Renewable energy optimization in isolated microgrids: A Python-based tool for cost-effective solutions using genetic algorithms

• 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.

One of the critical challenges for power energy engineers and decision-makers is to select an optimum size for a renewable energy-based electrification system as a standalone microgrid with as minimum investment as possible. Several researchers have used genetic algorithm (GA) and Particle swarm optimization (PSO) tools for solving optimization problems in engineering. We used both algorithms here to select the optimum size of renewable energy (RE) generation units. In our system design, photovoltaic (PV) modules, wind turbines, and battery-banks are used as the primary power units whereas diesel generator serves as a backup. We have selected a village with 260 households in Ethiopia's eastern part, the Somali region, Darahtoleh village (7.31567N,45.52884E). Metrological data from Ethiopian Metrological Agency are used as a primary source to analyze the potential of solar and wind energy resources. We have run GA and PSO algorithms for twenty different numbers of runs (20, 30,40,…, 200) with 100 iterations for each considering life cycle cost (LCC) as an objective function and fulfilling the constraint function of peak energy demands. The results from both algorithms based on minimum LCC value are further analyzed. Results suggest an optimal combination of generation units with a minimum levelized cost of energy (LCOE) of 0.179$/kWhr and 65% of renewable energy (RE) penetration.

Accelerated development of eco-friendly technologies, such as renewable energy (RE), smart grids, and electric transportation will shape the future of electric power generation and supply. The power consumption characteristics of modern power systems are designed to be more flexible and easily controllable, which will also affect the sizing of power generation system. This paper presents a methodology for the joint capacity optimization of a typical residential standalone microgrid (MG) employing RE sources, i.e., solar photovoltaic (PV), wind turbines (WTs), diesel generators (DGs), and battery energy storage system (BESS). The MG supplies a residential community load comprising of typical residential load plus electric vehicles (EVs) charging load. The realistic mathematical models of PV, WT, diesel generation system, BESS, and EV load are formulated to improve the capacity optimization methodology, which involves various realistic constraints associated with the RE sources, diesel generation system, BESS, and EV load. The labyrinthine optimization problem is formulated and solved innovatively to 1) minimize the cost; 2) reduce greenhouse gases (GHG) emissions; and 3) curtail dump energy. All three objectives have special significance in designing a standalone MG, for example, cost is related to the economics, GHG emissions deal with global warming, and dump energy is related to the stability and economics of the system. The optimization problem is solved for different possible combinations of PV, WT, DG, and BESS to determine the best possible combination to serve the load effectively and economically. In addition, the impact of load shifting on the sizes of distributed generators and BESS in terms of per-unit cost and GHG emissions is analyzed using the concept of controllable loads. This study could be assumed as a powerful roadmap for decision makers, analysts, and policy makers.

Microgrids, or distributed systems of local energy generation, transmission, and demand, are now technologically and operationally capable of providing power to communities, especially in rural and peri-urban regions of developing nations. The reliability of the system, the cost of power generation, and the operating environmental impact are the major issues when designing and evaluating the performance of an off-grid hybrid renewable energy microgrid (HREM). This paper presents an integrated method for optimal sizing and operation of an HREM for rural agricultural communities in the Southern Philippines composed of run-of-the-river hydropower, photovoltaics (PV), diesel generator, and a battery energy storage system (BESS) using multi-objective particle swarm optimization (MOPSO) and a proposed multi-case power management strategy. The three conflicting objective functions that were simultaneously minimized were: loss of power supply probability (LPSP), levelized cost of energy (LCOE), and greenhouse gas (GHG) emissions, subject to several constraints. The optimization generated 200 non-dominated or Pareto optimal alternative solutions, 4 of which were selected as solutions of interest. Based on the results, the optimal sizes of the main components for the reliable operation of the system are 100 panels with a rating of 0.25 kW for PV, 100 kWh for BESS, and 13 kW for the diesel generator, with corresponding LCOE, LPSP, and GHG emission values of 0.1795 USD/kWh, 0.05%, and 7874 kg, respectively, for 1 year. The effectiveness of the proposed HREM design was also analyzed, and the study yielded plenty of useful findings that could aid the electrification of the area.

Environmental challenges such as climate change have accelerated humanity's need for renewable alternative energy sources. For this reason, we propose in this paper a decision-making strategy that allows controlling the flows of energy into a micro-grid (MG) compound of solar energy, batteries, and diesel generator (DG), and connected to the distributed network (DN). Therefore, the power supply to the loads is obtained either from the energy produced by solar sources, from the batteries, from the DN, or from the DG when renewable energy (RE) and batteries are depleted. To make the final decision, we consider four parameters at the same time: the energy produced by solar energy, the requested load, the state of charge of batteries (SoC), and the purchase or sale price. Decision tree (DT) is used to build the energy management strategy to ensure the availability of power on demand by making logical decisions about charging batteries, discharging batteries, buying necessary energy from DN, selling excess energy to DN, and recovering necessary energy from DG. The suggested DT approach is applied to a real MG to minimize the cost-benefit balance, and the comparison analysis demonstrates good results when compared to related works.

Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies

This paper proposes a dynamic time-of-use (d-TOU) tariff scheme for microgrid (MG) systems in islanded mode. The main problem for the islanded MG is the high cost of electricity, and the output from renewable energy is uncontrollable compared to the traditional grid. Therefore, this paper focuses on developing a suitable tariff scheme that provides reliability and financial benefits for both utility and customer. The time zone energy prices based on the Levelized Cost of Energy (LCOE) are introduced for islanded MG. The results show a contradiction between islanded MG with the standard traditional power generation TOU. Even though the LCOE obtained for MG is higher than conventional electricity rates, the greenhouse gas (GHG) emissions rate is reduced by 85%. In conclusion, the proposed d-TOU tariff scheme is suitable for the islanded MG system and it is beneficial for both the utility and the customer by not causing a financial burden to the utility and encouraging the customer to make a demand response in the future.

Background: During the whole life cycle of an offshore wind farm, from cradle to grave, logistics is needed. Particularly during wind turbine construction, vessels are intensively used. However, no study has been found to evaluate logistics costs during this particular turbine construction phase. Objective: In order to close this gap, this paper aims to propose an Offshore Logistics Cost (OLC) framework, evaluate OLC impact on total cost and identify recommendations to reduce this impact. Methodology: This study considers two quantitative approaches to evaluate OLC share. First quantitative approach is based on cost information found in twenty related studies from literature. Second quantitative approach was developed in two steps. A first step is a bottom up calculation model. This model performs OLC calculations for each scenario identified. In a second step, OLC results are used as input to two existing calculation models: National Renewable Energy Laboratory (NREL) simplified model and commonly accepted model developed by Megavind. Variance of results are then analyzed to determine a representative range for OLC impact on total cost. Results: Using some related values from literature review, this study assesses wind turbine construction offshore logistics to represent around 1.2% of Levelized Cost of Energy (LCOE). Using bottom up approach, this study evaluates offshore logistics during wind turbine construction with a broader contribution, conservatively from 0.6 to 7.6% of LCOE for offshore wind turbines over 4 MW power rating. It is also demonstrated that the higher the wind turbine size, the lower offshore logistics impact on LCOE. Conclusion: Offshore wind logistics costs during construction phase can represent between 0.6 and 7.6% of LCOE and it appears necessary to optimize these costs to contribute to a competitive LCOE. As main offshore logistics cost drivers have been identified, areas to reduce LCOE impact are suggested: increasing weather limits to reduce waiting on weather costs, minimizing work offshore, improving processes, using economy of scale and optimizing vessel use.

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.

Technical and economical bi-objective design of a grid-connected photovoltaic/diesel generator/fuel cell energy system

We estimated system-wise levelized cost of electricity (LCOE) for a power grid with a high level of renewable energy using our grid optimization model. The estimation results of the system-wise LCOE are discussed in terms of the nexus of energy, environment, and economic growth for Small Island Developing States (SIDS) economies. While 100% renewable energy is technologically possible with the usage of electricity storage, the estimated LCOE is as high as 397 $/MWh which is substantially higher than electricity prices for residential consumers in the US and Japan. The susceptibility analyses imply that the estimated LCOE increase of 223% with a 100% renewable power grid corresponds to an as high as 11% decrease in economic growth. This decrease in economic growth would have a significant negative impact on SIDS economies. However, hydrogen production via the electrolysis of water using the excess energy supply from solar photovoltaics would reduce the LCOE, and therefore, higher economic growth would be attained with less {mathrm{CO}}_2 emission.

This article aims to search the technical, environmental, and economic model of an off‐grid hybrid power generation system that supplies electricity to a seawater reverse osmosis (RO) system. Net present cost (NPC) and levelized cost of electricity (LCOE) values were used to determine the optimal system sizing powering a reverse osmosis desalination system for different sites where is located south and west coast of Türkiye. In the proposed power systems, PV panels, wind turbines, diesel generators, lead‐acid batteries, and converters were used. In the instance where the lowest LCOE of 0.301$/kWh is calculated, the optimal system comprises of a 25.7 kW PV array, one wind turbine (rated at 10 kW), 152 kWh LA batteries, and a 6.76 kW converter. The levelized cost of water (LCOW) value for this case was calculated as 1.168 $/m3. The LCOE value was calculated as 0.529 $/kWh for the power system, which is considered as a base case and consists of only a diesel generator, where no renewable energy source is used. For the base case, the carbon footprint of electricity generation is 35,127 kg/year. According to CO2 sequestration analysis result, the number of trees (Pinus Brutia) to be planted was calculated as approximately 164 tree/year over the lifetime of the power system for base case.

Pakistan’s economic growth is being impacted by the energy crisis, because of nation’s reliance on expensive electricity, obtained from independent power producers (IPPs). To address this issue, herein, we report an analysis of various off‐grid and grid‐connected hybrid renewable energy systems (HRES), for cheap electrification of area under municipal committee (MC) Jhelum, district Jhelum, Pakistan. Various HRES systems are designed, simulated, and optimized using HOMER Pro software, with a focus on techno‐economic factors. The net present cost (NPC) and levelized cost of electricity (LCOE) for the proposed designs are obtained and refined by adjusting the sensitive parameters, that is, components costs, nominal discount rate, inflation rate, and annual capacity shortage of components. The results reveal that the most economical system obtained is grid‐connected system, which comprises of solar photovoltaic (PV), diesel generators (DGs), and batteries, with LCOE (0.03719 $/kWh) and NPC (1,797,396 $). Robustness analysis is carried out: (1) to confirm the technical reliability of proposed system, (2) comparing our system’s LCOE with government tariff and previous literature, which reveals that the proposed system provides cheaper electricity. Finally, we discussed potential barriers like technical, policy making, social awareness, social acceptance, and economic that affect implementation of such systems. We have provided recommendations to rectify these barriers for successfully deploying sustainable energy systems across the country.

Indonesia, as the world's largest archipelagic country, faces substantial challenges in achieving equitable energy access, particularly in remote regions. These areas are predominantly reliant on Diesel Power Plants (PLTD), which result in high operational costs, logistical complexities in fuel supply, and considerable carbon emissions. Despite these limitations, remote regions possess abundant renewable energy resources, particularly solar energy. However, the intermittency of solar generation due to weather fluctuations hampers its reliability as a primary energy source. To address these challenges, this study proposes the implementation of a hybrid energy system integrating Solar Photovoltaic (PV) systems and Battery Energy Storage Systems (BESS), supported by a load-following dispatch strategy and optimal capacity planning. The objective is to improve both the reliability and efficiency of the local power system. The study was conducted on Nusa Penida Island, specifically at the 20 kV Kutampi Substation, which is interconnected with the existing diesel power infrastructure. The methodology encompasses a comprehensive literature review, secondary data acquisition, manual system sizing, and simulation-based analysis. PV capacity potential was assessed using PVSyst software, while power flow and voltage simulations were performed for three operational scenarios: (i) existing diesel-only configuration (baseline), (ii) hybrid Diesel-PV-BESS configuration, and (iii) PV-BESS configuration without diesel generators. Power system simulations were carried out using a computer-based electrical analysis platform to evaluate the technical impact of integrating renewable energy into the local grid. Simulation results demonstrate that the integration of PV and BESS enhances voltage stability and ensures a more reliable energy supply. Furthermore, techno-economic analysis reveals that the hybrid Diesel-PV-BESS configuration yields the most favourable outcome, achieving a Levelized Cost of Energy (LCOE) of IDR 3,088 per kWh. These findings underscore the potential of hybrid renewable energy systems as a viable solution for sustainable energy development in remote island regions.

This paper presents technical and economic analysis of hybrid ac microgrid (MG). The MG comprises conventional source like diesel generator (DiGen), renewable energy source such as roof-top solar photo-voltaic (PV) installation and battery energy storage system (BESS). The primary objective of this analysis is to study and compare levelised cost-of-energy (LCoE) and net-present value (NPV) for blend of energy sources catering electrical energy to various set-ups along with other vital economic instruments like internal rate of return(IRR), return on investment(ROI) and simple payback period(SPP). The analysis has been performed with existing utility-grid in operation. In order to compare the combination of micro-sources, same average annual electricity consumption has been considered as reference for comparison for various load profiles, viz. academic institutional, residential pocket, commercial, community and small/medium industrial loads. Moreover, the two tariff structures, with grid selling and non-grid selling are considered. The model has been developed in HOMER application software. The reference location for academic institution selected for study is situated in southern part of Gujarat, India and has longitude and $21.17^{\circ}\mathrm{N}$ latitude of $72.83^{\circ}\mathrm{E}$.