ANNs) are widely used in prediction applications, including wind power. The Random Vector Functional Link (RVFL) is an efficient ANN model that can be employed in time-series forecasting applications. However, the configuration process of the RVFL needs to be improved. Thus, in this paper, we presented an optimized RVFL network using a new naturally inspired technique called the Capuchin search algorithm (CapSA). The main function of the CapSA is to boost the configuration of the traditional RVFL and enhance its prediction capability. We implement extensive evaluation experiments using public datasets from four wind turbines located in France, using several evaluation measures called RMSE, MAE, MAPE, and R2. The evaluation outcomes reveal that the CapSA-RVFL obtained the best prediction accuracy compared to the original RVFL and several variants of the RVFL model, which verifies that the application of CapSA has a significant contribution to improving the prediction capability of the RVFL.

Frequency regulation is critical for maintaining balance between supply and demand in interconnected power systems, ensuring grid stability and preventing disruptions. This becomes increasingly important with the integration of renewable energy sources, such as photovoltaic (PV) units, which introduce variability and complexity into power systems. In this regards, this study presents a novel approach to frequency regulation in a two-area interconnected power system comprising thermal and PV units. A Proportional-Integral (PI) controller is designed, and its parameters are optimally tuned using the flood algorithm (FLA). The innovative use of the FLA ensures robust performance and efficient frequency stabilization under varying operational conditions. The implementation details of the FLA-tuned PI controller are provided, and its performance is rigorously compared with PI controllers tuned using several state-of-the-art optimization techniques. These include sea horse optimization, salp swarm algorithm, whale optimization algorithm, shuffled frog-leaping algorithm, and firefly algorithm. The comparative analysis is based on numerical results of performance metrics, demonstrating the robustness and effectiveness of each tuning method. Performance indices, including maximum overshoot, settling time and steady-state error are used to evaluate the robustness of the designed PI controllers. The frequency variations for the two-area thermal and PV power system are analyzed post-optimization, highlighting the superiority of the FLA-based PI controller in maintaining system stability under various operational conditions. The proposed FLA-based PI controller achieved a reduction in maximum overshoot by 28.3 %, a decrease in settling time by 23.4 %, and an improvement in steady-state error by 15.7 % compared to the next best-performing optimization method. These results demonstrate the significant advantages of the FLA in optimizing frequency regulation.

Frequency regulation is critical for maintaining balance between supply and demand in interconnected power systems, ensuring grid stability and preventing disruptions. This becomes increasingly important with the integration of renewable energy sources, such as photovoltaic (PV) units, which introduce variability and complexity into power systems. In this regards, this study presents a novel approach to frequency regulation in a two-area interconnected power system comprising thermal and PV units. A Proportional-Integral (PI) controller is designed, and its parameters are optimally tuned using the flood algorithm (FLA). The innovative use of the FLA ensures robust performance and efficient frequency stabilization under varying operational conditions. The implementation details of the FLA-tuned PI controller are provided, and its performance is rigorously compared with PI controllers tuned using several state-of-the-art optimization techniques. These include sea horse optimization, salp swarm algorithm, whale optimization algorithm, shuffled frog-leaping algorithm, and firefly algorithm. The comparative analysis is based on numerical results of performance metrics, demonstrating the robustness and effectiveness of each tuning method. Performance indices, including maximum overshoot, settling time and steady-state error are used to evaluate the robustness of the designed PI controllers. The frequency variations for the two-area thermal and PV power system are analyzed post-optimization, highlighting the superiority of the FLA-based PI controller in maintaining system stability under various operational conditions. The proposed FLA-based PI controller achieved a reduction in maximum overshoot by 28.3 %, a decrease in settling time by 23.4 %, and an improvement in steady-state error by 15.7 % compared to the next best-performing optimization method. These results demonstrate the significant advantages of the FLA in optimizing frequency regulation.

The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms.

The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms.