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Battery energy storage systems enhance grid flexibility by enabling participation in frequency containment reserves (FCR), day-ahead (DA), and peak shaving (PS) markets—each with distinct operational and economic rules. Yet, operators face a key challenge: how to stack services without compromising reliability or lifespan? This study presents a unified mixed-integer linear programming framework for optimal multi-service stacking, rigorously integrating technical constraints — including real-world fuse limits and battery degradation — alongside market participation requirements. Uniquely, the model balances both droop-based and energy-based FCR participation, precise day-ahead market trading, and behind-the-meter cost management, all while tracking the interplay of physical and regulatory boundaries. The framework is tested using real industrial data from Sweden, under the coordinated rules of Svenska kraftnät. Results reveal that holistic co-optimization is not just a theoretical ideal but a practical economic lever: stacking services increases net profit by about 83% compared to the single-market strategy (DA). This highlights the need for a holistic approach that manages state of energy (SoE), degradation, and fuse limits. The analysis shows that moderate relaxations in fuse limits boost revenue, but benefits plateau, suggesting that reasonable sizing captures most economic gains without costly upgrades to fuses or grid infrastructure. 

Lithium plating, triggered by low-temperature and high-rate charging, leads to capacity degradation and poses significant safety risks in lithium-ion batteries (LIBs). To ensure safe and efficient LIB operation, this study improves the impedance-based lithium plating detection method and proposes a non-destructive online detection method for lithium plating based on the lithium stripping mechanism. By monitoring changes in battery relaxation impedance during brief charging pauses after every 1 % increment in the state of charge (SOC), the onset SOC for lithium plating is accurately identified. The method is theoretically validated using an electrochemical-thermal coupling model and experimentally verified under both low and room temperatures, as well as under fast and slow charging conditions, through voltage relaxation profiles and dynamic electrochemical impedance spectroscopy. Furthermore, a stepwise intermittent charging (SIC) strategy is developed, leveraging the progressively decreasing current and current pause to mitigate lithium plating. The SIC strategy reduces capacity degradation by 85.7 % after 80 cycles compared to constant current charging at the same charging speed. This research offers practical insights for enhancing fast and safe charging technologies in LIBs, providing a foundation for real-world applications. 

Battery degradation plays a critical role in determining battery performance and in predicting the remaining useful life (RUL). Several methods exist to monitor degradation. Electrochemical Impedance Spectroscopy (EIS) is well-suited for single cells, but its application becomes more challenging when analyzing battery modules composed of multiple cells connected in series. Differential capacity analysis, expressed as either differential capacity versus voltage (dQ/dV) or differential voltage versus capacity (dV/dQ), can be applied to both single cells and modules. EIS allows tracking the deterioration of specific internal mechanisms within a cell. In contrast, the differential methods provide partial insights into these mechanisms. Typically, the dQ/dV curve exhibits three distinct peaks, which can be monitored over time to quantitatively assess the degree of degradation. This, in turn, enables the estimation of the state of health (SOH), and, to a certain extent, the remaining useful life provided that the data is correlated with results from batteries that have been cycled under controlled conditions. The paper presents examples demonstrating this approach, including comparisons between single cells and cells arranged in series.

Recently, clean solar energy has aroused wide attention due to its excellent potential for electricity production. A highly accurate prediction of photovoltaic power generation (PVPG) is the basis of the production and transmission of electricity. However, the current works neglect the regional correlation characteristics of PVPG and few studies propose an effective framework by incorporating prior knowledge for more physically reasonable results. In this work, a hybrid deep learning framework is proposed for simultaneously capturing the spatial correlations among different regions and temporal dependency patterns with various importance. The scientific theory and domain knowledge are incorporated into the deep learning model to make the predicted results possess physical reasonability. Subsequently, the theory-guided and attention-based CNN-LSTM (TG-A-CNN-LSTM) is constructed for PVPG prediction. In the training process, data mismatch and boundary constraint are incorporated into the loss function, and the positive constraint is utilized to restrict the output of the model. After receiving the parameters of the neural network, a TG-A-CNN-LSTM model, whose predicted results obey the physical law, is constructed. A real energy system in five regions is used to verify the accuracy of the proposed model. The predicted results indicate that TG-A-CNN-LSTM can achieve higher precision of PVPG prediction than other prediction models, with RMSE being 11.07, MAE being 4.98, and R2 being 0.94, respectively. Moreover, the performance of prediction models with sparse data is tested to illustrate the stability and robustness of TG-A-CNN-LSTM. 

Offshore wind energy can be considered one of the renewable energy sources with high force potential installed in marine areas. Consequently, the best wind farm layouts identified for constructing combined offshore renewable energy farms are crucial. To this aim, offshore wind potential analysis is essential to highlight the best offshore wind layouts for farm installation and development. Furthermore, the offshore wind farm layouts must be designed and developed based on the offshore wind accurate assessment to identify previously untapped marine regions. In this case, the wind speed distribution and correlation, wind direction, gust speed and gust direction for three sites have been analyzed, and then two offshore wind farm layout scenarios have been designed and analyzed based on two offshore wind turbine types in the Northwest Persian Gulf. In this case, offshore wind farm layouts software and tools have been reviewed as ubiquitous software tools. The results show Beacon M28 and Sea Island buoys location that the highest correlation between wind and gust speeds is between 87% and 98% in Beacon M28 and Sea Island Buoy, respectively. Considerably, the correlation between wind direction and wind speed is negligible. The Maximum likelihood algorithm, the WAsP algorithm, and the Least Squares algorithm have been used to analyze the wind energy potential in offshore buoy locations of the Northwest Persian Gulf. In addition, the wind energy generation potential has been evaluated in different case studies. For example, the Umm Al-Maradim buoy area has excellent potential for offshore wind energy generation based on the Maximum likelihood algorithm, WAsP algorithm, and Least Squares algorithm.

The building sector in the European Union accounts for over 40% of the final energy use, where the usage of non-residential buildings may be up to 40% higher than the residential sector. Improving building energy efficiency across all categories of buildings is one key goal of the European energy policies, made prominent by the Climate and Energy package, Energy Performance of Building Directive and Energy Efficiency Directive. In this study, the profitability of an on-site combined heat and power supply system for an office building is investigated. A reference model utilizing solely district heating was constructed and used for validation purposes. Then, a photovoltaic assisted ground source heat pump model was developed and investigated with and without electrical storage to reveal the most cost-effective investment scenario in cold climate regions. The reference model was validated using consumption data provided by the facility owner, after which an investigation of the energy saving potential along with the economic viability of adapting a new heat- and power supply system was conducted. It was concluded that a ground source heat pump in combination with a standalone rooftop photovoltaic system, was successful in satisfying thermal requirements while lowering the building specific energy demand compared to utilizing a district heating system. The photovoltaic assisted ground source heat pump system including a battery bank is the most profitable when incentives are granted, a higher self-consumption of 93.1% is achieved with a battery capacity of 38.4 kWh. 

Visualization of energy consumption within the built environment, both in the private and public sectors, can be a potent tool for increasing conservation behavior. For instance, dynamics visualization could add new knowledge to the end-users to have a better understanding of the energy flows, dynamic mapping of the energy usage in order to avoid misplacing effort and resources, e.g. when it comes to selection of heating systems, investing in energy efficiency measures and renewables as well as when stakeholders are planning for new area to be populated with either commercial or residential buildings. This paper introduces an open-source visualization platform allowing various energy flows mapping in both time and space of a sports facilities. It further includes advanced functionalities such as key performance indicators and integrated prediction models to assist the benchmarking and decision making processes.

This paper aims to investigate the energy consumption in a sport facilities and elaborate a set of novel energy indicators to support decision making process. Sports facilities are complex systems having higher significant energy demand than other facilities for service and recreation. These facilities require massive demand of various energy (e.g. heat, cooling, electricity) to meet the requirement of different types of sports facilities leading to a high complexity to understand and describe such facility accurately. To tackle this problem, an energy flow mapping of different energy demand is developed to have more insights on the energy flow in both time and space domain within one of the biggest sports facilities in Sweden, Rocklunda arena. All the energy meters are virtually connected to design a comprehensive mapping of the energy streams. Then the data is processed and analyzed to elaborate a set of novel key performance indicators KPIs allowing a simplistic description of the different aspects of the system consumption profile and the related energy performance.

The introduction of Silicon Carbide (SiC) MOSFET based inverters into the traction drive makes it possible to increase the inverter switching frequency and reduce energy consumption. This paper describes how to model switching frequency dependent losses in the traction drive and compares the calculated losses to measurements done on a newly developed SiC MOSFET based traction drive. The results from the developed loss models of motor and inverter agrees well with the results from energy measurements. This paper concludes that the energy use of the traction motor and inverter can be simulated well using simple models where skin-effect losses in the motor are modelled in detailed. This paper also concludes that in terms of energy efficiency, the optimal switching frequency using a SiC MOSFET based inverter, is in the range of 3-6 kHz.

With the building sector standing for a major part of the world's energy usage it of utmost importance to develop new ways of reduce the consumption in the sector. This paper discusses the evolution of the regulations and policies of the Swedish electric and district heating metering markets followed by the development of a nonlinear autoregressive neural network with external input (NARX), with the purpose of performing heat demand forecasts for a commercial building in Sweden. The model contains 13 input parameters including; calendar, weather, energy and social behavior parameters. The result revealed that these input parameters can predict the building heat demand to 96% accuracy on an hourly basis for the period of a whole year. Further analysis of the result indicates that the current data resolution of the district heat measuring system limits the future possibilities for services compared to the electric metering system. This is something to consider when new regulation and policies is formulated in the future.