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This paper describes an energy management strategy for a DC microgrid that utilizes a hybrid renewable energy system (HRES) composed of a photovoltaic (PV) module, a wind turbine based on a permanent magnetic synchronous generator (PMSG), and a battery energy storage system (BESS). The strategy is designed to provide a flexible and reliable system architecture that ensures continuous power supply to loads under all conditions. The control scheme is based on the generation of reference source currents and the management of power flux. To optimize the supply–demand balance and ensure optimal power sharing, the strategy employs artificial intelligence algorithms that use previous data, constantly updated forecasts (such as weather forecasts and local production data), and other factors to control all system components in an optimal manner. A double-input single-output DC–DC converter is used to extract the maximum power point tracking (MPPT) from each source. This allows the converter to still transfer power from one source to another even if one of the sources is unable to generate power. In this HRES configuration, all the sources are connected in parallel through the common DC–DC converter. The strategy also includes a long short-term memory (LSTM) network-based forecasting approach to predict the available energy production and the battery state of charge (SOC). The system is tested using Matlab/Simulink and validated experimentally in a laboratory setting. [ABSTRACT FROM AUTHOR]

The development of hybrid renewable energy systems (HRESs) can be the most feasible solution for a stable, environment-friendly, and cost-effective power generation, especially in rural and island territories. In this studied HRES, solar and wind energy are used as the major resources. Moreover, the electrolyzed hydrogen is utilized to store energy for the operation of a fuel cell. In case of insufficiency, battery and fuel cell are storage systems that supply energy, while a diesel generator adds a backup system to meet the load demand under bad weather conditions. An isolated HRES energy management system (EMS) based on a Deep Q Network (DQN) is introduced to ensure the reliable and efficient operation of the system. A DQN can deal with the problem of continuous state spaces and manage the dynamic behavior of hybrid systems without exact mathematical models. Following the power consumption data from Basco island of the Philippines, HOMER software is used to calculate the capacity of each component in the proposed power plant. In MATLAB/Simulink, the plant and its DQN-based EMS are simulated. Under different load profile scenarios, the proposed method is compared to the convectional dispatch (CD) control for a validation. Based on the outstanding performances with fewer fuel consumption, DQN is a very powerful and potential method for energy management. [ABSTRACT FROM AUTHOR]

This paper describes an energy management strategy for a DC microgrid that utilizes a hybrid renewable energy system (HRES) composed of a photovoltaic (PV) module, a wind turbine based on a permanent magnetic synchronous generator (PMSG), and a battery energy storage system (BESS). The strategy is designed to provide a flexible and reliable system architecture that ensures continuous power supply to loads under all conditions. The control scheme is based on the generation of reference source currents and the management of power flux. To optimize the supply–demand balance and ensure optimal power sharing, the strategy employs artificial intelligence algorithms that use previous data, constantly updated forecasts (such as weather forecasts and local production data), and other factors to control all system components in an optimal manner. A double-input single-output DC–DC converter is used to extract the maximum power point tracking (MPPT) from each source. This allows the converter to still transfer power from one source to another even if one of the sources is unable to generate power. In this HRES configuration, all the sources are connected in parallel through the common DC–DC converter. The strategy also includes a long short-term memory (LSTM) network-based forecasting approach to predict the available energy production and the battery state of charge (SOC). The system is tested using Matlab/Simulink and validated experimentally in a laboratory setting.

In the Smart City, the Integrated Renewable Energy System (IRES) is playing a crucial role. Integrating the available renewable energy sources is useful in solving energy supply and demand-related issues. For a stable state of energy supply and energy demand, their proper size is needed to adapt to integrated renewable energy sources in the future. To address technical, economic and sizing problems, different algorithms needed to implement the integrated renewable energy scheme, as suggested by various authors. This paper provides a comprehensive review of various topics related to power generation for Smart City based on Integrated Renewable Energy System (IRES). It discusses in detail issues related to the integration of different energy sources, use of smart grids for integration, methods of IRES sizing using software followed by methods of sizing using artificial intelligence algorithms. This article reviews different AI algorithms that focus on the sizing of integrated renewable energy systems in smart cities. [ABSTRACT FROM AUTHOR]

This paper involves the hybrid power potential implementation in view of six climatic zones in Indian state of Tamil Nadu. An intertwined techno-economic feasible study and energy management analysis of Hybrid Renewable Energy System (HRES) has been proposed to cater to need of the electrical energy requirement in un-electrified village hamlets of Tamil Nadu. The HRES feasibility, size optimization, cost and sensitivity analyses are performed to satisfy the electrical energy requirements of the considered area. A combination of Demand Side Management (DSM) and optimum tilt solar panel approach has also been analyzed through HOMER Energy ® simulation. The selection of HRES configuration is based on real-time data collected from six different climatic zones. The optimization results of the considered system are presented and compared with and without DSM strategy. The optimum planning of HRES is based on ranking scheme which includes technical and ecological aspects for sustainable development. In addition, to evaluate the most feasible consideration of the system, sensitivity analysis has been performed upon the load variation, biomass and diesel price too. The simulation results of the proposed HRES configuration can improve the renewable fraction and offer more employment opportunities to the local people, compared to the existing PV-DG-Battery HRES. [ABSTRACT FROM AUTHOR]