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Hybrid power plants have recently emerged as reliable and flexible electricity generation stations by combining multiple renewable energy sources, energy storage systems (ESS), and fossil-based output. However, the effective operation of the hybrid power plants to ensure continuous energy dispatch under challenging conditions is a complex task. This paper proposes a dispatch engine (DE) based on mixed-integer linear programming (MILP) for the planning and management of hybrid power plants. To maintain the committed electricity output, the dispatch engine will provide schedules for operation over extended time periods as well as monitor and reschedule the operation in real time. Through precise prediction of the load and the photovoltaic (PV) and wind power outputs, the proposed approach guarantees optimum scheduling. The precise predictions of the load, PV, and wind power levels are achieved by employing a predictor of the Feed-Forward Neural Network (FFNN) type. With such a dispatch engine, the operational costs of the hybrid power plants and the use of diesel generators (DGs) are both minimized. A case study is carried out to assess the feasibility of the proposed dispatch engine. Real-time measurement data pertaining to load and the wind and PV power outputs are obtained from different locations in the Sultanate of Oman. The real-time data are utilized to predict the future levels of power output from PV and from the wind farm over the course of 24 h. The predicted power levels are then used in combination with a PV–Wind–DG–ESS–Grid hybrid plant to evaluate the performance of the proposed dispatch engine. The proposed approach is implemented and simulated using MATLAB. The results of the simulation reveal the proposed FFNN's powerful forecasting abilities. In addition, the results demonstrate that adopting the proposed DE can minimize the use of DG units and reduce a plant's running expenses. [ABSTRACT FROM AUTHOR]

Over the years, several achievements have been made in power generation and optimising hybrid renewable energy systems (HRES) to achieve nature conservation, achieve energy security, and reduce carbon emissions. However, there are many complexities in Renewable energy (RE) conversion, sizing, design, and implementation, that require optimisation techniques to achieve optimal results in terms of reliability, cost, and environmental protection over time. This paper presents an overview of research trends in Optimization methods in HRES which are classified into modern and conventional methods. These two classifications are further divided into control methods, Artificial intelligence, Iterative and mathematical operations. However, all mentioned techniques have inherent advantages and disadvantages which will be discussed in this survey. In addition, the review paper explored different types of research in computing intelligence (CI), an aspect of Artificial Intelligence (AI) that involves the development of nature-inspired algorithms for optimisation. Finally, general optimisation criteria, system sizing methods used in RES, Mathematical modelling of RES, and gaps for future work to achieve sustainability were also presented.

Hybrid power plants have recently emerged as reliable and flexible electricity generation stations by combining multiple renewable energy sources, energy storage systems (ESS), and fossil-based output. However, the effective operation of the hybrid power plants to ensure continuous energy dispatch under challenging conditions is a complex task. This paper proposes a dispatch engine (DE) based on mixed-integer linear programming (MILP) for the planning and management of hybrid power plants. To maintain the committed electricity output, the dispatch engine will provide schedules for operation over extended time periods as well as monitor and reschedule the operation in real time. Through precise prediction of the load and the photovoltaic (PV) and wind power outputs, the proposed approach guarantees optimum scheduling. The precise predictions of the load, PV, and wind power levels are achieved by employing a predictor of the Feed-Forward Neural Network (FFNN) type. With such a dispatch engine, the operational costs of the hybrid power plants and the use of diesel generators (DGs) are both minimized. A case study is carried out to assess the feasibility of the proposed dispatch engine. Real-time measurement data pertaining to load and the wind and PV power outputs are obtained from different locations in the Sultanate of Oman. The real-time data are utilized to predict the future levels of power output from PV and from the wind farm over the course of 24 h. The predicted power levels are then used in combination with a PV–Wind–DG–ESS–Grid hybrid plant to evaluate the performance of the proposed dispatch engine. The proposed approach is implemented and simulated using MATLAB. The results of the simulation reveal the proposed FFNN’s powerful forecasting abilities. In addition, the results demonstrate that adopting the proposed DE can minimize the use of DG units and reduce a plant’s running expenses.

Over the years, several achievements have been made in power generation and optimising hybrid renewable energy systems (HRES) to achieve nature conservation, achieve energy security, and reduce carbon emissions. However, there are many complexities in Renewable energy (RE) conversion, sizing, design, and implementation, that require optimisation techniques to achieve optimal results in terms of reliability, cost, and environmental protection over time. This paper presents an overview of research trends in Optimization methods in HRES which are classified into modern and conventional methods. These two classifications are further divided into control methods, Artificial intelligence, Iterative and mathematical operations. However, all mentioned techniques have inherent advantages and disadvantages which will be discussed in this survey. In addition, the review paper explored different types of research in computing intelligence (CI), an aspect of Artificial Intelligence (AI) that involves the development of nature-inspired algorithms for optimisation. Finally, general optimisation criteria, system sizing methods used in RES, Mathematical modelling of RES, and gaps for future work to achieve sustainability were also presented. [ABSTRACT FROM AUTHOR]

Renewable energy resources are becoming more appealing as energy demand and fossil fuel costs increase. The hybridization of these resources has the potential to reduce unpredictability and intermittency while increasing efficiency. The accuracy of size optimization of hybrid renewable energy systems (HRES) can be improved by using accurate weather data that can be obtained through forecasting. Thus, to increase the precision of the size optimization, hourly forecasting of global horizontal irradiation, temperature, and wind speed for one year has been performed using Gaussian process regression (GPR), Support Vector Regression, Extreme Gradient Boosting, and Decision Tree techniques. The results of all four forecasting models (FM) are then compared and revealed that the results obtained from GPR are better than those of other FM; therefore, the forecasted data for solar, wind, and temperature obtained from GPR are used for sizing the HRES. The net present cost is utilized to analyze the viability of the HRES while considering system reliability. Furthermore, recently developed optimization algorithms, namely the Colony Predation Algorithm (CPA), Tunicate Swarm Algorithm (TSA), and Aquila Optimization (AO) algorithms have been applied to the sizing of a grid-connected HRES to meet the energy needs of a remote site in the Indian province of Haryana. A comparison of CPA, AO, and TSA has been carried out and revealed that TSA offers more promising outcomes. In addition, the simulation results demonstrate a 0.42% reduction in per unit cost of energy when forecasted data has been used for size optimization. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate the economic, technical, and environmental performances of grid-tied and stand-alone hybrid renewable energy systems (HRESs) in 21 provinces in seven regions of Turkey, considering different regional solar radiation and wind speed diversity. HRES were designed and modeled using the Hybrid Optimization of Multiple Energy Resources software (HOMER PRO) to meet the daily load of 13.26 kWh/day of a household. The analysis results for each province were compared considering the cost of energy, net present cost (NPC), greenhouse gas emissions, renewable fraction (RF), and optimum system configuration. The findings demonstrated that the optimal system configurations are Grid/PV/WT and PV/WT/DG/BESS for grid-tied and stand-alone HRES, respectively. The value of NPC ranges from $\$ $ 2,540.00 to $\$ $ 8,951.00 for grid-tied HRES, while it varies from $\$ $ 23,372.00 to $\$ $ 40,858.00 for stand-alone HRES. The provinces of Çanakkale in the Marmara Region and Artvin in the Black Sea Coast Region have the lowest and highest NPC values, respectively, for all systems. The PV capital cost, WT capital cost, BESS capital cost, solar radiation, and wind speed are considered as sensitivity input parameters that might affect the economic output of the HRES in this study. According to the sensitivity analysis, the NPC value as an economic indicator input decreased for both on-grid and off-grid HRES as the wind speed and solar radiation increased. It was also found that when the capital cost of PV panels and WT were changed, the NPC of the stand-alone HRES was in the range of $\$ $ 21,402.27- $\$ $ 29,978.89 for the province of Çanakkale, while it was in the range of $\$ $ 37,518.11- $\$ $ 51,939.00 for the province of Artvin. Moreover, when solar radiation and wind speed were increased, the results showed that NPC and CO2 emissions decreased by 9.30% and 9.23%, respectively, for Çanakkale, and by 25.58% and 66.95%, respectively, for Artvin. Finally, the results indicated that the optimal system configuration changes depending on the PV and WT capital cost variations for the grid-tied HRES. This research can be useful for planning grid-tied and stand-alone HRES from different aspects in Turkey, as well as other countries around the world. It contributes to the literature by comparing grid-tied and stand-alone HRES to determine the optimum system configuration and to find the best optimization results in seven regions of Turkey under different climate conditions. In addition, most of the studies related to HRES for residential areas in the literature are reviewed in this research, which intends to serve as a guide for engineers and researchers.

The cost of electricity produced by thermal power plants in Republic of Djibouti is relatively high at about $0.32/kWh. This is due to its dependence on imported oil coupled with fluctuating oil prices. Consequently, the customer pays a high electricity bill. However, Djibouti is endowed with indigenous renewable energy resources such as a good solar irradiance of 5.92 kWh/ m2 day, a potential geothermal energy estimated up to 1000 MW, and few sites with annual wind speed higher than 6 m/s. The goal of this paper is, therefore, to assess an economic evaluation of different grid connected hybrid renewable energy systems to a residential urban house located in Tadjourah city (11.7913° N, 42.8796° E) in the North-Eastern part of Djibouti to reduce the cost of electricity from the grid. To reach this objective, a powerful software tool called HOMER (Hybrid Optimization Model for Electric Renewables) has been used to find the optimum hybrid energy system using real wind and solar irradiation data. The results obtained from this study show that the best economical suited combination of hybrid renewable energy system is a PV-Wind grid connected system. This study shows also that potentially the indigenous renewable energy contribution, in Tadjourah, can be as much as 77 % with 47 % of solar and 30% of Wind energy. The Net Present Cost, the Levelized Cost of Energy, and the operating cost of the optimal HRES are $337, $0.002/kWh and $1,025/year, respectively. When compared with the average cost of grid-only connection of $0.32/kWh, the optimal hybrid renewable energy system is more economical and will save 51 % of the cost that the customer must pay when using only the electricity from the grid.

The actual energetic situation has several challenges such as pollution, the rarefaction of fossil fuel and the dangers of nuclear. Renewable sources are proposed as a solution and suggested, such as a cost-effectiveness system. The paper deals with the problem of feeding a domestic load with electricity which should respect the ecologies factors, so this work is a design problem of the hybrid renewable energy systems; PV/biomass, PV/diesel/battery, PV/wind/diesel/battery, and wind/diesel/battery to choose the best one of them which feed the load with the lowest cost. The study’s goal is to design a microgrid system by the minimization of the total investment cost with respect to the required technical factor, the minimum allowed renewable energy fraction, and the minimum allowed availability factor. The methodology flowed utilizes frameworks based on a recent algorithm called Movable damped wave algorithm (MDVP). The proposed optimization algorithm is compared with other algorithms to prove its efficacy which are; the artificial electric field algorithm (AEFA), harris hawks optimization (HHO), and the grey wolf optimizer (GWO). The project case study is investigated in Al-Majmaah, Saudi Arabia. The contribution of this work is implementing a recent algorithm that proves its efficacy and finding the best microgrid configuration following many investigations and comparisons. The results confirm that the MDVP is better compared to the other algorithms, its computational time is fast, and its convergence is good; otherwise, the PV/biomass is considered the best configuration in the area of study with a size of 237.698 m2 from PV panel and 954.097 t/year of biomass, which obtained the best Net Present Cost (NPC) of $299504, and a cost of energy (LCOE) assumed as $0.228/kW. A sensitivity analysis is applied to prove the effect of size variation on project factors. The simple observation, by the way, is that any change in the PV size affects the output factors.

Adopting hybrid power systems to provide access to clean, reliable, and inexpensive energy is necessary for countries like India to accomplish their goals of sustainable development. This work proposes an optimization approach for sizing a grid‐connected hybrid renewable energy system (HRES) that includes photovoltaic, biomass, biogas, and a battery. In this research, a recently developed Aquila optimization algorithm is utilized to evaluate the optimal size of HRES for a cluster of villages near Sarai Jairam, Agra district, Uttar Pradesh, India using the MATLAB software package. The goal of this optimization approach is to reduce the net present cost for HRES while satisfying the operational constraints, including the reliability of the power supply and maximum utilization of the photovoltaic, biomass, and biogas complementary properties. Initially, three HRES configurations in the off‐grid mode were optimized using the suggested Aquila optimization technique. To confirm the findings, the optimization results for the same models of off‐grid HRES were obtained using the harmony search (HS) and particle swarm optimization (PSO) algorithms. The same algorithms were also used to optimize the grid‐connected SPV/biomass/biogas with the battery model of HRES. Finally, the outcomes from the off‐grid models presented above were compared to the grid‐linked model. The grid‐connected configuration which is obtained by the Aquila optimization approach provides an optimal solution with the least net present cos t (NPC) and minimum cost of energy (COE) when compared with HS and PSO. The optimal grid‐connected HRES includes 235 kW of PV, a 10 kW biogas generator, a 64 kW biomass generator, and a 50.40 kWh battery bank. The overall net present cost and the COE are found to be $547 670 and $0.0768/kWh, respectively. [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. [ABSTRACT FROM AUTHOR]

This study focuses on a technical and economic analysis of designing and operating an off-grid hybrid renewable energy system (HRES) in a rural community called Olooji, situated in Ogun state, Nigeria, as a case study. First, a size optimization model is developed on the basis of the novel metaheuristic particle swarm optimization (PSO) technique to determine the optimal configuration of the proposed off-grid system on the basis of the minimization of the levelized cost of electricity, by factoring in the local meteorological and electricity load data and details on the technical specification of the main components of the HRES. Second, a fuzzy-logic-controlled energy management system (EMS) is developed for the dynamic power control and energy storage of the proposed HRES, ensuring the optimal energy balance between the different multiple energy sources and the load at each hour of operation. The result of the size optimization model showed that an LCOE for implementing an HRES in the community would be 0.48 USD/kWh in a full-battery-capacity scenario and 1.17 USD/kWh in a half-battery-capacity scenario. The result from this study is important for quick decision-making and effective feasibility studies on the optimal technoeconomic synopsis of implementing minigrids in rural communities. [ABSTRACT FROM AUTHOR]

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions. [ABSTRACT FROM AUTHOR]

Knowledge on operational energy consumption and embodied energy, replacing embodied greenhouse gas (GHG) emissions in building materials, and national energy resources have become a necessity. In this study, the importance of investigating the stochastic nature of weather-dependent renewable energies is well documented. The management of the hybrid renewable energy system (HRES) was built and assessed, utilizing a decision-making algorithm and 13 case studies. When the proportion of renewable energy is at 24% and the average daily fossil fuel usage is 1.11 L per year, the HRES generates 1697 kWh per year with a net present value (NPV) of 553,68 USD, with a rate of return (IRR) of 21.4%, and a payback period (PP) of 15.7 years. With a renewable energy share of 54%, fossil fuel consumption dropped to 0.69 L per year, while yearly energy output was comparable to 1652 kWh per year, with an IRR of 19.5% and a PP of 17.6 years. To achieve zero greenhouse gas emissions, HRES Management employs 100% renewable energy sources to generate 1933 kWh per year at a net present value of −372.09 USD. This scenario is economically possible if the renewable energy feed-in tariff exceeds 0.06 USD. • Dynamic decision algorithms developed HRES technology. • To increase RE in weak economies, techno-economic study was undertaken. • Simulink MATLAB models prefabricated Hybrid Renewable Energy Systems. • For HRES, the configuration and size have been optimized. • Sustainable Green cottage and energy transition. [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]

[Display omitted] • This study examines synergy options of a Hybrid solar-wind renewable energy system optimized using iHOGA for green building. • This study develops operational conditions for an HRES simulating the energy consumption process over a year. • The perfect synergy produced 6988 kWh of energy, the building used 6759 kWh and energy loss was calculated as 276 kWh per year. • The CO 2 mitigation achieved 5273.14 kg/year. SO 2 and NO x , are reduced by 9601.512 kg and 32424.32 kg per year, respectively. This research article analyses the optimal feasible synergy options of renewable energy sources combined into a hybrid PV panel and wind energy power producing system to deliver green energy for the Renewable Energy Department building at Rajasthan Technical University Kota, India. The configuration of the modeled system is optimized to be estimated with improved hybrid optimization by genetic algorithm (iHOGA). This study generates operational conditions for this hybrid solar-wind energy system which simulates the energy usage process over one year. The optimal synergy configured between solar and wind hybrid systems provides peak-demand energy to the green office building with little surplus energy. The whole year's production of energy was 6988 kWh and the energy used by the building was 6759 kWh. The energy loss estimated is 276 kWh/year. The CO 2 mitigated by using the integrated renewable energy option is evaluated and observed at 5273.14 kg/year. The other pollutants like SO 2 and NO x are also being mitigated by 9601.512 kg/year and NO x 32424.32 kg/year respectively. [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.

Energy Consumption has been increasing at an alarming rate due to the growing energy need. More and more non-renewable sources are harvested to fulfill the energy demand resulting in and rising environmental health issues. However, harvesting Solar and Wind energy is considered as the best alternative in generating energy as these resources are renewable. Hybrid Renewable Energy System (HRES) has been grabbed the attention recently, as it involves with renewable, environmentally friendly sources to generate energy. The limitation of single Renewable Energy (RE) system is overcome by systems such as HRES. Even though it has been introduced different sizing and optimization techniques, due to the lack of system function, it had posed issues in calculating the optimized cost of a hybrid system considering the solar, wind resources and load demand as the optimization of the system cannot be predicted accurately. The aim of this research was to obtain optimization of a Hybrid PV-wind system in term of sizing and cost over the 20 years of the period of interest. The simulation of the PV-Wind Hybrid system using MATLAB for the verification purpose. This work includes detailed calculation using the Life Cycle Cost method for identifying all possible combinations. The combination of eleven Solar Panels, one Wind Turbine and nine Batteries was identified as the optimal Combination with LCC of RM 221,329.97 and has been verified using simulation results. Lastly, a sensitivity test was carried out using the exiting results of verified by the simulation to identify the most deterministic system in affecting LCC of the Hybrid system. Further, total Cost distribution for the Optimized hybrid PV-Wind system was conducted and identified that 50% of system cost was contributed by the Wind turbine. Determination of LCC, was done as a combination of Component and Operation costs. It was identified that Replacement cost contributed the highest while Wind turbine showed the highest Operation cost from the system cost. Thus, this work was included with the sensitivity test assuming 10% price increment for each component and it was concluded that price changes in Wind turbine results the greatest difference in LCC while further verified with the results of the simulation.

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.

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.

Hybrid renewable energy systems (HRES), which are based on doubly fed induction generators (DFIG), have good fault ride-through (FRT) capacity. However, to increase the fast response of compensation and good transient performance of HRES, FACT devices such as Distribution Static Compensators (DSTATCOM) are employed. Normally PI controllers are optimized to control the operation of DSTATCOM. Due to various disturbances arising owing nonlinearity of WECS, a more robust and fast, a fuzzy logic controller (FLC) is explored for DSTATCOM control and that can make the HRES to match the low-voltage ride-through (LVRT). This paper proposes a strategy through a battery energy storage system (BESS) embedded with fuzzy-based DSTATCOM. The proposed strategy improves the DC-link voltage, voltage at PCC, reactive power, and active power by 15.68%, 64%, 13%, and 33%, respectively, as compare to DSTATCOM alone. The design includes a simple controller-based series dynamic braking resistor (SDBR). The simulations of the control strategies were performed on MATLAB to study the LVRT performance of HRES with BESS embedded fuzzy-based DSTATCOM. Simulation results showed improved performance of the proposed strategy compared to DSTATCOM alone. [ABSTRACT FROM AUTHOR]

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]

A microgeneration scheme based on a wind–solar hybrid renewable energy system (HRES), suitable for grid-secluded applications, is described in this article. A conventional single-phase two-winding induction generator (IG) capable of operating in a wide speed range is used for wind energy conversion. A photovoltaic (PV) panel of suitable capacity is connected to the common dc bus as a part of the hybrid generation. A sequential current-based control for the IG is proposed in this article for balancing the currents of the two stator windings. A fixed-frequency inverter is used at the common dc bus for supplying single-phase ac domestic loads. The suitability of the proposed generation scheme is verified using proper simulations followed by appropriate experiments on a laboratory test setup. [ABSTRACT FROM AUTHOR]

Energy sectors are responsible for most of greenhouse gas emission in the world. Significant amount of energy is consumed by the residential sector, highlighting the importance of developing sustainable energy technologies for this sector. In the current study, the behavior of an earth - air heat exchanger is simulated numerically, and the results are validated against the existing experimental data. To improve reliability and sustainability, the heat exchanger is coupled with a hybrid renewable energy system, including wind, solar and hydrogen. The performance of this system is explored in continuous and intermittent modes over a period of one month. The results show that nearly half of the drop in total performance of the system occurs during the first working day. Further, running the system intermittently result in an 8% rise in the effectiveness and around 31.55 MJ energy delivery over the period of a month. This implies that the long-term behavior of the system can be determined by monitoring its behavior on the first day of operation. It is also demonstrated that adding geothermal energy to hybrid renewable energy system can lead to an improvement of about 5.5% of the renewable fraction, decrease emissions and diesel consumption by almost 48%. • Earth to air heat exchanger (EAHE) system to supply thermal energy. • EAHE is analyzed in continuous and intermittent operating. • Coupling EAHE to PV, WT, thermal load controller, hydrogen are investigated. • Obtaining the optimum size of PV, electrolyzer, hydrogen tank, electric boiler and WT. [ABSTRACT FROM AUTHOR]

• Proposed the concept of negative emission hybrid renewable energy system (NEHRES). • Developed the decision-support framework for the optimal design of the NEHRES. • Integrated first-principle and data-driven methods for the system modeling. • Conducted a case study on the design of the system for a standalone rural island. • Validated the economic and carbon sequestration feasibilities of the NEHRES. To tackle the increasing global energy demand the climate change problem, the integration of renewable energy and negative emission technologies is a promising solution. In this work, a novel concept called "negative emission hybrid renewable energy system" is proposed for the first time. It is a hybrid solar-wind-biomass renewable energy system with biochar production, which could potentially provide energy generation, carbon sequestration, and waste treatment services within one system. The optimization and the conflicting economic and environmental trade-off of such system has not yet been fully investigated in the literature. To fill the research gap, this paper aims to propose a stochastic multi-objective decision-support framework to identify optimal design of the energy mix and discuss the economic and environmental feasibilities of a negative emission hybrid renewable energy system. This approach maximizes energy output and minimizes greenhouse gas emissions by the optimal sizing of the solar, wind, combustion, gasification, pyrolysis, and energy storage components in the system. A case study on Carabao Island in the Philippines, which is representative of an island-mode energy system, is conducted based on the aim of achieving net-zero emission for the whole island. For the island with a population of 10,881 people and an area of 22.05 km2, the proposed optimal system have significant negative emission capability and promising profitability with a carbon sequestration potential of 2795 kg CO 2-eq /day and a predicted daily profit of 455 US$/day. [ABSTRACT FROM AUTHOR]

Abstract In the majority of isolated sites, the diesel generator is seen as the best solution for the electrification. For these sites, access to the electricity grid is either difficult or expensive. The rising price of fuel and the interest in reducing greenhouse gas emissions have led researchers to think otherwise: meeting the energy needs with minimal damage. Renewable energy sources (RES) have experienced a significant drop in the purchase price, making their use in these regions very advantageous compared to conventional sources. One of the barriers to scaling up renewable energy sources (RES) is the intermittency of these sources. The variability experienced by the resources (sun, wind, etc.) is not correlated with the load demand. For that there is always interest to couple several different sources and form what is called a hybrid system. This paper is a review of Hybrid Renewable Energy System (HRES) for electricity generation. It presents the operating mode, the different structure of the HRES and the sizing software used to evaluate the Hybrid Renewable System (HRS) behavior. [ABSTRACT FROM AUTHOR]

Solar and wind energy are considered as promising electrical generating sources. These renewable energies are omnipresent, with free access, and a friendly environmental impact. Their integration remains technically and economically advantageous for electrical generation in isolated areas (IS). In several cases, the separate use of solar and wind energy sources may result in considerable over-sizing, which makes the single renewable energy sources in implementation very costly. It is found that the use of one of the optimization sizing techniques could help to guarantee the maximum power reliability and the minimum system cost for the future hybrid implementation. Moreover, a remarkable interest is manifesting for the use of solar and wind renewable energy sources (RES), which provide a realistic form of electrical generation in isolated areas. This paper provides an updated literature review, of the most applied method and techniques used in sizing and optimization of PV-Wind based hybrid system (PWHS) for an isolated area aiming to reach the best compromise between power reliability and hybrid system costs. Furthermore, this work discusses a comparison of the most common topologies used for the implementation of PWHS, then, presents a mathematical model of the hybrid system components with an emphasis on the importance of power reliability and system cost, Finally, provides an extensive analysis of software tools and algorithm approach used in sizing optimization. [ABSTRACT FROM AUTHOR]

In the current smart grid scenario, the evolution of a proficient and robust maximum power point tracking (MPPT) algorithm for a PV subsystem has become imperative due to the fluctuating meteorological conditions. In this paper, an adaptive feedback linearization-based NeuroFuzzy MPPT (AFBLNF-MPPT) algorithm for a photovoltaic (PV) subsystem in a grid-integrated hybrid renewable energy system (HRES) is proposed. The performance of the stated (AFBLNF-MPPT) control strategy is approved through a comprehensive grid-tied HRES test-bed established in MATLAB/Simulink. It outperforms the incremental conductance (IC) based adaptive indirect NeuroFuzzy (IC-AIndir-NF) control scheme, IC-based adaptive direct NeuroFuzzy (IC-ADir-NF) control system, IC-based adaptive proportional-integral-derivative (IC-AdapPID) control scheme, and conventional IC algorithm for a PV subsystem in both transient as well as steady-state modes for varying temperature and irradiance profiles. The comparative analyses were carried out on the basis of performance indexes and efficiency of MPPT. [ABSTRACT FROM AUTHOR]

• The study investigates HRES sizes to achieve NZEB status for a hotel building. • An optimization analysis of a hotel's grid-connected HRES is presented. • The PV/wind grid-connected system has the lowest NPC and LCOE. • The electricity price of the proposed HRES, at 2.1 ¢/kWh. • The Egypt's retail electricity prices of 6.9 ¢/kWh. The tourism sector is a key source of national income, and the use of renewable energy resources promotes green and sustainable tourism. In addition, ensuring a reliable supply and efficient use of clean energy is a primary objective of Egypt's Vision 2030, as it has a significant impact on the sustainable development and growth of the tourism sector in Egypt. Net-zero energy buildings (NZEBs) are a promising decarbonization effort as they reduce energy consumption while increasing the share of renewable energy. Therefore, it is worth investigating the opportunities for NZEBs applications in tourism sector. This paper presents an optimization analysis of a hotel's grid-connected hybrid renewable energy system (HRES) based on its technical, economical, and environmental prospects. Seven scenarios are considered for on-site generation to match the energy requirements of the specific hotel building. The proposed hybrid systems incorporate photovoltaic (PV), wind turbine (WT), and biogas generators into a grid-connected system for supplying power to a tourist hotel. The size of the microgrid system is optimized to decrease the net present cost (NPC) and levelized cost of electricity (LCOE). The results reveal that the PV/wind grid-connected system, with renewable energy generators consisting of 143 kW of photovoltaic modules and one 20 kW wind turbine with a hub height of 18 m, has the lowest NPC and LCOE. This hybrid energy system has a net present cost of 388 k$, which is significantly lower compared to the electricity grid's cost of 873 k$. The electricity price of the proposed HRES, at 2.1 ¢/kWh, is highly competitive with Egypt's retail electricity prices of 6.9 ¢/kWh. Moreover, grid-connected HRES can lower the pressure on the electricity grid arising from the feed-in of electricity from HRES. Furthermore, the proposed HRES will help slow climate change by reducing GHG emissions and fossil fuel usage. Finally, this study will provide valuable guidance for the practical application of NZEBs in Egypt as well as similar global zones. [ABSTRACT FROM AUTHOR]