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Green hydrogen has become a central topic in discussions about the global energy transition, seen as a promising solution for decarbonizing economies and meeting climate goals. As part of the process of decarbonization, green hydrogen can replace fossil fuels currently in use, helping to reduce emissions in sectors vital to the global economy, such as industry and transport, as well as in the power and heat sectors. Whilst there is significant potential for green hydrogen, there are also challenges. The upfront costs for infrastructure and technology are high, and the availability and accessibility of the renewables needed for production varies by region. Green hydrogen production and storage technologies are continuously evolving and being promoted as the demand for hydrogen in many applications grows. Considering this, this paper presents the main methods for its production and storage, as well as its economic impact. Hence, the trend of governments and international organizations is to invest in research and development to make this technology more accessible and efficient, given the carbon reduction targets. [ABSTRACT FROM AUTHOR]

The increase in new energy grid connections has reduced the supply-side regulation capability of the power system. Traditional economic dispatch methods are insufficient for addressing the flexibility limitations in the system's balancing capabilities. Consequently, this study presents a day-ahead scheduling method for renewable energy systems that balances flexibility and economy. This approach establishes a dual-layer optimized scheduling model. The upper-layer model focuses on the economic efficiency of unit start-up and shut-down, utilizing a particle swarm algorithm to identify unit combinations that comply with minimum start-up and shut-down time constraints. In contrast, the lower-layer model addresses the dual uncertainties of generation and load. It employs the Generalized Polynomial Chaos approximation to create an opportunity-constrained model aimed at minimizing unit generation and curtailment costs while maximizing flexibility supply capability. Additionally, it calculates the probability of flexibility supply-demand insufficiency due to uncertainties in demand response resource supply and system operating costs, providing feedback to the upper-layer model. Ultimately, through iterative solutions of the upper and lower models, a day-ahead scheduling plan that effectively balances flexibility and economy is derived. The proposed method is validated using a simulation of the IEEE 30-bus system case study, demonstrating its capability to balance system flexibility and economy while effectively reducing the risk of insufficient supply-demand balance. [ABSTRACT FROM AUTHOR]

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Amidst mounting global pressure, governments worldwide have embarked on a collective journey towards decarbonising their economies within the next 30 years. This ambitious goal necessitates the phasing out of naturally occurring hydrocarbons such as coal, natural gas, and oil. However, our analysis of data from academic papers, Australian Federal Government publications, and reports from energy industry bodies and manufacturers of electricity‐generating equipment suggests that such a complete elimination of fossil fuel use is not feasible. The data we've gathered, however, indicate that transitioning to a connected energy island power generation topology could at least create a sustainably robust energy supply capable of propelling Australia towards its environmental targets while bolstering its future economic well‐being. Based on analysis of data drawn from academic papers, the Australian Federal Government, energy industry bodies, and manufacturers of electricity generating equipment, the authors believe at this time, elimination of fossil fuel use is simply not possible. By moving to a connected energy island power generation topology, the data suggest we can: 1. Create a sustainably robust energy supply. 2. Move Australia closer to its environmental targets. 3. Underpin Australia's future economic well‐being. [ABSTRACT FROM AUTHOR]

In response to the growing demand for electricity and the depletion of fossil fuel resources, nations are transitioning towards renewable energy systems as viable alternatives for power generation. Wind and solar photovoltaic (SPV) energy systems have emerged as promising, sustainable options. However, conventional multilevel inverters fail to control both wind and SPV energy simultaneously. Therefore, in this study, a hybrid SPV wind power system with a level converter (HPWPS-SLC) was developed using a wind-based permanent magnet synchronous generator and SPV energy grid sources. The HPWPS-SLC leverages the benefits of the genetic algorithm-optimized adaptive neuro-fuzzy inference system controller for efficient energy generation and management. In addition, a pulse width modulation controller with a hybrid asymmetric switching scheme was implemented to reduce the total harmonic distortion (THD). This approach enables high switching frequency while minimizing the switch count, thereby reducing the losses and costs associated with conventional techniques. Simulation results show that the proposed HPWPS-SLC system achieves a power factor of 0.7 and a THD of 25.02 % for grid voltages under fault conditions. Despite the fault conditions, maintaining a THD value of 25.02 % ensures a better grid voltage waveform quality and minimizes distortions for stable operation. Utilizing a 42-cycle signal with a fast Fourier transform of 17 cycles enables finer resolution in harmonic analysis up to the 16th order, thereby enhancing the overall system performance.

The whole world is now widely using green energy compared to fossil because of the depletion of fossil fuels, the rising temperature of the earth, and changing weather conditions, all these things are becoming a big threat to the life of the earth. This study proposed a stand-alone hybrid renewable energy system using different types of batteries. This model includes photovoltaic arrays, wind turbines, diesel generators, converters, and batteries. Lead-acid and lithium-ion batteries have been compared for the selection of optimal battery based on hybrid renewable energy system and sustainable development requirements. The purpose of this study is to find the optimal configuration, and techno-economic characteristics, using the hybrid optimization of multiple energy resources technique. The results of Lithium-ion and Lead Acid have been compared and it is found that the best configuration is photovoltaic arrays/wind turbines/ diesel generators /Battery/converter with lithium-ion Batteries. The net present cost and cost of energy are found to be 1.64M and 0.144$ respectively, for the selected study location. The carbon dioxide emission for configuration with LI batteries is 107314 kg/year as against the LA batteries which have 351288 kg/year. The results show LI batteries are technically as well as economically better than the LA batteries.

The transition to low‐carbon energy and energy independence of a country play an important role in the sustainable development of its energy sector. Another important issue of sustainable energy development is the cost competitiveness in the generation process; with new renewable energy technologies, a sustainable energy transition to carbon‐neutral society is possible. In this article, we present a view of sustainable energy transformation based on a case study of Malta. We have created a simulation of a Maltese electricity system with projected growth and dominance of photovoltaic energy in the electricity market. The study results suggest that a system with a high penetration of photovoltaics has significant advantage over a conventional system using fossil fuels. In particular, in the simulated Maltese system, the total annual cost of energy was reduced threefold, the CO2 emissions were reduced by 40%, and the energy independence of Malta increased by 60%. In the end, the article gives a recommendation for further research into the Maltese energy system. [ABSTRACT FROM AUTHOR]

The microbial fuel cell (MFC) is a renewable energy technology that utilizes the oxidative decomposition processes of anaerobic microorganisms to convert the chemical energy in organic matter, such as wastewater, sediments, or other biomass, into electrical power. This technology is not only applicable to wastewater treatment but can also be used for resource recovery from various organic wastes. The MFC usually requires an external controller that allows it to operate under controlled conditions to obtain a stable output voltage. Therefore, the application of a PID controller to the MFC is proposed in this paper. The design phase for this controller involves the identification of three parameters. Although the particle swarm optimization (PSO) algorithm is an advanced optimization algorithm based on swarm intelligence, it suffers from issues such as unreasonable population initialization and slow convergence speed. Therefore, this paper proposes an improved particle swarm algorithm based on the Golden Sine Strategy (GSCPSO). Using Circle chaotic mapping to make the distribution of the initial population more uniform, and then using the Golden Sine Strategy to improve the position update formula, not only improves the convergence speed of the population but also enhances convergence precision. The GSCPSO algorithm is applied to execute the described design process. The results of the simulation show that the designed control method exhibits smaller steady-state error, overshoot, and chattering compared with sliding-mode control (SMC), backstepping control, fuzzy SMC (FSMC), PSO-PID, and CPSO-PID. [ABSTRACT FROM AUTHOR]

The efficient production and sustainable operation of water electrolysis hydrogen production equipment within renewable energy systems, characterized by intermittency and volatility, necessitates dynamic simulation for the development of optimized control systems. This paper proposes the application and modification of a data-driven deep-learning model to simultaneously simulate the thermal and electrochemical responses of an alkaline water electrolyzer (AWE), yielding exceptional performance and versatility. By utilizing 4840 h of comprehensive experimental data, the causal relationships among electrolyzer operating parameters are resolved, classifying them into set values and response values for the proposed dynamic model. The model architecture is based on an attention mechanism and designed with hybrid input. Notable enhancements include embedding flow-leveraged polarization function, creating set-value cross-attention, and adopting probability-sparse attention, all of which are validated through both the validation and simulation results. Extensive exploration of various structural and training parameters leads to the selection of the best-performing model. When applied to simulation across all experimental data, the median mean percentage absolute error of the optimal model in cell voltage is a mere 1.3%, while the error in temperature is only 3.5%. Moreover, under typical operating conditions, the simulation error for both voltage and temperature remain below 2%, highlighting the outstanding dynamic modeling and simulation capabilities. These results demonstrate the potential of the proposed model for future hydrogen energy applications in dynamic control and operational optimization within renewable energy system for higher performance and better conversion efficiency. • Deep-learning model for dynamic thermoelectric simulation of water electrolyzers. • Flow-leveraged polarization function and set-value cross-attention mechanism. • Excellent accuracy, thermoelectric error of 3.5% and 1.3%, respectively. • Insights for optimizing control strategies of electrolyzer for higher efficiency. [ABSTRACT FROM AUTHOR]

The whole world is now widely using green energy compared to fossil because of the depletion of fossil fuels, the rising temperature of the earth, and changing weather conditions, all these things are becoming a big threat to the life of the earth. This study proposed a stand-alone hybrid renewable energy system using different types of batteries. This model includes photovoltaic arrays, wind turbines, diesel generators, converters, and batteries. Lead-acid and lithium-ion batteries have been compared for the selection of optimal battery based on hybrid renewable energy system and sustainable development requirements. The purpose of this study is to find the optimal configuration, and techno-economic characteristics, using the hybrid optimization of multiple energy resources technique. The results of Lithium-ion and Lead Acid have been compared and it is found that the best configuration is photovoltaic arrays/wind turbines/ diesel generators /Battery/converter with lithium-ion Batteries. The net present cost and cost of energy are found to be 1.64M and 0.144$ respectively, for the selected study location. The carbon dioxide emission for configuration with LI batteries is 107314 kg/year as against the LA batteries which have 351288 kg/year. The results show LI batteries are technically as well as economically better than the LA batteries. [ABSTRACT FROM AUTHOR]

The optimal design of the proportional-integral (PI) controller using the walrus optimization algorithm (WOA) with the aim to enhance the dynamic performance of a grid-connected wave energy conversion (WEC) system when subjected to diverse operational conditions is presented in this paper. The proposed system under study consists of an oscillating water column (OWC) device coupled with a permanent magnet synchronous generator (PMSG) to supply power to the grid. Power electronics devices in the form of a generator-side converter (GSC) and a grid-side inverter (GSI) are used to couple the grid and WEC systems. The GSC is used to minimize generator losses and maximize generator real power through the control of d-axis and q-axis currents (id, and iq) of the PMSG. The GSI is used to control the point of common coupling (PCC) and DC-link voltages (VPCC,VDC), respectively. The PI controllers, used to minimize the error between the actual current and voltage values with their respective reference values, are optimally designed using the WOA. The fitness function of the optimization problem is based on the integral square error criterion (ISE). Presented in this paper is a model for the OWC-WEC system and a control strategy to maximize generated power, minimize generator losses, and keep the VDC, VPCC at required values, the usage of WOA to design the PI controllers, and the simulations of system results. The proposed WOA-based PI controller design’s effectiveness is evaluated by comparing its simulation results with that obtained from using genetic algorithm (GA), grey wolf (GWO), particle swarm (PWO), and harmony search (HS) optimization-based PI controllers under symmetrical and unsymmetrical faults. The proposed strategy shows an enhancement in the dynamic performance of OWC wave energy systems when compared to the other optimization algorithm-based PI controllers, as well as achieving the least value for ISE, which reached 0.172.

Abstract Amidst mounting global pressure, governments worldwide have embarked on a collective journey towards decarbonising their economies within the next 30 years. This ambitious goal necessitates the phasing out of naturally occurring hydrocarbons such as coal, natural gas, and oil. However, our analysis of data from academic papers, Australian Federal Government publications, and reports from energy industry bodies and manufacturers of electricity‐generating equipment suggests that such a complete elimination of fossil fuel use is not feasible. The data we've gathered, however, indicate that transitioning to a connected energy island power generation topology could at least create a sustainably robust energy supply capable of propelling Australia towards its environmental targets while bolstering its future economic well‐being.

This paper presents a techno-economic evaluation of a Hybrid Renewable Energy System (HRES) for the University of Baghdad College of Engineering. The objectives include improving reliability and mitigating grid demand. The design of a customized grid-connected HRES for the university, incorporating solar PV and a diesel generator (DG). The optimal hybrid configuration is determined through a complete assessment of economic, technical, and environmental factors using Homer-Pro software. Besides it addresses critical challenges in provides valuable insights into sustainable energy solutions for educational institutions. Additionally, the study extends its scope by presenting a comprehensive analysis of four different proposed hybrid energy systems. The College of Engineering is anticipated to have an estimated daily load of 1110.48 kWh and a peak load of 494.56 kW. The optimal cost for a system comprising PV, DG, and grid is determined to be net present cost (NPC) is 288,348,027IQD and levelized cost of energy (LCOE) is 57.2896IQD/kWh. The analysis emphasizes the integration of both renewable and conventional sources to create a sustainable and efficient energy solution.

Abstract How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh.

This research investigates a hybrid renewable energy system (HRESs) connected to the grid that uses three energy sources: solar energy, wind energy, and battery energy. Unlike conventional wind turbine systems, this paper uses a direct-drive high torque low speed generator called the Vernier Doubly Salient Permanent Magnet Generator (V-DSPMG). The generator shaft is directly connected to the turbine shaft without a gearbox, which enhances the performance of the wind power system. A mathematical model of the components of the grid-connected system is presented. An efficient Tip Speed Ratio (TSR) based sliding mode control (SMC) maximum power point tracking (MPPT) algorithm based on constant plus proportional rate reaching law is utilized to extract the optimal power from the wind turbine. A perturb and observe (P&O) MPPT algorithm is used in the photovoltaic system because of its high efficiency and ease of implementation. To control the bidirectional power flow in the battery storage system, a proportional-integral (PI) current control is employed. Furthermore, this paper proposes a new super-twisting sliding mode control (NSTSMC) method to regulate the DC-Link voltage. To demonstrate its performance, a comparison is made with Super-Twisting SMC (STSMC) and conventional PI control. A five-level Active Neutral Point Clamped (5L-ANPC) DC/AC converter is proposed for efficient power flow, better output voltage harmonic spectrum, and better power quality injected into the grid. The considered system is performed under MATLAB/Simulink and OPAL-RT real time validation. The results demonstrate that this configuration establishes a highly reliable and efficient energy system. Each energy source is skillfully engineered to autonomously meet power demands. If there is a shortfall in power from any single source, the system is designed to balance this using the other sources. This interconnected and supportive arrangement significantly amplifies the system's reliability and sustainability, making it an exemplary model in the realm of hybrid renewable energy systems for HRES.

In response to the poor reliability in identifying fault direction in distribution networks with Inverter Interfaced Distributed Generators (IIDGs), considering the control strategy of low-voltage ride-through, a fault direction criterion based on post-fault positive-sequence steady-state components is proposed. Firstly, the output steady-state characteristics of IIDGs considering the low-voltage ride-through capability are analyzed during grid failure, and the applicability of existing directional elements in a distribution network with IIDGs connected dispersively is demonstrated. Subsequently, for the typical structure of an active distribution grid operating under flexible modes, the positive-sequence voltage and current are examined in various fault scenarios, and a reliable direction criterion is suggested based on the difference in post-fault positive-sequence impedance angles on different sides of the lines that are suitable whether on the grid side or the IIDG side. Lastly, the reliability of the proposed direction criterion is verified by simulation and the results indicate that the fault direction can be correctly determined, whereas phase-to-phase and three-phase short circuit faults occur in different scenarios, independent of the penetration and grid-connected positions of IIDGs, fault location, and transition resistance. It is suitable for fault direction discrimination of an IIDGs multi-point grid-connected system under a flexible operation mode. [ABSTRACT FROM AUTHOR]

Current building techniques in developing countries have become extremely critical. The net-zero energy concept has emerged as an essential strategy for minimising energy consumption and greenhouse gas emissions. However, its implementation, particularly in developing countries, remains a critical challenge. The purpose of this work is to assess the feasibility of achieving a net-zero energy building by combining energy-efficient design practices and renewable energy systems under the climatic conditions of the Republic of Congo. To achieve the stipulated objectives, DesignBuilder software was utilised for building modelling, energy load assessment and multiobjective optimisation of building energy efficiency measures, and the multicriteria energy optimisation was carried out using the HOMER tool. The optimisation results showed that the implementation of energy efficiency measures resulted in 39.15% energy savings, 43.45% thermal load savings and 55.68% discomfort hours reduction. Furthermore, the renewable energy system can provide 100% of the building's energy load and a total savings of 3341.84 kgCO2eq/year. The system's levelised cost of energy was estimated at 0.256 $/kWh, with a net present cost of $20,231, while the overall life cycle cost was calculated at 188.91 $/m2. [ABSTRACT FROM AUTHOR]

How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh. [ABSTRACT FROM AUTHOR]

This paper investigates the design of a robust non-linear backstepping controller for the DC-AC microgrid comprising a photovoltaic source and a battery energy storage system with grid integration, all feeding a non-linear load, to improve its power quality and dynamic stability. A unidirectional DC-DC boost converter and a bidirectional back boost converter are used on the DC side to connect the photovoltaic module and battery storage to the DC bus. The three phases of the voltage source inverter are connected to the electrical grid via an inductive filter on the AC side. The control objectives are fourfold: i) Produce the maximum power from the photovoltaic system regardless of variations in weather conditions (temperature and irradiation); ii) Keep the DC link voltage constant and close to a given reference value under various conditions to ensure a balance of power between the DC and AC sides; iii) Active power control by injecting excess power into the grid; iv) Propose an energy management strategy to optimize the energy consumption of each source: the solar source, the grid, and the battery storage. Simulation results under different operating conditions, parameter variations, and load disturbances are presented and discussed to verify the performance satisfaction of the proposed controllers. An improvement in the overall dynamic performance of the microgrid is demonstrated, with a reduction in voltage overshoot (4.8%) and settling time (5.6ms) on the DC bus, along with reduced total harmonic distortion (THD) in grid current (0.29%) when compared with the other controllers in this work: the proportional integral controller (voltage overshoot (5.8%), settling time (15.5ms); grid current THD (6.53%)) and sliding mode controller(voltage overshoot (4.6%), settling time (15ms), grid current THD (3.53%). finally, several tests have been conducted on the proposed microgrid to evaluate the controller's efficiency in maintaining power balance during irradiation distortion and non-linear load imbalance. [ABSTRACT FROM AUTHOR]

Incorporating renewable energy into forthcoming grid-connected or decentralized energy systems assumes an escalating significance and can potentially enhance endeavors toward accomplishing Sustainable Development Goal 7 (SDG7). Nevertheless, deploying sustainable renewable energy-intensive systems may present challenges related to their intermittency and cost instability. This paper proposes the development of a decision support model aimed at planning an optimal on-grid PV battery system. The model builds upon the Open Energy Modeling Framework (OEMOF) and defines asset capacities, energy dispatch, operational strategies, and optimal costs for the designated system. Key factors considered include energy demand profile, photovoltaic potential, electricity tariffs, and the availability of the National Grid. Furthermore, the model evaluation incorporates the computation of pertinent performance, feasibility, and viability indicators, notably the Levelized Cost of Energy (LCOE). To validate the framework, the article conducts a case study to determine the optimal sizing and planning of a grid-connected PV battery energy system. The objective is to cater to the electricity needs of an OCP (Office Chérifien des Phosphates) mining site in Morocco. The study considers the characteristics of the national power grid, incorporating time-varying electricity tariffs based on a Demand-Response program taking into account various rates according to the time of use (ToU). The case study includes a sensitivity analysis that examines different factors, namely the proportion of renewable energy, the investment costs of photovoltaic systems and batteries, and the financial parameter known as the weighted average cost of capital (WACC). Through this analysis, the study assesses the impact of these variables on the calculated cost of energy. Experimental results revealed a range of LCOE values from $0.07111/kWh to $0.11847/kWh for high renewable energies integration.

Technology for “wireless power transfer” (WPT) has advanced greatly in recent years, especially when used for renewable power sources. Environmental issues and the growing need for renewable energy sources have prompted a great deal of study in this area. This report provided an overview of the evolution of WPT technologies for renewable energy systems, including their advantages, disadvantages, and future possibilities. An introduction to WTP’s foundational ideas is provided, including techniques such as resonant pairing, capacitive connection, and microwave-based approaches. It delves into how these systems have been modified and improved to facilitate the long-distance transmission of renewable energy sources like wind and solar power. The study emphasizes the benefits of WPT in renewable energy systems, such as improved convenience, lower maintenance costs, and the opportunity to lessen negative effects on the environment. Problems with WPT technology in renewable energy applications are also analyzed critically. Power loss in transmission, misalignment, and electromagnetic interference are all examples of technical difficulties, and these are on top of the financial and legal obstacles. WPT’s effects on humans and animals are also explored since they must be taken into account before broad use of the technology.

Green hydrogen has become a central topic in discussions about the global energy transition, seen as a promising solution for decarbonizing economies and meeting climate goals. As part of the process of decarbonization, green hydrogen can replace fossil fuels currently in use, helping to reduce emissions in sectors vital to the global economy, such as industry and transport, as well as in the power and heat sectors. Whilst there is significant potential for green hydrogen, there are also challenges. The upfront costs for infrastructure and technology are high, and the availability and accessibility of the renewables needed for production varies by region. Green hydrogen production and storage technologies are continuously evolving and being promoted as the demand for hydrogen in many applications grows. Considering this, this paper presents the main methods for its production and storage, as well as its economic impact. Hence, the trend of governments and international organizations is to invest in research and development to make this technology more accessible and efficient, given the carbon reduction targets.

Renewable energy systems are mostly used in the world due to their inexhaustible and nonpolluting production. As a result of a large utilization of these energy sources in different areas, the electricity production rate is increasing every day. Previous studies clarified uses, modeling, configuration, energy management operation, and optimization objectives based on different energy sources. For this reason, this paper focuses on an overview of multi energy systems as renewable and conventional power sources with the integration of an energy storage system coupled to the on-off electrical network. Furthermore, a survey is done regarding global energy production, configuration energy systems, energy storage systems, power management strategies, and optimization methods based on different hybrid energy systems. Multiple optimization approaches have been implemented to reach the global best solution for the hybrid power systems. To ensure the best optimization result, it is preferable to take hybrid optimization methods into consideration. These methods have been invented recently and have proved their efficacy and performance mainly in power systems. [ABSTRACT FROM AUTHOR]

Owing to the current environmental concerns, the RESs have become popular as the microgrid structures for power generation. However, due to capricious weather and loading conditions, the generated power and thereby the microgrid frequency get adversely affected. This instant study puts forth the control strategy for the mitigation of frequency excursions, arising out of step load disturbance, in AC microgrid through adaptive network fuzzy inference system (ANFIS) scheduled fractional ordered proportional-integral-derivative (PID) control optimally tuned with a multiverse optimizer. The control strategy proposition is compared against multi-verse optimized PID and fractional order PI controls. Furthermore, the study investigates as to how EV affects in stabilizing the system frequency in the backdrop of a load disturbance. For a more realistic assessment, the proposition is assessed in the face of system nonlinearities and random load perturbations also to establish its robust and stable behavior. The results prove the efficacy of the multi-verse optimized ANFIS scheduled fractional ordered PID controller. Simulations are executed using MATLAB® software. The results are also validated by experimental studies employing a hardware-in-loop configuration on the OPAL-RT real-time simulator. [ABSTRACT FROM AUTHOR]

To operate the grid-connected renewable energy system economically, this study presents a dual-stage optimization scheduling model for grid-connected systems with hybrid energy storage, including day-ahead and intra-days stages. In the day-ahead stage, an economically optimal scheduling model is developed, considering the price peak-to-valley difference. This model aims to enhance the economic efficiency of the system by utilizing hybrid energy storage. In the intra-day stage, more accurate renewable energy forecasts with a shorter time scale are considered. The objectives are to minimize the curtailment rate of renewable energy and to track the day-ahead scheduling outcomes. The NSGA-II algorithm is employed for multi-objective optimization, achieving equilibrium solutions considering multiple optimization objectives. Compared to other published works, the proposed model achieves a balance between different optimization objectives, enabling the system to operate economically and stably. It provides a comprehensive approach to optimize the scheduling of grid-connected systems with hybrid energy storage by considering both economic and operational aspects. Overall, this proposed dual-stage optimization model presents a viable approach to improve economic efficiency and mitigate renewable energy curtailment in grid-connected systems. By effectively integrating renewable energy sources and optimizing their utilization, this model contributes to enhancing the sustainability and optimal operation of the power grid. [ABSTRACT FROM AUTHOR]

Greenhouses offer controlled microclimates that enable year-round cultivation, improving food security and agricultural productivity. However, greenhouses are energy-intensive, with heating accounting for a significant portion of the associated costs. This study explores optimal microgrid configurations, economic viability, and policy recommendations for sustainable greenhouse agriculture in Nigeria. An in-depth energy assessment of a reference greenhouse in a South Korean facility is conducted. Distinct climatic differences between South Korea and Nigeria are highlighted, emphasizing the need for tailored greenhouse designs and energy solutions. Shifting focus to Nigeria, this study investigates the feasibility of hybrid renewable energy systems with a focus on wind and solar power across six geopolitical zones in Nigeria. The analysis encompasses technical, economic, and policy aspects, providing a holistic perspective on renewable energy adoption. Notably, the study uses an advanced optimization model, Teaching and Learning–Based Optimization algorithm, to assess the net present cost and baseload supply reliability, offering valuable insights for investors and policymakers. The result indicates diverse energy requirements across Nigeria, with total monthly peak energy demands ranging from 5374.80 kWh in the Southeast to 17,115.76 kWh in the Northwest, and a notable variation in the Levelized Cost of Electricity (LCOE), with the lowest at $0.07327 in Kano. Specifically, in Ogun, the net present cost for the WT-PV-ESS system stood at $520,935.45, while the PV-ESS system cost was substantially lower at $500,444.41. This confirms the effectiveness of location-specific analysis and shows the suitability of photovoltaic–battery energy storage systems for Nigeria's diverse regions, with unique considerations for specific areas. Policy recommendations, including feed-in tariffs, renewable portfolio standards, net metering, research support, and market development, provide a holistic framework for the adoption of renewable energy and sustainable agriculture. Improving infrastructure, market access, and financing for smallholder farmers is integral for improving food security and standards of living in rural Nigeria. In conclusion, Nigeria can leverage renewable resources to revolutionize its energy and agriculture sectors, setting an example for a sustainable and resilient future.

This paper presents the energy planning problem (EPP) as an optimization problem to find the optimal schedules to minimize energy consumption costs and demand and enhance users’ comfort levels. The grey wolf optimizer (GWO), One of the most powerful optimization methods, is adjusted and adapted to address EPP optimally and achieve its objectives efficiently. The GWO is adapted due to its high performance in addressing NP-complex hard problems like the EPP, where it contains efficient and dynamic parameters that enhance its exploration and exploitation capabilities, particularly for large search spaces. In addition, new energy and real-world resources based on solar renewable energy systems (RESs) are combined with the proposed GWO to enhance its performance and ensure the optimisation of EPP objectives. Furthermore, EPP is presented as a multi-objective planning problem to optimize all objectives simultaneously. To efficiently investigate the proposed method performance, the results obtained by the GWO with the RESs are compared in three stages: comparison with original methods without RESs, comparison with methods using RESs, and comparison with state-of-the-art. The obtained results proved the robust performance of the proposed method in handling EPP and optimizing its objectives.

With the large-scale integration of massive, dispersed, and diverse electric heating flexibility resources into communities, traditional physical energy storage devices are difficult to apply on a large scale due to high construction costs. Electricity building suppliers (EBPs) are an effective way to participate in energy management and low-carbon economic operation of demand-side energy systems. Firstly, this article takes a co-generation type shared energy storage system consisting of high-temperature solid heat storage, waste heat boilers, and steam turbines as a typical case. Based on explaining the basic principles of system operation, the pricing mechanism and optimal load distribution mechanism of community-shared energy storage on the distribution side are studied. Secondly, a double-layer network market model consisting of a P2P energy trading network composed of renewable energy stations and user communities containing co-generation shared energy storage is proposed, and a reinforcement learning Nash bargaining combined ESP and multi-community double-layer energy management model is established. The outer layer optimizes the optimal pricing strategy between the ESP and EBPs alliance through reinforcement learning models, and the inner layer optimizes the “electricity heat” P2P trading strategy within the EBPs alliance through Nash bargaining models. Iterative optimization of inner and outer models. Finally, a case study was conducted on typical systems to analyze the optimal energy management strategy of MEBPs in P2P mode. The results showed that the proposed energy management method can effectively schedule electric heating, achieve maximum system revenue, and reduce carbon emissions.

The increase in new energy grid connections has reduced the supply-side regulation capability of the power system. Traditional economic dispatch methods are insufficient for addressing the flexibility limitations in the system’s balancing capabilities. Consequently, this study presents a day-ahead scheduling method for renewable energy systems that balances flexibility and economy. This approach establishes a dual-layer optimized scheduling model. The upper-layer model focuses on the economic efficiency of unit start-up and shut-down, utilizing a particle swarm algorithm to identify unit combinations that comply with minimum start-up and shut-down time constraints. In contrast, the lower-layer model addresses the dual uncertainties of generation and load. It employs the Generalized Polynomial Chaos approximation to create an opportunity-constrained model aimed at minimizing unit generation and curtailment costs while maximizing flexibility supply capability. Additionally, it calculates the probability of flexibility supply-demand insufficiency due to uncertainties in demand response resource supply and system operating costs, providing feedback to the upper-layer model. Ultimately, through iterative solutions of the upper and lower models, a day-ahead scheduling plan that effectively balances flexibility and economy is derived. The proposed method is validated using a simulation of the IEEE 30-bus system case study, demonstrating its capability to balance system flexibility and economy while effectively reducing the risk of insufficient supply-demand balance.

Grid-connected photovoltaic (PV) system is often needed whenever utilities fail to provide consumers with a reliable, sufficient and quality power supply. It provides more effective utilization of power, however, there are technical requirements to ensure the safety of the PV installation and utility grid reliability. In solar systems there is often excessive use of components, resulting in high installation costs. Consequently, appropriate measures must be taken to develop a cost-effective grid-connected PV system. An optimally sized PV system incorporated into an existing unreliable grid-connected commercial load for Mount Olive food processing is presented in this paper. The study focused on providing a reliable electricity supply which is cost-effective and environment-friendly. The techno-economic analysis of grid-connected PV/Diesel/Battery Storage systems was carried out using HOMER Pro software. Results showed that Grid/PV/BSS are technically, economically and environmentally feasible with the cost of energy at 0.136$/kWh and net present cost at $254,469. Also, the excess electricity produced by this combination is 13,264kWh/year, which generates income for the company by selling excess generated energy back to the grid if net metering were to be implemented. Furthermore, the CO2 emissions for these combinations decreased to 10,081.6 kg/year as compared to the existing systems (Grid/Diesel Generator) with emissions of 124,480 kg/year. This is an additional advantage in that it improves the greenhouse effect. A sensitivity analysis was carried out on the variation of load change, grid power price and schedule outages for the optimal system.

The design of a horizontal-axis propeller hydrokinetic turbine (HPHT) depends on several geometric parameters affecting its hydrodynamic efficiency which is measured through the power coefficient (CP). In this study, a 1 kW turbine with 1.6 m of rotor diameter (D) was used as the prototype to know the relationship between the CP and the turbine design parameters, such as the skew (ϕ) and rake (γ) angles. A full-factorial design of experiments, as a response surface methodology technique, and computational fluid dynamics simulation were used to determine the significance of the factors considered and their interaction in the maximization of the response variable (CP). A 3D computational domain in ANSYS Fluent software and the k-ω SST turbulence model were utilized, for the unsteady flow simulations. Under optimal design conditions, i.e., when ϕ and γ were equal to 13.30° and -18.06°, respectively, the highest CP was 0.4571. For these optimal values, a scaled model with 0.24 m of diameter was numerical and experimentally studied and the findings were compared. A good agreement was found between the numerical results regarding the lab-scale turbine and the experimental data for the CP values obtained as a function of the tip speed ratio.

Ecological Footprint of electric vehicle (EV) charging stations primarily focuses on three parameters: direct/indirect emissions, manpower and physical land requirement. Electric vehicle charging stations rely on electricity to charge EV batteries, and electricity source can significantly influence their environmental impact. The Ecological Footprints of EV charging station is estimated as 40.69 gha. The potential EF reduction of EV charging station is 89.9 % by using proposed hybrid power system. The Ecological Footprint of EV charging is about 3.1 × 10−4 gha/kWh of batteries charging. However, proposed hybrid system may reduce environmental impact of battery charging as 3.15×10−5 gha/kWh of batteries charging. The lifecycle cost of batteries charging is estimated as 0.168 $/kWh. It may reduce as 0.107 $/kWh of batteries charging with installation of proposed hybrid system. Thus, it is crucial to promote the use of renewable energy sources to power electric vehicle charging stations and minimize their environmental impact.