Search Results (238)

This paper presents an annual, real-world evaluation of the performance and economics of a residential photovoltaic (PV) system coupled with a battery energy storage system (BESS) in southern Poland. The system, monitored with 5 min resolution, operated under time-of-use (TOU) electricity tariffs. Seasonal variation was significant; self-sufficiency exceeded 90% in summer, while winter conditions increased grid dependency. The hybrid system reduced electricity costs by over EUR 1400 annually, with battery operation optimized for high-tariff periods. Comparative analysis of three configurations—grid-only, PV-only, and PV + BESS—demonstrated the economic advantage of the integrated solution, with the shortest payback period (9.0 years) achieved with financial support. However, grid voltage instability during high PV production led to inverter shutdowns, highlighting limitations in the infrastructure. This study emphasizes the importance of tariff strategies, environmental conditions, and voltage control when designing residential PV-BESS systems. Full article

The research demonstrates, through simulation and laboratory validation, the development of a low-voltage DC-link (LVDC) back-to-back converter system that enables multi-frequency power transfer. The system operates in two distinct modes, which include a three-phase grid-connected converter transferring fundamental and 5th and 7th harmonic power to a three-phase residential inverter supplying a clean 50 Hz load and another mode that uses a DC–DC buck–boost converter to integrate a battery storage unit for single-phase load supply. The system allows independent control of each harmonic component and maintains a clean sinusoidal voltage at the load side through DC-link isolation. The LVDC link functions as a frequency-selective barrier to suppress non-standard harmonic signals on the load side, effectively isolating the multi-frequency power grid from standard-frequency household loads. The proposed solution fills the gap between the multi-frequency power systems and the single-frequency loads because it allows the transfer of total multi-frequency grid power to the traditional household loads with pure fundamental frequency. Experimental results and simulation outcomes demonstrate that the system achieves high efficiency, robust harmonic isolation, and dynamic adaptability when load conditions change. Full article

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic load conditions for 800 residential units and 50 charging points rated at 7.4 kW each. Simulation results, validated through SCADA-based performance monitoring using MATLAB/Simulink and OpenDSS, reveal substantial technical improvements: a 31.5% reduction in peak transformer load, voltage deviation minimized from ±5.8% to ±2.3%, and solar utilization increased from 48% to 66%. The AI framework dynamically predicts user demand using a non-homogeneous Poisson process and optimizes charging schedules based on a cost-voltage-user satisfaction reward function. The study underscores the critical role of intelligent optimization in improving grid reliability, minimizing operational costs, and enhancing renewable energy self-consumption. The proposed system demonstrates scalability, resilience, and cost-effectiveness, offering a practical solution for next-generation urban EV charging networks. Full article

This study introduces a novel approach for optimizing residential energy systems by combining linear policy graphs with stochastic dual dynamic programming (SDDP) algorithms. Our method optimizes residential solar power generation and battery storage systems, reducing costs through strategic charging and discharging patterns. Using stylized test data, we evaluate battery storage optimization strategies by comparing various SDDP model configurations against a linear programming (LP) benchmark model. The SDDP optimization framework demonstrates robust performance in battery operation management, efficiently handling diverse pricing scenarios while maintaining computational efficiency. Our analysis reveals that the SDDP model achieves positive financial returns with small-scale battery installations, even in scenarios with limited photovoltaic generation capacity. The results confirm both the economic viability and environmental benefits of residential solar–battery systems through two key strategies: aligning battery charging with renewable energy availability and shifting energy consumption away from peak periods. The SDDP framework proves effective in managing battery operations across dynamic pricing scenarios, achieving performance comparable to LP methods while handling uncertainties in PV generation, consumption, and pricing. Full article

Off-grid and isolated rural communities in developing countries with limited resources require energy supplies for daily residential use and social, economic, and commercial activities. The use of data from space assets and space-based solar power is a feasible solution for addressing ground-based energy insecurity when harnessed in a hybrid manner. Advances in space solar power systems are recognized to be feasible sources of renewable energy. Their usefulness arises due to advances in satellite and space technology, making valuable space data available for smart grid design in these remote areas. In this case study, an isolated village in Namibia, characterized by high levels of solar irradiation and limited wind availability, is identified. Using NASA data, an autonomous hybrid system incorporating a solar photovoltaic array, a wind turbine, storage batteries, and a backup generator is designed. The local load profile, solar irradiation, and wind speed data were employed to ensure an accurate system model. Using HOMER Pro software V 3.14.2 for system simulation, a more advanced AI optimization was performed utilizing Grey Wolf Optimization and Harris Hawks Optimization, which are two metaheuristic algorithms. The results obtained show that the best performance was obtained with the Grey Wolf Optimization algorithm. This method achieved a minimum energy cost of USD 0.268/kWh. This paper presents the results obtained and demonstrates that advanced optimization techniques can enhance both the hybrid system’s financial cost and energy production efficiency, contributing to a sustainable electricity supply regime in this isolated rural community. Full article

The proposed study incorporates a stochastic demand side management (SDSM) strategy for a self-sufficient residential system powered from a PV source with a hybrid battery–hydrogen storage system to minimize the total degradation costs associated with key components, including Li-io batteries, fuel cells, and PEM electrolyzers. The uncertainty in demand forecasting is addressed through a scenario-based generation to enhance the robustness and accuracy of the proposed method. Then, stochastic optimization was employed to determine the optimal operating schedules for deferable appliances and optimal water heater (WH) settings. The optimization problem was solved using a genetic algorithm (GA), which efficiently explores the solution space to determine the optimal operating schedules and reduce degradation costs. The proposed SDSM technique is validated through MATLAB 2020 simulations, demonstrating its effectiveness in reducing component degradation costs, minimizing load shedding, and reducing excess energy generation while maintaining user comfort. The simulation results indicate that the proposed method achieved total degradation cost reductions of 16.66% and 42.6% for typical summer and winter days, respectively, in addition to a reduction of the levelized cost of energy (LCOE) by about 22.5% compared to the average performance of 10,000 random operation scenarios. Full article

Building-integrated photovoltaic (BIPV) systems coupled with energy storage systems offer promising solutions to reduce the dependency of buildings on non-renewable energy sources and provide the building sector with environmental benefits by reducing the buildings’ environmental footprint. Hence, the economic viability of such energy systems must be further assessed, particularly regarding the market price and required initial investments. This paper aims to evaluate the net present cost (NPC) and saving-to-investment ratio (SIR) of the electrical storage system coupled with BIPV in smart residential buildings with a focus on optimum sizing of the battery systems under varying market price scenarios. Therefore, a parametric energy model of a residential building, a life cycle cost analysis approach, and a Monte Carlo analysis are carried out to elaborate the dynamism between the storage size, market price, and net present cost of the system over its life cycle. The results provide a decision-support tool to find the cost-optimum size of the battery systems and to realize the interplay between the battery system size, the market price, and the economic feasibility of the electrical storage system coupled with residential BIPV. In more detail, the results reveal that the economic viability thresholds of the battery systems’ market price are in the range of 250–300 €/kWh depending on the chosen life cycle cost indicators, while the cost-optimum size of the battery systems varies noticeably according to the market price of battery systems. Furthermore, the paper provides insight to designers, policymakers, manufacturers, and the market for developing scenarios to accelerate the implementation of energy storage systems in the building sector. Full article

Buildings account for about a third of global energy and it is thus imperative to eliminate the use of fossil fuels to power and provide for their thermal needs. Solar photovoltaic (PV) technology can provide power and with electrification, heating/cooling, but there is often a load mismatch with the intermittent solar supply. Electric batteries can overcome this challenge at high solar penetration rates but are still capital-intensive. A promising solution is thermal energy storage (TES), which has a low cost per unit of energy. This review provides an in-depth analysis of TES but specifically focuses on phase change material (PCM)-based TES, and its significance in the building sector. The classification, characterization, properties, applications, challenges, and modeling of PCM-TES are detailed. Finally, the potential for integrating TES with PV and heat pump (HP) technologies to decarbonize the residential sector is detailed. Although many studies show proof of carbon reduction for the individual and coupled systems, the integration of PV+HP+PCM-TES systems as a whole unit has not been developed to achieve carbon neutrality and facilitate net zero emission goals. Overall, there is still a lack of available literature and experimental datasets for these complex systems which are needed to develop models for global implementation as well as studies to quantify their economic and environmental performance. Full article

This paper presents SEAMS (Solar Energy Aggregator Management System), an optimization-based framework for managing solar energy trading in smart communities under Thailand’s regulatory constraints. A major challenge is the prohibition of residential grid feed-in, which limits the use of conventional peer-to-peer energy models. Additionally, fixed pricing is required to ensure simplicity and trust among users. SEAMS coordinates prosumer and consumer households, a shared battery energy storage system (BESS), and a centralized aggregator (AGG) to minimize total electricity costs while maintaining financial neutrality for the aggregator. A mixed-integer linear programming (MILP) model is developed to jointly optimize PV sizing, BESS capacity, and internal buying price, accounting for Time-of-Use (TOU) tariffs and local policy limitations. Simulation results show that a 6 kW PV system and a 70–75 kWh shared BESS offer optimal performance. A 60:40 prosumer-to-consumer ratio yields the lowest total cost, with up to 49 percent savings compared to grid-only systems. SEAMS demonstrates a scalable and policy-aligned approach to support Thailand’s transition toward decentralized solar energy adoption and improved energy affordability. Full article

Lithium-ion batteries (LIBs) are widely deployed in electric vehicles due to their high energy density and long cycle life. Even after retirement, typically at around 80% of their rated capacity, LIBs can still be repurposed for second-life applications such as residential energy storage systems (ESSs). However, effectively grouping these heterogeneous cells is crucial to optimizing performance of the module. Retired LIBs can be effectively repurposed for numerous second-life applications such as ESSs, and other power backups. In this paper, we compare four clustering approaches including random grouping, equal-number Support Vector Clustering, K-means, and an equal-number Gaussian Mixture Model (GMM) to organize 60 retired cells into 48 V modules consisting of 15-cell groups. We verify the performance of each method via simulations of a 15S2P configuration, focusing on the standard deviation of final charge voltage, average charge throughput, delta capacity, and coulombic efficiency. Based on the evaluation metrics analyzed after regrouping the battery cells and simulating them for second-life ESS applications, the GMM-based clustering method demonstrates better performance. Full article

Integrating Electric Vehicle (EV) charging stations into buildings is becoming increasingly important due to the rapid growth of private EV ownership and prolonged parking durations in residential areas. This paper proposes robust, building-integrated charging solutions that combine mobile energy storage systems (ESSs), station linkage data, and traffic volume data. The proposed system promotes eco-friendly EV usage, flexible energy management, and carbon neutrality through a polyfunctional Vehicle-to-Grid (V2G) architecture that integrates decentralized energy networks. Two core strategies are implemented: (1) configuring Virtual Power Plant (VPP)-based charging packages tailored to station types, and (2) utilizing EV batteries as distributed ESS units. K-means clustering based on spatial proximity and energy demand is followed by heuristic algorithms to improve the efficiency of mobile ESS operation. A three-layer framework is used to assess improvements in energy demand distribution, with demand-oriented VPPs deployed in high-demand zones to maximize ESS utilization. This approach enhances station stability, increases the load factor to 132.7%, and reduces emissions by 271.5 kgCO₂. Economically, the system yields an annual benefit of USD 47,860, a Benefit–Cost Ratio (BCR) of 6.67, and a Levelized Cost of Energy (LCOE) of USD 37.78 per MWh. These results demonstrate the system’s economic viability and resilience, contributing to the development of a flexible and sustainable energy infrastructure for cities. Full article

Accurate predictions of the temperature of battery energy storage systems (BESSs) are crucial for ensuring their efficient and safe operation. Effectively addressing both the long-term historical periodic features embedded within long look-back windows and the nuanced short-term trends indicated by shorter windows are key factors in enhancing prediction accuracy. In this paper, we propose a BESS temperature prediction model based on a convolutional neural network (CNN), patch embedding, and the Kolmogorov–Arnold network (KAN). Firstly, a CNN block was established to extract multi-scale periodic temporal features from data embedded in long look-back windows and capture the multi-scale correlations among various monitored variables. Subsequently, a patch-embedding mechanism was introduced, endowing the model with the ability to extract local temporal features from segments within the long historical look-back windows. Next, a transformer encoder block was employed to encode the output from the patch-embedding stage. Finally, the KAN model was applied to extract key predictive information from the complex features generated by the aforementioned components, ultimately predicting BESS temperature. Experiments conducted on two real-world residential BESS datasets demonstrate that the proposed model achieved superior prediction accuracy compared to models such as Informer and iTransformer across temperature prediction tasks with various horizon lengths. When extending the prediction horizon from 24 h to 72 h, the root mean square error (RMSE) of the proposed model in relation to the two datasets degraded by only 11.93% and 19.71%, respectively, demonstrating high prediction stability. Furthermore, ablation studies validated the positive contribution of each component within the proposed architecture to performance enhancement. Full article

South Africa’s persistent energy shortages and high utility costs have led to increased interest in rooftop solar photovoltaic (PV) systems. However, understanding their economic and environmental viability in urban residential contexts remains limited. This study investigates the feasibility of integrating rooftop solar PV systems with local energy storage and grid electricity in residential housing complexes in Benoni, Gauteng Province. A hybrid energy system was proposed and modeled using detailed consumption data from a typical community in Benoni. The system includes rooftop PV installations, lithium-ion storage, and connection to the national grid. A techno-economic analysis was conducted over a 25-year project lifespan to evaluate energy cost, payback period, net present cost, and carbon dioxide emissions. The optimal system configuration—Solar PV + Storage + Grid—achieved average annual utility bill savings of USD 30,207, with a payback period of 1.0 year, a net present cost (NPC) of USD 40,782, and an internal rate of return (IRR) of 101.7%. Annual utility costs were reduced from USD 30,472 to USD 267, and the system resulted in a net reduction of 130 metric tons of CO₂ emissions per year. The levelized cost of energy (LCOE) was USD 0.0071/kWh. The integration of rooftop solar PV and energy storage with grid electricity presents a highly cost-effective and environmentally sustainable solution for residential communities in urban South Africa. The findings support policy initiatives aligned with Sustainable Development Goal (SDG) 7: “Affordable and Clean Energy”. Full article

This paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm progresses through the optimization hyperspace. This modification addresses issues of poor convergence and suboptimal search in the original algorithm. Both the modified and standard algorithms were employed to design an HRES system comprising photovoltaic panels, wind turbines, fuel cells, batteries, and hydrogen storage, all connected via a DC-bus microgrid. The components were integrated with the microgrid using DC-DC power converters and supplied a designated load through a DC-AC inverter. Multiple operational scenarios and multi-objective criteria, including techno-economic metrics such as levelized cost of energy (LCOE) and loss of power supply probability (LPSP), were evaluated. Comparative analysis demonstrated that mSAO outperforms the standard SAO and the honey badger algorithm (HBA) used for the purpose of comparison only. Our simulation results highlighted that the PV–wind turbine–battery system achieved the best economic performance. In this case, the mSAO reduced the LPSP by approximately 38.89% and 87.50% over SAO and the HBA, respectively. Similarly, the mSAO also recorded LCOE performance superiority of 4.05% and 28.44% over SAO and the HBA, respectively. These results underscore the superiority of the mSAO in solving optimization problems. Full article