Design Of Renewable Energy System Research Articles

Design, optimization, and data analysis of solar-tidal hybrid renewable energy system for Hurawalhi, Maldives

In the modern era, every country work towards sustainable development with the help of effective utilization of renewable energy system. The design and planning of multi-renewable energy system networks for Hurawalhi, Maldives, with an approximate 450.09 KW load, is proposed in this study. The first resource assessment for solar radiation, wind velocity, and the tidal range is done through linear regression and decision tree-based data analysis. Design of the system is done through the HOMER software, where the electricity production (KWh/Year) through the solar and tidal systems are 1,401,086 and 197,509, respectively. The energy generation through the proposed system is 1593.6 kWh/day (Baseline) and 2424.25 (Scaled). Cost optimization is done through the Chaotic Particle Swarm Optimization and Cuckoo Optimization techniques. Further survival measurement is done through the logrank and probit analysis.

Techno-economic assessment and optimal design of hybrid power generation-based renewable energy systems

This study presents a techno-economic analysis of five different hybrid energy systems (HES)-based renewable energy sources (RES) in the northern region of Saudi Arabia. It aims to provide valuable insights into the economic feasibility, technical compatibility, and environmental implications of these systems. To carry out this analysis, hybrid optimization of multiple energy resources (HOMER) software is used. Parameters such as total net price cost (TNPC), cost of energy (COE), initial capital cost (ICC), energy generation and consumption, excess and unmet energy, renewable energy (RE) fraction, and emissions were considered to assess the different HES configurations. The findings demonstrate that the grid-connected photovoltaic/wind turbines (PV/WT) system is the best option in terms of economic perspective with TNPC and COE of $213,099 and $0.0480/kWh, respectively followed by grid-connected PV/fuel cell (FC)/WT and stand-alone PV/diesel generator (DG)/WT/battery systems. The PV/battery and PV/FC/WT/battery green hybridization are the highest cost-effective due to their high initial and replacement battery costs. The grid-connected PV/WT and PV/DG/WT/battery systems are the most efficient in meeting load demand, while the PV/battery and PV/FC/WT/battery hybridization have the highest excess and unmet energy. From an environmental perspective, the stand-alone HES consisting solely of RESs, i.e., PV and batteries, has the lowest emissions, making it one of the most environmentally friendly options. Following closely is the PV/FC/WT/battery configuration, which also demonstrates low emissions. On the other hand, the grid-connected PV/WT system exhibits the highest total GHG emissions, rendering it the least environmentally friendly option. This research provides decision-makers, researchers, and stakeholders with valuable information for selecting the optimal hybrid energy systems, taking into account economic, technical, and environmental considerations.

Comprehensive sustainability assessment and multi-objective optimization of a novel renewable energy driven multi-energy supply system

The pressing challenge of over-reliance on fossil fuels and the resultant greenhouse gas emissions necessitates innovative solutions in the energy sector. Addressing this, our study delves into the design and optimization of a multi-source renewable energy system, distinctively integrating wind, photovoltaic, concentrated solar power, proton exchange membrane electrolyzers, and fuel cells. The system is designed to meet user energy demands while reducing grid power consumption. The approach is novel in its comprehensive energy management strategy, which not only mitigates the inconsistencies of wind and solar energy but also aligns with user demand proactively. To assess the system's sustainability, we employed a life cycle assessment method, encompassing energy, economic, environmental, and social dimensions. A unique aspect of our study is the exploration of how varying meteorological conditions and geographical locations influence the system's sustainability. Key results indicate that, with an increase in photovoltaic capacity from 25 MW to 150 MW, the hybrid system reduces grid power consumption by 18,457.9 MWh annually. This leads to a decrease in the average energy cost by 0.0324$/kWh and an increase in the net present value by 0.2418 M$. Impressively, the system, when integrated with 150 MW of photovoltaic generation, achieves an optimal combined sustainability index of 0.999. Under the best capacity configuration, the levelized cost of energy stands at 0.3096$/kW, with CO2 emissions limited to 630 t/y. A noteworthy correlation emerged: a 1 % decrease in onsite CO2 emissions corresponds to a 1.714 % surge in the levelized cost of energy. This research enhances sustainable energy policy development and decision-making processes by offering a comprehensive understanding of multi-energy supply system sustainability. The results are invaluable for the future design and operation of such systems, facilitating a transition towards more sustainable energy systems.

Optimizing Security Systems with an Optimum Design of a Hybrid Renewable Energy System

Security systems play a crucial role in protecting individuals and assets within diverse infrastructures, including seaports. The uninterrupted operation of these systems heavily relies on a continuous power supply, as any disruptions can lead to severe consequences. Therefore, security systems are classified as critical loads requiring uninterrupted power availability. This study focuses on the investigation of an optimal hybrid energy system (HES) to ensure a reliable power supply for security systems in two seaports located in Turkiye. Through the utilization of the HOMER software, optimization analyses were conducted, considering both conventional sources such as grid-generator or grid-generator-battery configurations, as well as off-grid and on-grid HES solutions integrating photovoltaic (PV) and wind turbine technologies. The findings reveal that on-grid HES solutions incorporating PV and wind technologies offer a more cost-effective and dependable energy supply for security systems in seaports, surpassing traditional alternatives. This study represents a significant contribution to the existing literature, as it presents the first comprehensive optimization study on the design of HES for security systems. The outcomes serve as a valuable reference for future research endeavors in this field.

Design Optimization and Analysis of on-Grid Hybrid Renewable Energy System for Audio-Visual Chain

Rapid advancement of various renewable energies (especially wind and solar energies) as well as energy storage technologies dramatically changes the current energy configuration including the television buildings. In this paper, a micro-grid with high penetration of clean energy systems has been designed for an audio-visual chain. The design and performance optimization of on-grid hybrid renewable energy system using hybrid optimization software is developed and analyzed. The micro-grid design containing photovoltaic panels, wind turbines and storage batteries connected to the grid with a backup diesel generator. The technical and economic advantages of a micro-grid based on clean energy sources for an audio-visual chain located in Tunisia is presented to improve the performance of industrial buildings by providing a green environment with alternative energy sources to meet the high energy demand. This analysis is accomplished by studying solar and wind energy potentials and by aggregating data from various sources. The system was analyzed using HOMER software, which simulate and optimize the proposed micro-grid to a specific demand (audio-visual production chain) with a sensitive/normal loads and energy sources. For the economic analysis, the net present cost was used as objective function in the optimization process. The obtained results show that, in the selected configuration of micro-grid, 49.4% of all energy is supplied by renewable sources with a Total Net Present Cost of $5.02 M under a minimum COE of $0.0609/kWh.

Optimal Design and Operation of Hybrid Renewable Energy Systems for Oakland University

This research paper presents a comprehensive study on the optimal planning and design of hybrid renewable energy systems for microgrid (MG) applications at Oakland University. The HOMER Pro platform analyzes the technical, economic, and environmental aspects of integrating renewable energy technologies. The research also focuses on the importance of addressing unmet load in the MG system design to ensure the university’s electricity demand is always met. By optimizing the integration of various renewable energy technologies, such as solar photovoltaic (PV), energy storage system (ESS), combined heat and power (CHP), and wind turbine energy (WT), the study aims to fulfill the energy requirements while reducing reliance on traditional grid sources and achieving significant reductions in greenhouse gas emissions. The proposed MG configurations are designed to be scalable and flexible, accommodating future expansions, load demands changes, and technological advancements without costly modifications or disruptions. By conducting a comprehensive analysis of technical, economic, and environmental factors and addressing unmet load, this research contributes to advancing renewable energy integration within MG systems. It offers a complete guide for Oakland University and other institutions to effectively plan, design, and implement hybrid renewable energy solutions, fostering a greener and more resilient campus environment. The findings demonstrate the potential for cost-effective and sustainable energy solutions, providing valuable guidance for Oakland University’s search for energy resilience and environmental surveillance, which has a total peak load of 9.958 MW. The HOMER simulation results indicate that utilizing all renewable resources, the estimated net present cost (NPC) is a minimum of USD 30 M, with a levelized energy cost (LCOE) of 0.00274 USD/kWh. In addition, the minimum desired load will be unmetered on some days in September.

Operational strategy and capacity optimization of standalone solar-wind-biomass-fuel cell energy system using hybrid LF-SSA algorithms

This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system.

Different Scenarios for Reducing Carbon Emissions, Optimal Sizing, and Design of a Stand-Alone Hybrid Renewable Energy System for Irrigation Purposes

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.

Improving solar radiation source efficiency using adaptive dynamic squirrel search optimization algorithm and long short-term memory

Artificial intelligence and machine learning are used to optimize the design parameters of renewable energy sources, which are now regarded as vital components in current clean energy sources. As a result, system requirements can be reduced, and a well-designed system can improve performance. Artificial intelligence approaches in renewable energy sources and system design would significantly cut optimization time while maintaining high modeling accuracy and optimum performance. This study examines machine learning in depth, emphasizing how it can be used in developing renewable energy sources because of the vast range of technologies it can use. This paper approximates the hourly tilted solar irradiation using climate factors. The irradiance is estimated using a hybrid ensemble-learning approach. This approach combines a proposed adaptive dynamic squirrel search optimization algorithm (ADSSOA) with long short-term memory (LSTM) methods. To the best of our knowledge, this combination has not been used for solar radiation. The results are analyzed and contrasted with the outcomes of several recent swarm intelligence algorithms, such as the genetic algorithm, particle swarm optimization, and gray wolf optimizer. The binary ADSSOA approach performed as expected, with an average error of 0.1801 and a standard deviation of 0.0656. The ADSSOA–LSTM model had the lowest root mean square error (0.000388) compared to LSTM’s (0.001221). In addition, the statistical analysis uses 10 iterations of each presented and evaluated method to provide accurate comparisons and reliable results.

Optimizing upside variability and antifragility in renewable energy system design

Despite the considerable uncertainty in predicting critical parameters of renewable energy systems, the uncertainty during system design is often marginally addressed and consistently underestimated. Therefore, the resulting designs are fragile, with suboptimal performances when reality deviates significantly from the predicted scenarios. To address this limitation, we propose an antifragile design optimization framework that redefines the indicator to optimize variability and introduces an antifragility indicator. The variability is optimized by favoring upside potential and providing downside protection towards a minimum acceptable performance, while the skewness indicates (anti)fragility. An antifragile design primarily enhances positive outcomes when the uncertainty of the random environment exceeds initial estimations. Hence, it circumvents the issue of underestimating the uncertainty in the operating environment. We applied the methodology to the design of a wind turbine for a community, considering the Levelized Cost Of Electricity (LCOE) as the quantity of interest. The design with optimized variability proves beneficial in 81% of the possible scenarios when compared to the conventional robust design. The antifragile design flourishes (LCOE drops by up to 120%) when the real-world uncertainty is higher than initially estimated in this paper. In conclusion, the framework provides a valid metric for optimizing the variability and detects promising antifragile design alternatives.

Design and operational management of sustainable Multi- networks green energy system for study area at India

In the modern era, every country work towards the sustainable development with the help of effective utilization of renewable energy system. Sometime single renewable energy source doesn’t fulfill the load demand, so in that case single renewable energy resource is replaced through the multi-network renewable energy system. This paper presents design and operational management of multi-network renewable energy system for Himachal Pradesh Cricket Association Dharamshala, India with approximate 250 KW load, is proposed in this study. First resource assessment for solar radiation, wind velocity, water velocity and waste material is done through the Rapid Miner based logistic regression analysis. Further aggregate operational planning of multi-network renewable energy system and after that feasible design is done through the HOMER software. According to the assessment, the net present cost of hydro-biomass energy system is minimum compare to the other multi-network renewable energy system.

A novel cloud-based framework for optimal design of stand-alone hybrid renewable energy system considering uncertainty and battery aging

This paper performs a new optimal framework for a hybrid photovoltaic-wind system design integrated with battery storage (PV/WT/Battery), considering cloud-based uncertainty modeling and battery degradation based on real meteorological data from the Sarein-Ardabil region in Iran. The objective function is presented as minimizing the total net present cost (NPC), load loss, and battery degradation cost. The decision variables include the number of PVs, WTs, batteries, inverter power, and the angle of PVs installation, which is optimally determined via a new meta-heuristic optimization algorithm named opposition-based learning and Gradient-based optimizer (OBLGBO). In the proposed framework, the cloud theory method based on combining fuzzy theory and probability statistics has been applied for modeling the energy resources and load demand uncertainties. The simulation results indicate that considering battery degradation costs increases the overall cost of designing hybrid systems. As the cost of degradation increases, reliability indices improve due to an increase in the number of wind turbines and a decrease in the number of batteries. Also, the results demonstrated that incorporating the uncertainties based on the cloud theory increases the design cost, and the system reliability is weakened. Therefore, the proposed optimal framework has presented a real and accurate approach to the optimal design of energy systems with more accurate knowledge of electricity generation costs and the cost of improving reliability in conditions of uncertainties. Moreover, the superior capability of the OBLGBO compared with traditional GBO and the well-known particle swarm optimization (PSO) and grey wolf optimizer (GWO) is proved to achieve lower design cost and better reliability indices.

Hybrid Renewable Energy System Design: A Machine Learning Approach for Optimal Sizing with Net-Metering Costs

Hybrid renewable energy systems with photovoltaic and energy storage systems have gained popularity due to their cost-effectiveness, reduced dependence on fossil fuels and lower CO2 emissions. However, their techno-economic advantages are crucially dependent on the optimal sizing of the system. Most of the commercially available optimization programs adopt an algorithm that assumes repeated weather conditions, which is becoming more unrealistic considering the recent erratic behavior of weather patterns. To address this issue, a data-driven framework is proposed that combines machine learning and hybrid metaheuristics to predict weather patterns over the lifespan of a hybrid renewable energy system in optimizing its size. The framework uses machine learning tree ensemble methods such as the cat boost regressor, light gradient boosting machine and extreme gradient boosting to predict the hourly solar radiation and load demand. Nine different hybrid metaheuristics are used to optimize the hybrid renewable energy system using forecasted data over 15 years, and the optimal sizing results are compared with those obtained from 1-year data simulation. The proposed approach leads to a more realistic hybrid renewable energy system capacity that satisfies all system constraints while being more reliable and environmentally friendly. The proposed framework provides a robust approach to optimizing hybrid renewable energy system sizing and performance evaluation that accounts for changing weather conditions over the lifespan of the system.

Techno-economic study and the optimal hybrid renewable energy system design for a hotel building with net zero energy and net zero carbon emissions

The tourism sector is a key source of national income, and the use of renewable energy resources promotes green and sustainable tourism. In addition, ensuring a reliable supply and efficient use of clean energy is a primary objective of Egypt's Vision 2030, as it has a significant impact on the sustainable development and growth of the tourism sector in Egypt. Net-zero energy buildings (NZEBs) are a promising decarbonization effort as they reduce energy consumption while increasing the share of renewable energy. Therefore, it is worth investigatingthe opportunities for NZEBs applications in tourism sector. This paper presents an optimization analysis of a hotel’s grid-connected hybrid renewable energy system (HRES) based on its technical, economical, and environmental prospects. Seven scenarios are considered for on-site generation to match the energy requirements of the specific hotel building. The proposed hybrid systems incorporate photovoltaic (PV), wind turbine (WT), and biogas generators into a grid-connected system for supplying power to a tourist hotel. The size of the microgrid system is optimized to decrease the net present cost (NPC) and levelized cost of electricity (LCOE). The results reveal that the PV/wind grid-connected system, with renewable energy generators consisting of 143 kW of photovoltaic modules and one 20 kW wind turbine with a hub height of 18 m, has the lowest NPC and LCOE. This hybrid energy system has a net present cost of 388 k$, which is significantly lower compared to the electricity grid's cost of 873 k$. The electricity price of the proposed HRES, at 2.1 ¢/kWh, is highly competitive with Egypt's retail electricity prices of 6.9 ¢/kWh. Moreover, grid-connected HRES can lower the pressure on the electricity grid arising from the feed-in of electricity from HRES. Furthermore, the proposed HRES will help slow climate change by reducing GHG emissions and fossil fuel usage. Finally, this study will provide valuable guidance for the practical application of NZEBs in Egypt as well as similar global zones.

Design and feasibility analysis of grid-connected hybrid renewable energy system: perspective of commercial buildings

Design and feasibility analysis of grid-connected hybrid renewable energy system: perspective of commercial buildings

On the behavior of renewable energy systems in buildings of three Saudi cities: Winter variabilities and extremes are critical

Integration of renewable energy systems emerges as a key enabler for future low-carbon electricity supply. To maintain a secure and effective supply, the design and operation of these systems must carefully account for the influence of weather variability on the availability of renewable energy resources and on energy demand. This study examines the impact of weather variability on the optimal design and operation of renewable energy systems for office buildings in Saudi Arabia. The building electricity demand is supplied by a photovoltaic/wind/battery storage system, with an additional connection to the grid. The optimization process then aims at finding the optimal capacities of the renewable energy system components, so as to strike a trade-off between life cycle cost and CO2 emissions by considering weather datasets with different levels of variability. The results show that accounting for the full weather variability, particularly the extreme weather events, leads to higher investment costs in fully renewable energy system, namely due to increased battery storage (up to 288%). Neglecting extreme events at the design stage, however, leads to a significant performance gap that could be compensated by grid integration, which adds to the overall CO2 emissions, for a negligible change in the system cost. Such extreme weather events are then associated with cloudy and high temperatures days, the impact of which drives down the photovoltaic power output and increases the demand for energy from the grid or from the storage. The study further examines the electricity price impacts on renewable energy system design and operation. With global weather variability challenges at the forefront, the paper findings draw insightful recommendations related to energy policy.

An effective design of hybrid renewable energy system using an improved Archimedes Optimization Algorithm: A case study of Farafra, Egypt

The Improved Archimedes Optimization Algorithm (IAOA) is presented and applied to design a hybrid renewable energy system (HRES) for a microgrid system in the Farafra region of Egypt. The studied microgrid consists of three scenarios based on PV panels, wind turbine systems, diesel generators, and a battery energy storage system (BESS). The objective is to minimize the design function of the net present cost (NPC) that englobes all expenses during the project lifetime, respecting three constraints: the renewable fraction index, loss of power supply probability, and availability. The simulation results are compared with several known algorithms, such as the original Archimedes optimization algorithm (AOA), artificial electric field algorithm (AEFA), Equilibrium optimizer (EO), Grey Wolf optimizer (GWO), and Harris Hawks Optimization (HHO) algorithms. The results prove the ability of the proposed algorithm to solve the problem design, and they also demonstrate its superior efficiency to competing algorithms. The best-found HRES is the PV panel/wind turbine/diesel generator/battery storage system HRES, the NPC is $187,181, equivalent to cost energy (LCOE) of 0.213 $/kWh. The constraints are respected, the reliability is approximately 5%, the renewable fraction index is close to 90%, and the availability is approximately 100%. From the results, it is observed that the synergy of PV and wind systems is mandatory in such areas, and the battery also plays an important role in managing and arranging the energetic flow in HRES systems. The benchmark functions are tested using 23 functions, which proved that the IAOA performed better than the original AOA algorithm.

Study of Meta-heuristic Optimization Methodologies for Design of Hybrid Renewable Energy Systems

Exponentially rising hydrocarbon fuel consumption creates several environmental issues that have necessitated the integration of renewable energy systems (RES) into the grid. Solar photovoltaic and wind energy constitutes the most matured hybrid renewable energy system (HRES) alternative technology against conventional fossil fuels as it is pollution-free, easily available, low-price, and available in abundance. The intermittent nature of solar irradiation and fluctuation in wind speed makes the system design inappropriate, making it either oversized or undersized. Due to this, deploying solar PV-Wind-based HRES is becoming either expensive or inefficient. Thus, there is an immediate requirement for optimization problem-solving methodologies to minimize the HRES costs. This paper mainly focuses on a critical comparative analysis of the many applied and promising optimization methodologies. The methodologies used to design the optimal capacity of HRES, are categorized as traditional, modern, and hybrid on the basis of identified objective functions, decision variables, and evaluation indicators. While most of the present studies have been based on the technical reliability and economical perspective of HRES, in this paper the environmental indicators have got major emphasis. The facts emerging out of the study indicate that application of hybrid meta-heuristic optimization techniques for engineering applications is growing significantly due to their flexibility and efficiency. The application of economical indicators was observed to be more prevalent compared to other reliability and environmental indicators. However, the percentage of multi-objective functions of economical and reliability is a growing trend for the optimal design process of HRES.

Optimal sizing of standalone for hybrid renewable energy system by using PSO optimization technique

Providing electricity to rural regions is difficult for developing countries, such as Iraq, particularly in remote parts without grid connections. The electrical demands of Zerbattiya, a community in southern Iraq near the Iranian border, are discussed in this paper. The proposed system includes wind turbines, solar panels, diesel engines, batteries, etc. This study suggests a techno-economic viable and optimal size for each component to generate electricity for this area. This research uses particle swarm optimization techniques (PSO). The best hybrid renewable energy system (HRES) design is achieved by balancing the lowest possible cost of energy (COE) with the lowest possible loss of power supply probability (LPSP) and the greatest possible reliability factor value. As a result of the findings, the respective ideal values of number of photovoltaics (NPV), number of wind turbines (NWT), number of diesel generator (NDG), number of batteries (NBT), COE, LPSP, and reliability are 138, 43, 2, 324, US$/KWh 0.129, 0.0508%, and 99.9492%, respectively. Finally, it was discovered that implementing a HRES is an effective way to address the electrical demands of remote rural regions in Iraq and other developing countries with similar climates.

Optimum hybrid renewable energy system design for on and off grid buildings: Hotel, education and animal hospital building case

Optimum design of hybrid renewable systems is essential for the efficient utilization of the renewable sources and uninterruptible energy supply. The size and backup capacity of the system are effected by the hourly energy demand profile of the building.In this study, optimal design of PV/wind/battery/diesel hybrid renewable energy system (HRES) for the electrification of a hotel building (HB), education building and animal hospital (AH) for on and off grid cases are evaluated to compare buildings with different demand profiles. The hourly electricity consumption of the buildings is measured during January 2023. Nonlinear swarm optimization algorithm was used to minimize the system cost of electricity (COE) and maximize the renewable fraction.Optimal off-grid HRES found to include PV and wind turbine together. AH represented the most stable and highest hourly demand which resulted in minimum COE (0.18 $/kWh and 0.321 $/kWh) for on and off grids.Then annual electricity consumptions of the buildings are equalized to compare buildings with the same annual but different hourly load. On-grid HB found to have minimum COE (0.177 $/kWh) due to the closer hourly demand profile on weekends and weekdays then other buildings. Therefore, on-grid HRES configurations are found to be more cost effective in buildings utilized seven days of the week. However, in off-grid case minimum COE (0.321 $/kWh) was obtained for AH and maximum was found for HB which was due to the peak demands. The result shows that buildings with sudden increases require high battery capacity which results in high COE.