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The integration of renewable energy sources into power systems has increased significantly in recent years. Among various types of renewable energy, the use of wind energy is growing rapidly due to its low operating cost, wide distribution worldwide, and no greenhouse gas emissions. However, power systems integrated with wind energy may face stability and reliability issues due to the intermittent nature of wind power. Therefore, in power systems connected to wind farms, it is usually required to use some compensators such as static synchronous series compensator (SSSC) to increase the system performance under abnormal conditions. On the other hand, for an SSSC to be effective in improving the system performance, it must be equipped with a suitable controller. In this paper, a fuzzy logic controller (FLC) is used for the SSSC because of its advantages over conventional controllers. Extensive research has been conducted in power systems with wind turbines in which SSSC or FLC has been used; however, their simultaneous application in such systems has received less attention. Therefore, this article aims to fill this gap. The proposed method is implemented on two power systems and the simulation results are analyzed. In both systems, the dynamic behavior of three different wind farms is examined. In the first and second wind farms, either a squirrel cage induction generator (SCIG) or doubly-fed induction generator (DFIG) are used, whereas in the third one which is a combined wind farm (CWF), an equal number of SCIG and DFIG are employed. In wind farms with SCIG or DFIG, an SSSC is also utilized. Furthermore, an FLC is employed for the SSSC to improve its efficacy. A proportional integral (PI) controller is also considered for the SSSC, and its results are compared with FLC results. The simulation results confirm the superiority of FLC over PI controller. [ABSTRACT FROM AUTHOR]

The rapid deployment of electric vehicles (EVs) in Jordan requires massive efforts to prepare the infrastructures that serve the transportation sector. The lack of EV charging stations is the major obstacle that faces EV drivers. Utilizing renewable energy in EV charging stations contributes to the spread of these stations. Renewable energy technologies are environmentally friendly as they mitigate greenhouse gases that cause the global warming phenomenon. In this paper, an EV charging station, based on a PV-biodiesel-battery hybrid system, is investigated. The importance of this article is to discuss the hybrid system of PV and waste vegetable oil (WVO) along with storage that applies the maximum reliability supply, having an environmentally friendly supply and achieving the lowest energy cost of charging. This station is designed based on renewable and WVO utilization. In fact, WVO, coming from restaurants, is exploited to produce electricity by a diesel generator through direct burning after converting it into biodiesel. The capacity of each station is 14 cars/day with a medium-speed charger of 7 kW. The system is simulated and optimized using iHOGA software where multiobjective optimization is applied to achieve the minimum net present cost (NPC) and CO2 emissions considering three cases, PV-diesel, PV-biodiesel, and PV-WVO, all with battery-hybrid system. These values for the EV charging station that uses the PV-biodiesel-battery hybrid system are 624408 € and 15.4 tons/year, respectively. The corresponding values are 781473 € and 15.14 tons/year and 615310 € and 18.84 tons/year for the systems that work with diesel and WVO, respectively, and energy cost is achieved in best solution to be 0.11 Euro/kWh. Simulation results show that the proposed technique led to enhanced operational efficiency in terms of both NPC and annual CO2 emissions. [ABSTRACT FROM AUTHOR]

In this paper, a new simultaneous framework of distribution network operation is proposed based on the scheduling of photovoltaic/wind/battery multi-microgrids with network reconfiguration considering self-healing. The objective function is considered to minimize the energy losses, and multi-microgrids cost and improve the reliability indices including minimization of energy not-supplied (ENS) and minimizing system average interruption duration (SAIDI) and system average interruption frequency (SAIFI) indices. The optimization variables are defined as the situation of the distribution network switches to find the network's optimal configuration with the installation location and size of renewable resources and battery energy storage during 24 hours. An improved beluga whale optimization (IBWO) based on a nonlinearly diminishing inertia weight (NDIW) approach is used to find the optimal variable set of the problem. The recommended methodology is implemented on 33-bus and real 59-bus distribution networks. The results demonstrated that by obtaining an optimal state of the network switches in the event of a fault, as well as the optimal scheduling of the two microgrids, the energy losses have decreased and the reliability indices have improved. The outcomes of the proposed methodology based on the hybrid multi-microgrid allocation and network reconfiguration are stated that the losses, ENS, SAIDI, and SAIFI are reduced by 66.39%, 54.00%, 50.24%, and 33.61%, respectively, for 33-bus network and are declined by 65.29%, 57.44%, 48.63%, and 62.33%, respectively, for the real 59-bus Ahvaz network in comparison with the base network. The obtained results illustrated that in the condition of the network line outage, by simultaneously implementing the reconfiguration with the change of network switches and the optimal allocation and scheduling of HMGs in the network, self-healing is provided to prevent a significant weakening of the network performance so that the resilience is improved in addition to minimizing the energy losses and the cost of HMG energy injection compared to the base network. The findings revealed that the objectives including losses, ENS, SAIFI, and SAIDI are increased by 78.29%, 33.00%, 83.33%, and 35.11%, respectively, due to outage of line 22 of the 33-bus network and these objectives are increased by 52.32%, 10.30%, 32.29%, and 79.66%, respectively, due to outage of line 12 of the 59-bus network compared with not considering the line outage. Moreover, the superior capability of the recommended NDIW-based IBWO has been confirmed in comparison with the well-known particle swarm optimizer (PSO) and ant lion optimizer (ALO) in solving the problem to achieve better objective value. [ABSTRACT FROM AUTHOR]

Available transfer capability (ATC) is an important measurement index to evaluate the security margin of interconnected power grids and serve as a reference for the transmission right transaction. In modern power systems, ATC is affected by the transmission network topology, renewable power output uncertainty, and load demand uncertainty. Traditional works usually model the power source-load uncertainty by using robust optimization, interval optimization, or chance-constraint optimization, which cannot fully reflect the probabilistic distribution of the daily source-load uncertainty. This paper proposes an ATC assessment methodology based on the typical stochastic scenarios of renewable output and load demand of multiarea power systems. Furthermore, the conditional generative adversarial network (CGAN) algorithm is adopted to generate and select representative scenario sets based on historical raw data, which can fully reflect the usual operating condition of a system with high renewable energy penetration. The scenario set that is fed into the ATC assessment model can fully characterize the impact of source-load uncertainty on daily ATC. Finally, the proposed method is verified by a modified three-area IEEE 9-bus system and a real-world provincial power system. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) are becoming more popular globally as a reaction to critical energy concerns. Modern energy management technologies are used to maximize their efficiency while preserving the reliability of the grid. A hybrid energy storage system (HESS) connects to the DC microgrid through the bidirectional converter, allowing energy to be transferred among the battery and supercapacitor (SC). In this paper, a fuzzy logic control (FLC) technique is developed for PV-based DC microgrid systems that use both batteries and SCs. The proposed method uses the unbalanced energy from the battery pack to enhance the overall effectiveness of the HESS. The FLC approach is performed to validate under conditions of variable irradiance using MATLAB Simulink. When sudden changes in irradiance occur, the proposed FLC brings the voltage back to the desired level in terms of transient response like 33 ms settling times and 19% overshoot values. The results exhibit that the proposed method is more efficient in terms of time response, power output, increasing battery life, and ensuring a continuous supply of the PV system. [ABSTRACT FROM AUTHOR]

This paper presents a multistrategy fusion particle swarm optimization model for dynamic economic dispatching of renewable energy in distribution networks. The objective is to minimize active network losses and system voltage deviation while considering the integration of distributed energy sources and static reactive power compensators. The algorithm incorporates specific strategies, including a particle position change strategy based on the midpipeline convergence approach, a strategy for generating exploding particles near the optimal particles, and a particle velocity update strategy relying on the global optimal particle position. The inertia weights and particle position update methods of the simplified particle swarm optimization algorithm are also utilized. Simulation experiments are conducted on an IEEE 33 bus radial distribution system, demonstrating the effective optimization of system losses while ensuring system voltage stability. This research contributes to the scientific understanding of renewable energy integration in distribution networks and its economic dispatching. [ABSTRACT FROM AUTHOR]

Stochastic programming has become increasingly vital in energy applications, especially in the context of the growing need for renewable energy solutions. This paper presents a significant advancement in this field by introducing an efficient and robust algorithm for optimally sizing hybrid renewable energy systems. Utilizing a two-stage stochastic programming approach, the proposed algorithm addresses the challenges posed by the unpredictability of renewable energy sources. The proposed solution leverages the three-block alternating direction method of multipliers (ADMM), a cutting-edge technique that facilitates parallel computation and enhances computational efficiency. The distinctiveness of this method lies in its ability to solve complex stochastic optimization problems without compromising the mathematical integrity of the model. This is achieved by applying first-order optimality conditions, ensuring both robustness and efficacy. To demonstrate the practical applicability and superiority of the algorithm, a case study was conducted in a rural area of South Africa. The proposed algorithm was applied to design an optimal hybrid renewable energy system, and its performance was compared against traditional methods such as progressive hedging and Monte Carlo techniques. Results affirm the superiority of the approach, saving approximately 8.16% capital cost when compared to progressive hedging. In addition, the proposed algorithm outperforms the Monte Carlo method both in terms of CPU time and the number of cost function evaluations. [ABSTRACT FROM AUTHOR]

In recent years, due to the increase in electricity consumption and environmental problems, power system expansion planning requires new technologies. In this regard, the incorporation of renewable energy sources (RESs) and utilization of demand response (DR) programs need disruptive variations in the present power system configurations. This paper proposes a mixed-integer linear robust multiobjective model for generation and transmission expansion planning (GEP-TEP) taking into account wind farms (WFs) and a DR program based on time-of-use pricing. The suggested model is presented via mixed-integer nonlinear programming (MINLP) at the first stage and then transformed into mixed-integer linear programming (MILP) using the Big M linearization technique. Moreover, long- and short-term uncertainties of load demand and WFs are incorporated into the recommended model to achieve more accurate results. The interval-based method is applied for taking into account long-term uncertainties while the scenario-based stochastic model is applied for modeling short-term uncertainties in the recommended GEP–TEP model. Lastly, the suggested model is investigated on various standard test systems to evaluate the effectiveness of the GEP-TEP model. [ABSTRACT FROM AUTHOR]

The increased integration of renewable energies (REs) raised the uncertainties of power systems and has changed the approach to dealing with power system challenges. Hence, the uncertain nature of all the power system variables needs to be considered while dealing with the optimal planning and operation of modern power systems. This paper presents a probabilistic optimal active and reactive power dispatch (POARPD) based on the point estimate method (PEM), considering the uncertainties associated with load variation and wind power generation. In the POARPD, the deterministic optimal active and reactive power dispatch (OARPD) is performed in two stages, which gives a deterministic two-stage OARPD (TSOARPD). The objectives of TSOARPD are the operating cost (OC) minimization in stage 1 and voltage stability (VS) maximization in stage 2, whereas the VS is improved by maximizing the system's reactive power reserve (RPR). In this paper, instead of using multiobjective optimal power flow, this TSOARPD is used to give more importance to VS when the system is substantially loaded. The POARPD problem is solved using PEM for modified IEEE-9 bus and standard IEEE-30 bus test systems by considering the correlation between the loads. The results are compared with Monte Carlo simulation (MCS). While solving POARPD, the voltage-dependent load model is used to account for the real-time voltage dependency of power system loads. This paper discusses the detailed procedure of solving POARPD by considering correlation and the increased nonlinearities by giving more importance to VS when the system is heavily loaded. [ABSTRACT FROM AUTHOR]

China's renewable energy products have an important place in international trade, and the conclusion of the RCEP agreement can create favourable external conditions for China's renewable energy product exports. This paper measures the export potential of China's renewable energy products to other RCEP countries through a trade gravity model. The results show that (1) the GDP of the importing and exporting countries, the energy consumption of the importing countries, and the particulate emissions damage significantly enhance China's renewable energy product exports; (2) among the RCEP countries, China has a greater potential to export renewable energy products to five countries in 2020, which are mainly located in East and Southeast Asia; and (3) from a dynamic perspective, China's renewable energy trade potential is increasing, while in the Oceania region it is weakening, while trade links to Southeast Asian countries are developing in a more mature direction. Based on the above research, this paper puts forward corresponding policy recommendations. [ABSTRACT FROM AUTHOR]

The surge in interest surrounding renewable energy stems from concerns regarding pollution and the finite supply of nonrenewable resources. Solar PV and wind hybrid renewable energy systems (HRES) are increasingly recognized as practical and cost-effective solutions, particularly in remote areas. However, the intermittent nature of solar and wind power presents a challenge. To address this, incorporating a hydrogen source into the system has been proposed. This study focuses on modelling and sizing a hybrid energy system tailored for remote areas, accommodating both home and electric vehicle loads. The simulation is conducted for Siliguri, West Bengal, India, with the goal of optimizing productivity, minimizing expenses, and considering economic factors using HOMER Pro software. The integration of green hydrogen-based power generation with photovoltaic and wind HRES emerges as an effective solution. Solar power, in particular, showcases promising opportunities for the electrolysis process and HRES systems. The presented work facilitates the modelling of a green hydrogen-based green energy system, taking into account capacity, cost, and emission constraints. Various case studies are conducted to enhance system efficiency and reduce the costs of energy (COE). In this paper, three cases of grid-connected and three cases of off-grid or grid-disconnected systems are considered for highlighting the benefits of hydrogen energy incorporation in both types of systems. This research contributes to sustainable energy solutions, advancing a greener and more efficient energy landscape, especially in addressing the recent development in load combinations of home and electric vehicle loads in both grid-connected as well as grid-disconnected system. [ABSTRACT FROM AUTHOR]

As the integration of distributed energy into the power grid continues to rise, the significance of electricity transactions in promoting renewable energy consumption grows substantially. This necessitates the establishment of a robust electricity trading framework to facilitate reliable and trustworthy transactions for prosumers. This paper introduces an innovative blockchain-based electricity trading framework. Within this framework, we present a decentralized collaborative model training approach aimed at predicting the power generation of distributed photovoltaic power stations. Simultaneously, we propose a graph-based algorithm for efficiently matching producers and consumers. Our experiments, conducted on the Hyperledger Fabric blockchain platform using real-world datasets, demonstrate enhanced prediction accuracy compared to existing models. The framework effectively handles transactions within one second, sustaining a sending rate of up to 200 transactions per second. The outcomes of this study not only surpass the performance of existing prediction models but also provide valuable insights for the development of trading frameworks in actual production environments. The proposed framework stands as a reference point for constructing reliable trading infrastructures, thereby contributing to the acceleration of green electricity trading. [ABSTRACT FROM AUTHOR]

This paper presents a performance comparison between switching and reduced-order models of grid-following inverters. Different models have been developed to study the inverter's operation using MATLAB and PSIM simulation tools. These models offer a tradeoff between accuracy and computational burden. The switching model fully represents the discontinuities in the inverter due to switching, but such a model demands high computational resources. The inverter representation is simplified using reduced-order models, which is common in the literature. However, the fidelity of these simplified models might not be adequate for some applications. Therefore, it is essential to investigate the performance of such models closely to ensure their suitability depending on the purpose of the analysis. Hence, a comparative study between switching and reduced-order models is presented here by simulating different scenarios and operation conditions. The results have validated the efficacy of the reduced-order model in representing the grid-following inverter's operation in both steady state and response to a step change resulting from active or reactive power variation. Unlike the switching model, the findings illustrate that the reduced-order modeling fails to represent harmonics and losses. After simulating different scenarios, this work highlights the main differences between the switching and reduced-order models to facilitate the choice of the proper modeling method for researchers. [ABSTRACT FROM AUTHOR]

In recent years, a sharp increase in integration of renewable energy sources (RESs) in power system network has been observed. High penetration of RES interfaced with power electronics converters-inverters with reduced or no inherent inertia compromises modern power system's overall stability. Due to low inertia, voltage and frequency deviations far off the allowable threshold occur. To overcome this challenge, an adaptive inertia control strategy based on optimization technique is proposed. The improved particle swarm optimization (PSO) and genetic algorithm (GA) optimization techniques-based PID controller has been used to generate the appropriate virtual inertia coefficient for effective emulation of inertia in the presence of energy storage system. The conventional PSO suffers local optima stagnation, resulting in premature convergence during searching process in order to achieve global and local position. To address this issue, the velocity update equation was modified on inertia weight (w) using an additional exponential term with linear decreasing inertia weight PSO (LDIW-PSO). In this paper, exponential power is taken strategically instead of squaring it in order to reduce the number of iterations for faster convergence. Finally, a microgrid based on wind and solar energy is simulated using MATLAB/Simulink where three cases, 2% disturbance, 3% disturbance, and 4% disturbance, have been considered. Here, the evaluation of proposed system is carried out based on four main performance indices (ITAE, IAE, ISE, and ITSE). Furthermore, validation was done through hardware prototype to get experimental results in real time. The results from MATLAB simulation and experimental setup are in sync. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources into modern electric grids has led to a rise in uncertain factors that must be managed to maintain voltage security during reactive power optimization (RPO). Traditional deterministic RPO methods fail to account for these uncertainties, which can result in power grid security issues such as voltage violations. To address these challenges, this paper proposes a data-driven interval-based reactive power optimization method (IRPOM). The IRPOM represents the uncertainties associated with renewable power generation and load demands as intervals within the RPO problem formulation. The proposed method uses an improved particle swarm optimization algorithm to solve the RPO problem. In each iteration, the uncertain power flow is solved using the optimizing-scenarios method- (OSM-) based interval power flow (IPF) algorithm. This approach calculates the real power losses and checks whether state quantities, including voltage, power flow, and generator output, exceed their limits. Furthermore, a data-driven modeling approach is introduced to reduce the conservativeness of the IRPOM solutions. The effectiveness of the proposed method is demonstrated through detailed computational analysis on a modified IEEE 30-bus system. The results show that the proposed approach ensures economic efficiency while maintaining a low bus voltage threshold crossing probability close to zero. [ABSTRACT FROM AUTHOR]

The global demand of electrical power has increased enormously due to various reasons such as fast-changing and challenging technologies, climatic change, economic growth, and lifestyle of mankind. Due to global warming issues and the depletion of conventional sources of power generation, the utilization of renewable energy sources has drastically increased. So, many challenges exist in integrating the microgrid with AC grid and load. The alternative arm converter (AAC) is among the most innovative converter topologies used in high voltage direct current (HVDC) applications. This research work presents a new control strategy to generate pulses to trigger the switches in the AAC in a proper sequence to obtain a smooth output waveform. The AAC output is controlled by implementing various controllers such as the proportional-integral-derivative (PID) controller, fractional order PID (FOPID) controller, and FOPID controller tuned by metaheuristic algorithm-bacterial foraging optimization technique (BFOT). Also, a comparative analysis is performed based on the spectral analysis of the output voltage obtained. In comparison to other controllers, the FOPID controller optimized by the bacterial foraging optimization technique (BFOT) produced the least total harmonic distortion (THD) of the AAC output voltage. In addition, this paper also discusses about the performance and analysis on the design of an energy management system (EMS) to optimally utilize the energy sources such as PV system, wind system, and battery based on their availability which feed the AAC. The energy management system controls the entire integrated system in association with an integrated CUK-SEPIC converter to fulfil the load demand at the point of common coupling, either from the microgrid or the AC grid. A nine-level AAC model is designed integrating the microgrid, grid, and industrial loads with an energy management system using MATLAB/Simulink. The performance parameters of the entire model are analysed at every stage to provide stabilized output to meet the load demand. [ABSTRACT FROM AUTHOR]

Recent research has been focussed on renewable energy due to the rising need for electrical energy. Renewable energy has a low environmental impact compared to other energy sources. As a result, renewable energy sources (RESs) are the best option for generating electricity. Solar photovoltaic is one of the largest renewable power generators. Solar photovoltaic (PV) is connected to the load via power electronic converters. Most PV installations need a two-stage conversion process consisting of a boost converter to increase the load voltage and an AC-to-DC voltage source inverter to power the load. The Z-source inverter (ZSI) can confront the shortcomings of VSI and two-stage conversions. ZSI connects the PV system to the load and is used to increase the system's performance. This paper discusses the performance of various topologies of ZSI, such as traditional Z-source inverters (XZSIs); for integrating a PV source into a load, switched inductor Z-source inverters (SIZSIs) and transient Z-source inverters (TZSIs) are used. Also, artificial neural networks (ANNs), fuzzy logic controller (FLC), and adaptive neuro-fuzzy inference system (ANFIS)-based MPPT techniques are discussed for obtaining maximum power from PV panels. Based on the maximum power, the shoot-through duty ratio has been adjusted. [ABSTRACT FROM AUTHOR]

Considering the increasing penetration of renewable energy sources (RESs) into power grids, adopting efficient energy management strategies is vital to mitigate the uncertainty issues resulting from the intermittent nature of their output power. This paper aims to resolve the energy management problem by presenting an adaptive robust optimization (ARO) model in which uncertainties associated with solar and wind powers, consumer demand, and electricity prices are considered. The proposed model comprises three stages, one master and two subproblems, using both linearized and convexified power flow equations. Meanwhile, a new optimization-based bound tightening (OBBT) method is also presented to strengthen the relaxation. The proposed solution method solves the microgrid (MG) operation management problem with high accuracy due to considering the convexification method in a reasonable computational time and reducing operational costs compared to conventional models. The numerical results indicate the benefits of the proposed ARO and solution approach over traditional methods. [ABSTRACT FROM AUTHOR]

Summary: Since the depletion of fossil fuels, the world has started to rely heavily on renewable sources of energy. With every passing year, our dependency on renewable sources of energy is increasing exponentially. As a result, complex and hybrid generation systems are being developed to meet the energy demands and ensure energy security in a country. The continual improvement in the technology and an effort toward the provision of uninterrupted power to the end‐users is strongly dependent on an effective and fault‐resilient Operation & Maintenance (O&M) system. Ingenious algorithms and techniques are hence been introduced aiming to minimize equipment and plant downtime. Efforts are being made to develop robust prognostic maintenance systems that can identify the faults before they occur. To this aim, complex Data Analytics and Artificial Intelligence (AI) algorithms are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. We pay a particular focus to the approaches, challenges, including data‐related issues, such as the availability of quality data and data auditing, feature engineering, interpretability, and security issues. Being a key aspect of ML‐based solutions, we also discuss some of the commonly used publicly available datasets in the domain. The paper also identifies the key future research directions to further enhance the prognostics maintenance procedures. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a new robust distributed approach for the energy management of rural multi‐microgrids communities in Chile. These rural communities are often geographically isolated and have a poor connection to the main electricity grid. Operating them as individual microgrids allows the community to connect to the grid or behave as a stand‐alone system (islanded). Connecting and coordinating the operation of the microgrids can improve the system's robustness to energy and demand uncertainty, as well as reducing the dependency on the main grid. However, this coordination can be challenging because the communication between the different microgrids can sometimes deteriorate, moreover, both load and local generation profiles are challenging to predict. For these reasons, in this paper, a new robust distributed hierarchical Energy Management System (EMS) for coordination of multiple microgrids (multi‐microgrids) is introduced, which uses robust distributed Model Predictive Control (MPC). Distributed optimisation permits improving reliability in the presence of poor or lack of communications between microgrids. To consider the uncertainty associated with renewable energy generation and load consumption, fuzzy prediction interval models are included in the MPC design. The proposed method is compared with robust centralised MPC and deterministic distributed MPC to assess its performance. Simulation results are presented using data representative of a rural community in coastal Chile applied to the robust MPC design. The outcomes of this work demonstrate that the proposed robust distributed EMS can improve the power supply, apply peak shaving, and can successfully operate when there is a loss of communication between microgrids. The use of decreasing fuzzy intervals allows an accurate prediction of both load and renewable energy generation, making the system robust to uncertainty in the available resources. Moreover, results show that in absence of communication, the power supply obtained under different scenarios with the distributed EMS is better than the equivalent centralised EMS. [ABSTRACT FROM AUTHOR]

In order to actively respond to the "14th Five-Year Plan," the PGA algorithm is used to develop a new energy planning strategy in this paper. The project can make full use of my country's abundant renewable energy resources, encourage energy conservation and reduction of emissions, improve the energy structure's low-carbon level, support the development of smart green energy, and achieve ecological civilization construction. This solution can show users how much greenhouse gas emissions can be reduced through some environmental changes, as well as the basic issues of meeting the future energy needs. It can display the benefits, costs, and emissions data under different scenarios in the future and use the scenario demonstration method to show energy planning to make energy data more vivid. It allows people, technicians, and decision makers to understand what will happen to China's carbon emissions over time in the next 15 years. This paper innovatively combines a particle swarm optimization algorithm with a genetic algorithm and designs a PGA algorithm for path optimization. In terms of carbon emission reduction, comparative trials demonstrate that the PGA algorithm's path optimization is 58.06 percent greater than the genetic algorithm; In terms of cost, the PGA algorithm's path optimization is 15.72% less expensive than the genetic algorithm's. This article provides a reference path for selecting the best results for future energy planning schemes and provides a new strategy for the "14th Five-Year" energy plan. [ABSTRACT FROM AUTHOR]

DC microgrids have been quite popular in recent times. The operational challenges like control and energy management of the renewable-driven standalone DC microgrids have been an interest of research. This paper presents a bidirectional quasi Z-source DC-DC converter (BQZSDC). This converter topology has been developed based on a conventional buck-boost type bidirectional converter, and it interfaces the storage system and the common DC bus. The challenge, however, lies in effectively managing the uncertain renewable energy sources and the storage system and catering for the loads simultaneously. An effective control strategy is needed for that energy management and to achieve various microgrid objectives. This paper deals with one such effective control strategy implemented for BQZSDC. That is, the fixed-frequency double-integral sliding mode control (FF-DISMC) controls the converter to regulate the DC bus voltage and battery current. A detailed analysis of the controller is conducted, and its performance is evaluated for both charging (buck) and discharging (boost) modes. Simulations have been performed in MATLAB, showing that the controller performs satisfactorily in achieving the objectives of voltage regulation and battery current regulation. Finally, the performance of the proposed controller is validated with the hardware setup. [ABSTRACT FROM AUTHOR]

South Africa first implemented rolling blackouts known as load shedding to protect the integrity and maintain stability of their ailing electrical grid over a decade ago. Since then, load shedding has become a daily occurrence, which South Africa plans to alleviate through the addition of grid-connected renewable generation. Significant renewable energy (RE) additions can however negatively affect a grid, prompting South Africa to develop RE-specific grid code requirements as mitigation. Given this strategy to address the country's supply challenges using RE, and the still unknown effects of the country's aggressive RE integration planned, South Africa must perform the necessary simulations tailored to the country's unique grid conditions, and local RE-specific grid code requirements to ensure a RE-driven solution are indeed viable. Prompted by the lack of a suitably tailorable simulation platform, this paper proceeds to discuss the development of a tailorable grid code-guided renewable power plant (RPP) behavioral studies testbed. The testing methodology involves feeding grid disturbance event data to an RPP modeled after Eskom's Sere wind farm in South Africa, which is operated in line with local RPP-specific grid code voltage and frequency requirements to assess the testbed's grid code-guided RPP operating approach. Results show the testbed to effectively distinguish between grid code-specified no-fault, fault ride-through, and trip conditions, operating the simulated RPP accordingly. Consequently, as compared to comparable RE integration simulation platforms, the reviewed testbed has the potential of producing individualized results, owing to its tailorable grid, RPP, and incorporated grid code guiding specifications. [ABSTRACT FROM AUTHOR]

Irrigation systems to supply water to agricultural land are essential in remote and isolated areas. However, these areas often face challenges and obstacles in obtaining energy for use in irrigation since many depend on diesel generators (DGs) to produce electricity. A farm located in a remote area in Al-Jafr, Jordan, uses a 100 kW DG to supply its need for electric energy for irrigation purposes. Its energy consumption is 500 kWh/day at $0.29/kWh. This paper designs a new hybrid renewable energy system (HRES) for this farm by conducting simulations using the HOMER (Hybrid Optimization of Multiple Energy Resources) software. This new system consists of solar photovoltaics (PVs), batteries, an inverter, and a 100 kW DG. The results showed a clear difference between the baseline DG-only system and the hybrid system regarding energy cost and carbon emissions. The energy price for the HRES is $0.107/kWh, and carbon dioxide emissions are reduced to 27,378 kg/yr from 184,917 kg/yr for the DG-only system. In addition, simulations and comparisons for an alternative HRES with a 60 kW DG were conducted. Based on the simulation results, the energy price was $0.091 instead of $0.19, and carbon dioxide (CO2) emissions were 15,847 kg/yr instead of 115,090 kg/yr. It was concluded that using hybrid renewable energy systems to power the irrigation of remote areas successfully reduced the energy cost, fuel consumption, emissions, and overall cost. The HOMER program makes an accurate comparison over extended periods between the four strategies (load following, cycle charging, combined dispatch, and predictive dispatch) and selects the optimal system based on the cost, emissions, fuel consumption, and percentage of renewable energy from the system. [ABSTRACT FROM AUTHOR]

Energy is one of the basic inputs and driving forces for economic and social development modernization. Sustainable energy supply is one of the major challenges in the modern world. Sustainable energy supply can be obtained through alternative energy sources, and efficient energy use is a high priority to optimize the environmental impact. This paper investigates the potential for energy savings involving five end-load equipment in academic institutions. The saving potential is obtained through energy efficiency analysis and feasibility analysis of the solar power generation systems. An energy audit is conducted on end-use consuming equipment (i.e., fans, lights, air conditioning, ICT equipment, etc.) in these five end-load sectors to find out the major energy-consuming equipment, energy consumption, and potential energy savings. The analysis of energy consumption per equipment helps determine the appropriate type of equipment to be upgraded and replaced for energy saving. The feasibility of rooftop solar power generation has been also analysed to integrate into the energy end load. The research findings confirm that laboratory equipment, lighting, and air conditioning are vital to energy consumption in academic buildings. The energy savings, bill savings, and carbon emissions reductions have been estimated based on integrating rooftop solar power generation. The feasibility analysis of onsite energy generation using a hybrid solar system found that the total energy-saving potential and bill savings within five years are 311,131 kWh and RM 113,563, respectively. This value refers to comprehensive energy-saving and bill for all academic buildings involved in this research. [ABSTRACT FROM AUTHOR]

This paper presents an energy management peer-to-peer (P2P) and peer-to-grid (P2G) trading strategy for power sharing between prosumers with grid-connected photovoltaic/wind turbine/battery storage systems. The optimal results are obtained using the "Fmincon" solver due to its ability to solve large-scale optimization problems. Two prosumers sectors are considered in this study that is the residential and commercial. The results show that for the residential consumer side, the load is satisfied by the energy from the grid, its own renewable energy system (RES), and the energy from the commercial consumer side RES during the first off-peak period (00:00 AM to 05:00 AM). During the peak pricing period (17:00 PM to 22:00 PM), the load is satisfied by the residential consumer's battery and the commercial consumer's battery, and no energy is bought from the grid due to the high price. Furthermore, 37.69% and 39.40% cost savings for residential and commercial, respectively, and a reduction of the bill by $42.26/day and $42.97154/day, respectively, are recorded. Significant grid energy savings for the developed P2P/P2G trading for prosumers and maximum daily cost savings are achieved compared to existing cases such as P2G only and P2P only. Finally, the residential consumer can achieve a greenhouse gases (GHG) emissions reduction of 141.88 kgCO2-eq and 176.99 kgCO2-eq net savings, while the commercial consumer can achieve 122.34 kgCO2-eq GHG emissions mitigation and 196.53 kgCO2-eq net savings. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RESs) such as solar photovoltaic (PV) systems are increasingly used as distributed generation for replacing the conventional energy. At the same time, energy storage systems like battery (BAT) must be applied for maintaining the balance between fluctuating energy production and load consumption. BAT's state of charge (SOC) should be maintained within their design limits unaffected by RES intermittency and/or load power variations. This necessitates advanced power control and management methodologies for overcoming challenging conditions. This paper discusses and evaluates an optimal DC bus voltage regulation approach: an intelligent controller using an adaptive fuzzy logic controller (FLC) and a novel supervisory power management strategy for PV systems with BAT. The objectives are to keep a stable power flow in the system and guarantee the continuity of service by ensuring that the system components do not exceed their limits. In this manner, the DC bus voltage regulation of the PV/BAT system can be improved in comparison with conventional regulation. Therefore, the most important contributions of this work are as follows. (1) Development of comprehensive and modular novel energy management system (EMS): its originality is related to the inclusion of the control system limits with faster SOC balancing and smaller DC bus voltage fluctuation. (2) Providing a simple power flow management implementation that considers the optimal energy flow between PV system, BAT system, and load: a balance between minimal energy flow in the connecting line and the least requirements of BAT capacity is kept, reducing component constraints with a very straightforward structure. (3) Furthermore, FLC offers high robustness and smooth performances. FLC is added to the control strategy design requirements to reduce DC bus voltage deviation. (4) Real-time simulation/experimentation-based complete cases utilizing Matlab/Simulink and DSpace are illustrated to testify the effectiveness of the proposed FLC and EMS. [ABSTRACT FROM AUTHOR]

Power quality is the main problem with the power system network. Poor electricity quality may cause disruptions and financial challenges for consumers. Additionally, it could cause electronic gadgets to overheat, be damaged, or operate inadvertently. Transformers and other components of the power distribution system may also overheat and experience core saturation. This study investigates potential problems with the quality of the electricity in a photovoltaic linked power system. This paper suggests a new optimization method for day-ahead trading and control in DC microgrid power management (MG). The goal of the multiobjective optimization dispatch (MOOD) problem is to lower overall operational costs as well as the costs associated with power loss in efficient conservation systems and exhaust emission quantities such as nitrogen oxides, sulphur dioxide, and carbon dioxide. Using the weighted sum approach, the multiobjective optimization problem is reduced to a single optimization problem. The analytical hierarchy process (AHP) method is then used to calculate the weight coefficients while accounting for each objective function's preferences. Power balancing, high levels of renewable energy penetration, the most effective scheduling of battery charging and discharging, control of load curtailment, and the technical limitations of the system are all taken into account when evaluating the system's performance in both grid-connected and standalone operation modes. The ant lion optimizer (ALO) technique is taken into account to tackle MOOD, comparing the effectiveness of the proposed method to other well-known heuristic optimization techniques. The simulation's results demonstrate how effectively the proposed strategy can address the coordinated control and optimization dispatch difficulties. They also found that operating the MG system economically in grid-connected mode can save overall costs by roughly 4.70% compared to doing it in independent mode. [ABSTRACT FROM AUTHOR]

In this paper, a modified resonant voltage multiplier rectifier (RVMR) has been developed to improve the voltage gain and efficiency of the proposed converter on PV interconnected renewable energy systems. To acquire the most energy from the PV source, the improved RVMR scheme uses an altered Perturb and Observe (P&O)-based MPPT approach. This paper develops a modified variable step size P&O MPPT algorithm that can increase the PV system's dynamic and steady-state performance at the same time. The recommended variable step size technique addresses the constraints of traditional P&O MPPT. The suggested study uses the MATLAB/Simulink environment to analyze and evaluate voltage gain and efficiency. [ABSTRACT FROM AUTHOR]

The paper is proposed an improved equilibrium optimizer (IEO) algorithm to solve the optimal power flow (OPF) problem with the participation of a renewable energy source (RES). In the proposed IEO method, the "exponential term" is replaced by a function that does not dependent on the number of iterations. This modification of the IEO algorithm increases exploration ability compared to EO algorithm. In addition, the exploration of the proposed IEO algorithm will not decrease according to the number of iterations which avoids to get stuck at the local optimal solution. The IEO algorithm is tested on two IEEE 30-bus and IEEE 118-bus systems with three different objective functions. The performance of the proposed IEO method is compared with equilibrium optimizer (EO), artificial ecosystem optimization (AEO), cuckoo search algorithm (CSA), teaching-learning-based optimization (TLBO), artificial bee colony (ABC), and many other existing methods. Besides, a simple probabilistic formula for calculating RES output power based on the Monte-Carlo simulation model is proposed in this paper to reduce the computation time for the OPF problem with RES. The simulation results obtained show that the proposed IEO algorithm has better quality of the solution as well as stability level compared to the original EO algorithm and other algorithm. Thus, the proposed IEO algorithm is also one of effective and reliable algorithms for handling OPF problem with RES. [ABSTRACT FROM AUTHOR]

Summary: During the last years, the electricity networks worldwide have rapidly developed, especially with integrating many types of renewable energy sources (RESs). The optimal operation is an opportunity to increase the penetration level of stochastic RESs into the power grid to maximize energy efficiency. Generally, the optimal power flow (OPF) problem is a highly complex, non‐convex, and non‐linear optimization problem. The complexity of the OPF problem is further increased as stochastic RESs are incorporated into the network. This paper presents an effective solution to the OPF problem for a traditional power generation with stochastic RESs. For solving this problem, chaotic Bonobo optimizer (CBO) is proposed in this paper based on the Chaos Theory to avoid the stuck in the local minimum by applying the original Bonobo optimization (BO). The performance of BO is enhanced using the chaotic maps sequences technique to enhance its global search capability and prevent getting stuck into local solutions. Uncertainty of the output power generated by RESs is forecasted based on probabilistic models. To minimize the total operating cost, the direct, underestimation, and overestimation costs of RESs are considered. Three different objective functions are considered, minimizing total operating cost, emissions, and power losses. Moreover, a carbon tax is incorporated in the objective function problem to minimize carbon emissions. The proposed OPF model and CBO technique are verified on the modified IEEE‐30 and IEEE‐57 bus test systems to confirm the superiority and effectiveness of the proposed CBO to achieve the optimal solution. The simulation results prove the efficiency and robustness of CBO for finding the best solution to the OPF problem with stochastic RESs. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is a crucial issue to maintain the reliable operation of power systems. However, achieving this objective is not easy, especially when renewable energy sources (RESs) are penetrated into the power system due to their uncertainty nature. This paper provides an optimal solution for the power flow problem including two different types of RESs based on a marine predator algorithm (MPA). The OPF model used in this paper has three different types of energy resources (thermal, wind, and solar). The output power from wind or solar generator has two probabilities either underestimation or overestimation consequently. These two probabilities have been translated into the objective function by two extra costs, penalty cost, and reserve cost, respectively. To check the validity of the proposed algorithm, it is applied to a modified IEEE-30 and IEEE-57 bus systems. The obtained results are compared with some recent optimization methods. The results show the effectiveness of marine predator algorithm in providing the optimal solution for the power flow problem with maintaining the power system constraints inviolate. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, power consumptions are increasing due to the modern behaviour of technology. To accomplish the power demand more power generation is needed. The usage of fossil fuel causes many health problems and environmental pollutions so the researchers notice the renewable energy resources (RER) to generate electricity. RER consume large land areas to produce a low amount of power. In this paper, floating photovoltaic (FPV) is used to produce the maximum amount of power to compensate the load demand. Normally, a battery is used for a storage device in the solar PV system. In this paper, the energy storage device is used for compressed air energy storage system which stores electricity in the form of compressed high‐pressure air. Power quality issues like swell, sag, harmonics, and interruption, as well as disturbance are arisen at the time of linking non‐linear loads in the power system. A controller is placed in the system to mitigate these power quality issues. Here, unified power quality conditioner (UPQC) is used to reduce the above power quality issues. UPQC injects the real power into the transmission line, so the unbalance voltage and current are compensated. Such that the inject voltage and current are controlled with the help of the series part and shunt part, respectively. The mitigation of UPQC is enhancing with the support of a proportional‐integral (PI) controller. The PI controller produces two conditions of the signal according to the need for power; one is a proportional signal, the other one is proportional to the integral of the signal. The PI controller optimal gain value is appropriately selected based on the black widow Optimization algorithm. The proposed algorithm carries off the load variation and avoid disturbance in the floating PV system. The projected method is designed and executed in the MATLAB/Simulink software. The proposed FPV with ACESS design is verified with the load conditions of 180 e3 V, 50 Hz. The proposed method efficiency is validated and contrast with the prevailing methods such as Cuckoo Search Optimization Algorithm, Whale Optimization Algorithm, Particle Swarm Algorithm, and Gravitational Search Algorithm that validate the innovative method is more efficiently reducing the power quality issues. [ABSTRACT FROM AUTHOR]

With the sharply increased development of variable renewable energy resources (VRERs) in recent years, the hydro-wind-photovoltaic (PV) hybrid system (HWPHS) has the prospective to enhance the grid integration of VRERs. Nevertheless, the intense variation associated with wind and PV generation causes uncertainties in the long-term operation of the HWPHS. To overcome this drawback, this paper develops a novel method to derive adaptive operating rules for a cascade HWPHS. First, a scenario-generating method coupling Kernel density estimation with the copula function is proposed to characterize the wind and PV forecast errors. Second, based on the power generation scenarios, an optimal scheduling model for the cascade HWPHS considering transmission section constraints is proposed to simulate the hydro-wind-PV complementary operation; finally, the long-term operating rules for the cascade HWPHS are extracted by grey relational analysis and BP neural network. As a case study, the HWPHS of the Wu River basin in China is chosen. Results demonstrate that the proposed model can effectively utilize the flexibility of cascade hydropower stations, improve transmission section utilization efficiency, and promote clean energy absorption. [ABSTRACT FROM AUTHOR]

Due to the strong uncertainties of renewable energy and load demands, the new type of power systems is facing severe challenges in terms of generation control and load dispatch. Considering the uncertainties of the penetrated renewable energy and diversified load demands, this paper proposes a random fuzzy power flow analysis method (RFPF) to exactly depict the impact on the various flows of power systems. In the RFPF, the random fuzzy models of wind turbine generators, photovoltaic generators, and load demands are established utilizing the stochastic probability functions and fuzzy interval to reveal the uncertainties with high precision. Afterwards, the random fuzzy mean value is developed as an index of accuracy, and the fuzzy number of the output variables is extracted under the 95% confidence level. Furthermore, the proposed RFPF model is executed by applying a three-point estimate method (3PE) to figure out the corresponding power flow of the power system, costing less computation burden compared with the Monte Carlo simulation. Simulation studies conducted on the IEEE-33 system verifies the accuracy of the RFPF and the efficiency of the 3PE. [ABSTRACT FROM AUTHOR]

This paper presents grid-oriented multiobjective harmony search algorithm (GOMOHSA) to incorporate the multiple grid parameters for minimization of the active power loss, reactive power loss, and total voltage deviations (TVD) in a part of practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Limited (RVPN) in southern parts of Rajasthan state of India. This is achieved by optimal deployment of optimally sized renewable energy (RE) generators using GOMOHSA. Performance indexes such as active power loss minimization index (APMLI), the reactive power loss minimization index (RPMLI), and the total voltage deviation improvement index (TVDII) are introduced to evaluate the health of the test network with different load scenarios. Performance of proposed GOMOHSA has been tested for five different operating scenarios of loads and RE generation. It is established that the proposed GOMOHSA finds the optimal deployment of optimally sized RE generators, and the investment cost of deployment of these RE generators can be recovered within a time period that is less than 5 years. Performance of GOMOHSA is superior compared to a conventional genetic algorithm (GA) in terms of performance indexes, RE generator capacity, payback period, and parameter sensitivity. Study is performed using MATLAB software for loading scenario of base year 2021 and projected year 2031. [ABSTRACT FROM AUTHOR]

A coupled-inductor-based high step-up high step-down DC-DC converter with bidirectional capability suitable for electric vehicle (EV) applications is proposed in this paper. The voltage gain in both step-up and step-down operations is increased by a two-winding coupled inductor. The current ripple in the low-voltage port is low making it suitable for renewable energy sources. In addition, the low number of components causes the efficiency of the proposed converter to be competitive with similar topologies in the literature. Due to the use of only three bidirectional switches with low voltage stress, the cost and volume of the converter are reduced. To demonstrate the performance of the proposed bidirectional converter, an analysis of the operation modes in the steady state is presented. Also, a comparison between the suggested converter and other similar converters in the literature is provided. The results of these comparisons show that the proposed converter has a lower number of components and higher efficiency than the others. In addition, lower voltage stress and noticeable voltage gain in comparison with others are further results of these comparisons. Eventually, the experimental results in step-up and step-down operations are presented. In step-up operation, the experimental prototype is tested under fs = 50 kHz, VLow = 20 V, VHigh = 110 V, rated power = 120 W, with 94.60% efficiency. Furthermore, VHigh is considered 100 V with VLow = 33 V and 94.43% efficiency at 50 W rated power in step-down mode operation. [ABSTRACT FROM AUTHOR]

Microgrids are considered to be smart power grids that can integrate Distributed Energy Resources (DERs) in the main grid cleanly and reliably. Due to the random and unpredictable nature of Renewable Energy Sources (RESs) and electricity demand, designing a control system for microgrid energy management is a complex task. In addition, the policies of microgrid agents are changing over time to improve their expected profits. Therefore, the problem is stochastic and the policies of the agents are not stationary and deterministic. This paper proposes a fully decentralized multiagent Energy Management System (EMS) for microgrids using the reinforcement learning and stochastic game. The microgrid agents, comprising customers, and DERs are considered as intelligent and autonomous decision makers. The proposed method solves a distributed optimization problem for each self-interested decision maker. Interactions between the decision makers and the environment during the learning phase lead the system to converge to the optimal equilibrium point in which the benefits of all the agents are maximized. Simulation studies using a real dataset demonstrate the effectiveness of the proposed method for the hourly energy management of microgrids. [ABSTRACT FROM AUTHOR]

The global trends toward increasing clean and sustainable power generation have brought significant changes and developments in electrical distribution networks. The existing passive networks are being restructured to facilitate renewable distributed generations to ensure high degree of reliability of power supply to customers. A distribution system reliability assessment (DSRA) is crucial to analyze the risk and cost associated with power interruptions and take necessary preventive and corrective actions. This paper presents an approach to DSRA and carries out a comprehensive reliability assessment of an active distribution network (ADN) connected with solar, wind, and tidal energy sources. A multi‐state reliability model is developed, combining the Markov and well‐being frameworks to determine the reliability of a network energized by multiple renewable energy sources (RESs). The load point reliability and worth‐oriented indices, namely SAIDI, SAIFI, CAIDI, AENS, EENS, ECOST and IEAR, are evaluated to analyze the impacts of the three RESs on the reliability of the ADN. The paper also proposes two new metrics to estimate the benefits of adding RESs from the reliability worth and cost perspectives. The analyses have been carried out on the Roy Billinton Test System. The results show that the maximum reliability worth and cost benefits can be extracted from a combined operation of multiple RESs. A combination of solar‐wind‐tidal energy sources, therefore, can be an effective solution for countries having long coastlines to fulfill not only the sustainable energy goals but also to improve the grid's reliability and reduce the monetary losses incurred due to customer interruptions. [ABSTRACT FROM AUTHOR]

In recent years, the development of renewable energy sources (RESs) and their integration with the conventional power network have increased significantly. Due to the power grid transformation brought about by the large penetration of power electronics converter‐based RESs such as wind and solar photovoltaic (PV), the operation and control of the interconnected power system has become a challenging task with respect to sustaining its stability and reliability. Power systems are confronted by several new stability issues because the dynamic behavior of converter‐interfaced renewable sources differs from conventional generation. In this respect, static synchronous compensator (STATCOM), a shunt connected flexible AC transmission system (FACTS), is recognized as a fundamental solution for maintaining power system stability. This paper presents a thorough and state‐of‐the‐art review of STATCOM control in wind‐ and/or PV‐interfaced power systems for enhancing system performance by addressing key stability issues related to rotor angle stability, voltage stability, and resonance stability. A comprehensive analysis of various control techniques of STATCOM based on conventional, adaptive, nonlinear, predictive, robust, and coordinated control and soft computing techniques is provided. Furthermore, the function of the grid‐side converter of the wind/PV plant as a STATCOM is also evaluated. Finally, the shortcomings of existing research are highlighted, current control challenges are presented, and several topics for future research are suggested. This paper provides researchers the opportunity to consider the current state of research and develop new control schemes for STATCOM to further improve the stability of modern power grids. [ABSTRACT FROM AUTHOR]

Selecting and optimizing propulsion architecture is one of the most practical goals and the most difficult decisions in designing modern commercial ships. That process faces several challenges, namely greenhouse gas emissions from the consumption of fossil fuels, International Maritime Organization's (IMOs) strict international legal system on environmental pollution prevention, and new energy efficiency standards for design and management of commercial fleets. With the integration of electrical systems and power supply equipment in maritime transport in the past several decades, the availability of electric propulsion systems have brought several benefits to ship owners, including improved ability to comply with international laws, increased flexibility and reliability, reduced operating costs, and enhanced access to advanced automation capabilities. However, the introduced benefits of electrical and hybrid electric propulsion system architectures in recent years have put considerable pressure on ship owners and designers when optimizing the vessel's propulsion and power system and their control strategies. This paper presents a comprehensive review of conventional and advanced electrical propulsion systems on current commercial vessels and future smart ships. By highlighting the characteristics of the propulsion system configuration, electric power source, and propulsors, the authors present a detailed comparison and analysis in optimizing the choice of electric or hybrid powertrain systems for commercial fleets. Taking into account the increasingly stringent standards related to energy efficiency and emissions regulated by IMO, this paper also addresses strategies for the development of sustainable maritime transport by proposing the efficient use of renewable energy sources and electrical energy storage system. [ABSTRACT FROM AUTHOR]

Sustainable generation is impacted by the adoption of renewable energy, the growth of energy markets, and economic strategies. This paper offers a sustainable strategy and a technoeconomic analysis of off-grid hybrid energy systems (HES) in remote islands of Iran, including Lavan, Larak, and Failaka, utilizing PV module, wind turbine, and hydrokinetic turbines. Hourly wind speed, solar irradiation, and hydrovelocity have been implemented under load following (LF) and cycle charging (CC) dispatch strategies in order to ascertain the most appropriate systems. Lavan Island achieves the winning HES with a CC dispatch strategy, which consists of 3 hydroelectric turbines, 1 wind turbine, 349 kW of solar power, 150 kW of generator power, 316 kWh of batteries, and 287 kW of the converter. This ideal HES, which generates a consistent generation profile and reasonable net present cost (NPC) and cost of energy (COE) of M0.160$ and $0.013 kWh, respectively, can be practically attained in these areas. LF-controlled optimal solutions use less fuel than CC-based ones, leading to a higher share of renewable energy. Compared to Larak and Lavan, the CC- and LF-controlled options on Failaka Island generate cleaner electricity with emissions that are 57% and 44% lower. Regarding the ability to recoup the project's initial investment costs, long-term energy production would be more financially viable than short-term. Short-term projects with higher financial uncertainty due to the salvage cost should use the CC method. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy systems are becoming widely prevalent in warships due to their reliability and acceptability. However, the uncertainty caused by using renewable energy resources is one of the primary challenges. Therefore, this paper investigates the implementation of a dynamic voltage restorer (DVR) with a new control strategy in a hybrid solar power generation system, including photovoltaic (PV) panels, diesel generators, battery storage, and conventional and sensitive loads. Furthermore, a new metaheuristic-based active disturbance rejection control (ADRC) strategy for fast and accurate DVR control is proposed. In this regard, a novel chimp optimization algorithm (ChOA)-based (i.e., ChOA-ADRC) strategy is suggested to increase the stability and robustness of the aforementioned hybrid system. The ADRC controller's parameters are updated in real-time using the ChOA approach as an automatic tuning mechanism. In order to evaluate the performance of the proposed control strategy, the model is evaluated under two and three-phase fault case scenarios. Also, a comparison with the conventional PI controller has been performed to further evaluate the proposed method. Simulation findings reveal the suggested control strategy's remarkable effectiveness in correcting fault-caused voltage drop and maintaining sensitive load voltage. Additionally, the results show that ChOA-ADRC presents a better dynamic response compared to conventional control strategies and increases the reliability of the hybrid power generation system. [ABSTRACT FROM AUTHOR]

Summary: The need to increase the share of renewable energy supply in buildings has been a subject of concern for many authors. Renewable energies can offset building electrical and thermal energy loads, thus reducing constrains on national grid networks. The aim of this paper is to construct, model and optimize a hybrid renewable energy supply system under transient conditions. The supply system consists of solar‐PV panels and vertical axis wind turbines, while batteries are used for energy storage. TRNSYS software is used to model the energy demand of an office building that operates under the average standard building as specified in EnergyPlus software. In order to determine the optimal size of the energy supply system an Artificial Neural Network (ANN) combined with a Sobol algorithm are employed. Two objective functions are developed to: (a) minimize the cost of purchasing solar‐PV panels and wind turbines, and (b) minimize the electricity required from the national grid network. The simulation and optimization results show that the best condition is achieved with 461 panels and one vertical axis wind turbine. At this best condition, the amount of electricity required from the national grid network is 5.55 MWh/year, and this will cost 2.4 $/h. Moreover, 3% of electricity will be obtained from the grid, 31% from batteries and 66% are supplied directly through panels and turbines. [ABSTRACT FROM AUTHOR]

Summary: This research paper proposes a detailed design problem of electrical vehicle (EV) fast‐charging stations to maximize the net profit. The charging station is integrated with the renewable energy sources (RES) and battery energy storage system (BESS) to minimize the energy demand from the grid. The performance indices of the design problem, such as the number of chargers and their rating, installed RES power, energy and power of the storage units, and dynamic power contracted by the grid to the EV charging station, is estimated through the proposed algorithm. The detailed modelling of the charging process considers the EV behavioural characteristics like arrival time, state of charge, departure time, and battery capacity and is simulated through sequential Monte‐Carlo method. The hybrid Crow Search Algorithm (hCSA), along with the particle swarm optimization (hCSA‐PSO), is adopted for the first time to optimize the charging station's installation and operational cost. The effectiveness of the proposed method is compared with a hybrid genetic pattern search algorithm, CSA, and PSO. Several case studies are considered, and it is observed that hCSA‐PSO provides the best‐optimized results in profit maximization. The cost‐benefit analysis is also performed to estimate the financial feasibility of both RES and BESS. [ABSTRACT FROM AUTHOR]

Globally, climate change has increased various environmental concerns, and there is a very high and still ever greater penetration of renewable energy power into energy grids. The intermittent nature of these energy sources has demanded a strong power electronic interface to maintain an uninterrupted power flow to the external load. This paper deals with a novel dual input quasi Z source inverter (qZSI) that can operate with two intermittent sources and perform a single stage power conversion. The novelty of the proposed methodology is the implementation of qZSI with a reduced number of switches, increased voltage gain, and a high boosting factor which results in increased efficiency. A simple boost control-based pulse width modulation (PWM) technique has been adopted, which has been observed after analysis, to have reduced the stress on the switches and simultaneously increased system efficiency. Perturb and observe method-based maximum power point tracking (MPPT) was integrated into the study with the control technique, and its performance was observed using MATLAB/Simulink and further validated with a scale down model. [ABSTRACT FROM AUTHOR]

Electricity has become one of the most essential components of establishing a quality standard of living in any country. Consequently, considerable work has been focused on designing a sophisticated load frequency control (LFC) system. However, in light of limited resources and real-world challenges, computationally based control algorithms that are more effective and less expensive remain critically needed. Thus, this paper employs a modified honey badger algorithm (HBA) in conjunction with the concepts of Lévy flight and inertia weight to optimize the parameters of a new cascaded two-degree-of-freedom fractional-PID structure coupled with a proportional derivative (2DOF + FOPIDN)-PD controller to solve LFC problems in an interconnected power system (IPS) comprising conventional and renewable energy sources (RES). The proposed control technique is applied to a two-area IPS under diverse load conditions and in the presence of nonlinear elements and electronic devices. The proposed method is evaluated with respect to a range of performance metrics, such as settling time, undershoots, and error criteria values. The collective performance of the established control scheme indicated that the suggested control approach provides excellent reliability under various load condition scenarios, sensitivity tests, and perturbations, proving the system's efficacy and dependability. [ABSTRACT FROM AUTHOR]

Rural and remote area electrification is of grave concern around the globe. Therefore, well-planned and cost-effective microgrids integrating renewable energy sources are emerging as effective solutions. However, the microgrid's stable operation and its future deployment is affected by the perturbations caused due to uncertainity in renewable sources, dependency on the battery state of charge, and load variation. So, considering the possible concerns affecting the planning and development of a microgrid for any given region, this paper proposes a comprehensive performance assessment of the hybrid residential microgrid based on a load model of Kibber village in Himachal Pradesh, India. The proposed approach is divided into three parts for the best planning of microgrids. Firstly, the MATLAB–Simulink software technically analyzes the system performance under perturbations considering the available renewable sources. Secondly, an economic analysis using HOMER Pro software is done to examine the cost-effectiveness of the proposed microgrid model through the simulation of electrical loads for Kibber village, considering the available renewable sources. Lastly, a real-time analysis of the proposed prototype of programmable logic controller-based hardware test bench has been developed, aiming for future regional microgrid deployment. System voltage, frequency, power shared, percentage of load met, energy cost, available renewable energy resource, etc. have been considered for validating the proposed controller. The proposed comprehensive assessment of the microgrid model is reproducible with necessary modifications for any geographical location. It will be helpful for its future deployment aiming at rural and remote electrification. [ABSTRACT FROM AUTHOR]

Summary: Human activities are directly related to fossil fuel consumption and cause global warming and climate change effects by emitting greenhouse gas into the lower atmosphere. To reduce and adapt to such effects, renewable energy sources such as surface dams, including hydroelectric energy (HE) generation units, are gaining importance. In the current literature, HE potential calculations are based on formulations that are quite simple, clear and do not distinguish between different situations. In this paper, two classification methodologies are proposed for better assessment of HE production. The first one is based on the concept of the hypsographic curve to distinguish different drainage basin features into "Young," "Mature" and "Old." The second classification takes into account the newly defined energy index (EI) principle, which helps to classify the HE generation capability of drainage basins into "Very low," "Low," "Normal," "High" and "Very high." The application of the proposed methodology is presented for three drainage basins from the upstream sub‐basins of the Tigris River in Turkey. A comparison of the results shows that the proposed methodologies not only give numerical values but also sets linguistically rational and logical rules for the HE potential of a drainage basin. In the upstream basin of the Tigris River, the EI values vary between 0.705 and 0.122, corresponding to the "High" and "Low" categories, respectively. It is recommended that these two classification procedures be considered before the construction of any dam for HE production. [ABSTRACT FROM AUTHOR]

This paper provides sufficient evidence on how the crop-residual-derived charcoal could effectively restore the soil polluted by the heavy metal. In this paper, straw char at three temperatures of 300°C, 500°C, and 700°C, labeled as RS300, RS500, and RS700, was prepared by low temperature pyrolysis technique using straw as raw material, and the competitive adsorption desorption of Pb2+, Cd2+, Cu2+, and Zn2+ in acidic solution and the mechanism were investigated by static adsorption experiments. Since the crop-residual-derived charcoal could effectively restore the nutritional structure of the soil, which contributes to preventing the decrease in grain yield, and it is also a kind of renewable environment-friendly resource by itself, which could be used in control the pollution of heavy metal ions, it is expected that the crop-residual-derived charcoal will be a new adsorption material that could be used to control the heavy metal pollution in the future; the adsorption effect of biochar as new adsorption material on heavy metal ions has a distinct advantage over traditional adsorbent materials, and biochar is a renewable energy source, which is cheap and better for recycling resources. [ABSTRACT FROM AUTHOR]