Design and optimization of grid-connected hybrid renewable energy systems with EV integration for supermarkets

• Developed a techno-economic model optimizing PV/WT/DG/BT hybrid systems using real annual meteorological and load data. • Introduced a rule-based energy management strategy tailored for grid-connected HRES with strict reliability and REF constraints. • Benchmarked four metaheuristic algorithms under identical conditions; MFOA outperformed in speed, accuracy, and sustainability. • Achieved 100% renewable fraction with zero diesel use while minimizing LCOE to $0.1443/kWh. • Proposed a replicable optimization framework adaptable to regions with high tariffs and renewable integration targets. This study proposes an integrated optimization framework for the techno-economic sizing and performance evaluation of a grid-connected hybrid renewable energy system (HRES) comprising photovoltaic (PV) panels, wind turbines (WT), battery storage (BTS), and a diesel generator (DG). A real-world case study is conducted on a university campus in Turkey using high-resolution hourly meteorological and load data over a full year (8760 hours). The objective is to minimize the annualized cost of the system (ACS), levelized cost of energy (LCOE), and total net present cost (TNPC), while ensuring high reliability through a constraint on the loss of power supply probability (LPSP) at 0.5%. The decision variables include the optimal capacities of PV, WT, DG, BT, and inverter components, bounded by technical, economic, and operational constraints, including a minimum renewable energy fraction (REF) requirement. The system's energy production, storage, and grid interactions are modeled using detailed mathematical formulations. Optimization is performed using the Moth-Flame Optimization Algorithm (MFOA) and benchmarked against the Whale Optimization Algorithm (WOA), Flower Pollination Algorithm (FPA), and Genetic Algorithm (GA). Simulation results identify the PV/WT/BT configuration as the most cost-effective and reliable, achieving an LCOE of $0.1342/kWh, a TNPC of $3.2542 × 10⁶, and an ACS of $2.9214 × 10⁵. These values reflect a 33% cost reduction compared to the off-grid configuration. Additionally, the system enables annual grid electricity purchases of up to 4.4086 × 10⁵ kWh and sales of up to 1.2114 × 10⁶ kWh. Notably, the achieved LCOE is significantly lower than the prevailing commercial grid tariff of $0.35/kWh in Turkey, demonstrating the financial competitiveness of the proposed system for institutional and commercial users. In terms of algorithmic performance, MFOA outperforms the other methods by delivering the fastest convergence, highest optimization stability, and a fully renewable solution (REF = 100%) without DG operation. This solution achieves an LCOE of $0.1443/kWh and a TNPC of $3.5085 × 10⁶, which is slightly higher than the absolute minimum cost but demonstrates the ability to reach 100% renewable penetration without diesel usage. The system also reports the shortest execution time (336.5 seconds), confirming its suitability for real-time or iterative design tasks. Overall, the proposed HRES configuration offers a technically feasible, economically advantageous, and environmentally sustainable solution for campus electrification and broader smart grid applications, and serves as a replicable decision-support model for renewable energy planning in regions with high electricity tariffs.

The transition from fossil fuels to renewable energy demands energy systems that are not only technically and economically sound but also environmentally and socially sustainable. This study proposed a novel multi-objective optimisation (MOO) framework for the design of a grid-connected hybrid renewable energy system (GC-HRES) that explicitly integrated Job Creation (JC) as a fourth objective along with traditional technical, economic, and environmental objectives, such as Loss of Power Supply Probability (LPSP), Cost of Energy (COE), and Renewable Energy Fraction (REF). Prior studies often treated JC as a post-optimisation metric, while this study incorporated JC into the MOO using employment technology-specific factors. Metaheuristic algorithms, Multi-Objective Particle Swarm Optimisation (MOPSO) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), were applied to find the optimal number of solar panels, wind turbines, and battery banks under scenarios which excluded and included JC. The results demonstrated that including JC reshaped the Pareto front, revealed new objective compromises and led to diverse configurations. MOPSO favoured solutions with higher JC and REF at the expense of cost and reliability, while NSGA-II achieved more balanced, cost-effective and reliable solutions with competitive JC values. Additional constraint sensitivity analysis further demonstrated the influence of battery bank constraints on solution feasibility. The results highlighted that integrating JC into the optimisation framework enriched design possibilities and fostered a more inclusive transition to renewable energy with a focus on GC-HRESs. This paper provided a flexible and replicable MOO framework that sets the foundation for socially attuned GC-HRES designs that align with broader sustainability goals.

Grid-connected hybrid renewable energy systems for supermarkets with electric vehicle charging platforms: Optimization and sensitivity analyses

Optimizing green hydrogen integration in grid-connected hybrid renewable systems: Techno-economic and sensitivity analysis in hot desert environments

Integrating renewable resources with existing power system are rapidly increasing day by day, becoming an effective way to rural electrification where distribution system extension is not economically feasible. This research aims to design such an optimal grid-connected hybrid renewable energy system (HRES) modelled using solar, wind energy, diesel generator, battery storage, thermal loads, thermal load controller, and boiler. This system is designed to meet the load demand requirement of chintalayapalle village, Andhra Pradesh, India. The techno-economic, sensitivity analysis and cost-effective optimal configuration of this system have been performed using HOMER software. Totally eight configurations have been designed here and the optimal configuration have been identified based on the minimum net present cost (NPC), lowest cost of energy (COE), and the highest renewable fraction (RF). Results obtained shows that the optimally configured system is more environmentally friendly because of less carbon emission. Also, it would be more cost-effective when wind power becomes the main energy source and combined with moderate capacity of solar photovoltaic and battery.

In the context of global warming and fossil fuel depletion, electric vehicles (EVs) have become increasingly popular for reducing both carbon emissions and fossil fuel consumption. However, as the demand for EV charging power rises along with the expansion of EVs, conventional power plants require more fuel, and carbon emissions increase. This suggests that the goal of promoting EV adoption to mitigate climate change and reduce reliance on fossil fuels may face significant challenges. Therefore, there is a need to adopt renewable energy generation for EV charging stations to maximize the effectiveness of EV distribution in an eco-friendly way. This paper aims to propose an optimal renewable energy generation system for an EV charging station, with a specific focus on the use of an actual load profile for the station, the consideration of carbon emissions and economic evaluation, and the study of a specific case location in Korea. As a case study, an EV charging station in Korea was selected, and its renewable energy fractions (REF) of 0%, 25%, 50%, 75%, and 100% were considered for comparison of carbon emissions and economic evaluation with the help of HOMER software. In addition, the system with 25% REF was analyzed to find the best operating strategy considering the climate characteristics of the case site. The results show that the system configuration of PV/ESS is the most economical among all the REF cases, including PV, WT, and ESS, due to the meteorological characteristics of the site, and that the system with REF below 25% is the most optimal in economic terms and carbon emissions.

Sizing and energy management of grid-connected hybrid renewable energy systems based on techno-economic predictive technique

The increasing adoption of electric vehicles (EVs) presents both challenges and opportunities for reducing greenhouse gas (GHG) emissions. While EVs are essential for decarbonising the transport sector, the emissions from charging vary greatly depending on the generation mix at the time. This study investigates the impact of various EV charging strategies on GHG emissions in different regions in the Australian National Electricity Market (NEM). The study focuses on four key charging strategies–Control Tariff, Timer, Solar Soak, and Convenience Charging. Using real-world data, the analysis evaluates both average and marginal emissions across regions with varying levels of renewable energy integration. Sensitivity analysis showed that coarser temporal resolution in emissions calculations can lead to variances of up to 6.3 %, emphasising the importance of using higher resolution data when available. It was found that the Solar Soak strategy is the most effective in minimising EV charging emissions and can also help with challenges associated with increasing solar exports in the distribution network. The choice between average and marginal emissions intensity factors is also critical in determining outcomes. In Tasmania and South Australia, where renewable energy sources dominate, the use of marginal emission factors resulted in higher EV charging emissions than average emissions due to their reliance on coal and gas as the marginal generators. The sensitivity analysis carried out with emissions data between 2019 and 2023 revealed a negative relationship between renewable energy fraction and emissions intensity and highlighted the importance of aligning EV charging with high renewable generation periods to achieve maximum GHG reductions. • Real-world EV charging data is used to assess GHG emissions. •Four charging strategies—Control Tariff, Timer, Solar Soak, and Convenience Charging—are evaluated •Solar Soak charging yields the lowest emissions. •Marginal emissions can exceed average in high-renewable states. • Coarse temporal data can misestimate emissions by up to 6.3 %.

The increasing environmental and economic drawbacks of fossil fuels have accelerated the global transition to renewable energy sources. In this context, the optimal design of hybrid renewable energy systems (HRES) that combine solar, wind, and energy storage technologies is critical for achieving sustainable and cost-effective power generation. This study addresses the problem of optimally sizing a grid-connected HRES composed of photovoltaic (PV) panels, wind turbine (WTs), batteries (BTs), and supercapacitors (SCs). A mathematical model is developed to minimize the annual cost of the system (ACS) while ensuring high renewable energy utilization and system efficiency. To solve this optimization problem, five advanced meta-heuristic algorithms-Hunger Games Search (HGS), Spider Wasp Optimizer (SWO), Kepler Optimization Algorithm (KOA), Fire Hawk Optimizer (FHO), and Coronavirus Disease Optimization Algorithm (COVIDOA)-were applied and statistically validated. The model was tested on real meteorological and load data from a university campus in Turkey. Results show that HGS achieved the most favorable performance, with an ACS of $603,538.44, a cost of energy (COE) of $0.23801/kWh, and a renewable energy fraction (REF) of 80.04%. This configuration offers significant economic advantages compared to purchasing electricity directly from the grid at $0.35/kWh. The proposed system proves commercially viable for large consumers and demonstrates the practical effectiveness of meta-heuristic methods in energy system design. MATLAB was used for simulation, while R programming was employed for statistical validation of the algorithmic performance. The study establishes a reproducible and validated framework that can guide future research and implementation in the field of hybrid energy optimization.

Optimal design of grid-connected hybrid renewable energy systems using multi-objective evolutionary algorithm

The economic operation of an electric vehicle (EV) parking lot under different cases are explored in the paper. The parking lot is equipped with EV charging stations with a vehicle-to-grid (V2G) function, renewable energy sources (RESs), and energy storage system (ESS). An optimisation problem is formulated to maximise the profit of the parking lot from EV charging and feed-in energy to the grid under various charging modes while considering the uncertain factors, ESS degradation, and diverse EV parking conditions. The electricity market price, solar radiation and wind speed are considered as uncertain factors, and the scenred toolbox of MATLAB is used to generate scenarios. Based on the parking time of different EVs, the model classifies the EVs entering the charging station and dynamically determines the charging price according to their charging demand through a linear price-demand relationship. The efficacy of the proposed model is verified by the comparison with two other models under three different cases. It is shown that the proposed model gains the most profit based on the proposed V2G services and dynamic charging price.

This paper presents a techno-economic evaluation of three hybrid renewable energy configurations for the electrification of Khadva village in the Kutch district of Gujarat, India. Using HOMER Pro software, the study analyzes three scenarios: Case 1 (PV + Grid + Battery), Case 2 (Wind + Grid + Battery), and Case 3 (PV + Wind + Grid + Battery), assessing their performance based on Total Net Present Cost (NPC), Levelized Cost of Energy (COE), capital and operating expenditures, renewable fraction, and grid dependency. Among the evaluated options, Case 3 emerged as the most optimal configuration, offering the lowest NPC (₹1.23 Cr) and COE (₹7.18/kWh), along with the highest renewable fraction (63.4%), indicating effective integration of solar and wind resources with minimal reliance on grid electricity. Case 2 also showed competitive performance with considerable renewable contribution (58.3%) and moderate costs, while Case 1, though simpler in design, resulted in the highest COE and the lowest renewable contribution (9.4%). The results underscore the value of combining diverse renewable sources to enhance system reliability, sustainability, and economic viability in rural electrification. This analysis provides a practical framework for optimizing decentralized hybrid energy systems in off-grid or weak-grid regions, promoting clean energy access in remote areas.

Techno-economic optimization and sensitivity analysis of off-grid hybrid renewable energy systems: A case study for sustainable energy solutions in rural India

Optimal Coordinated Charging and Routing Scheme of Electric Vehicles in Distribution Grids: Real Grid Cases

Optimal design and economic analysis of a hybrid renewable energy system for powering and desalinating seawater