Optimal Component Sizing Research Articles

A tri-level hybrid stochastic-IGDT dynamic planning model for resilience enhancement of community-integrated energy systems

In this paper, a resilience-oriented dynamic planning framework is developed for optimal sizing of the community-integrated energy system components, including photovoltaic system, wind turbine, boiler, power to gas technology, combined heat and power, and storage devices. The proposed framework is formulated as a tri-level linear programming that performs Community-Integrated Energy Systems design under normal conditions at the first level while evaluating system operation during disastrous conditions at the second level. In the third level, re-planning is done based on information-gap decision theory to enhance community-integrated energy systems’ resilience against various natural disasters. Stochastic programming is also employed at all levels to address the uncertainty of electricity market price, energy demand, solar radiation, and wind speed. A detailed P2G system including, a methanation device, electrolysis, and hydrogen storage is designed to improve the resilience of the system. In addition, the power to gas proposed in this model is coupled with a carbon capture unit to mitigate carbon emission by reusing emitted carbon from the flue gas of the boiler and combined heat and power. Various economic metrics and technical constraints are also considered to achieve a realistic design. Numerical simulation results demonstrate that the positive interplay of renewable energy resources and energy storage technologies, specifically P2G, assisted the CIES in maintaining a stable and uninterrupted energy supply during extreme events. The results exhibit that increasing only 10 % of the resilience budget can decrease >93 % of unserved demand and helps reduction of >37 % of carbon emissions.

A study of powertrain parameter optimization based on performance requirements of a vehicle platform

Powertrain top down design is a vital process for vehicle platformization and performance development. To obtain the optimal component size of a plug-in hybrid electric vehicle (PHEV) platform, this study proposes an optimization framework with the goal of minimizing performance redundancy. First, we analyze the characteristics of the powertrain architecture, and propose the indicators through performance gradient planning of the vehicle platform. Then we establish the mathematical model of the powertrain, and formulate the optimization problem. Particle Swarm Optimization (PSO) is used to solve the problem and a platform optimization problem with eight vehicle models is performed. Optimization results show that compared to traditional single vehicle powertrain optimization, platform based powertrain optimization can greatly reduce the number of components and save development costs through the sharing of components. With only three electric machines, two engines, two generators, and two sets of transmissions, the performance requirements of the entire platform can be met, and the maximum performance redundancy of different vehicle models is only 0.205. It can be seen that platform based powertrain optimization can greatly reduce vehicle costs and improve profit margins.

Towards standalone commercial buildings in the Mediterranean climate using a hybrid metal hydride and battery energy storage system

Given the critical role of hybrid energy storage systems in the building sector for enhancing renewable energy reliability and integration, this study examines the techno-economic feasibility of adopting a dual-level energy storage system for a PV-driven commercial building in the Mediterranean climate. The proposed system encompasses both hydrogen metal hydride and battery storage units. Aimed at off-grid electrification, the optimal component sizing of the hybrid system is identified by establishing a statistical optimization framework using the response surface methodology. Dynamic simulations of the hybrid system are performed through a TRNSYS model coupled with a numerical code that simulates the metal hydride system. Regarding optimal net-zero solutions, it is shown that the share of the direct PV electricity supply to end-use fluctuates within a limited range for all scenarios, namely between 57.7 and 60.5 %, while the share of each storage system varies distinctively in each solution. The results indicate a striking difference between scenarios in terms of economic aspects; differences of $ 19,791 and $ 32,621 are observed between the minimum and maximum values of the initial investment and life cycle cost, respectively. The levelized cost of electricity varies between 0.354 and 0.403 $/kWh, in which the case having the lowest payback period (10.8 years) demonstrates the lowest levelized cost of electricity too. Furthermore, an inverse relationship between the levelized cost of hydrogen and production rate is observed; the lowest levelized cost and the highest annual production are achieved in the scenario with the lowest BESS capacity and the highest electrolyzer power and hydrogen storage volume, which are equal to 5.77 $/kg and 304.7 kg H2/yr, respectively.

Adaptive-DMPC based energy management and pre-installation techno-economic analysis of a grid-tied cyber-physical community microgrid

The trends for creating smart cities is increasing as it gives assurance of high-quality living standards, however, converting an existing city into a smart one needs more time, investments and is highly tedious. Another concept of community based sustainable living that can be installed in and around outskirts of any city. All the features of smart cities can be brought in by most economical, sustainable, and environmental friendly way. A cyber-physical Community Microgrid (CMG) can have various loads such as heating, cooling, security and lighting purposes. The dependency on grid supply can be reduced by using renewable sources of electricity. Solar water heaters and biogas plant can take care of heating loads. Handling uncertainties in generation, time-varying load scenarios, and dynamics of supply grid are crucial for smooth operation of cyber-physical CMG. In this work, an Energy Management Strategy (EMS) for CMG is proposed using an adaptive Distributed Model Predictive Control (DMPC) that improves scalability with modular architecture, allow enhanced flexibility and adaptability, robustness and reliability, enhanced optimization and predictive capabilities. All possible intermittencies types due to renewables, time-variable demand profiles, smooth charging/discharging cycles and longevity of storage batteries, curtailing of noncritical loads, and dynamic price of grid consumption are applied. Further, use of EMS outcomes is done to develop pre-installation techno-economic analysis for establishing a CMG with optimal component sizing. The given DMPC scheme for CMG is verified by conducting comprehensive test case studies over 24 hrs interval on the Matlab/Simulink and hardware-in-loop testing.

Electricity and hydrogen fuel generation based on wind, solar energies and alkaline fuel cell: A hybrid IWGAN–AVOA approach

Addressing the pressing need for sustainable energy solutions, this paper proposes a novel hybrid energy system integrating wind, alkaline fuel cells and solar energies. This paper introduces a pioneering hybrid energy system designed to produce electricity and hydrogen fuel through the integration of wind, solar energies, and an alkaline fuel cell. The proposed approach, coined as IWGAN–AVOA technique, synergizes the improved Wasserstein generative adversarial network (IWGAN) and the African vultures optimization algorithm (AVOA). The primary aim is to delineate a distinctive energy cycle reliant on renewable sources, featuring a Stirling engine, electrolyzer, alkaline fuel cell, wind turbine, and solar photovoltaic system, with the objective of generating hydrogen fuel and energy. The AVOA enhances fuel cell capabilities, simulates optimal system component sizes, and rectifies erroneous configurations based on geographical specifics. By validating the efficacy of IWGAN, the study achieves optimal configurations for device capacities and enhances power flow efficiency. Comparative analyses reveals that the IWGAN–AVOA approach surpasses existing techniques, like particle swarm optimization (PSO), wild horse optimizer (WHO), and heap based optimizer (HBO), with a remarkable efficiency rate of 98%, outperforming current methods. The last figure illustrates the effectiveness comparison, showcasing efficiencies of 62%, 79%, and 85% for HBO, PSO, and WHO, respectively, against the superior 98% efficiency achieved by the IWGAN–AVOA approach, affirming its superiority in energy system optimization.

A case study on optimizing industrial air conditioning with thermal solar energy in Egypt

This study investigates the feasibility of implementing a solar-assisted adsorption chiller in an industrial building at the Oriental Weavers International factory located in 10th of Ramadan City, Cairo, Egypt. The objective is to replace an inefficient split air conditioning system currently used to cool the Jacquard units during carpet manufacturing. The research begins by analyzing the performance of the existing cooling system to establish a baseline. It then explores the potential energy savings achievable by replacing the current system with a solar-assisted adsorption chiller. The existing oversized boiler will serve as an auxiliary heater for the new system. TRNSYS simulation tools are employed to model the building, simulate its thermal performance, and develop a solar-assisted cooling system. A parametric analysis investigates the impact of varying collector area and hot/cold-water storage tank volumes on key energy performance indicators. This analysis aims to determine the optimal component sizes needed for efficient system operation. Results indicate that a collector area of 90 m2 offers the optimal balance between performance and cost. There are minimal benefits to increasing the collector area beyond 100 m2. Larger hot water storage tanks demonstrate reduced outlet temperatures, reaching a maximum solar fraction at a capacity of 4 m³. The impact of cold-water storage tank volume on the system is minimal. The economic assessment reveals a payback period of 7.6 years, an Internal Rate of Return (IRR) of 14.3 %, and a Return on Investment (ROI) of 34.5 % over a 10-year period, indicating the financial viability of the proposed system. Furthermore, the solar-assisted adsorption chiller system has the potential for substantial environmental benefits. The system has the capacity to reduce CO2 emissions by up to 7200 metric tons. This highlights not only the technical feasibility of the system but also its economic and environmental advantages.

Effects of energy sharing and electricity tariffs on optimal sizing of PV-battery systems for grid-connected houses

This paper investigates the optimal sizing of solar photovoltaic (PV) and battery energy storage (BES) for grid-connected houses based on mutually-agreed energy sharing prices by considering the flat and time-of-use (TOU) tariffs. The grid-tied house with PV-BES, referred to house 1 (H1) in the paper shares electricity with house 2 (H2) whenever needed with mutually-agreed electricity tariffs. The main objective function of the study is to minimize the cost of electricity (COE) for the H1 while decreasing COE for H2. Eight different schemes are investigated with the combination of flat and TOU tariffs for buying, selling, and mutually-agreed rates, respectively. For each scheme, optimal sizing of components and COE for both houses are evaluated. Realistic hourly-arranged annual data of temperature, solar irradiation, and load consumption of two houses are used as the input data. For each scheme, the results are compared with the situation when H1 does not have PV-BES and there is no electricity sharing. Sensitivity, operational, and uncertainty analyses are provided for the scheme with the lowest COE.

Performance modeling and cost optimization of a solar desalination system using forward osmosis with energy storage

This study presents the design and performance evaluation of a solar-driven water desalination system based on forward osmosis (FO) with thermally responsive ionic liquids (ILs). FO is a two-step process involving dilution of the ILs with water, followed by heating above a critical temperature to induce phase separation of liquid water from the IL. This regeneration step can be achieved by integrating FO with low-grade solar energy, which is abundant in regions that face severe water scarcity. A system design is presented that couples an FO membrane module with a compound parabolic concentrator solar collector and thermal energy storage to minimize solar intermittency and produce clean water at a distributed scale of 10 m3/day. To determine the optimal sizing of components in the system, a technoeconomic analysis using the TRNSYS software is performed with the goal of minimizing the levelized cost of water (LCOW). Notably, over 96 % of the energy required for regeneration comes from solar energy and/or the thermal energy storage (TES) in all cases, with auxiliary heat from electricity being used to maintain a continuous process. To evaluate the potential of solar-FO in three different locations within the United States, a case study is presented for Phoenix, AZ; San Diego, CA; and Atlanta, GA. The simulation results reveal that for a small-scale desalination system, LCOW values as low as $1.31/m3 can be attained, with the potential to approach $1/m3 by lowering the costs of the solar collector and FO module based on a sensitivity analysis.

An Analysis of Hybrid Renewable Energy-Based Hydrogen Production and Power Supply for Off-Grid Systems

Utilizing renewable energy sources to produce hydrogen is essential for promoting cleaner production and improving power utilization, especially considering the growing use of fossil fuels and their impact on the environment. Selecting the most efficient method for distributing power and capacity is a critical issue when developing hybrid systems from scratch. The main objective of this study is to determine how a backup system affects the performance of a microgrid system. The study focuses on power and hydrogen production using renewable energy resources, particularly solar and wind. Based on photovoltaics (PVs), wind turbines (WTs), and their combinations, including battery storage systems (BSSs) and hydrogen technologies, two renewable energy systems were examined. The proposed location for this study is the northwestern coast of Saudi Arabia (KSA). To simulate the optimal size of system components and determine their cost-effective configuration, the study utilized the Hybrid Optimization Model for Multiple Energy Resources (HOMER) software (Version 3.16.2). The results showed that, when considering the minimum cost of energy (COE), the integration of WTs, PVs, a battery bank, an electrolyzer, and a hydrogen tank brought the cost of energy to almost 0.60 USD/kWh in the system A. However, without a battery bank, the COE increased to 0.72 USD/kWh in the same location because of the capital cost of system components. In addition, the results showed that the operational life of the fuel cell decreased significantly in system B due to the high hours of operation, which will add additional costs. These results imply that long-term energy storage in off-grid energy systems can be economically benefited by using hydrogen with a backup system.

Improving fuel efficiency and durability in fuel cell vehicles through component sizing and power distribution management

The proposed study presents a novel approach to determining the optimal component sizes and power distribution for a vehicle, taking into account various power sources in order to prolong the lifespan of the fuel cell stack. Moreover, this technique aims to increase the vehicle's overall fuel economy. The available experimental data of Toyota Mirai has been used to model the vehicle in the MATLAB-SIMULINK software. The validated model is implemented to evaluate the vehicle power request in different standard driving cycles and routes to size the vehicle components. The arithmetic average of the steady power of different driving cycles and routes has been attributed to the fuel cell stack's maximum efficiency point. The battery size should support the requested dynamic power in any situation. Thus, the maximum power of the fuel cell stack, the high voltage battery size, and the electric motor maximum power are assigned as 96 kW, 3.05 kWh, and 59 kW, respectively. The results of fuel consumption of the vehicle is also improved at least by 10% up to 21% in different driving conditions. Economic analysis reveals that adopting this strategy can lead to a substantial reduction in the initial vehicle cost by $1,328. The operational cost has also decreased 691$ annually due to lower fuel consumption of the new strategy implementation. These advancements along with better fuel cell stack durability hold promise for prospective buyers, allowing for greater accessibility and financial flexibility.

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.

An optimal standalone wind-photovoltaic power plant system for green hydrogen generation: Case study for hydrogen refueling station

Sustainability goals include the utilization of renewable energy resources to supply the energy needs in addition to wastewater treatment to satisfy the water demand. Moreover, hydrogen has become a promising energy carrier and green fuel to decarbonize the industrial and transportation sectors. In this context, this research investigates a wind-photovoltaic power plant to produce green hydrogen for hydrogen refueling station and to operate an electrocoagulation water treatment unit in Ostrava, Czech Republic's northeast region. The study conducts a techno-economic analysis through HOMER Pro® software for optimal sizing of the power station components and to investigate the economic indices of the plant. The power station employs photovoltaic panels and wind turbines to supply the required electricity for electrolyzers and electrocoagulation reactors. As an off-grid system, lead acid batteries are utilized to store the surplus electricity. Wind speed and solar irradiation are the key role site dependent parameters that determine the cost of hydrogen, electricity, and wastewater treatment. The simulated model considers the capital, operating, and replacement costs for system components. In the proposed system, 240 kg of hydrogen as well as 720 kWh electrical energy are daily required for the hydrogen refueling station and the electrocoagulation unit, respectively. Accordingly, the power station annually generates 6,997,990 kWh of electrical energy in addition to 85595 kg of green hydrogen. Based on the economic analysis, the project's NPC is determined to be €5.49 M and the levelized cost of Hydrogen (LCH) is 2.89 €/kg excluding compressor unit costs. This value proves the effectiveness of this power system, which encourages the utilization of green hydrogen for fuel-cell electric vehicles (FCVs). Furthermore, emerging electrocoagulation studies produce hydrogen through wastewater treatment, increasing hydrogen production and lowering LCH. Therefore, this study is able to provide practicable methodology support for optimal sizing of the power station components, which is beneficial for industrialization and economic development as well as transition toward sustainability and autonomous energy systems.

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.

Renewable hydrogen production through electrolysis: An analysis of the cost gap for its economic competitiveness in Italy

With global policies targeting greenhouse gas emission reduction, renewable hydrogen produced by water electrolysis powered with renewable electricity appears a promising alternative to fossil fuels. However, still high capital costs, reliance on intermittent renewable electricity sources, and high electricity cost hinder the economic viability of electrolyzers. This work develops a comprehensive analysis for assessing the need of an incentive to make renewable hydrogen economically viable, identifying different archetypes of electrolysis plant representative of a wide range of applications. A techno-economic optimization model is set up that minimize the average hydrogen production cost and compute the cost gap that makes hydrogen economically competitive with the fossil alternative, defining the optimal size and operation of the plant components while satisfying the hydrogen demand. The model is applied to the Italian case study. Sensitivity analyses investigate the impact of economy of scales, renewable electricity availability and introduction of the emission trading system. Cost gaps ranging between 8.1 €/kg and 29.9 €/kg highlights the need of an incentive to allow the deployment of this technology, thus meeting the hydrogen penetration targets.

A reliable spacecraft power supply subsystem based on discrete water cycle multi-objective optimization

The successful execution of spacecraft missions relies heavily on the performance and reliability of their power supply subsystems. Achieving optimal component size and configuration is crucial for efficient power generation, distribution, and storage. Designing an efficient power supply subsystem for spacecraft missions is a complex and multi-objective optimization problem. Traditional optimization techniques, such as genetic algorithm and particle swarm optimization, have been employed, but their limitations in handling discrete variables and achieving global optima necessitate the exploration of alternative methods. In this context, this research paper explores the application of discrete water cycle algorithm (DWCA) optimization as a novel approach for optimizing the component size and configuration of spacecraft power supply subsystems. By emulating the dynamic flow and distribution of water, DWCA seeks to efficiently explore the design space and identify optimal solutions.Through comprehensive experimentation and comparative analysis, this research demonstrates that the proposed DWCA outperforms/surpasses other traditional optimization techniques in optimizing component size and configuration for spacecraft power supply subsystems. It consistently determines designs that are more efficient in power generation and storage. Moreover, DWCA enhances the efficiency and reliability of spacecraft power supply systems, especially in mission-critical scenarios. The successful optimization of spacecraft power supply subsystems is paramount to the overall success of space missions. Therefore, this advancement contributes significantly to the ongoing efforts to ensure the success of future space missions.

Profit-effective component sizing for electric delivery trucks with dual motor coupling powertrain

This study proposes a novel component sizing method for electric delivery trucks (EDTs) employing dual motor coupling powertrain (DMCP) to enhance both the energy efficiency and operating profitability. A control-oriented model for the EDT is first established, encompassing the three-mode DMCP dynamics. Variations in component size and mass have been modeled, with consideration of their effects on the load capacity. To maximize the average profit per kilometer over the truck’s lifespan, four objective functions are defined to accommodate to the diverse types of cargo being transported. We formulate the optimization problem in a bi-level form, and propose a solution method that combines particle swarm optimization (PSO) handling parameter filtering with iterative dynamic programming (IDP) to minimize energy consumption. Three real-world delivery tests show that component sizing leads to an increase in the average profit per kilometer by 2.62%–8.10%. Upon evaluating the impact of powertrain and battery mass/volume on cargo capacity, the battery pricing ceases to impact the sizing of components. However, the electricity price and freight significantly influence the optimal size of components. Moreover, a sensitivity analysis focusing on market price factors underscores the importance of component sizing for maximizing profit, particularly in scenarios where freight costs fluctuate in commercial settings.

Technical note for intraoperative determination of proper acetabular cup size in primary total hip arthroplasty.

Selecting the optimal size of components is crucial when performing a primary total hip arthroplasty. Implanting the accurate size of the acetabular component can occasionally be exacting, chiefly for surgeons with little experience, whilst the complications of imprecise acetabular sizing or over-reaming can be potentially devastating. To assist clinicians intraoperatively with a simple and repeatable tip in elucidating the ambivalence when determining the proper acetabular component size is not straightforwardly achieved, specifically when surgeons are inexperienced or preoperative templating is unavailable. This method was employed in 263 operations in our department from June 2021 to December 2022. All operations were performed by the same team of joint reconstruction surgeons, employing a typical posterior hip approach technique. The types of acetabular shells implanted were: The Dynasty® acetabular cup system (MicroPort Orthopedics, Shanghai, China) and the R3® acetabular system (Smith & Nephew, Watford, United Kingdom), which both feature cementless press-fit design. The mean value of all cases was calculated and collated with each other. We distinguished as oversized an implanted acetabular shell when its size was > 2 mm larger than the size of the acetabular size indicator reamer (ASIR) or when the implanted shell was larger than 4 mm compared to the preoperative planned cup. The median size of the implanted acetabular shell was 52 (48-54) mm, while the median size of the preoperatively planned cup was 50 (48-56) mm, and the median size of the ASIR was 52 (50-54) mm. The correlation coefficient between ASIR size and implanted acetabular component size exhibited a high positive correlation with r = 0.719 (P < 0.001). Contrariwise, intraoperative ASIR measurements precisely predicted the implanted cups' size or differed by only one size (2 mm) in 245 cases. In our study, we demonstrated that the size of the first acetabular reamer not entering freely in the acetabular rim corroborates the final acetabular component size to implant. This was also corresponding in the majority of the cases with conventional preoperative templating. It can be featured as a valid tool for avoiding the potentially pernicious complications of acetabular cup over-reaming and over-sizing in primary total hip arthroplasty. It is a simple and reproducible technical note useful for confirming the predicted acetabular cup size preoperatively; thus, its application could be considered routinely, even in cases where preoperative templating is unavailable.

Optimal Design of Grid-Connected Hybrid Renewable Energy System Considering Electric Vehicle Station Using Improved Multi-Objective Optimization: Techno-Economic Perspectives

Electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) have been widely integrated into distribution systems. Electric vehicles (EVs) offer advantages for distribution systems, such as increasing reliability and efficiency, reducing pollutant emissions, and decreasing dependence on non-endogenous resources. In addition, vehicle-to-grid (V2G) technology has made EVs a potential form of portable energy storage, alleviating the random fluctuation of renewable energy power. This paper simulates the optimal design of a photovoltaic/wind/battery hybrid energy system as a power system combined with an electric vehicle charging station (EVCS) using V2G technology in a grid-connected system. The rule-based energy management strategy (RB-EMS) is used to control and observe the proposed system power flow. A multi-objective improved arithmetic optimization algorithm (MOIAOA) concept is proposed to analyze the optimal sizing of the proposed system components by calculating the optimal values of the three conflicting objectives: grid contribution factor (GCF), levelled cost of electricity (LCOE), and energy sold to the grid (ESOLD). This research uses a collection of meteorological data such as solar radiation, temperature, and wind speed captured every ten minutes for one year for a government building in Al-Najaf Governorate, Iraq. The results indicated that the optimal configuration of the proposed system using the MOIAOA method consists of eight photovoltaic modules, two wind turbines, and thirty-three storage batteries, while the fitness value is equal to 0.1522, the LCOE is equal to 2.66 × 10−2 USD/kWh, the GCF is equal to 7.34 × 10−5 kWh, and the ESOLD is equal to 0.8409 kWh. The integration of RESs with an EV-based grid-connected system is considered the best choice for real applications, owing to their remarkable performance and techno-economic development.

Multi-objective optimization and sustainable design: a performance comparison of metaheuristic algorithms used for on-grid and off-grid hybrid energy systems

Alternative energy sources are needed for a sustainable world due to rapidly increasing energy consumption, fossil fuels, and greenhouse gases worldwide. A hybrid renewable energy system (HRES) must be optimally dimensioned to be responsive to sudden load changes and cost-effective. In this study, the aim is to reduce the carbon emissions of a university campus by generating electricity from a hybrid energy production system with solar panels, wind turbine, a diesel generator, and battery components. On the university campus where the hybrid energy system will be installed, the ambient temperature, solar radiation, wind speed, and load demands have been recorded in our database. Optimization algorithms were used to select the power values of the system components to be installed using these data in an efficient and inexpensive manner according to the ambient conditions. For optimal sizing of HRES components, gray wolf optimizer combined with cuckoo search (GWOCS) technique was investigated using MATLAB/Simulink. In this way, it has been tried to increase their efficiency by combining current optimization techniques. The cornerstone of our optimization efforts for both on-grid and off-grid models pivots on a constellation of critical decision variables: the power harvested from wind turbines, the productivity of solar panels, the capacity of battery storage, and the power contribution of diesel generators. In our pursuit of minimizing the annual cost metric, we employ a tailor-made function, meticulously upholding an array of constraints, such as the quotient of renewable energy and the potential risk of power disruption. A robust energy management system is integral to our design, orchestrating the delicate power flow balance among micro-grid components—vital for satisfying energy demand. Upon analyzing the outcomes of the study, it is apparent that the proposed Scenario 1 HRES effectively utilizes solar and battery components within the off-grid model, surpassing the efficiency of four other hybrid scenarios under consideration. Regarding optimization processes, the off-grid model exhibits superior results with the implementation of the GWOCS algorithm, delivering faster and more reliable solutions relative to other methodologies. Conversely, the optimization of the on-grid model reaches its optimal performance with the application of the cuckoo search algorithm. A comprehensive comparison from both technical and economic view points suggests the on-grid model as the most feasible and suitable choice. Upon completion of the optimization process, the load demand is catered to by a combination of a 2963.827-kW solar panel, a 201.8896-kW battery, and an additional purchase of 821.9 MWh from the grid. Additionally, an energy surplus sale of 1379.8 MWh to the grid culminates in an annual cost of system (ACS) of 475782.8240 USD, a total net present cost of 4815520.2794 USD, and a levelized cost of energy of 0.12754 USD/kWh. Solar panels cover the entire system, and the renewable energy fraction is 100%.

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 &lt; 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.