Cost Of Grid Electricity Research Articles

Water, energy, and food nexus in a solar-powered brackish water desalination plant in Jordan

Abstract The growing demand for water in domestic, agricultural, and energy production applications poses a significant challenge for Jordan. This work assesses the role of brackish water desalination as an alternative to alleviate water scarcity in semi-arid regions. Desalination is still limited in its application in Jordan due to high electricity tariffs. Shifting to renewable sources such as solar energy, abundant in the country, is a feasible way to power technologies with a high energy demand. In this work, we study the brackish water desalination plant at the Hashemite University in Jordan that is powered by a photovoltaic (PV) solar system (the HU PV-BWRO). The plant’s performance was evaluated in the context of the water-energy nexus as a hybrid water supply solution. While this work integrates essential elements, such as water availability, technical options, economic viability, and agricultural management, the analysis primarily focuses on the technical and economic aspects related to water, energy, and food. Water assessment results indicate that the groundwater wells near the HU campus are at risk of quality degradation over time, as they have shown a slight increasing trend in salinization from 2015 to 2023. Energy assessment results show a promising performance from the HU PV-BWRO desalination plant, with a specific energy consumption (SEC) value of 1.2 kWh m−3 (140% to 400% less energy consumption compared with other Jordanian desalination plants of similar capacity). Unit price comparisons indicate that the energy cost of PV (0.042 USD/m3) is 5 times less than the cost of grid electricity (0.24 USD/m3). The operational cost of the solar desalination plant at full capacity, is USD 0.23/m3. This is about 260% less than the operation cost for local, grid-powered desalination plants. Finally, it is estimated that by operating the plant at 50% of its total capacity, the produced water could be sufficient to irrigate up to 80% of the HU campus to increase agricultural production. This study highlights the importance of decreasing reliance on energy for water and food production, and it shows that the use of solar powered desalination could be used as an example in semi-arid regions, particularly in terms of integrating renewable energy and energy efficiency.

Optimal energy management and techno-economic assessment of hydrogen energy production system incorporated with photovoltaic and battery storage

Green hydrogen (H2) has garnered significant attention in recent years as a key component of a zero-emission future owing to its diverse range of applications. The energy management (EM) strategy of the green H2 production system (HPS) plays a crucial role in facilitating efficient and cost-effective green H2 production. This can be achieved by regulating the operating point of the electrolyzer (EL) to obtain economic and operational benefits. This study proposes an optimal EM scheme for a photovoltaic-based battery (BT) grid-connected green HPS. The HPS is intended to meet H2 and electricity demands of an industrial HPS. The objective of the EM scheme is to minimize the cost of green H2 production by minimizing the total system costs of the HPS, while ensuring that the system operates reliably. The objective is realized by incorporating the EM strategy model with Z-score statistical values of past grid electricity costs, which reflect seasonal electricity price trends. This enables green H2 production when the utility grid electricity prices are relatively low. The EM variables considered for the optimization include: EL status (on/off operation of the EL), electricity purchased from the grid, input power to the EL and output power from EL (H2 production rate), energy stored in the H2 storage tank and BT, discharging and charging power of the BT and H2 tank. Four test case systems (Case #1 - #4) were used to demonstrate the techno-economic benefits of the proposed EM scheme model for green HPS. Finally, a sensitivity analysis is performed on the green HPS to investigate the impact PV power and EL capacity on the cost of H2 and EL efficiency.

Exogenous-spatial scale techno-economic methodology for energy application assessment based on levelized cost of electricity: A case study in Norway

Amidst the escalating electricity prices in Norway and Europe, a compelling need arises for alternative and sustainable energy solutions. This study introduces a novel techno-economic method to assess micro-combined heat and power (m-CHP) systems, specifically those powered by Micro Gas Turbines (MGTs). Employing both numerical and experimental analyses, the investigation focuses on the techno-economic viability of MGT-based m-CHP systems fueled by natural gas (NG) and a blend of NG-H2 (23 % Vol.%). A 20-year operational lifespan serves as the basis for calculating the Levelized Cost of Electricity (LCoE) and comparing it with grid-based electricity costs. The technical model relies on 3 kW MGT experiments and numerical system modelling using IPSEPro® software. Economic modelling involves developing a cost model and employing a unique Techno Economic Analysis (TEA)-rooted methodology for LCoE calculation. Through 16 techno-economic scenarios, substantial cost-benefit advantages of m-CHP systems over grid-based electricity are demonstrated, supported by point LCoE values. To address uncertainties, a “Risk and Uncertainty” analysis is conducted using Monte-Carlo Simulation, generating a LCoE distribution. This method incorporates the standard deviations and mean values of key parameters, ensuring economic consistency and reliability with minimal fluctuations. Findings reveal negligible variation from the expected LCoE at 100 % power with NG, providing 98 % accuracy in assessing cost-benefit. The final result yields a Ceq, LCoE of 1.42 Current NOK/kWh (0.13 USD/kWh) and 1.19 constant NOK/kWh (0.11 USD/kWh). In contrast to grid electricity costs (2.54 NOK/kWh (0.24 USD/kWh) with current NOK and 1.78 NOK/kWh (0.17 USD/kWh) with constant NOK, the analysis underscores the significant and sustained cost benefits of m-CHP systems. Moreover, the proposed methodology offers flexibility for transitioning to a “carbon-neutral” operation with hydrogen (H2) as its prices decrease.

Energetic and economic analysis of novel cascade systems for ammonia-fed solid oxide fuel cell

This study proposes an ammonia-fed cascade recirculation system comprising: (1) two stacks connected in series and (2) an anode off-gas recirculation configuration to enhance electrical efficiency of the system. The proposed system is both energetically and economically investigated. Under energetic analysis, the effects of fuel utilization, temperature, and recirculation rate, are clarified. Information from energetic analysis is employed to investigate levelized cost of electricity (LCOE) value in the entire working range. Due to the high potential of the cascade configuration, a cascade system without recirculation is also investigated using the same methods. The results reveal that the cascade-only and cascade recirculation systems obtain electrical efficiency up to 67.78% and 68.80%, respectively. The minimum LCOEs are 0.485 and 0.486 $/kWh, respectively, which are both higher than grid electricity cost; however, compared with other ammonia-fed SOFC systems, the two cascade configuration-based systems show significant improvements in both efficiency and cost.

Fuzzy logic-based controller of the bidirectional direct current to direct current converter in microgrid

Microgrids are small-scale power networks that include renewable energy sources, load, energy storage systems, and energy management systems (EMS). Lithium-ion batteries are the most used battery for energy storage in microgrids due to their advantages over other types of batteries. However, to protect the battery from the explosion and to manage to charge and discharge based on state-of-charge (SoC) value, this type of battery requires the use of an energy management system. The main objective of this paper is to propose an intelligent control strategy for energy management in the microgrid to control the charge and discharge of Li-ion batteries to stabilize the system and reduce the cost of electricity due to the high cost of grid electricity. The proposed technique is based on a fuzzy logic controller (FLC) for voltage control. The FLC is based on the measured voltage of the direct current (DC) bus and the fixed reference voltage to generate buck/boost converter signal control. The proposed technique has been simulated and tested using MATLAB/Simulink software which illustrates the tracking of desired power and DC bus voltage regulation. The simulation results confirm that the proposed systems can diminish the deviations of the system's voltage.

Scheduling and Sizing of Campus Microgrid Considering Demand Response and Economic Analysis.

Current energy systems face multiple problems related to inflation in energy prices, reduction of fossil fuels, and greenhouse gas emissions which are disturbing the comfort zone of energy consumers and the affordability of power for large commercial customers. These kinds of problems can be alleviated with the help of optimal planning of demand response policies and with distributed generators in the distribution system. The objective of this article is to give a strategic proposition of an energy management system for a campus microgrid (µG) to minimize the operating costs and to increase the self-consuming energy of the green distributed generators (DGs). To this end, a real-time based campus is considered that currently takes provision of its loads from the utility grid only. According to the proposed given scenario, it will contain solar panels and a wind turbine as non-dispatchable DGs while a diesel generator is considered as a dispatchable DG. It also incorporates an energy storage system with optimal sizing of BESS to tackle the multiple disturbances that arise from solar radiation. The resultant problem of linear mathematics was simulated and plotted in MATLAB with mixed-integer linear programming. Simulation results show that the proposed given model of energy management (EMS) minimizes the grid electricity costs by 668.8 CC/day ($) which is 36.6% of savings for the campus microgrid. The economic prognosis for the campus to give an optimum result for the UET Taxila, Campus was also analyzed. The general effect of a medium-sized solar PV installation on carbon emissions and energy consumption costs was also determined. The substantial environmental and economic benefits compared to the present situation have prompted the campus owners to invest in the DGs and to install large-scale energy storage.

Firefly algorithm and learning-based geographical task scheduling for operational cost minimization in distributed green data centers

Green Data Centers (GDCs) are more and more deployed world-wide. They integrate many renewable sources to provide clean power and decrease their operating cost. GDCs are typically deployed over multiple locations where renewable energy availability, bandwidth prices and grid electricity cost have high geographical diversity. This paper focuses on delay-bounded applications in distributed GDCs (DGDCs) and performs cost and energy-effective scheduling of multiple heterogeneous applications verifying delay bound constraints of different tasks. DGDCs’ operational cost minimization problem is formulated and successfully optimized using an innovative modified Firefly Algorithm (mFA). Real-life data trace-driven experiments are conducted to evaluate the effectiveness of the proposed mFA in solving this problem. High performance task scheduling results are obtained. The operational cost of each GDC is minimized, the utilization of solar and wind renewable energy from the different geographical locations is maximized while delay bound constraints of all tasks are strictly met. Compared to Bat Algorithm, Simulated-annealing Bat Algorithm and basic firefly algorithm, mFA can produce a schedule that outperforms its peers’ drastically in terms of operational cost of DGDCs. Moreover, mFA finds more rapidly both global or local optima than its peers. It succeeds to meet all equality and inequality constraints at all time slots while its peers may sometimes fail to find satisfactory solutions at some particular time slots.

Effects of power tariffs and aeration dynamics on the expansion of water resource recovery facilities

Water resource recovery facilities are major points of electricity demand and exhibit dynamic electricity demand due to their influent load as well as their diurnal and seasonal cycles. This dynamic demand along with the diurnal and seasonal nature of grid electricity cost as well as greenhouse gas emission intensity (i.e., emission produced to generate unit grid electricity) together demonstrate the importance of modelling these facilities dynamically to accurately assess their energy demand, operating cost, extent of removal, and greenhouse gas emissions. This article reports the results of a dynamic modelling effort that examines the effect of an expansion project on the extent of treatment, electricity demand, carbon footprint, and the operating cost of a large water resource recovery facility in California. This expansion consists of the addition of a differently configured activated sludge unit and associated secondary clarifiers to this plant's existing unit. The presence of these two parallel activated sludge configurations in the expanded plant was expected to provide an opportunity to achieve electricity demand and cost savings especially during the facility's coinciding peak of electricity demand with the grid's demand by maximizing the use of more energy efficient configuration. This electricity saving approach is an example of a grid demand-side flexibility project. However, the observed inverse correlation of the facility electricity demand and its cost with the overall operating cost and extent of treatment after the addition of the second activated configuration, which was determined to be the less energy efficient of these two configurations, demonstrated the infeasibility of such a grid demand-side flexibility project. This inverse correlation is changed to a positive correlation if the electricity efficiency of the added activated sludge configuration as part of this expansion project exceeds the existing electricity efficiency. As a result, recommendations were made accordingly for future studies to improve this configuration's electricity efficiency and to make the proposed grid demand-side flexibility project feasible. Such electricity efficiency improvement is also expected to lower the plant's overall carbon footprint which was not significantly impacted as part of this expansion project.

Optimal Energy Management of a Campus Microgrid Considering Financial and Economic Analysis with Demand Response Strategies

An energy management system (EMS) was proposed for a campus microgrid (µG) with the incorporation of renewable energy resources to reduce the operational expenses and costs. Many uncertainties have created problems for microgrids that limit the generation of photovoltaics, causing an upsurge in the energy market prices, where regulating the voltage or frequency is a challenging task among several microgrid systems, and in the present era, it is an extremely important research area. This type of difficulty may be mitigated in the distribution system by utilizing the optimal demand response (DR) planning strategy and a distributed generator (DG). The goal of this article was to present a strategy proposal for the EMS structure for a campus microgrid to reduce the operational costs while increasing the self-consumption from green DGs. For this reason, a real-time-based institutional campus was investigated here, which aimed to get all of its power from the utility grid. In the proposed scenario, solar panels and wind turbines were considered as non-dispatchable DGs, whereas a diesel generator was considered as a dispatchable DG, with the inclusion of an energy storage system (ESS) to deal with solar radiation disruptions and high utility grid running expenses. The resulting linear mathematical problem was validated and plotted in MATLAB with mixed-integer linear programming (MILP). The simulation findings demonstrated that the proposed model of the EMS reduced the grid electricity costs by 38% for the campus microgrid. The environmental effects, economic effects, and the financial comparison of installed capacity of the PV system were also investigated here, and it was discovered that installing 1000 kW and 2000 kW rooftop solar reduced the GHG generation by up to 365.34 kg CO2/day and 700.68 kg CO2/day, respectively. The significant economic and environmental advantages based on the current scenario encourage campus owners to invest in DGs and to implement the installation of energy storage systems with advanced concepts.

Optimization of electrical energy waste in house using smart appliances management System-A case study

A substantial quantity of energy is consumed by residential buildings. The appliance runs on a set schedule in most of the houses. As a result, the appliance remains switched ON whether user need it or not. This results in significant energy waste and high electricity bill. To address this problem a smart energy management system (SEMS) has been proposed that generates switch ON/OFF control actions for appliances depending on physical characteristics. The SEMS optimize the energy waste and energy cost by reducing the operative time of the appliances without affecting the user comfort. The SEMS further reduces the electricity bill, if public utility offer the time varying electricity price for the residential consumers. A residential building microgrid system with smart appliances, rooftop mounted photovoltaic system, energy storage (electric car, and battery) linked to the public utility is regarded to assess the performance of the SEMS. The performance of the SEMS is tested for both flat and real time electricity price system. The potentiality and functioning of SEMS are verified by measuring energy and environmental performance, as well as user comfort and acceptance of the control system, throughout the course of a normal day. The findings show a potential for energy savings of 31% and a electricity energy cost reduction by 35%. The appliance smart scheduling and management of appliances according to the renewable power generation or the grid electricity cost are reflecting the novality and originality of the proposed SEMS system.

Development of low-cost reflective plate thermal energy absorber systems for application in South Africa

An innovative solar heat collector system was developed from low-cost materials by applying innovative design technology and using adaptive technologies. The system design entails placing long black polymer pipes connected in series circulation, mounted and positioned in grooves of inverted box rib galvanised steel plating. The grooves reflect and focus the incident rays on the centre piping, concentrating incident sun rays on the black piping and increasing the absorption of thermal energy in the piping filled with circulating water. The circulation of the water is facilitated by a direct-current solar-driven pump powered by a small photovoltaic panel. The thermal energy as collected is stored in a standard household geyser tank. A 3 × 4 m heat absorber of this type collects approximately 10 kWh of energy per day. The estimated cost of the system, considering the capital outlay over an expected ten-year life cycle for the product was estimated at R0.25 per kWh. This pricing competes extremely favourably with the general cost of grid electricity in South Africa for medium-sized households, which is of the order of R2 per kWh up to 1000 kWh per month, including taxes. With an estimated electricity cost escalation of above 5% every year, the ten-year saving for a household using this prototype can be approximately R200 000. The technology and designs developed through the study can create many new business opportunities in South Africa in both urban and rural environments, through manufacturing, installing, repairing, and maintaining the systems.

Environmental and Economic Impact of Employing Solar Chimney and Photovoltaic Cells in Buildings with Various Climates

In this research, Solar Chimney (SC) as a passive system in the building, is used to provide thermal comfort and reduction of energy consumption. The operation of SC will have different effects in different climates. Investigating this issue can make it possible to use this system in places where its operation is more efficient in terms of thermal comfort and economic issues. EnergyPlus was applied to model a house with SC. Potential of cooling and heating generated by SC was investigated in Humid (Babol) and Semi-arid (Tehran) climates. The result shows that in Babol, SC saves approximately 21% in cooling load and 16% in heating load consumption. On the other hand, in Tehran, using SC can save 35% in cooling load and 20% in heating load consumption. For both humid and semi-humid climates, Summer-SC (ventilation mode) performs better than Winter-SC (heating mode). In continue, for each city, the cost of saving electricity consumption by SC was spent on the purchase and installation of photovoltaic cells (PV). According to calculations, SC and PV systems annually produce 4717 kWh of clean energy in Babol and 6965 kWh in Tehran. In addition to producing clean energy, despite the low cost of grid electricity in Iran, employing these systems in each house, annually reduces the cost of electricity consumption by $ 8 and $ 14, respectively, in the cities of Babol and Tehran. Of course, this amount would be considerable for all the houses in a city.

Optimal storage systems for residential energy systems in British Columbia

In recent years, deployment of low carbon energy systems to supply electricity in residential buildings has increased. These energy systems typically integrate different renewable energy resources with energy storage systems to meet electrical energy demand. This paper applies the ”energy hub” model to various energy systems for residential buildings in British Columbia considering several scenarios. We explore the energy system changes in on-grid, off-grid and 100% renewable scenarios. In on-grid systems, the trade off between grid connection and energy storage is explored; the results shows that even the cheapest energy storage system is not feasible with the current cost of grid electricity. For off-grid systems with a diesel generator, storage technologies are used in some energy systems, however the systems still have carbon emissions. Finally, for 100% renewable off-grid systems, both Li-ion and pumped hydro storage systems are used to handle diurnal and seasonal intermittency.

Optimization of the Configuration of the CCHP System

In this article, we have optimized the total operational cost of combined cooling heating and power system (CCHP). This cost is further divided into three major parts, which are the capital cost of the equipment, fuel cost, and the grid electricity cost. The heat storage tank has also been used to store the excessive heat that can be utilized when needed. A genetic algorithm has been used for the optimization of the whole system. The decision variables chosen were 74. The optimization resulted in significant reduction in the total cost. The final results show that the cooling demand was significantly supplied by the Grid through the electric chillers because of higher coefficient of performance, while the power demand was met through the power generation unit. Results shows that the natural gas cost consumption is much greater then the grid and equipment cost.

Design of Grid-tied Hybrid Diesel-Renewable Energy Systems Using Power Pinch Analysis

Diesel power systems have been widely applied for energy supply generation. This power scheme however requires periodical maintenance and contributes to the emissions of greenhouse gases. These challenges may be mitigated by integrating existing diesel station with renewable energy (RE) technologies into a hybrid system. Integration of diesel plants with RE systems has been mostly implemented using software and mathematical programming approaches, where the focus of the study is mainly on grid-independent hybrid system. Grid-connected hybrid systems have the potential to supply electricity at a lower cost in comparison to the standalone hybrid power systems. This work aims to design a system that integrates diesel plant with RE technologies into grid-tied hybrid system using insight-based Power Pinch Analysis (PoPA) method. The interactions between diesel generator, RE sources and the grid in meeting the load demands were considered in the methodology development. Various load sharing scenarios between the multiple generation sources were assessed to establish the optimal system operation. Economic assessment was performed to ensure the trade-off between the costs of generation sources and grid electricity cost can be maximized. Based on the demonstrated Illustrative Case Study, load sharing based on peak/off peak period contributes to the lowest net present cost of MYR 32,813,708, hence is deduced as the optimal on-grid hybrid diesel RE system.

Optimal Scheduling for Campus Prosumer Microgrid Considering Price Based Demand Response

Existing energy systems face problems such as depleting fossil fuels, rising energy prices and greenhouse gas (GHG) emissions which seriously affect the comfort and affordability of energy for large-sized commercial customers. These problems may be mitigated by the optimal scheduling of distributed generators (DGs) and demand response (DR) policies in the distribution system. The focus of this paper is to propose an energy management system (EMS) strategy for an institutional microgrid (μG) to reduce its operational cost and increase its self-consumption from green DGs. For this purpose, a real-time university campus has been considered that is currently feeding its load from the national grid only. However, under the proposed scenario, it contains building owned solar photovoltaic (PV) panels as non-dispatchable DG and diesel generator as dispatchable DG along with the energy storage system (ESS) to cope up with the intermittency of solar irradiance and high operational cost of grid energy. The resulting linear mathematical problem has been mapped in mixed-integer linear programming (MILP) and simulated in MATLAB. Simulations show that the proposed EMS model reduces the cost of grid electricity by 35% and 29% for summer and winter seasons respectively, while per day reductions in GHG emissions are 750.46 kg and 730.68 kg for the respective seasons. The effect of a half-sized PV installation on energy consumption cost and carbon emissions is also observed. Significant economic and environmental benefits as compared to the existing case are enticing to the campus owners to invest in DG and large-scale energy storage installation.

Cooperative energy management of a community of smart-buildings: A Blockchain approach

Demand Response (DR) is progressively moving from a centralized, unidirectional structure to a set of advanced decentralized mechanisms that better balance distributed supply and demand. This paper presents a decentralized cooperative DR framework to manage the daily energy exchanges within a community of Smart-Buildings, in the presence of local Renewable Energy Sources (RES). The proposed algorithm taps into the flexibility of the participants to let them decide of a day-ahead community power profile, and subsequently ensures the forecast tracking during the next day. In practice, the algorithm is fully decentralized by the Blockchain technology, that enables a trusted communication medium among the participants and enforces autonomous monitoring and billing via Smart-Contracts. With such an energy management framework, participating Smart-Buildings can together aim at a common objective, such as carbon-free resources usage or aggregated grid services, without depending on a centralized aggregator/utility. Simulations on realistic Swiss building models demonstrate that nearly all the renewable production resources could be harnessed locally through the presented framework, compared to selfish individual optimization. Under a quadratic cost of grid electricity, the considered community profile could dramatically be flattened, hence avoiding costly peaks at the grid interface. A scalability analysis shows that, considering the current public Ethereum Blockchain, the framework could handle a community size of up to 100 Smart-Buildings.

Techno-economic optimization and social costs assessment of microgrid-conventional grid integration using genetic algorithm and Artificial Neural Networks: A case study for two US cities

Through two case studies, a methodology is presented that assessed the techno-economic and environmental performance of microgrid-conventional grid integration scenarios for fifty homes located in US cities of Fargo and Phoenix. The microgrid was composed of seven components - solar photovoltaics, wind-turbines, lead acid batteries, biodiesel generators, fuel cells, electrolyzers and H2 tanks. Firstly, mathematical models that predicted the hourly power generation were developed for every microgrid component. Secondly, Artificial Neural Networks were utilized to predict hourly electricity demand and its results were validated with actual available data. Thirdly, through an electricity dispatch strategy and a Genetic Algorithm optimization technique, microgrid configurations were determined that had lowest levelized cost of energy, $/kWh. From peak power standpoint, four microgrid-conventional grid integration scenarios were examined, namely, microgrid possessing penetration level of 25%, 50%, 75%, 100%. Based on the environmental life cycle assessment of power generation, three carbon taxes were imposed -$12, $48, $72/tonne carbon dioxide emitted. Microgrid's electricity cost was found to be $0.43–0.86/kWh. Imposing carbon taxes barely showed any effect on microgrid's electricity cost nor its optimum configuration, but conventional grid's electricity cost was found to increase by 7–33% as its carbon emissions were five times as that of microgrid.

Solutions for adapting photovoltaics to large power irrigation systems for agriculture

The adaptation of the mature PV water-pumping technology to large power irrigation applications requires the problems associated to PV power intermittencies and matching PV production with the irrigation needs to be resolved. This paper presents the innovations developed, implemented and tested in a PV irrigation prototype installed in a real well at an Irrigator Community in Alicante, Spain. The innovations eliminate the intermittences due to passing clouds, reducing the PV power by 80% in just few seconds without batteries, just using control algorithms in the frequency converter, avoiding water hammers and overvoltages. A North-South horizontal axis tracker has been used to extend the number of hours of irrigation per day and to provide daily constant profiles of PV power during the irrigation period. Moreover, the use of this tracker allows the nominal power of the PV generator to be reduced by 45% as compared to a fixed structure for the same volume of water. The analysis of the economic feasibility of the prototype compared with the cost of grid electricity shows savings of 60%. This prototype has been operating in the South of Morocco delivering more than 235 m3/day for the last three years.

Techno-economic analysis of off-grid PV/wind/fuel cell hybrid system combinations with a comparison of regularly and seasonally occupied households

In Turkey, it is common to move to seasonal vacation homes/second homes during summer months. Electrical energy demand of these homes, which are usually located in the coastal regions where solar radiation is high, can be met by renewable energy sources. In this study, meeting electrical energy demand of off-grid vacation homes via photovoltaic/wind/fuel cell hybrid energy systems is investigated from a techno-economical perspective. 24 different simulations were performed in the HOMER software under geographic and climatic conditions of Çeşme, İzmir that has relatively high solar and wind energy potential for Turkey conditions and is a place where seasonal vacation homes are located. Two occupancy scenarios (seasonal and regular) of the households were investigated under two storage options (hydrogen and battery) and each storage option consisted of six hybrid system configurations (three 100% renewable + three minimum 95% renewable). As a result, the levelized cost of electricity (COE) of off-grid renewable energy systems are found to be above the cost of grid electricity, however, a lot less when compared to previous years. The techno-economic analysis showed that, battery storage, which is a more mature technology, is still economically superior to hydrogen storage.