Loss Of Power Supply Probability Research Articles

Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.

Enhancing Ethiopian power distribution with novel hybrid renewable energy systems for sustainable reliability and cost efficiency

Economic development relies on access to electrical energy, which is crucial for society’s growth. However, power shortages are challenging due to non-renewable energy depletion, unregulated use, and a lack of new energy sources. Ethiopia’s Debre Markos distribution network experiences over 800 h of power outages annually, causing financial losses and resource waste on diesel generators (DGs) for backup use. To tackle these concerns, the present study suggests a hybrid power generation system, which combines solar and biogas resources, and integrates Superconducting Magnetic Energy Storage (SMES) and Pumped Hydro Energy Storage (PHES) technologies into the system. The study also thoroughly analyzes the current and anticipated demand connected to the distribution network using a backward/forward sweep load flow analysis method. The results indicate that the total power loss has reached its absolute maximum, and the voltage profiles of the networks have dropped below the minimal numerical values recommended by the Institute of Electrical and Electronics Engineers (IEEE) standards (i.e., 0.95–1.025 p.u.). After reviewing the current distribution network’s operation, additional steps were taken to improve its effectiveness, using metaheuristic optimization techniques to account for various objective functions and constraints. In the results section, it is demonstrated that the whale optimization algorithm (WOA) outperforms other metaheuristic optimization techniques across three important objective functions: financial, reliability, and greenhouse gas (GHG) emissions. This comparison is based on the capability of the natural selection whale optimization algorithm (NSWOA) to achieve the best possible values for four significant metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), and GHG Emissions. The NSWOA achieved optimal values for these metrics, namely 0.0812 €/kWh, 3.0017 × 106 €, 0.00875, and 7.3679 × 106 kg reduced, respectively. This is attributable to their thorough economic, reliability, and environmental evaluation. Finally, the forward/backward sweep load flow analysis employed during the proposed system’s integration significantly reduced the impact of new energy resources on the distribution network. This was evident in the reduction of total power losses from 470.78 to 18.54 kW and voltage deviation from 6.95 to 0.35 p.u., as well as the voltage profile of the distribution system being swung between 1 and 1.0234 p.u., which now comply with the standards set by the IEEE. Besides, a comparison of the cost and GHG emission efficiency of the proposed hybrid system with existing (grid + DGs) and alternative (only DGs) scenarios was done. The findings showed that, among the scenarios examined, the proposed system is the most economical and produces the least amount of GHG emissions.

Enhancing the economic viability and reliability of renewables based electricity supply through Power-to-Gas-to-Power with green hydrogen

This study explores the role of green hydrogen in the Power-to-Gas-to-Power process as a solution to grid instability caused by the rise of volatile renewable energy sources. A comprehensive numerical model is developed to evaluate the economic viability and reliability of electricity supply, using key performance indicators such as system levelized cost of electricity and loss of power supply probability. Through simulations based on hourly renewable energy production profiles for a scenario with an average power demand of 100 MW, the effectiveness of batteries and green hydrogen is compared. Findings indicate that while batteries are suited to photovoltaic systems, green hydrogen excels in wind energy applications, particularly in wind-solar hybrid setups. Here, green hydrogen substantially enhances the power supply reliability (loss of probability reduced from 0.23 to 0.06) and achieves a competitive system levelized cost of electricity at 0.157 USD/kWh. Sensitivity analysis regarding system size and cost variations further highlights green hydrogen's potential, providing strategic insights for improving grid stability and economic efficiency.

Optimal sizing of standalone hybrid renewable energy system based on reliability indicator: A case study

The economical electrification of rural settlements extensively utilizes renewable energy sources. In recent years, the deployment of solar photovoltaic, wind turbine, and biomass gasifier-based power generation technologies for electricity accessibility in remote regions has received greater prominence. This paper presents an optimal component design of a hybrid solar PV-wind and battery system along with a biomass gasifier to provide continuous electricity for rural communities of Uttarakhand, India. In this study, a recently developed population-based giza pyramids construction (GPC) methodology is applied to determine the optimal component sizing. The findings of the GPC method are contrasted with the particle swarm optimization (PSO) methodology to assess the effectiveness of the proposed method. The annualized system cost and levelized cost of energy of the hybrid renewable energy system have been minimized subject to technical reliability indicators such as loss of power supply probability and renewable fraction.Moreover, a systematic exploration of the system has been conducted to assess its optimal feasibility across different values of power loss probability ranging from (0% to 4%). The simulation indicates that the proposed method adeptly identifies globally optimal values, leading to a $177 decrease in annual cost and 8% reduction in average simulation time compared to PSO. The research reveals that the balanced point (relative attainment), where system reliability and cost-effectiveness intersect optimally, is achieved at a 3% power loss level, resulting in the most significant cost reduction of $199 compared to other probability levels.

Designing an optimal multi-energy system with fast charging and hydrogen refueling station under uncertainties

As the demand for battery-powered and hydrogen-based electric vehicles continues to rise, the need for integrated charging and refueling systems with energy systems becomes more critical. Therefore, it is crucial to consider the stations serving diverse vehicle users during the system design process. This paper presents an optimal sizing strategy for a multi-energy system that covers clean energy sources and two energy storage technologies under the presence of a fast-charging station and a hydrogen refueling station for various types of car users. For the considered system, the optimal sizing problem aims to minimize the loss of power supply probability (LPSP) and annualized total life cycle cost (TLCC) through a two-stage stochastic programming-based multi-objective optimization approach. Using a scenario-based approach, the study considers the uncertainties associated with renewables, homeowner’s vehicle demands, and energy demands from homes and stations. Different cases are produced to evaluate the impacts of the different stations with the availability of various electric vehicle profiles under different levels of reliability. The TLCC decreases if LPSP increases in each case. Also, the cost of installing a fast charging station is significantly lower than that of a hydrogen refueling station for all LPSP limits. Moreover, the overall cost of the system design is at its lowest when homeowners’ vehicles provide vehicle-to-grid services. It is worth noting that when the station loads which come from the customers’ vehicle increase by 10%, the TLCC increases by 1.19%. Additionally, the revenue from hydrogen refueling station is approximately 70% less than the fast charging station.

Techno-economic assessment of hydrogen-based energy storage systems in determining the optimal configuration of the nuclear-renewable hybrid energy system

Population growth and economic development have significantly increased global energy demand. Hence, it has raised concerns about the increase in the consumption of fossil fuels and climate change. The present work introduced a new approach to using carbon-free energy sources, such as nuclear and renewable, to meet energy demand. The idea of using the Nuclear-Renewable Hybrid Energy System (N-R HES) is suggested as a leading solution that couples a nuclear power plant with renewable energy and hydrogen-based storage systems. For this purpose, using a meta-heuristic method based on Newton's laws, the configuration of the N-R HES is optimized from an economic and reliability point of view. The optimal system is selected from among six cases with different subsystems such as wind turbine, photovoltaic panel, nuclear reactor, electrolysis, fuel cell, and hydrogen storage tank. Furthermore, the performance of hydrogen-based energy storage systems such as high-temperature electrolysis (HTE) and low-temperature electrolysis (LTE) is evaluated from technical and economic aspects. The results of this work showed that using nuclear energy to supply the base load increases the reliability of the system and reduces the loss of power supply probability to zero. More than 70 % of the power is produced by nuclear reactors, which includes more than 80 % of the system costs. The key findings showed that despite HTE's higher efficiency, using LTE as a storage system in N-R HES is more cost-effective. Finally, due to recent developments and the safer design of nuclear reactors, they can play an important role in combination with renewable energies to support carbon-free energy sectors, especially in remote areas, for decades to come.

The integration of solar-hydrogen hybrid renewable energy systems in oil and gas industries for energy efficiency: Optimal sizing using Fick's Law optimisation Algorithm

The global demand for sustainable energy solutions in the oil and gas industry has stimulated interest in the integration of renewable energy sources. This paper investigates the techno-economic and environmental aspects of incorporating a solar-hydrogen-based hybrid renewable energy system (SHRES) into oil and gas processing facilities.The proposed SHRES, comprising Solar PV modules, hydrogen production and storage systems, and a gas turbine generator (PV-FC-EL-Tank-GT), aims to enhance energy efficiency and reduce the carbon footprint of energy-intensive operations. The study utilises a multidisciplinary approach, encompassing engineering, economics, and environmental analysis. The techno-economic evaluation employs detailed modelling and optimisation using Fick's Law Algorithm, ensuring efficient energy production and cost-effectiveness. Ecological assessments quantify potential reductions in greenhouse gas emissions and other environmental impacts.The technical and economic evaluation identifies the electrolyser system as the most significant cost contributor (57%). The optimised system balances energy sources with 45% from PV modules, 35% from fuel cells, and 20% from gas turbines, showcasing substantial reliance on renewable energy while maintaining required power generation.Addressing the urgent need for sustainable energy solutions in the oil and gas industry, the study highlights the potential of SHRES integration. By employing Fick's Law Algorithm, alongside with 80 % of renewable energy integration, the research optimises system components to optimise the annualised cost to be $57,539.85, decrease to zero the Loss of Power Supply Probability (LPSP), and mitigate CO2 emissions to a lowest value of 0.147 gCO2eq/kWh (by 80 %). The electrolyser system's pivotal role and the distribution of power generation sources are emphasised.This study presents a novel approach to integrating solar-hydrogen systems into oil and gas processing facilities, aiming for sustainability. The findings underscore the significance of the electrolyser system and the distribution of power generation sources in optimising the SHRES. Recommendations for renewable energy integration and potential reductions in CO2 emissions and costs are discussed, providing valuable insights for future energy policies.

Optimal sizing and location of grid-interfaced PV, PHES, and ultra capacitor systems to replace LFO and HFO based power generations

The impacts of climate change, combined with the depletion of fossil fuel reserves, are forcing human civilizations to reconsider the design of electricity generation systems to gradually and extensively incorporate renewable energies. This study aims to investigate the technical and economic aspects of replacing all heavy fuel oil (HFO) and light fuel oil (LFO) thermal power plants connected to the electricity grid in southern Cameroon. The proposed renewable energy system consists of a solar photovoltaic (PV) field, a pumped hydroelectric energy storage (PHES) system, and an ultra-capacitor energy storage system. The economic and technical performance of the new renewable energy system was assessed using metrics such as total annualized project cost (TAC), loss of load probability (LOLP), and loss of power supply probability (LPSP). The Multi-Objective Bonobo Optimizer (MOBO) was used to both size the components of the new renewable energy system and choose the best location for the solar PV array. The results achieved using MOBO were superior to those obtained from other known optimization techniques. Using metaheuristics for renewable energy system sizing necessitated the creation of mathematical models of renewable energy system components and techno-economic decision criteria under MATLAB software. Based on the results for the deficit rate (LPSP) of zero, the installation of the photovoltaic field in Bafoussam had the lowest TAC of around 52.78 × 106€ when compared to the results for Yaoundé, Bamenda, Douala, and Limbe. Finally, the project profitability analysis determined that the project is financially viable when the energy produced by the renewable energy systems is sold at an average price of 0.12 €/kWh.

Achieving green mobility: Multi-objective optimization for sustainable electric vehicle charging

This study optimizes and evaluates a Photovoltaic-Wind-Battery/Electric Vehicle Charging Station (PVWB/EVCS) system using four Multi-Objective Optimization (MOO) techniques: MOPSO, NSGAII, NSGAIII, and MOEA/D. The main goals are to minimize the Total Net Present Cost (TNPC) and Loss of Power Supply Probability (LPSP) of the system, which are crucial for sustainable electric vehicle charging. The study analyzes the economic, operational, and sustainability aspects of the optimized system and compares it with HOMER software. The results show that NSGA-II is the best MOO technique for this problem, as it has the best performance and robustness. The Discounted Cash Flow analysis confirms the economic feasibility and sustainability of the optimized system over its lifetime. The technical analysis demonstrates the system's ability to use renewable energy from solar and wind sources, along with efficient energy storage and distribution. The study also conducts a sensitivity analysis to investigate the effects of changes in load, irradiance, wind speed, and component costs on the system performance. The findings reveal the system's resilience and adaptability under different scenarios, thus enhancing its suitability for renewable energy generation and electric vehicle charging. The study showed that the optimized PVWB/EVCS system is a promising solution for reducing reliance on non-renewable sources and promoting a more eco-friendly sustainable future.

Multi-objective size optimization and economic analysis of a hydrogen-based standalone hybrid energy system for a health care center

The main contribution of this study is the multi-objective size optimization of a standalone hybrid energy system (HES) to supply the electricity demand of a health care center in Kerman province, south of Iran, in which fuel cell, photovoltaic, and diesel generator produces electricity, while the combination of electrolyzer and hydrogen tank is the storage system. The non-dominated sorting genetic algorithm II (NSGA-II) is applied to find the Pareto front of optimal solutions, where the loss of power supply probability (LPSP) and total net present cost (TNPC) are the objective functions. The results show that considering operating reserve and uncertainty, the TNPC increases 30.50% compared to the base load. Moreover, the fuel cell has a smaller share of energy production, which is due to the high cost of the storage system, while about 50% of the demand load is provided by DG in the cold seasons. The sensitivity analysis reveals that TNPC decreases by 8.93% for a 50% reduction of the storage system cost, while increases by 24.55% for a 50% increase in the fuel cost. Moreover, AHP/TOPSIS-based multi-criteria decision-making is implemented to find the best solution according to the opinions of three different managers/experts, which shows that the HES with LPSP = 0.2503% and TNPC = 2.1038 × 106 $ is the best solution in which uncertainties and operating reserve are also considered.

Feasibility study: Economic and technical analysis of optimal configuration and operation of a hybrid CSP/PV/wind power cogeneration system with energy storage

In this study, a hybrid photovoltaic-wind-concentrated solar power renewable energy system and two cogeneration models are proposed. Evaluation criteria are employed, including the levelized cost of energy (LCOE) and the loss of power supply probability (LPSP). The optimal configuration and dynamic dispatch strategy of the hybrid system are determined through multi-objective optimization using the natural resources of Xining City. The economic feasibility of the system is assessed through comparison and analysis. The findings reveal that both cogeneration modes of the system effectively meet the power output requirements and dynamic thermal load. In thermal energy storage tanks' heat production mode without a battery storage system, the system achieves a minimum LCOE of 0.0526$/kWh and a maximum LPSP of 6.86%. With a battery storage system in this mode, the system attains a minimum LPSP of 4.83% and a maximum LCOE of 0.160$/kWh. Additionally, in the turbine spent vapor recovery heat supply mode, the system exhibits an LCOE of 0.127$/kWh and an LPSP of 4.96%, the system has better flexibility and wind-solar complementarity in this mode. This study also analyzed the importance of affordable electric energy storage for stable energy supply in hybrid energy systems.

Socio‐techno‐economic‐environmental sizing of hybrid renewable energy system using metaheuristic optimization approaches

AbstractElectricity supply reliability in an electricity distribution network is majorly affected due to unexpected power failures and power cuts. A hybrid renewable energy system (HRES) with the optimal size of renewable energy sources can substantially improve power reliability. Therefore, this article develops an objective function incorporating socio‐techno‐economic‐environmental (STEE) factors for HRES optimal sizing to supply reliable power to a rural village. The factors considered in the objective function are namely social (employment generation factor, human progress index, and land cost), technical (excess energy factor, renewable energy portion, and loss of power supply probability), economical (total net present cost, cost of energy, and annualized cost of system), and environmental (emission cost). In this article, for the first time, marine predators algorithm (MPA) based metaheuristic optimizer is devised to address the sizing optimization problem of HRES. Three HRES configurations, having different arrangements of diesel generator (DG), biogas generator (BG), battery (BAT), wind turbine (WT), and photovoltaic (PV), are examined utilizing MPA, particle swarm optimization (PSO), salp swarm algorithm (SSA), and genetic algorithm (GA) for optimal configuration. Due to the lowest value of economical and environmental factors and the highest value of the social factor, the PV‐WT‐BAT‐BG‐DG configuration is optimal compared to other investigated configurations with MPA. Comparing the four optimizers, MPA has the best STEE factor values, as well as stronger convergence, greater ability to escape from local minima, and higher ability to approach the global optimum. Additionally, by contrasting it with the PSO result for the three HRES configurations, the MPA result quality is confirmed. Furthermore, the cost of energy (0.1799 $/kWh) of the optimal configuration is less than the latest addressed in the literature.

Comparative techno-economic analyses and optimization of standalone and grid-tied renewable energy systems for South Asia and Sub-Saharan Africa

This study evaluates the economic, technical, and environmental performance of stand-alone and grid-tied HRES in Khiriya Bharka, India; Thayet Township, Myanmar; Lower Manya Krobo, Ghana; and Mamfe, Cameroon, considering different regional solar radiation, wind speed diversity, and climate. The HRES are designed and modeled using the Hybrid Optimization of Multiple Energy Resources software (HOMER PRO) to meet consumers' daily loads in the selected places. The analysis results are compared considering the levelized cost of energy (LCOE), net present cost (NPC), greenhouse gas (GHG) emission, renewable fraction (RF), and optimum system configuration. The optimal system configurations are the combination of PV-WT-DG-BAT-CON for grid-tied and stand-alone HRES in India, Ghana, and Cameroon, respectively, and the best configuration for Thayet Township, Myanmar, is WT-DG-BAT-CON. The LCOE of the considered places without grid connection is $0.127/kWh, $0.145/kWh, $0.174/kWh, and $0.143/kWh, respectively. The LCOE of grid-tied HRES is 0.026$/kWh, 0.0286$/kWh, 0.01$/kWh, and 0.0281$/kWh for Khiriya Bharka, Thayet Township, Lower Manya Krobo, and Mamfe, respectively. This research can be useful for planning grid-tied and stand-alone HRES between Asia and African countries by comparing grid-tied and stand-alone HRES to determine the optimum system configuration and finding the best optimization results under different climate conditions. These results are further validated by finding loss of power supply probability (LPSP) and LCOE using particle swarm optimization (PSO). To better inform policymakers and stakeholders in various regions, this research aims to assess and compare renewable energy solutions in terms of their feasibility, cost-effectiveness, environmental impact, and sustainability across a range of geographical, social, and economic contexts.

OPTIMAL SIZING OF SOLAR-WIND HYBRID MICROGRID USING IMPROVED GREY WOLF OPTIMIZATION ALGORITHM A CASE STUDY OF KADUNA - NIGERIA

This paper presents an improved grey wolf optimization algorithm (IGWOA) for optimal sizing of an isolated photovoltaic (PV), wind turbine (WT), and battery energy storage (BES) hybrid microgrid. To demonstrate the effectiveness of the proposed approach, atmospheric data sets comprising of wind, solar, and temperature of Kaduna International Airport were collected from Nigerian Meteorological Agency while the load demand data was collected from Kaduna International Airport Electricity Distribution Center. The microgrid optimal sizing was formulated as a constrained single objective optimization problem. Constraints including, loss of power supply probability (LPSP), power balance, generation limits and battery state of charge (SOC) were imposed. Three simulation scenarios were considered. Firstly, the target allowable maximum LPSP was fixed at 25% and the algorithm was able to determine the optimal sizing of the hybrid microgrid components and minimize the initial cost from 169,880.00 USD to 112,356.40 USD per annum resulting in 34% savings in cost. Secondly, the effect of the target allowable maximum LPSP variation was investigated, and it was found that the total installed capacity of the system decreases with increase in LPSP thereby decreasing the total cost. Additionally, a novel electricity price index (EPI) was introduced in order to quantify the degree of optimality of the solution. The EPI was found to increase exponentially with increase in LPSP, resulting in an EPI of < 0.05USD/kWh at 20% LPSP. Lastly, to validate the proposed approach, a comparative analysis between the IGWOA and other algorithms was carried out, and the proposed IGWOA proved applicable.

Optimal design and analyzing the techno-economic-environmental viability for different configurations of an autonomous hybrid power system

AbstractIn the present day, there is widespread acceptance of autonomous hybrid power systems (AHPSs) that rely on renewable energy sources (RESs), owing to their minimal adverse effects on the environment. This paper evaluates and compares three various AHPS configurations comprising photovoltaic (PV) modules, wind turbines (WTs), batteries, and diesel generators (DGs), using a recent optimization approach. A new optimizer 'Dandelion-Optimizer' (DO) is applied to tackle design problems. Real-time meteorological data from Siwa Oasis in northwest Egypt was utilized to determine an optimum design of system components for the purpose of providing sustainable power to this remote region. The system configurations are effectively modelled and optimized to achieve the minimum cost of energy (COE), while also minimizing the loss of power supply probability (LPSP) and carbon dioxide (CO2) emissions. As per the results, the last configuration (PV with both backup equipment) is the most optimal one in terms of the lowest cost, whereas the first configuration (PV and WT with both types of backup equipment) is the most optimal one with regards to the lowest carbon emissions.

Optimal performance of stand-alone hybrid microgrid systems based on integrated techno-economic-environmental energy management strategy using the grey wolf optimizer.

Recently, global interest in organizing the functioning of renewable energy resources (RES) through microgrids (MG) has developed, as a unique approach to tackle technical, economic, and environmental difficulties. This study proposes implementing a developed Distributable Resource Management strategy (DRMS) in hybrid Microgrid systems to reduce total net percent cost (TNPC), energy loss (Ploss), and gas emissions (GEM) while taking the cost-benefit index (CBI) and loss of power supply probability (LPSP) as operational constraints. Grey Wolf Optimizer (GWO) was utilized to find the optimal size of the hybrid Microgrid components and calculate the multi-objective function with and without the proposed management method. In addition, a detailed sensitivity analysis of numerous economic and technological parameters was performed to assess system performance. The proposed strategy reduced the system's total net present cost, power loss, and emissions by (1.06%), (8.69%), and (17.19%), respectively compared to normal operation. Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) techniques were used to verify the results. This study gives a more detailed plan for evaluating the effectiveness of hybrid Microgrid systems from a technical, economic, and environmental perspective.

Mountain gazelle optimizer for standalone hybrid power system design incorporating a type of incentive-based strategies

The main objective of this research study is to improve the performance of a standalone hybrid power system (SHPS) that consists of photovoltaic modules (PVMs), wind turbines (WTs), battery system (BS), and diesel engine (DE). The emphasis is on optimizing the system's design by incorporating demand response strategies (DRSs). Incorporating these strategies into the system can enhance system performance, stability, and profitability while also reducing the capacity of SHPS components and, consequently, lowering consumers' bills. To achieve this objective, the sizing model incorporates a novel indicator called the load variation factor (LVF). This paper assesses and contrasts various scenarios, including SHPS without DRS, with DRS, and with DRS but no DE. In this article, interruptible/curtailable (I/C) as one of the DRSs is incorporated into the model used for sizing issues. A newly developed optimization algorithm called the mountain gazelle optimizer (MGO) is utilized for the multi-objective design of the proposed SHPS. The utilization of MGO will facilitate achieving the lowest possible values for each of the following: cost of energy (COE), loss of power supply probability (LPSP), and carbon dioxide (CO2) emissions. This work introduces a mathematical model for the entire system, which is subsequently simulated using MATLAB software. The results reveal that among all the scenarios analysed, scenario iii — which has an LVF of 30% — is the most cost-effective. It has the lowest COE, at 0.2334 $/kWh, hence the lowest net present cost (NPC), at 6,836,445.5 $.

Techno-economic optimization for isolated hybrid PV/wind/battery/diesel generator microgrid using improved salp swarm algorithm.

The main objective of this study is to develop a new method for solving the techno-economic optimization problem of an isolated microgrid powered by renewable energy sources like solar panels, wind turbines, batteries, and diesel generators while minimizing greenhouse gas emissions. An Improved Salp Swarm Algorithm (ISSA) with a position adaptation mechanism for the salp leader that involves a leader salp that moves about depending on both food availability and its previous position has been proposed to overcome the convergence problem. In the original SSA, as the approach converges, it can no longer find optimal solutions and becomes trapped in a local minimum. Three Microgrid System (MS) configurations are discussed: PV/WT/BESU/DG, PV/BESU/DG, and WT/BESU/DG. The proposed method seeks to find a middle ground between technical criteria and environmental concerns when deciding on PV, WT, BESU, and DG sizes. The findings indicate that the proposed ISSA approach gives superior results compared to other well-known algorithms like the original SSA, the Ant Lion Optimizer (ALO), the Dragonfly Approach (DA), and the Moth-Flame Optimization Algorithm (MFO), which, after significant investigation, has been proven to help determine the appropriate microgrid size. With PV sizes of 10, 9 WT, 24 BESU, and 3 DG, the PV/WT/BESU/DG configuration offers the highest level of cost-effectiveness with Cost of Energy (COE) of 0.2109 $/kWh, Net Present Cost (NPC) of 376,063.8 $, Loss of Power Supply Probability (LPSP) of 4%, Renewable Energy Fraction (REF) of 96%, and CO2 emission of 12.4457 tons/year. ISSA is brought up as a possible solution to both the problem of rising energy prices and the difficulties inherent in microgrid design.

Optimal Design of PV/WT/Battery Based Microgrid for Rural Areas in Leh Using Dragonfly Algorithm

This study proposes an optimal microgrid design for rural electrification in India’s Leh and Ladakh regions, using wind energy, solar, energy, and battery energy storage system. The Dragonfly Algorithm (DA) is used to calculate the optimal number of microgrid units, and results are compared with popular optimization algorithms such as Grey Wolf optimization (GWO), Differential Evolution (DE), and Discrete Harmony Search (DHS). The optimal design is based on an objective function to minimize the Levelized cost of energy (LCOE) while keeping the loss of power supply probability (LOPSP) as a reliability constraint. Three configuration studies are carried out, with three cases, each with a different maximum permissible LOPSP (LOPSPmax) value. The results show that optimal design and efficient energy management reliably meet the load demand. The energy generated from the proposed microgrid is clean compared to the grid supply, and the amount of greenhouse gas (GHG) emissions is reduced by 91.2% from Configuration-I, Case-I, which is the most economical configuration. The LCOE obtained from Configuration-I, Case-I is 0.129 $/kWh, the lowest among similar systems available in the literature. To determine the parameter cost with supply, the LCOE and Total life cycle cost (TLCC) sensitivity to LOPSPmax are considered. Furthermore, statistical analysis shows that DA outperforms GWO, DE, and DHS in terms of accuracy and convergence rate.

Capacity configuration strategy for wind-photovoltaic-battery system based on optimized simulated annealing

Abstract Photovoltaic (PV) and wind power are very promising renewable energy sources. Wind-PV has good complementarity, and the battery can better smooth the power fluctuation of wind-PV, so the wind-PV-battery system has been widely used. The capacity configuration of the wind-PV-battery system is a complex issue because many factors affect it. Unlike other studies that focus on many meaningless parameters, the contribution of this paper is that we focus on three key elements, system reliability, cost, and wind-PV energy discard rate (EDR), which are the key factors affecting the capacity configuration of the system because system reliability and cost are factors that cannot be ignored during system operation. At the same time, the EDR is the government’s minimum requirement for system operation. Based on this, this paper establishes an optimization function to minimize the loss of power supply probability (LPSP), cost, and EDR, and then optimizes a simulated annealing (SA) algorithm to improve its optimization speed as well as accuracy, and finally verifies through simulation, that the optimized SA has better performance and can obtain a satisfactory reference configuration for wind-PV-battery capacity. Further, we have also analyzed the effectiveness obtained by applying retired batteries to the energy storage system. In our example, the cost of the system after using retired batteries is only about 61% of the cost of using new batteries, which suggests that utilizing retired batteries instead of new batteries has a better cost performance.