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Extended power outages are not only a nuisance but a critical problem in the modern world, which demands a continuous supply of decent quality electricity. Hybrid renewable energy systems (HRES) within a microgrid (MG) play an important role in delivering energy to rural and off-grid areas and avoiding potential power outages. This research describes an in-depth study of the three phases, design, optimization, and performance analysis of a stand-alone hybrid microgrid for a residential area in a remote area in the province of Adrar in southern Algeria. The system is composed of photovoltaic (PV) modules and a wind turbine, a set of batteries as an energy storage unit, a diesel generator as a backup energy source, and an inverter. This paper investigates four recent methodologies based on Multi-objective Particle Swarm Optimization (MOPSO), Multi-objective Ant Lion Optimizer (MOALO), Multi-objective Dragonfly Algorithm (MODA), and Multi-objective Evolutionary Algorithm (MOGA) to identify the optimal sizing of a microgrid (MG) integrated with hybrid renewable energy sources (RES). The proposed methods are carried out to select the optimal system size, which is a multi-objective problem involving the minimization of the annual cost of electricity (COE), and the loss of power supply probability (LPSP) simultaneously. To achieve this, the proposed methods are combined with energy management strategy (EMS) rules that coordinate energy flows between the various system components. The findings reveal that the MOPSO method has the most efficient hybrid renewable configuration with an annual generation cost of electricity (COE) of 0.2520 $/kWh and loss of power supply probability (LPSP) of 9.164%, which dominates the performance of MOALO (COE of 0.1625$/kWh and LPSP of 8.4872%), MOGA (COE of 0.1577$/kWh and LPSP of 10%), and MODA (COE of 0.02425$/kWh and LPSP of 7.8649%). Furthermore, a sensitivity analysis is performed for the effect that COE variants may have on the design variables. [ABSTRACT FROM AUTHOR]

The stochasticity of power flow of distributed generations (DGs) and load in the microgrid has great influence on power flow distribution and voltage quality of the distribution network. For improving the voltage quality of the distribution network, the questions need to be further studied, which include the description of the stochasticity of the power flow in the microgrid and the impact of the microgrid into the distribution network on the power flow. Therefore, a novel stochastic power flow calculation and optimal control method for the microgrid based on multivariate stochastic factors fusion-sensitivity (MSFF-sensitivity) is proposed in this paper. Firstly, the multivariate stochastic factors fusion (MSFF) function is developed by using the probability density function to extract the stochasticity and correlation of power flow among different stochastic factors in the microgrid, which are effectively unified. Furthermore, the fusion-sensitivity (F-sensitivity) of the power flow in the microgrid integrated into the distribution network is constructed to accurately characterize the influence degree of various stochastic factors in the microgrid on the power flow of the distribution network. Based on this, the output power of the stochastic factor is adjusted to optimally control the power flow of the distribution network. Finally, the algorithm verification suggests that, compared with the conventional power flow methods, the method proposed in this paper is more suitable for the microgrid. The influence of stochastic power flow on the distribution network can be effectively reduced and the voltage quality of the distribution network can be improved by optimizing control of the power flow in the microgrid integrated into the distribution network. [ABSTRACT FROM AUTHOR]

In isolated microgrids, distributed energy resources (DERs) such as small-scale generators, energy storage systems, and flexible loads operate independently from the main grid. The challenge is to optimize these resources to minimize user costs while ensuring microgrid stability and efficiency. This paper presents an optimization framework for DERs, leveraging a game-theoretical approach to demand-side management (DSM) in an isolated microgrid environment. Each participant aims to minimize their total cost by strategically managing renewable energy generation, storage, and consumption. The framework models the DSM problem as a noncooperative game, identifying equilibrium points where no user can unilaterally reduce costs. The proximal decomposition algorithm is employed to iteratively update user strategies, ensuring convergence to a Nash equilibrium. Furthermore, a blockchain-based system with smart contracts is integrated to automate critical processes, including registration, event detection, DSM actions, and incentive distribution. This integration enhances transparency, security, and efficiency in the microgrid. During the registration phase, all devices are authenticated and authorized through a secure, transparent blockchain ledger. Event detection is managed by the microgrid Energy Management System (EMS), which continuously monitors voltage and frequency levels, triggering predefined smart contract responses to maintain stability. DSM actions are automatically executed by smart contracts, adjusting energy loads, generation, and storage to balance supply and demand dynamically. The smart contracts also manage the economic incentives that drive participant engagement. They calculate and distribute incentives based on predefined criteria, ensuring accurate and prompt allocation. This process is recorded on the blockchain, providing an immutable and auditable trail of actions and rewards. By leveraging blockchain technology and a game-theoretical approach, the proposed framework ensures continuous optimal operation despite fluctuations in energy demand and renewable generation. This dynamic and adaptive model promotes decentralized and efficient energy management within the microgrid, fostering a resilient and sustainable energy ecosystem. [ABSTRACT FROM AUTHOR]

As a global community our use of data is increasing exponentially with emerging technologies such as artificial intelligence (AI), leading to a vast increase in the energy demand for data centres worldwide. Delivering this increased energy demand is a global challenge, which the rapid growth of renewable generation deployment could solve. For many data centre giants such as Google, Amazon, and Microsoft this has been the solution to date via power purchase agreements (PPAs). However, insufficient investment in grid infrastructure globally has both renewable generation developers and data centre developers facing challenges to connect to the grid. This paper considers the costs and carbon emissions associated with stand-alone hybrid renewable and gas generation microgrids that could be deployed either before a grid connection is available, or to allow the data centre to operate entirely off-grid. WindPRO 4.0 software is used to find optimal configurations with wind and solar generation, backed up by battery storage and onsite gas generation. The results show that off-grid generation could provide lower cost and carbon emissions for each of Europe's data centre hotspots in Frankfurt, London, Amsterdam, Paris, and Dublin. This paper compares each generation configuration to grid equivalent systems and an onsite gas-only generation solution. The results showed that each hybrid renewable generation configuration had a reduced levelized cost of energy (LCOE) and reduced CO2eq emissions compared to that of its grid and gas-only equivalent. Previous literature does not consider the economic implications caused by a mismatch between generation and consumption. Therefore, this paper introduces a new metric to evaluate and compare the economic performance of each microgrid, the levelized cost of energy utilised (LCOEu) which gives the levelized cost of energy for a given microgrid considering only the energy which is consumed by the data centre. The LCOEu across all sites was found to be between 70 and 102 GBP/MWh with emissions between 0.021 and 0.074 tCO2eq/MWh. [ABSTRACT FROM AUTHOR]

The globalization of electric vehicle development and production is a significant goal. The availability of charging stations helps to encourage the global transition to electric vehicles, which may lead to a decrease in traditional fuel consumption. Nevertheless, the rise in the number of electric vehicles is accompanied by sustainability issues, such as managing the grid's electrical demand, building more charging stations, and providing electricity from renewable resources in an efficient and sustainable manner, especially in Saudi Arabia. This work focused on three challenges regarding the installation of fast charging stations (FCSs) for electric vehicles (EVs) on highways. The first challenge is choosing optimal locations on highways to address the range of anxiety of EV drivers. The second challenge is to fuel these FCSs using renewable resources, such as photovoltaic (PV) panels, to make FCSs sustainable. The last challenge is to design FCSs by considering both highway driving behavior and the available renewable energy resources in order to cover charging demand. All of these challenges should be considered while planning the EV charging infrastructure of Saudi highways from both technical and economic perspectives. Thus, using the HOMER® Grid software (version 1.10.1 June 2023), locations on Saudi Arabian highways were selected based on the renewable resources of several roads that support a large number of vehicles traveling on them. These roads were the Makkah to Riyadh, Makkah to Abha, Riyadh to Dammam, Riyadh to NEOM, and Jeddah to NEOM roads. Electric vehicle charging stations with a capacity of 200 kW, 300 kW, and 500 kW were designed on these roads based on their natural renewable resources, which is PV energy. These roads are the most important roads in the Kingdom and witness heavy traffic. An economic study of these stations was carried out in addition to considering their efficiency. This study revealed that the 500 kW station is ideal for charging electric vehicles, with an annual energy production of 3,212,000 kWh. The 300 kW station had better efficiency but higher capital expenses. The 200 kW station could charge 6100 vehicles annually. The three stations on the Makkah to Riyadh, Makkah to Abha, and Riyadh to Dammam roads can charge 65,758 vehicles annually. The total cost of the project was USD 2,786,621, with the 300 kW plant having the highest initial investment, which can be potentially justified due to its higher power output. This study provides a comprehensive overview of the project costs and the potential returns of using solar power plants for charging electric vehicles. [ABSTRACT FROM AUTHOR]

In recent years, the growth of utilizing rural microgrids (RMGs) has been accompanied by various challenges. These necessitate the development of appropriate models for optimal generation in RMGs and RMGs' coordination. In this paper, two distinct models for RMGs are presented. The first model includes an islanded rural microgrid (IRMG) and the second model consists of three RMGs that are interconnected with one another and linked to the distribution network. The proposed models take into account the uncertainty in load, photovoltaics (PVs), and wind turbines (WTs) with consideration of their correlation by using a scenario-based technique. Three objective functions are defined for optimization: minimizing operational costs including maintenance and fuel expenses, reducing voltage deviation to maintain power quality, and decreasing pollution emissions from fuel cells and microturbines. A new optimization method, namely the Improved Multi-Objective Crow Search Algorithm (IMOCSA), is proposed to solve the problem models. IMOCSA enhances the standard Crow Search Algorithm through three key improvements: an adaptive chaotic awareness probability to better balance exploration and exploitation, a mutation mechanism applied to the solution repository to prevent premature convergence, and a K-means clustering method to control repository size and increase algorithmic efficiency. Since the proposed problem is a multi-objective non-linear optimization problem with conflicting objectives, the idea of the Pareto front is used to find a group of optimal solutions. To assess the effectiveness and efficiency of the proposed models, they are implemented in two different case studies and the analysis and results are illustrated. [ABSTRACT FROM AUTHOR]