Search Results (2,615)

The growing share of variable renewable energy sources in power systems is increasing the need for short-term operational flexibility—particularly from large industrial electricity consumers. This study proposes a practical, two-stage optimization framework to unlock this flexibility in cement manufacturing and support participation in electricity balancing markets. In Stage 1, a mixed-integer linear programming model minimizes electricity procurement costs by optimally scheduling the raw milling subsystem, subject to technical and operational constraints. In Stage 2, a flexibility assessment model identifies and evaluates profitable deviations from this baseline, targeting participation in Spain’s manual Frequency Restoration Reserve market. The methodology is validated through a real-world case study at a Spanish cement plant, incorporating photovoltaic (PV) generation and battery energy storage systems (BESS). The results show that flexibility services can yield monthly revenues of up to €800, with limited disruption to production processes. Additionally, combined PV + BESS configurations achieve electricity cost reductions and investment paybacks as short as six years. The proposed framework offers a replicable pathway for integrating demand-side flexibility into energy-intensive industries—enhancing grid resilience, economic performance, and decarbonization efforts. Full article

Residential photovoltaic and battery energy storage systems (PV/BESS systems) are gaining attention as a measure against natural disasters and rising electricity prices. This paper aims to propose an operational strategy that balances electricity cost reductions, battery lifespans, and outage mitigation for the residential PV/BESS system. The optimization model considering battery degradation determines normal operations with balancing cost reductions and degradation. Additionally, a rule-based approach simulates system performance during various outages and evaluates supply continuity using a resilience metric: the percent continuous supply hour. Outage mitigation benefits are quantified by considering the distribution of residential values of lost load (VoLLs). Results show that the operation considering degradation maintains a high state of charge (SoC) at all times. For 25.7% of households with large demand, electricity cost reductions exceed equipment costs. Outage simulations demonstrate that the mean energy supplied during a 48-h outage ranges from 14 kWh to $26.7$ kWh. Furthermore, the proposed operation increases the resilience metric from 20% to 30% under severe and unpredictable outages. Finally, incorporating outage mitigation benefits increases the proportion of households adopting PV/BESS systems by 21.5% points. Full article

Renewable Energy Communities (RECs) are a cornerstone of the European Union’s energy transition strategy, promoting decentralized and participatory energy models. A fundamental design aspect of RECs is the choice of Keys of Repartition (KoRs), which govern the allocation of locally generated energy among participants. This study evaluated the economic and technical impacts of four KoR strategies—static, dynamic (based on load or production), and hybrid—within the Portuguese regulatory framework. A simulation-based methodology was employed, considering both small and large-scale communities, with and without energy storage systems, including stationary batteries and electric vehicles (EVs). Results show that storage integration markedly improves self-sufficiency and self-consumption, with stationary batteries playing the most significant role, while EVs provided only a residual contribution. Furthermore, the results demonstrated that the choice of KoR has a decisive impact on REC performance: in small-scale communities, outcomes depend strongly on participant demand profiles and storage availability, whereas in large-scale communities, operational rules become the key factor in ensuring efficient energy sharing, higher self-consumption, and improved balance between generation and demand. Full article

Despite growing interest in positive-energy and net-zero-energy buildings (NZEBs), few studies have addressed the integration of biobased construction with building-integrated photovoltaics (BIPV) under hot–dry climate conditions, particularly in Morocco and North Africa. This study fills this gap by presenting a simulation-based evaluation of energy performance and renewable energy integration strategies for a residential building in the Fes-Meknes region. Two structural configurations were compared using dynamic energy simulations in DesignBuilder/EnergyPlus, that is, a conventional concrete brick model and an eco-constructed alternative based on biobased wooden materials. Thus, the wooden construction reduced annual energy consumption by 33.3% and operational CO₂ emissions by 50% due to enhanced thermal insulation and moisture-regulating properties. Then multiple configurations of the solar energy systems were analysed, and an optimal hybrid off-grid hybrid system combining rooftop photovoltaic, BIPV, and lithium-ion battery storage achieved a 100% renewable energy fraction with an annual output of 12,390 kWh. While the system incurs a higher net present cost of $45,708 USD, it ensures full grid independence, lowers the electricity cost to $0.70/kWh, and improves occupant comfort. The novelty of this work lies in its integrated approach, which combines biobased construction, lifecycle-informed energy modelling, and HOMER-optimised PV/BIPV systems tailored to a hot, dry climate. The study provides a replicable framework for designing NZEBs in Morocco and similar arid regions, supporting the low-carbon transition and informing policy, planning, and sustainable construction strategies. Full article

Maximizing the optimal scheduling capability of a virtual power plant (VPP) over its aggregated resources is crucial for increasing its revenue. However, the limited dispatchable resources in single-energy VPPs hinder maximum economic efficiency. To address this issue, in this paper, a multienergy virtual power plant (MEVPP), which aggregates distributed electrical, thermal, and demand-side flexible resources, is introduced. Furthermore, a low-carbon economic dispatch strategy model is proposed for the coordinated operation of the MEVPP with shared energy storage. First, an MEVPP model incorporating shared energy storage is constructed, with equipment modeling developed from both electrical and thermal dimensions. Second, a low-carbon dispatch strategy that incorporates multiple types of demand responses is formulated, accounting for the effects of electrical and thermal demand responses, as well as carbon emissions, on dispatch. The simulation results demonstrate that, compared with models that do not consider the multienergy demand response, the proposed model reduces system operating costs to 54.2% and system carbon emissions to 42%. Additionally, the MEVPP can leverage energy storage by charging during low-price periods and discharging during high-price periods, thereby enabling low-carbon and economically viable system operation. This study offers valuable insights for the optimized operation of MEVPP systems. Full article

The main objective of this article is to model, simulate, and analyze the interaction of energy storage systems with BIPV installations. Currently, due to the instability of energy generation, the economic challenges of integrating PV installations into the electricity grid, and the desire to increase self-consumption, energy storage facilities are becoming increasingly popular. Subsidy programs most often favor PV installations, including BIPV, that work with energy storage devices. Therefore, there is a justified need to model energy storage devices for use with BIPV. The article describes the rationale for the benefits of using energy storage systems within current billing models, using Poland as an example. The introduction also provides an overview of the most popular energy storage technologies compatible with renewable energy installations. To achieve these objectives, appropriate system solutions were designed in the MATLAB environment and used to perform simulations, taking into account variable energy demand. An economic analysis of the system’s operation was conducted using a prosumer net-billing model, and adjustments were made to the system configuration. It has been shown that the use of appropriate energy storage solutions, cooperating with photovoltaic installations, allows for increased self-consumption and more efficient management of electricity obtained in BIPV, which has a positive impact on the payback time and economic profits. The analysis method used and the results obtained are true for the assumed known load profile; however, the method can be successfully applied to various load profiles. Full article

With increasing penetration of distributed renewable energy sources (RES) in distribution networks, spatiotemporal mismatches arise between static time-of-use (TOU) pricing and real-time carbon emission factors. This misalignment hinders demand-side flexibility deployment, potentially increasing high-carbon-period consumption and impeding low-carbon operations. To address this, the paper proposes an adjustable carbon emission factor (ADCEF) which decouples electricity from carbon liability using storage. The strategy leverages energy storage for carbon responsibility time-shifting to build a dynamic ADCEF model, introducing a negative-carbon incentive mechanism which quantifies the value of surplus renewables. A revenue feedback mechanism couples ADCEF with electricity prices, forming dynamic price troughs during high-RES periods to guide flexible resources toward coordinated peak shaving, valley filling, and low-carbon responses. Validated on a modified IEEE 33-bus system across multiple scenarios, the strategy shifts resources to carbon-negative periods, achieving 100% on-site excess RES utilization in high-penetration scenarios and, compared to traditional TOU approaches, a 27.9% emission reduction and 8.3% revenue increase. Full article

Efficiently predicting the lifespan of lithium iron phosphate (LFP) batteries early in their operational life is critical to accelerating the development of energy storage systems while reducing testing time, cost, and resource consumption. Traditional degradation models rely on full-cycle testing to estimate long-term performance, which is both time- and resource-intensive. This study proposes a novel semi-empirical degradation model that leverages a small fraction of early-cycle data with just 5% to accurately forecast full-lifetime performance with high accuracy, with less than 1.5% mean absolute percentage error. The model integrates fundamental degradation physics with data-driven calibration, using an improved musical chairs algorithm modified with chaotic map dynamics to optimize model parameters efficiently. Trained and validated on a diverse dataset of 27 LFP cells cycled under varying depths of discharge, current rates, and temperatures, the proposed method demonstrates superior convergence speed, robustness across LFP operating conditions, and predictive accuracy compared to traditional approaches. These results provide a scalable framework for rapid battery evaluation and deployment, supporting advances in electric mobility and grid-scale storage. Full article

In recent years, the manufacturing industry and power sector have collectively accounted for nearly 60% of global carbon emissions, presenting a formidable obstacle to achieving net-zero targets by 2050. To address the urgent need for industrial decarbonization, this paper proposes a profit-driven framework for low-carbon manufacturing that synergistically integrates green certificates, demand response, distributed generation, and carbon capture, utilization, and storage (CCUS) technologies. A comprehensive optimization model is formulated to enable manufacturers to maximize profits through strategic participation in electricity, carbon, green certificate, and industrial manufacturing product markets simultaneously. By solving this optimization problem, manufacturers can derive optimal production decisions. The framework’s effectiveness is demonstrated through a case study on lithium-ion battery manufacturing, which reveals promising outcomes: meaningful profit growth, substantial carbon emission reductions, and only minimal impacts on production output. Furthermore, the proposed demand response strategy achieves significant reductions in electricity consumption during peak hours, while the integration of distributed generation systems markedly decreases reliance on the main grid. The incorporation of CCUS extends the clean operation periods of thermal power units, generating additional revenue from carbon trading and CO₂ utilization. In summary, the proposed model represents the first unified profit-maximizing optimization framework for low-carbon manufacturing industries, shifting from traditional cost minimization to profitability optimization, addressing gaps in fragmented low-carbon strategies, and providing a replicable blueprint for carbon-neutral operations while enhancing profitability. Full article

This paper develops an optimal dispatch model for an integrated wind–solar hydrogen-to-ammonia system to address the mismatch between renewable-energy fluctuations and chemical production loads. The model incorporates renewable variability, electrolyzer dynamics, hydrogen-storage regulation, and ammonia-synthesis load constraints, and is solved using a multi-time-scale MILP framework. An efficiency-priority power allocation strategy is further introduced to account for performance differences among electrolyzers. Using real wind–solar output data, a 72-h case study compares three operational schemes: the Balanced Scheme, the Steady-State Scheme, and the Following Scheme. The proposed Balanced Scheme reduces renewable curtailment to 2.4%, lowers ammonia load fluctuations relative to the Following Scheme, and decreases electricity consumption per ton of ammonia by 19.4% compared with the Steady-State Scheme. These results demonstrate that the integrated dispatch model and electrolyzer-cluster control strategy enhance system flexibility, energy efficiency, and overall economic performance in renewable-powered ammonia production. Full article

Technical constraints to be faced in microgrids have become more frequent with high renewable integration. In this context, Thermal Energy Storage (TES) has emerged as a promising solution to enable consumers’ flexibility to contribute to the solution of such operational issues. This paper examines the integration of the novel system ECHO-TES (a Thermal Energy Storage System developed within the European Project ECHO) in microgrids to address technical constraints, utilizing OpenDSS and Python simulations. Building on that, the Efficient Compact Modular Transaction Simulation System (ECHO-TSS) adds a layer of virtual automated transactions, coordinating multiple ECHO-TES assets to simulate not only energy flows and electricity consumption, but also the associated economic interactions. The study explores the critical role of TES in enhancing microgrid efficiency, flexibility, and sustainability, particularly when coupled with renewable energy sources. By analyzing diverse demand scenarios, the research aims to assess its impact on grid stability and management. The paper highlights the importance of advanced modeling tools like OpenDSS in simulating complex microgrid operations, including the dynamic behavior of TES systems. It also investigates demand-side management strategies and the potential of TES to mitigate challenges associated with renewable energy variability. The findings contribute to the development of robust, adaptive microgrid systems and support the global transition towards sustainable energy infrastructure. Full article

South Africa’s persistent electricity shortages and recurrent load shedding remain among the most pressing challenges to national economic growth and social stability. This paper presents a techno-economic framework to assess how optimized deployment of photovoltaic (PV) and battery energy storage systems (BESSs) can mitigate these disruptions under realistic grid and regulatory constraints. Despite recent operational improvements at Eskom—including a 10-month period without load shedding in 2024—energy insecurity persists due to aging coal assets, limited transmission capacity, and slow renewable integration. Using hourly demand and solar-resource data for 2023, combined with Eskom’s load-reduction records, a Particle Swarm Optimization (PSO) model identifies cost-optimal hybrid system configurations that minimize the Levelized Cost of Electricity (LCOE) while maximizing coverage of unserved energy. Three deployment scenarios are analyzed: (i) constrained regional grid capacity, (ii) flexible redistribution of capacity across six provinces, and (iii) unconstrained national deployment. Results indicate that constrained deployment covers about 86% of curtailed load at 1.88 USD kWh⁻¹, whereas flexible and unconstrained scenarios achieve over 99% coverage at ≈0.58 USD kWh⁻¹. The findings demonstrate that targeted PV–BESS expansion, coupled with selective grid reinforcement, can effectively eliminate load shedding and accelerate South Africa’s transition toward a resilient, low-carbon electricity system. Full article

Monitoring the battery state of health (SOH) has become increasingly important for electric vehicles (EVs), renewable storage systems, and consumer gadgets. It indicates the residual usable capacity and performance of a battery in relation to its original specifications. This information is crucial for the safety and performance enhancement of the overall system. This paper develops an explainable machine learning protocol with Bayesian optimization techniques trained on electrochemical impedance spectroscopy (EIS) data to predict battery SOH. Various robust ensemble algorithms, including HistGradientBoosting (HGB), Random Forest, AdaBoost, Extra Trees, Bagging, CatBoost, Decision Tree, LightGBM, Gradient Boost, and XGB, have been developed and fine-tuned for predicting battery health. Eight comprehensive metrics are employed to estimate the model’s performance rigorously: coefficient of determination (R²), mean squared error (MSE), median absolute error (medae), mean absolute error (MAE), correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and root mean squared error (RMSE). Bayesian optimization techniques were developed to optimize hyperparameters across all models, ensuring optimal implementation of each algorithm. Feature importance analysis was performed to thoroughly evaluate the models and assess the features with the most influence on battery health degradation. The comparison indicated that the GradientBoosting model outperformed others, achieving an MAE of 0.1041 and an R² of 0.9996. The findings suggest that Bayesian-optimized tree-based ensemble methods, particularly gradient boosting, excel at forecasting battery health status from electrochemical impedance spectroscopy data. This result offers an excellent opportunity for practical use in battery management systems that employ diverse industrial state-of-health assessment techniques to enhance battery longevity, contributing to sustainability initiatives for second-life lithium-ion batteries. This capability enables the recycling of vehicle batteries for application in static storage systems, which is environmentally advantageous and ensures continuity. Full article

As a critical energy component in electric vehicles, energy storage systems, and other applications, the accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for performance optimization and safety assurance. To this end, this paper proposes a hybrid model named Transformer–KAN, which integrates Transformer architecture with Kolmogorov–Arnold Networks (KANs) for precise SOH estimation of lithium-ion batteries. Initially, five health features (HF1–HF5) strongly correlated with SOH degradation are extracted from the historical charge–discharge data, including constant-voltage charging duration, constant-voltage charging area, constant-current discharging area, temperature peak time, and incremental capacity curve peak. The effectiveness of these features is systematically validated through Pearson correlation analysis. The proposed Transformer–KAN model employs a Transformer encoder to capture long-term dependencies within temporal sequences, while the incorporated KAN enhances the model’s nonlinear mapping capability and intrinsic interpretability. Experimental validation conducted on the NASA lithium-ion battery dataset demonstrates that the proposed model outperforms comparative baseline models, including CNN–LSTM, Transformer, and KAN, in terms of both RMSE and MAE metrics. The results indicate that the Transformer–KAN model achieves superior estimation accuracy while exhibiting enhanced generalization capabilities across different battery instances, indicating its strong potential for practical battery management applications. Full article

The efficiency of using electric vehicles largely depends on the availability of charging stations in power supply systems (PSS). To improve the power quality and the ability to control power flows, charging stations can be connected via energy routers built on the basis of solid-state high-frequency transformers. The paper proposes incorporating an energy storage device in the DC circuit of the energy router to improve the reliability of the power supply. The paper presents the results of modeling the operation of a power system supplying DC charging stations based on an energy router with an energy storage device. The study aimed to test the efficiency of the developed regulation system of the energy router with an energy storage device and its impact on the voltage in the power supply system and harmonic distortion levels. An algorithm for stabilizing voltage in the DC and AC networks of the energy router is proposed relying on the transformation of three-phase coordinates a–b–c into the d–q–0 system. The diagrams and descriptions of the models of the power supply system with DC charging stations, as well as an energy router with an energy storage device and a converter for control in normal and emergency modes are presented. The modeling results reveal that the proposed regulator of the energy router with an energy storage device reduces voltage drops when connecting a high-power load and ensures acceptable power quality indicators to meet the criterion of harmonic components. By implementing the control system of the energy storage device within the energy router and electric vehicle charging stations, we can effectively maintain voltage at consumers during emergencies. Thus, the use of energy routers with an automatic voltage regulation system will ensure the sustainable development of modern power supply systems with the ability to connect renewable energy sources, energy storage devices, and electric vehicle charging stations. Full article