This study presents a novel hybrid operation strategy for a wind energy conversion system (WECS) with a battery energy storage system (BESS). The proposed strategy is applied to support frequency regulation using coordinated control of WECS and BESS operations in power system. The coordinated control of the WECS consists of active power control and frequency support control based on permanent magnet synchronous generators for variable wind speed conditions. Active power control is achieved using maximum power point tracking and deloaded operation to ensure a certain power margin. In addition to this comprehensive control of the active power, frequency support control based on kinetic energy discharge control is developed to regulate the short‐term frequency response and to ensure reliable operation of the power system. Concurrently, the output power command of the BESS is determined according to the state of charge and frequency deviations. The effectiveness of the hybrid operation strategy is verified by a numerical simulation in power system computer aided design/electro‐magnetic transient direct current (PSCAD/EMTDC), and the results show that the proposed approach can improve the frequency regulation capability of the power system.

Distributed generation (DG) and capacitor bank (CB) allocation in distribution systems (DS) has the potential to enhance the overall system performance of radial distribution systems (RDS) using a multiobjective optimization technique. The benefits of CB and DG injection in the RDS greatly depend on selecting a suitable number of CBs/DGs and their volume along with the finest location. This work proposes applying a hybrid enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO) algorithm for optimal placement and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves. On the other hand, PSO is a swarm-based metaheuristic optimization algorithm that finds the optimal solution to a problem through the movement of the particles. The advantages of both techniques are utilized to acquire mutual benefits, i.e., the exploration ability of the EGWO and the exploitation ability of the PSO. The proposed hybrid method has a high convergence speed and is not trapped in local optimal. Using this hybrid method, technical, economic, and environmental advantages are enhanced using multiobjective functions (MOF) such as minimizing active power losses, voltage deviation index (VDI), the total cost of electrical energy, and total emissions from generation sources and enhancing the voltage stability index (VSI). Six different operational cases are considered and carried out on two standard distribution systems, namely, IEEE 33- and 69-bus RDSs, to demonstrate the proposed scheme’s effectiveness extensively. The simulated results are compared with existing optimization algorithms. From the obtained results, it is observed that the proposed EGWO-PSO gives distinguished enhancements in multiobjective optimization of different conflicting objective functions and high-level performance with global optimal values.

The increasing adoption of electric vehicles has led to new and unpredictable load conditions for electric vehicle charging stations (EVCSs), making charging demand forecasting important to the profitable deployment of EVCSs. Although existing spatial-temporal forecasting models have made significant progress, they ignore the realistic topologies of EVCS networks and the influence of external interference on charging demand. Moreover, limited research exists on developing forecasting models from the perspective of EVCSs participating in the energy market. This paper proposes a novel parallel-structured spatio-temporal mutual residual graph convolution-combined bi-long short-term memory for predicting the charging demand of EVCSs. First, a new mutual adjacency matrix considering both the static and dynamic attributes of EVCSs is constructed. This matrix is then combined with graph convolution and residual blocks to capture multi-level spatial dependencies and map relations between nodes and external factors. Second, to address temporal dependencies, CBi-LSTM combining Bi-LSTM with an additional predictor that considers day-type tendency features is developed. Finally, a parallel structure is adopted to obtain the final prediction results and preserve the integrity of spatiotemporal dependencies. Case studies validated the performance of the proposed model, which demonstrated high forecasting accuracy and the potential for profitable application in the energy market.

This research paper conducts an extensive exploration of onshore, offshore, and floating offshore wind turbines, pivotal components in the landscape of sustainable energy generation. The study thoroughly investigates various wind energy systems, shedding light on their distinct attributes, advantages, and limitations. The examination of onshore wind turbines encompasses aspects such as installation procedures, size considerations, wind dynamics, accessibility challenges, and visual impact assessments. Similarly, the analysis of offshore wind turbines covers considerations such as location selection, installation complexities, size variations, wind conditions, environmental impacts, and visual aesthetics. The paper also delves into the emerging technology of floating offshore wind turbines, highlighting their unique features, advantages, and technological advancements. A thorough comparative examination is conducted to assess the economic, environmental, and social aspects of onshore, offshore, and floating offshore wind turbines. The research paper concludes by offering insights into policy considerations, addressing research gaps, and outlining prospects for the seamless integration and advancement of these diverse wind energy systems. This comprehensive review aspires to contribute to the existing knowledge base in sustainable energy generation, fostering informed decision-making for a greener and more sustainable future.

With the significant development in photovoltaic (PV) systems, focus has been placed on inexpensive, efficient, and innovative power converter solutions, leading to a high diversity within power converters and new system configurations for grid-connected PV (GCPV) systems. During the last decade, multilevel inverter (MLI) designs have gained popularity in GCPV applications. This article provides a wide-ranging investigation of the common MLI topology in contrast to other existing MLI topologies for PV applications. Furthermore, the various modulation techniques used in MLI switching are elucidated and contrasted. The modulation strategies are reviewed with particular regard to their comparative suitability for the modulation of MLIs for PV applications. This article also provides a comparative analysis of available MLI control techniques and controllers for GCPV applications in recent times.