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The electrification of the transportation sector poses an opportunity for reducing greenhouse gas (GHG) emissions from passenger vehicles. Electric vehicle (EV) charging through dynamic wireless power transfer (DWPT), known as roadway electrification, could shift EV demand profiles to better coincide with renewable electricity generation. However, this would be a very large new load and few studies evaluate the regional impacts of DWPT charging in a power transmission system. This paper defines methods that address dataset generation for passenger vehicle trips and models to evaluate regional impacts for this emerging technology. Household vehicle miles traveled (VMT) data form localized EV demand profiles through discrete-event simulation. This data serves as exogenous inputs for a Production Cost Model (PCM) of a synthetic transmission system based on the Electric Reliability Council of Texas's (ERCOT) network. EV charging methods are compared for both a 2018 baseline generation mixture and a high-renewable generation case incorporating 20 GW of installed solar photovoltaic (PV) capacity. The PCM employs unit commitment and economic dispatch (UC&ED) models to compare financial, environmental, and grid reliability impacts from EV charging across passenger EV adoption levels. In-transit charging could reduce grid operational costs by as much as 1.49%, with up to $13.7B saved in annual vehicle operational costs for consumers compared to gas-powered vehicles. Health impacts analysis from power plant and vehicle tailpipe emissions from this study show net health benefits increase by 40% for in-transit charging coupled with high renewable generation. Renewable resources provide an avenue for cost-effective in-transit charging with reduced emissions. The combination of dataset generation and open-source power system modeling establish a foundation for the holistic evaluation of regional DWPT impacts. • PEV dynamic charging with increased renewable generation has positive system impacts. • Public data informs vehicle-miles traveled datasets for energy consumption analysis. • In-transit wireless charging from PEVs decreases power system peak demand hours. • In-transit PEV charging aligns with solar power to reduce curtailment and total cost. • Net health benefits from PEVs improve with in-transit charging and renewable sources. [ABSTRACT FROM AUTHOR]

Transitioning energy systems to renewable sources requires a paradigm shift in system operation: Rather than only dispatching central generators to match volatile demand, the demand side must also be adjusted flexibly to match renewable generation. Electrified heating is one source of such flexibility, via demand response and heat storage. In energy system analysis, demand response is often modelled as a direct control problem, where central decisions set demand levels. We consider this an over-simplification and have previously proposed Frigg, a framework for integrating price-based indirect demand response models in energy system analysis. In this article, a reformulation is proposed that solves central previous shortcomings, such as modelling a larger number of intertemporal constraints. This allows wide application of Frigg in energy system modelling. In this paper, Frigg is applied to soft-link plan4EU, a European electricity dispatch model, and the Flexibility Function. Based on this modelling setup, we conduct a case study on the role of power-to-heat demand flexibility, in the form of demand response and heat storage, in the Danish electricity system of 2050. Our results highlight the significance of Denmark as an electricity transit country: We find that power-to-heat demand response offers mild cost savings in the Danish electricity system, mainly through lower-cost electricity imports and higher-cost exports. Similarly, heat storage allows utilisation of the Danish geographical position. Heat storage achieves significantly higher savings than only demand response. Combining heat storage with demand response achieves similar operational savings but lowers heat-storage investment costs, leading to an overall cost reduction of approximately 7% in 2050. [Display omitted] • Frigg 2.0 integrates price-based demand response into energy system planning. • plan4EU models the European electricity system of 2050. • Frigg links it to Flexibility Function for Danish power-to-heat demand flexibility. • Mild cost-savings through demand response, higher through heat storage. • And highest when combining both (approx. 7%). [ABSTRACT FROM AUTHOR]

RANS (Reynolds-averaged Navier-Stokes) has become one of the mainstream methods to determine the wind field in the wind resource assessment of the complex terrain. However, before the simulation of wind field by RANS, defining the inlet boundary condition often depends on user's experience, and lacks of theoretical guidance, which brings great uncertainties to the accuracy of simulation results. In this paper, based on the vertical multi-point wind speed data (including wind direction) obtained from the on-site measurement and the wind speed data simulated by RANS, a data fusion method called the RANS-VMM (Vertical Multi-point on-site Measurement) method is proposed. In this method, through the polar coordinate diagram (PCD) that can express the wind direction and wind profile index at the same time, the vertical multi-point measured wind speed of wind measurement tower and the RANS simulation results are fused via multiple iterations, which can reconstruct the wind field distribution with spatial variability in complex mountainous areas, thus improving the accuracy of wind resource assessment in the complex terrain. The evaluation and verification of the proposed method are conducted through an real wind farm with complex terrain, and the results indicate that the RANS-VMM method can improves the accuracy of wind resource assessment in the complex terrain. • A wind resource assessment method considering vertical multi-point measured wind field data for mountainous wind farm. • The polar coordinate diagram (PCD) is proposed, which can express the wind direction and wind profile index. • Vertical multi-point wind speed of wind measurement tower and the RANS simulation results are fused via multiple iterations. • Wind field distribution with spatial variability in complex mountainous areas can be reconstruct. • The evaluation and verification of the proposed method are conducted through an real wind farm with complex terrain. [ABSTRACT FROM AUTHOR]

The solar furnace is key equipment for the thermochemical reaction research which uses focused solar energy to drive the reactor, and the solar flux distribution on the reactor surface plays a decisive role in the regulation of the thermochemical reaction process and formation of target products. In this paper, a reflective solar furnace with a thermal power of 30 kWth in Yanqing, Beijing, is taken as the research objective. Firstly, the Monte Carlo Ray Tracing (MCRT) method is used to establish the mathematical model for the optical calculation, and the distribution characteristics of solar flux are deeply analyzed. The accuracy of the model is verified through the comparison with the SolTrace software and experimental measurements respectively. Secondly, an improved four-quadrant and adjustable focusing solar furnace experimental equipment is developed, and a new solar flux distribution regulation strategy is proposed by using the inward crossover and outward expansion offset of the four-quadrant focusing points coupled with the heat-absorbing platform movement. The results show that the non-uniform factor and the peak value of the solar flux distribution in the set target area can be optimized by selecting an appropriate inward crossover offset and movement of the heat-absorbing platform, respectively. Moreover, when the DNI varies between 480 W/m2 and 800 W/m2, through the optimized control strategy, a non-uniformity factor with the value of less than 0.10 can be achieved in the target area of a square reactor surface with a side length of 10 cm, and the peak flux can also be controlled in the range from 3000 kW/m2 to 3300 kW/m2 with a fluctuation range of less than 10.0% as set by the thermochemical reaction. The optimized control strategy can effectively meet the uniformity and stability requirements of solar flux distribution in solar thermochemical reaction and finally achieve the goal of regulating the target products. • An improved four-quadrant adjustable focusing solar furnace is developed. • An innovative solar flux distribution regulation strategy is proposed. • A non-uniformity factor with the value of less than 0.10 can be achieved. • The peak flux can be controlled in the range from 3000 kW/m2 to 3300 kW/m2. [ABSTRACT FROM AUTHOR]

Deploying negative emission technologies has become crucial for limiting the global temperature rise to approximately 1.5 °C above preindustrial levels. DAC technologies are being explored as one of the prospective options. These technologies have been thoroughly investigated as a potential project to capture CO 2 emissions and provide purified air, natural gas, or fuel oil. An applied approach, on the other hand, was not taken into consideration while evaluating the influence that this technology has on emissions. For this reason, British Columbia provides a substantial chance to examine emissions that were produced after the DAC actions were put into place in 2015. In this study, the difference-in-differences methodology is employed for the very first time to compare the emissions that are produced by the transport sectors in British Columbia with those emitted by other provinces in Canada. The role that GDP and population play in the release of emissions is also taken into consideration in this paper. Based on the research results, it can be observed that the implementation of DAC initiatives has yielded notable effects. Evidence shows that the DAC effort has led to an average reduction of 0.08 in logarithmic CO 2 emissions in the transport sector. By accounting for GDP and population, the empirical results indicate that DAC technology reduced CO 2 emissions in British Columbia compared to provinces without DAC facilities. DAC initiatives are expected to become increasingly prevalent between the mid-2030s and 2040s. Overall policy implications suggest that there is a need for DAC technologies to collaborate with alternative mitigation technologies, or alternative technologies should collaborate with DAC technologies that are more efficient to achieve the targeted goals in a short time. • The study offers empirical evidence of DAC technology's impact, providing insights beyond theoretical conjectures. • Based on the research results, it can be seen that the implementation of DAC facilities has yielded notable effects. • DAC technology in British Columbia has led to a significant and negative impact on CO 2 emissions compared to other provinces without DAC facilities. • The study highlights the differential effectiveness of DAC across transport sectors, pointing toward a need for tailored implementation strategies. [ABSTRACT FROM AUTHOR]

In this paper, a swirl combustion furnace is designed and manufactured to realize MILD combustion. The influence of swirling air proportion, equivalence ratio and thermal power on MILD combustion are studied. The results reveal that the combustion chamber temperature is highest and the NO and CO emissions are lowest when the proportion of swirling air is 100%. Additionally, an appropriate equivalence ratio increases the combustion chamber temperature while reducing the NO and CO emissions during MILD combustion. Furthermore, CO emissions reach their minimum at a specific thermal power, while the temperature and NO emissions increase with thermal power. Overall, MILD combustion exhibits high stability under all experimental conditions, with NO emissions consistently below 30 ppm. Indeed, the findings underscore that the swirl combustion furnace is capable of achieving MILD combustion with superior combustion performance. This highlights the considerable potential of the combustion furnace for various applications, especially in areas where efficient and clean combustion is paramount. • Design and manufacture a swirling air MILD combustion furnace. • Realize MILD combustion under non preheating condition. • Study the effect of equivalence ratio, power and swirling air on MILD combustion. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources (RES) significantly increases, their inherent uncertainty has brought about a paradigm shift in distribution system planning and operation. The existing planning methodologies usually consider the uncertainty during the investment and day-ahead stages while neglecting the interactive power fluctuations during the intraday stage, resulting in planning schemes with a risk of inadequate regional stability. In light of this, this paper proposes a novel adaptive robust distribution system planning approach that integrates local uncertainty management (LUM) to provide a comprehensive and refined consideration of uncertainty. First, we introduce the concept of LUM that can effectively address the deviations between predicted and actual power of RES and load. Second, we propose the adaptive robust planning model for the distribution system, in which the investment and day-ahead operation are considered in the first stage, while LUM is considered in the second stage to maintain the local stability of interactive power. The proposed model can actively manage uncertainties of RES and load demand, reaching the optimal balance between the investment cost, operational cost, and LUM cost. Finally, by employing the column-and-constraint generation (C&CG), the adaptive robust planning model is decoupled into a master min problem and a slaver max-min problem, after which the max-min problem is converted into a single mixed-integer linear programming (MILP) problem through the strong duality theory and big-M method. The effectiveness of the proposed model is demonstrated using a real-world distribution system located in Fujian Province, China. The simulation results indicate that the proposed model achieves a quantitative decrease of approximately 10% in total cost compared to deterministic planning. Sensitivity analysis is also conducted to reveal the trade-off between economic cost and robustness level. • Local uncertainty management (LUM) is incorporated into distribution system planning. • A three-stage framework is developed for the planning problem incorporating LUM. • An adaptive robust distribution system planning model incorporating LUM is proposed. [ABSTRACT FROM AUTHOR]

With the significant trend towards variable frequency in heating ventilating and air-conditioner (HVAC) loads, the inverter-based air conditioner load (IACL) clusters are poised to become the mainstay in participating in demand response (DR). During the process of DR, once the indoor temperature exceeds the comfort constraint, HVAC load must be forced to exit DR, which brings correlation to the behaviour of the preceding and following periods, making cluster optimization modelling and solution difficult. In order to solve the problems, this paper proposes an analogue ON/OFF state-switching (AOSS) control method suitable for IACL cluster participating in DR. Firstly, the critical working point corresponding to the comfort constraint is analysed for eliminating the temporal coupling for IACL. Then, an AOSS control method is proposed based on the critical point analysis. The feasibility of the proposed AOSS control is validated by the experimental results, with the Hisense KFR-75W/T08SBp-A2 inverter-based air conditioning DR experimental platform. Furthermore, by using the proposed AOSS control, a day-ahead scheduling model is constructed, and, the traditional state-queuing (SQ) model is transplanted to the IACL cluster for short-term power control. And the case studies verified the effectiveness and applicability of the day-ahead scheduling model and the transplanted SQ model. • Analysing the critical working point corresponding to the comfort constraint. • AOSS control is proposed to eliminate the temporal coupling for IACL. • Aggregation and optimal dispatching for IACL cluster in day-ahead time scale. • Ultra-short time scale IACL cluster power control. [ABSTRACT FROM AUTHOR]

A large part of French electricity consumption variation is due to temperature fluctuations. While HVAC (heating, ventilation and air-conditioning) systems consumption are directly affected by the temperature, other systems (refrigerator, freezer, water heater) can also be driven by weather changes making thermal contribution to overall consumption difficult to extract. This paper presents a "by-design" unsupervised data-driven method to separate the consumptions due to the weather in the overall electricity consumption. The proposed deep-learning model is based on the separation of meteorological parameters from calendar ones within the model architecture. The performances of this model, in particular its ability to split consumption mechanisms, is tested on a synthetic dataset and on the french consumption dataset. Being relatively simple and interpretable, this approach can be generalized to other countries whereasenergy sobriety represents an important challenge we are facing. • The hourly power load of a country due to temperature is unsupervisingly identified. • Deep learning model provides Large Scale Non Intrusive Load Separation. • Engineering-driven neural network design separates weather and calendar consumption. • Model's performance is assessed on country's actual electricity consumption. [ABSTRACT FROM AUTHOR]

Energy plays a vital part in stimulating economic progress, and the shift towards a cleaner energy system is highly significant for ensuring the sustainable development of the economy. China's energy structure urgently needs to be transitioned. The fast advancement and implementation of artificial intelligence (AI) has provided a new and important tool for promoting the transition of energy structure. So, what is the relationship between the application of artificial intelligence and the transition of the energy structure? This research introduces artificial intelligence into the energy sector, focusing on the relationship between artificial intelligence and energy transition. Since nonlinear models are better able to study the complex effects and phase differences of artificial intelligence. Using China's provincial panel data spanning from 2006 to 2019, this study employs nonlinear modeling to explore the stage differences in the process of AI in facilitating energy structure transformation. This paper derives the following findings based on empirical research. First, there is a U-shaped relationship between artificial intelligence and the transition of energy structure. Specifically, before the inflection point, the initial application of artificial intelligence, artificial intelligence may adversely impact energy transition. When the inflection point is passed, AI will help facilitate the energy transition. Second, the U-shaped relationship between AI and energy transition is more pronounced in coastal and non-resource-based regions. Third, energy intensity, government investment in science and technology, and informatization will moderate the U-shaped relationship between artificial intelligence and energy transition, changing the steepness of the original U-shaped relationship and even reversing it. Hence, it is imperative to effectively utilize the technological benefits of artificial intelligence through the development patterns and distinctive features of different regions, thereby facilitating the smooth transition of the energy structure. [Display omitted] • The role of artificial intelligence in the energy transition process is studied. • Stage differences in the role of AI are analyzed. • The nonlinear relationship between AI and energy transition is investigated. • Provide insights into the factors affecting the relationship between AI and energy transition. • This research helps the government to advance the energy structure transition. [ABSTRACT FROM AUTHOR]

This paper delves into an econometric analysis to uncover the differences between Nordic European Countries - NEC (Norway, Denmark, Finland, Sweden) and South-East European Countries - SEEC (Greece, Bulgaria, Romania, Hungary). The focus is on exploring the interplay among electricity generation from Renewable Energy Sources (RES), CO 2 emissions, urban population and GDP per capita in these two European regions. The study employs cross-sectional dependence, first and second-generation panel unit root tests, Pedroni panel and Westerlund panel cointegration tests, ARDL, Dumitrescu-Hurlin causality test to establish the relation among variables. The findings reveal that in both regions, CO 2 emissions are significantly influenced by GDP per capita, urbanization and RES generation. Furthermore, urbanization plays an important role. When examining causality in SEEC, three bidirectional relationships are observed: urbanization and CO 2 , urbanization and GDP, urbanization and generation from RES, and two unidirectional causalities: CO 2 and GDP, generation from RES and CO 2. In contrast, for the NEC, there are two bidirectional causalities: urbanization and CO 2 , urbanization and generation from RES, and two unidirectional causalities: GDP and CO 2 , generation from RES and CO 2. These results highlight the nuanced and varied nature of environmental and economic dynamics across various European regions. • Relationship between CO 2 emissions, GDP per capita and electricity production from RES. • Investigating two groups of EU countries located in the North and SE Europe. • Estimations with Fully Modified and Dynamic Ordinary Least Squares. • Causality and cointegration between variables. [ABSTRACT FROM AUTHOR]

Local electricity markets (LEMs) such as peer-to-peer (P2P) and community-based markets allow prosumers and consumers to exchange electricity products and services locally. In order to coordinate electricity trading and flexibility services, this paper proposes a hierarchical prosumer-centric market framework with a hybrid LEM and a local flexibility market (LFM). Multi-cut Benders decomposition (MCBD) is employed to decompose the integrated hybrid LEM into a centralized P2P market and multiple community-based markets. The aggregators coordinate energy sources and demands of households in low voltage (LV) distribution networks (DN) as virtual power plants (VPPs) and engage in trading through a P2P market over the medium voltage (MV) DN. In addition, a modified MCBD (M-MCBD) approach is proposed to accelerate the convergence process. The LFM is operated by the distribution system operator (DSO) and is formulated as a mixed-integer nonlinear programming (MINLP) problem which is further relaxed to a mixed-integer second-order cone programming (MI-SOCP) problem. The case study demonstrates that aggregators were able to collaborate on trading within the hybrid LEM to minimize the costs incurred by prosumers within the network. Furthermore, the proposed M-MCBD method improves the scalability of the MCBD by enhancing its convergence speed and accuracy, as demonstrated by testing on problems of varying scales. • A hierarchical market framework is designed comprising a hybrid LEM and an LFM. • The hybrid LEM combines a P2P market and community-based markets with VPPs. • The P2P market and community-based markets are coordinated by a modified MCBD. • The LFM is formulated as an MI-SOCP problem, with VPPs providing flexibility service. • VPPs bid block orders so that the operational characteristics of DERs are captured. [ABSTRACT FROM AUTHOR]

Enhancing the reliability of energy networks and minimizing downtime is crucial, making self-healing smart grids indispensable for ensuring a continuous power supply and fortifying resilience. As smart grids increasingly incorporate decentralized prosumers, innovative coordination strategies are essential to fully exploit their potential and improve system self-healing capabilities. To address this need, this paper presents a novel bi-level strategy for managing the self-healing process within a smart grid influenced by Hydrogen Refueling Stations (HRSs), Electric Vehicle Charging Stations (EVCSs), and energy hubs. This approach taps into the combined potential of these prosumers to boost system self-healing speed and reliability. In the initial stage, the Smart Grid Operator (SGO) conducts self-healing planning during emergencies, communicating required nodal capacities to prevent forced load shedding and outlining incentives for smart prosumers. Subsequently, prosumers schedule their activities and contribute flexible capacities to the SGO. Bridging the first and second stages, an adaptive Alternating Direction Method of Multipliers (ADMM) algorithm ensures convergence between the SGO and prosumer schedules within a decentralized framework. This strategy underwent implementation on a 118-node distribution system using GAMS. Results demonstrate that the proposed concept reduces Forced Load Shedding (FLS) by 32.04% and self-healing costs by 17.48% through effective utilization of smart prosumers' flexible capacities. Furthermore, outcomes indicate that the SGO reduces FLS by 6.69% by deploying Mobile Electrical Energy Storages (MEESs) and Mobile Fuel Cell Trucks (MFCTs) to critical nodes. • Introducing a bi-level self-healing strategy for smart grids with smart prosumers • Developing an adaptive ADMM for decentralized coordination among SGO and prosumers • Utilizing the flexible capacities of energy hubs, EVCSs and HRSs to reduce FLS • Embedding a robust strategy into the bi-level methodology to address uncertainties [ABSTRACT FROM AUTHOR]

Conventional microgrid optimization schemes fall short in achieving global optimality for both sizing and scheduling aspects. In response to the demand for simultaneous optimization, this paper presents a novel inner-outer layer framework that includes an outer layer dedicated to sizing optimization and an inner layer focused on Energy Management System (EMS) operational optimization. This approach has a distinctive mechanism of two-layer interaction, where the outer layer focuses on the size assignment and renewal, while the inner layer is constrained by the size assignment and provides feedback on the corresponding operational cost to the outer layer. It enhances the capability of global optimization since the optimal sizes are searched within a fixed space. To establish the range of initial generation values, the framework commences with a pre-optimization phase. Based on the convexity nature of optimization problems, a combined solution methodology involving Rolling Horizon Optimization (RHO) and Particle Swarm Optimization (PSO) is implemented. The case study demonstrates that the algorithm exhibits a good convergence within 30 generations, reaching a global satisfactory solution with a population size of 50. Furthermore, our analysis of optimal sizes across diverse scenarios fills the research gap in understanding the relationship between optimal sizes and operational scenarios. These results underscore that the impact of the time-of-day tariff strategy is significant, and it is risky to determine the optimal sizes by rule-based strategies. Finally, the comparison to other co-optimization methods shows that the proposed framework can reduce the annual total cost by up to 23.1%, which highlights its greater capability of global optimization. [Display omitted] • A novel inner-outer layer framework for microgrid co-optimization is introduced. • An integrated solution methodology is employed. • A comprehensive analysis of optimal sizes across diverse scenarios is conducted. • A comparative analysis demonstrates its superior global optimization capability. [ABSTRACT FROM AUTHOR]

Accurately estimating the state of charge (SOC) of lithium-ion batteries by the battery management system (BMS) is crucial for efficient energy management and power distribution control in electric vehicles (EVs). By far, data-driven methods have been extensively used in estimating the SOC of lithium-ion batteries to efficiently operate EVs with limited resources. However, the learning ability of these methods needs further enhancement. Therefore, this paper aims to analyze the SOC performance of an extended-input long short-term memory (ELSTM) model. The inputs of the ELSTM are improved with identified battery parameters based on an adaptive multi-timescale identification method with frequency difference decomposition. Second, an adaptive singular value decomposition-transformed unscented Kalman filter (ASVDUKF) is proposed to integrate all unknown variables and guarantee the positive definiteness of the error covariance matrix into a vector to form a multi-fusion model that robustly outputs the denoised and optimized SOCs based on the estimations of the ELSTM. The SOC results demonstrate that the ELSTM outperforms the LSTM, which uses conventional input data. Following that, the ELSTM-ASVDUKF model ensures high accuracy and stability with optimal mean absolute error, root-mean-square error, and mean absolute percentage error of 0.0939%, 0.1074%, and 0.1257%, respectively. The proposed model is validated using various full-cycle current rate and temperature data from two different batteries, establishing it as a promising solution for future development. • A multi-fusion method is proposed based on an ELSTM for SOC estimation of LIBs. • The ELSTM uses battery parameters as extended input to improve its training. • An ASVDUKF method is proposed to further enhance the robustness of the method. • The multi-fusion method is validated under various full-cycle and driving conditions. [ABSTRACT FROM AUTHOR]

This paper presents data-driven models for photovoltaic module temperature prediction and analyzes the relation and effects of ambient conditions to module temperature. A total of 12 different machine learning and regression algorithms are implemented, with a large experimental dataset of 345,600 × 7. Prior to implementing those algorithms, data preprocessing is performed to prepare the datasets and determine the informative attributes for the models. Using PCA with module temperature as target to predict, the selected features for models' inputs were determined to be ambient temperature, solar radiation, wind speed, and relative humidity, and each algorithm is cross-validated and tuned with optimal performance parameters. Results show that while relative humidity is more likely to introduce less information to the model, other aforementioned features are the important parameters to predict the module temperature. While for linear modeling, LASSO algorithm provided the best performance, the ANN model demonstrated the best overall results as it produced the most accurate predictions with lowest errors. A similar performance is attained by the proposed non-linear model, KRR and Gradient Boosting algorithm, with a slight advantage to the KRR model. Furthermore, in comparison to experimental data, the ANN model and the proposed non-linear model provided an R2 values of 0.986 and 0.981, with a MAE of 0.982 and 1.476, and MSE of 2.181and 3.464, respectively. Moreover, the proposed model supplied accurate results when compared to models from literature in an out-of-sample testing, it also proven to be robust and accurate when used to predict the PV power output. • Prediction of photovoltaic module temperature considering ambient weather conditions. • Predictive models have been developed using twelve different machine learning and regression algorithms. • Ambient temperature and solar radiation are key parameters and important variables to predict the PV module temperature. • The performance of developed models was evaluated and compared with other models in the literature. [ABSTRACT FROM AUTHOR]

Electricity market liberalization and the absence of cost-efficient energy storage technologies have led to the transformation of state-owned electricity companies into complex electricity market entities, each having a different time horizon. Deregulation has intensified competition, giving rise to increased uncertainty caused by a multitude of interrelated exogenous factors, resulting in unexpected fluctuations in electricity prices. As a consequence, market participants encounter elevated risks and seek effective mitigation strategies. In this paper, the challenges described in the literature are addressed by studying price distribution histograms in the German and Finnish electricity markets. The objective is to identify normal price intervals that can serve as a foundation for an integrated Day-Ahead price forecasting methodology. A novel approach utilizing the Extreme Learning Machine in combination with Bootstrap intervals is proposed and applied to both markets. The findings demonstrate that Bootstrap intervals effectively capture normal prices, whereas extremely high prices typically align with the upper limits of Bootstrap intervals. Conversely, negative prices tend to fall outside the lower boundaries of the intervals. In order to assess the performance of the proposed methodology, a comparative analysis of its forecasting accuracy against the well-established Generalized AutoRegressive Conditional Heteroskedasticity and AutoRegressive Fractionally Integrated Moving Average models is conducted. In addition, both the computational efficiency and forecasting accuracy of the Extreme Learning Machine in comparison to the Artificial Neural Network are assessed. The results reveal the superior efficiency of the Extreme Learning Machine. The developed forecasting model could potentially assist market participants in making well-informed decisions and executing optimal bidding strategies in response to various scenarios before the Day-Ahead market closes. Notably, the proposed methodology transcends the limitations of fixed price thresholds and effectively addresses market nuances, including the occurrence of negative prices, thus offering a more comprehensive approach for electricity price forecasting. • Analysis of German & Finnish of Day-Ahead prices reveals new insights. • Proposed a new method of classifying market prices into unique classes. • ELM & Bootstrap forecast methodology with very good performance. • ELM & Bootstrap methodology outperforms traditional ones in accuracy & efficiency. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) possess untapped potential as mobile power banks for actively delivering electricity between different energy communities, known as Community-to-Vehicle-to-Community (C2V2C) service. While C2V2C represents an effective means of inter-community electricity sharing, limited research explores EVs' role in electricity delivery between locations. Suitable control approaches of EV charging for the C2V2C service are lacking, and it is unclear how robust the C2V2C service is and how its performance is affected by different factors. This paper aims to bridge these research gaps by developing an advanced control of EV smart charging/discharging to facilitate the C2V2C service. By comparing the power balance in the EVs' current-connecting and next-destination communities, the advanced control derives a target state-of-charge for the EVs in the current-connecting community, which can optimize the electricity delivery between the two communities. Then, the robustness of the C2V2C service is analyzed by evaluating its performances under different scenarios. Major factors like community combinations, renewable energy system (RES) configurations, EV battery capacity and numbers are examined for their impacts on C2V2C performance. The findings demonstrate that the C2V2C service significantly enhances energy balance across diverse community combinations, particularly in workplaces with substantial RES capacity. A large EV battery capacity is beneficial for performance improvements, but the impact diminishes at higher values due to limited surplus renewables availability. The increasing EV number enhances both electricity delivery capability and utilization of self-produced renewables. This study validated the effectiveness of the C2V2C service and provides valuable insights into optimizing its application across different scenarios. • Propose Community-to-Vehicle-to-Community (C2V2C) for inter-community energy sharing. • Study mechanisms of electricity delivery and sharing via EV over-charging/discharging. • Develop an advanced control to optimize EVs' electricity delivery and sharing. • Analyze robustness of the C2V2C service under different scenarios. • Reveal how building-/RES-/EV-related factors affect the C2V2C service performance. [ABSTRACT FROM AUTHOR]

Sensitivity analysis has been widely used in building energy analysis to identify key variables influencing building energy and carbon emissions. However, most previous studies only focus on the single time scale energy use of buildings when conducting sensitivity analysis. The multiple time-scale sensitivity analysis would lead to increased complexity and challenges in building energy analysis. This paper proposes a new systematical procedure for global variance-based sensitivity analysis of multiple time-scale energy use of buildings. A case study of an office building is used to demonstrate the application of this approach using the Bayesian adaptive spline surface (BASS) models at three different time scales (annual, monthly, and daily). The results indicate that the procedure proposed here can provide fast and reliable sensitivity results for the multiple time-scale building energy assessment. The BASS learning models have the advantages of high predictive performance and the fast computation of sensitivity indicators in a closed form without Monte Carlo integrals. The application of principal component analysis can deal with the highly correlated multi-output energy use at a smaller time scale, which can further reduce computational costs. Moreover, the sensitivity analysis at the different time scales can provide new insights into energy characteristics due to the intrinsic variations of weather conditions. This systematical procedure can provide useful guidance for the multiple time-scale model calibration and multi-step ahead predictions of buildings. Moreover, the method proposed here can be extended to the sensitivity analysis of energy use in different time scales (sub-hourly, hourly, or daily) for other energy systems (such as PV, solar thermal, and wind). • Bayesian adaptive spline surfaces (BASS) in sensitivity analysis of building energy. • Multiple time-scale sensitivity analysis of buildings using BASS models. • Building energy assessment using principal component analysis and BASS. • Reduce computational time for sensitivity analysis of building energy using BASS. [ABSTRACT FROM AUTHOR]

Current lithium-ion battery (LIB) management technique relying solely on the limited time-domain measurements appears to reach its limit, and incorporating new sensing information, particularly the impedance of LIB, offers a promising path for improvement. Using the non-stationary random driving profile of electric vehicle (EV), a method to extract online passive electrochemical impedance spectroscopy (OPEIS) is proposed and validated, with relevant factors influencing its efficacy also investigated. The particularity of actual driving profile is revealed both theoretically and experimentally, and beyond expectation, the highly differentiated driving profiles yield a similar spectral pattern, which facilitates the acquisition of OPEIS. Continuous OPEIS characterized by the distribution of relaxation time (DRT) ranging from 0.2 Hz to 3 kHz are analyzed in detail under different battery conditions. Compared with offline reference EIS, most of the measurement errors are <3%. Based on the acquired spectral information and OPEIS, a safety-relevant detector and an internal temperature estimator for LIB are presented, and they can both realize a near instant electrochemical sensing up to 1 Hz. As the underlying opportunities of OPEIS are outlined, challenges in its reliable acquisition and engineering implementation are evaluated as well. By comprehensively discussing the realization of OPEIS, research in this paper is expected to provide valuable references for a more effective battery management. • A straightforward OPEIS acquisition and preprocessing strategy is presented. • Spectral features of the driving profile in day-to-day use of EV are revealed. • The feasibility and performance of OPEIS are evaluated from multiple dimensions. • The positive role of OPEIS in battery management and the challenges in accessing it are outlined. [ABSTRACT FROM AUTHOR]

The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid hydrogen–electricity Storage System (HSS) in a residential microgrid (RMG). The HEMS dispatches an electrolyzer-fuel cell-based hydrogen energy storage (ES) unit for seasonal energy shifting and an on-site battery stack for daily energy allocation against the uncertainty from the renewable energy source (RES) and demand side. The online decision-making of the proposed HEMS is realized through two parallel fuzzy logic (FL)-based controllers which are decoupled by different operating frequencies. An original local energy estimation model (LEEM) is specifically designed for the decision process of FL controllers to comprehensively evaluate the system status and quantify the electricity price expectation for the HEMS. The proposed HEMS is independent of RES prediction or load forecasting, and gives the optimal operation for HSS in separated resolutions: the hydrogen ES unit is dispatched hourly and the battery is operated every minute. The performance of the proposed method is verified by numerical experiments fed by real-world datasets. The superiority of the HEMS in expense-saving manner is validated through comparison with PSO-based day-ahead optimization methods, fuzzy logic EMS, and rule-based online EMS. • EMS performance degrades without considering energy storage dynamics. • A hierarchical EMS for hybrid storage system redistributes renewable energy. • A local energy estimation model establishes the EMS intelligent awareness. • Fuzzy logic-based controllers realize system real-time operation. • Frequency-decoupling controllers benefit energy storage units' cooperation. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage distributionally robust approach for offering and pricing strategy of a virtual power plant (VPP) acting as a price-maker in the day-ahead market. The model incorporates uncertainties related to distributed photovoltaic (PV) output, real-time market price and charging stations demand. In the first stage, the VPP determines its day-ahead market bids/offers as a price maker and releases the retail price to the charging stations based on the worst case of probability distribution realizations. In the second stage, the VPP optimizes energy storage operations and the power transactions in the real-time market to maximize its revenue according to the optimal first stage decisions. To efficiently solve the two-stage model, a customized column-and-constraint generation algorithm (C&CG) is designed that decomposes non-convex subproblems into several small-scale linear programming problems that are independent of each other. The effectiveness of the proposed strategy is validated through case studies conducted with the IEEE 33-bus system. • A two-stage robust offering and pricing optimization model for a price-maker virtual power plant is proposed. • A customized column-and-constraint generation algorithm is developed to efficiently solve the model. • The model incorporates uncertainties related to PV output, real-time market price and charging stations' demand. [ABSTRACT FROM AUTHOR]

With the high penetration of renewable generation systems in the power grid, the accurate simulation of the uncertainty in renewable energy generation is vital to the safe operation of the power system This paper proposes a novel controllable method for renewable scenario generation based on the improved VAEGAN model. The standard VAEGAN model is first improved using spectral normalization technique and the generator of GAN is trained using VAE. Then, the external and internal interpretable features in the latent space are learned as the controllable vector utilizing the principle of mutual information maximization. Finally, the renewable energy scenarios with overall features are generated using the external universal meteorological features, and renewable energy scenarios with specific features are generated by tuning along the internal interpretable feature of the controllable vector in the latent space. The proposed approach is used to produce real-time series data for renewable energy including wind and solar power. Experiments demonstrate that our method has a better performance in terms of controllable generation and enables the generation of preference patterns covering various statistical features. • A novel VAEGAN model with controllable interpretable features is proposed. • External universal meteorological features are used to generate scenarios with unknown patterns. • Internal interpretable latent features are designed to generate scenarios with specific features. • The results show a better performance of the proposed method on controllable scenario generation. [ABSTRACT FROM AUTHOR]

Across recent years, there has been a growing prevalence of extreme weather events throughout the United States, posing significant challenges to the reliable and resilient operation of power systems. Specifically, summer droughts threaten to severely reduce available generation capacity to meet regional electricity demand, potentially leading to power outages. This underscores the importance of accurate resource adequacy (RA) assessment to ensure the reliable operation of the nation's energy infrastructure. Accurately evaluating the usable capacity of regional generation fleets is a challenging undertaking due to the intricate interactions between power systems and hydro-climatic systems. This paper proposes a systematic and analytical framework to evaluate the impacts of extreme summer drought events on the available capacity of various generating technologies, incorporating both meteorological and hydrologic factors. The framework provides detailed plant-level capacity derating models for hydroelectric, thermoelectric, and renewable power plants, facilitating evaluations with high temporal and spatial resolution. The application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system within the PJM and SERC regions of the United States yields insightful results. By analyzing the daily usable capacity of 6,055 at-risk generators across the study region, it shows that the summer capacity deration is most significant for hydroelectric and once-through thermal power plants, followed by recirculating thermal power plants and combustion turbines. In the event of the recurrence of the 2007 southeastern summer drought event in the near future, the generation fleet could experience a substantial reduction in available capacity, estimated at approximately 8.5 GW, compared to typical summer conditions. The sensitivity analysis reveals that the usable capacity of the generation fleet would suffer an even more significant decrease under conditions of increasingly severe summer droughts. The proposed approach and the findings of this study provide valuable methodologies and insights, empowering stakeholders to bolster the resilience of power systems against the potentially devastating effects of future extreme drought events. [Display omitted] • A systematic summer droughts generating capacity assessment framework is developed. • Capacity derating models for various generation technologies are presented. • The impacts of summer droughts on generating fleet in PJM and SERC are studied. • Impact of extreme summer drought on usable capacity of generators is significant. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) and their charging infrastructure have witnessed rapid adoption into the smart grid owing to their ability to reduce the transportation sector's emissions. However, this hasty deployment has left the grid prone to attacks initiated through the charging ecosystem. This paper develops a family of robust dynamic load-altering attacks that manipulate the EV load to destabilize the power grid. The attack strategy is formulated based on feedback control theory and Linear Matrix Inequalities (LMIs) and incorporates mathematical modeling of the power grid uncertainties. The effectiveness of our proposed robust EV attack strategy is demonstrated through extensive simulations of attack scenarios against the New England (NE) 39-bus grid. The results demonstrate that the proposed attacks were able to destabilize the grid frequency inducing a deviation of 1.5 Hz and forcing the generators to trip after compromising as little as 6.6% of the total grid load. We also present a two-tier Deep Learning (DL) attack detection mechanism. We optimize this mechanism to achieve optimal results against dynamic and switching attacks. The proposed mechanism was evaluated against several other models and proved to be superior with an accuracy of 99.9%. • We propose a robust EV Dynamic Attack Design that takes advantage of compromised EVs to cause power grid instability. • The controller design accounts for several uncertainties that can impact the attack success. • We perform extensive time domain simulations to demonstrate the devastating impact of such attacks even in the presence of uncertainties. • The proposed scheme demonstrates its ability to cause generator tripping even with just 6.7% of the total grid load. • We then propose a two-tier detection mechanism that achieved a detection accuracy above 99.9%. [ABSTRACT FROM AUTHOR]

Battery energy management systems have been studied in control communities for many years. This paper proposes a new perspective by integrating control and scheduling for battery-powered autonomous systems. This is motivated by the observations that battery closed-loop control can significantly improve the DC-bus stability but reduce the battery durability, while load scheduling can considerably improve the battery durability by smoothing the load power. In view of the above findings, we propose a proactive energy management strategy for the battery energy management system to improve both the DC-bus stability and battery durability. We first analyze the schedulability of load tasks to check if they are schedulable. Then, the power of schedulable loads is scheduled with an active load scheduling algorithm to smooth the fluctuation of battery current and extend the battery lifetime. Thereafter, the scheduled load power is integrated with a feedforward control to restrain DC-bus voltage fluctuation. In addition to the classical sporadic/periodic load tasks model, we propose a new aperiodic load task model to characterize the load power triggered by events in practical applications. An experimental platform is built to verify the effectiveness of the proposed proactive energy management method. Experimental results show that the proposed proactive energy management method can suppress the 15.71% DC-bus voltage fluctuation and reduce the 8.93% battery current fluctuation, extending the battery lifetime by 7.57% compared to existing energy management strategies. • A proactive energy management strategy integrated scheduling and control is proposed. • An aperiodic load model is proposed for characterizing events-triggered loads. • An active scheduling method based fixed-priority is proposed to guarantee real-time. • Li-ion battery lifetime and DC-bus voltage regulation are improved. • An experiment platform is built to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The global energy shortage is increasingly becoming a problem of major concern worldwide. Since the pumps represent the largest part in the energy conversion devices, improving their energy characteristics is very important for energy conservation and efficiecy. However, the traditional pump design methods have generally overlooked the transient flow characteristics inside the pumps leading to high hydraulic losses and low operatinal efficiency, especially in applications that have pronounced transient effects. To address this problem, a novel approach is proposed in this paper which combines the response surface optimization method with the computational fluid dynamics (CFD) technology to optimize the energy characteristics of pumps during the transient processes. The main objective of the study is to enhance the internal flow state and the transient energy performance of the pump during the start-up process. A comparative analysis is conducted on the energy characteristics and the internal flow field of a model pump before and after the optimization, thereby validating the effectiveness of the proposed energy characteristics optimization method. The numerical results reveal that the inlet placement angle (α), the outlet placement angle (β), the blade envelope angle (φ), and the blade thickness coefficient (θ) significantly impact the weighted average head and weighted average efficiency of the pump. After optimization, the weighted average head and the weighted average efficiency of the pump increase by 2.97% and 8.91% respectively, while the transient efficiency of the pump at all times surpasses that of the traditional, non-otimized design approach. These research findings provide valuable insights for enhancing the performance of the pumps oerating in the transient flow conditions. • Based on the response surface optimization model and computational fluid dynamics (CFD), an innovative general method for the energy characteristics optimization of pump transient processes is proposed. • The new method can improve the internal flow state of the startup process, and realize the improvement of the transient energy performance of the startup process. • The head and efficiency of the start-up process were effectively improved. The weighted average head of the optimized mixed flow pump increased by 2.97% and the weighted average efficiency increased by 8.91%. [ABSTRACT FROM AUTHOR]

The low-temperature cold start has become one of the leading technical bottlenecks limiting the large-scale commercial application of proton exchange membrane(PEM) fuel cells. This paper establishes the 1-D numerical model of the fuel cell stack to study the reactant gas, charge transfer, heat transfer, and water phase transition process. Then the critical parameters in the numerical model of the fuel cell stack are identified based on the cold start experiment data. The identification results show that the cold start model is consistent with the experimental results, and the model's output voltage and average temperature errors are under the acceptable range. Based on the numerical model, with the cold start time as the optimization objective and load current and initial membrane water content as the optimization variable, the cold start strategy is optimized under different ambient temperatures. The results show that the optimized current loading strategy can reduce the cold start time of the fuel cell stack and achieve a fast, successful cold start for −20 °C and − 25 °C conditions. The cold start time of the optimized cold start strategy is reduced by 42.2% compared with the strategy of step stair current under −20 °C. For the lower ambient temperature,-30 °C or below, to realize the successful cold start of fuel cell stacks, two aspects are needed: the redesign of the bipolar plate and endplate structure and the optimization of the cold start strategy. • The supercooled water phase change process is established based on JMAK theory. • Parameters identification and error analysis for the numerical model are conducted. • Initial membrane and segmented currents are optimized for the cold start strategy. [ABSTRACT FROM AUTHOR]

The ever-intensifying threat of climate change renders the electric power system undergoing a profound transition toward net-zero emissions. Energy efficiency measures, such as demand response, facilitate the transformation to jointly relieve consumers' financial burden and improve the operability of the electric power grid, in a carbon-free way. In this paper, we design a cluster-based appliance-level-of-use demand response program, based on the massive volume of appliance consumption data, to expand the role demand response can play in the power grid's low-carbon transition. We systematically model the appliance-level utility function to distinguish consumers' distinct consumption patterns. We then develop a bi-level optimization model to capture the interactions between individual consumers and a distribution system operator (DSO) and enable appliance-level-of-use demand response functions. To further improve the efficiency and scalability of the proposed mechanism, we propose a cluster-based approach to capture the heterogeneity of users based on their energy consumption behaviors. Simulation results show that by capturing the detailed appliance-level response patterns, the proposed approach can systematically improve overall social welfare compared with conventional demand response mechanisms. • Data Empowered Appliance-level Utility Modeling. • Demand Response Design to Offer Appliance-level Incentive. • Heterogeneity Characterization to Improve Scalability and Efficiency. [ABSTRACT FROM AUTHOR]

The concept of the virtual power plant (VPP) has attracted extensive attention due to its distinguished capability of integrating various types of distributed energy resources (DERs). Defining the feasible operation region (FOR) is a crucial prerequisite for VPP's participation in the provision of power system services. However, considering the heterogeneity and uncertainty of DERs, it is always a challenging task to calculate the accurate FOR of VPP. In this paper, we propose a novel internal resource aggregation approach to estimate the FOR of VPP. The design of the proposed aggregation model consists of two stages. In the first stage, all the operational constraints of various DERs at the same node are expressed with a unified polytope form, and then these constraints are aggregated into one single polytope operation constraint by the computational geometry method. In the second stage, based on the aggregated polytope operation constraint at each node, combined with the dynamic line rating (DLR) and network constraints, a data-driven distributionally robust FOR estimation problem is formulated to determine the boundaries of the FOR. Numerical experiments have been conducted to validate the feasibility and superiority of the proposed approach and its applicability in handling multi-period FOR estimation. • A data-driven distributionally robust feasible operation region estimation framework is established. • The proposed approach balances computational efficiency and accuracy. • Dynamic line rating contributes to the expansion of the feasible operation region. • Time-varying feasible operation region estimation is conducted. [ABSTRACT FROM AUTHOR]

The characteristics of thermal runaway in batteries exhibit significant differences depending on their states of charge (SOCs), posing considerable challenges in accurate and timely warning for thermal runaway accidents. This paper conducts an experimental investigation into the external expansion force, voltage, and temperature behaviours of batteries under different SOCs under thermal abuse scenarios. The expansion force signal was found first to exhibit abnormal characteristics (with an indicator value of 5 N/s), offering an earlier warning signal >642 s prior to thermal runaway, when the battery back temperature is merely approximately 318.15 K. To analyse the main causes of abnormal expansion, ultrasonic nondestructive tests were performed and suggested that a higher SOC intensify the side reactions and cause gas generation inside batteries. Notably, compared to normal-charged batteries (0% ≤ SOC ≤ 100%), over-charged batteries (SOC > 100%) are more sensitive to expansion force than normal-charged batteries. Furthermore, compared with voltage and temperature signals, the early warning time of the expansion force rises with the increase in the SOC of an overcharged battery, in contrast to normal-charged batteries. Finally, a novel hazard classification safety warning strategy for battery failure is effectively proposed. These novel findings hold substantial practical implications for creating more accurate early warning systems for battery thermal runaway, enhancing the safe and reliable operation of battery systems. • Effect of SOC on multidimensional signal evolution is comprehensively revealed. • Expansion force can serve as an early warning signal of battery failure. • Expansion force evolution of batteries with different states of charge is revealed. • Nondestructive tests are conducted to analyse the main causes of abnormal expansion. • A novel warning strategy with hazard classification for battery failure is proposed. [ABSTRACT FROM AUTHOR]

A novel sensitive thermo-chromic microcapsules with inorganic/organic nanoparticles-embedded composite shell was designed and synthesized, and the thermal properties, thermo-chromic performance, crystallization behavior of ethyl myristate (EM) in confined spaces were studied in this paper. As the reaction proceeded, the aminated silica (NH 2 -SiO 2) nanoparticles modified outer shell in the aqueous phase and isophorone diisocyanate (IPDI) in the oil phase gradually self-assembled at the inner interface, leading to the formation of an inorganic/organic polymers composite shell. The effect of NH 2 -SiO 2 nanoparticles content on the crystallization behavior, microstructure, morphology, and phase-change properties as well as the thermal stability of MPCMs were investigated in detail. Interestingly, the covalent attachment of NH 2 -SiO 2 nanoparticles on the capsules surface offered long-term chemical stability compared to that of conventional physical adsorption. The NH 2 -SiO 2 nanoparticles were found distributed uniformly on the surface of MPCMs, and the onset crystallization temperature of MPCMs increased by 7.3 °C with NH 2 -SiO 2 nanoparticles addition of 15% content, which counteracts the super-cooling phenomenon. Microcapsule temperature change optical microscopy and non-isothermal crystallization kinetics revealed that the crystallization of RS-MPCMs were homogeneous as well. RS-MPCMs have significant potential in green energy applications due to their latent heat storage, thermal stability, thermal conductivity, leakage prevention, and mechanical properties. [Display omitted] • Thermal energy-regulated microcapsules with raspberry-shaped morphology were designed and synthesized successfully. • Sensitive thermochromic performance of fabricated microcapsules was researched. • Crystallization behavior of microencapsulated phase change materials was investigated using Jeziorny theory. [ABSTRACT FROM AUTHOR]

The operation of energy systems has changed significantly with the increase of intermittent renewable energy sources. New market players that produce, consume, and store electricity- prosumages, along with the different global factors, influence price spreads in the electricity market. This paper gives a comprehensive analysis of the economic viability of two of the currently most cost-effective electricity storage technologies: pumped hydro storage (PHS) and lithium-ion (Li-ion) when used for price arbitrage. The analysis is conducted using a price-taker approach for electricity market prices from 2011.-2019. in which the market has changed. The mixed-integer optimization model is applied with the subtraction of the total storage costs. Arbitrage opportunities for two different energy systems- one with a high generation of renewables and one with high shares of fossil generation are analyzed. Results indicate that pumped hydro storage with a total cost of 0.032 €/kWh is economically justified contrary to Li-ion batteries with a total cost of 0.217 €/kWh. The average yearly profit of PHS, for a study case with 83% of electricity generation from renewables, is 65% lower compared to the case highly dependent on fossil generation. Comparison between study cases indicates that profits of price arbitrage are dependent on full load hours, electricity price spreads, and flexibility options available, rather than on installed shares of renewables. • Storage economic viability depends on the full load hours, electricity market prices and total costs of the technology. • Average yearly arbitraging profit of PHS in Austria is 65% lower compared to the Bosnia and Herzegovina case. • Total costs for 2000 full load hours are: Li-ion 0.217 €/kWh and PHS 0.032 €/kWh. • Energy arbitrage of Li-ion storage is unprofitable. • Increase of energy storage profits isn't proportional to higher shares of renewable generation. [ABSTRACT FROM AUTHOR]

Accurate and rapid fault detection is essential for the safe operation of lithium-ion batteries in electric vehicles. However, conventional fault detection methods dependent on constant thresholds may have false alarms or missing alarms due to the inevitable disturbances resulted from the battery system modeling errors and measurement noises. In this paper, we design a multi-objective nonlinear fault detection observer for lithium-ion batteries, which is robust against disturbances but sensitive to battery multi-fault. We then perform formal stability and L ∞ / H _ performance analysis for the resultant estimation error system. Furthermore, tractable design procedures for the observer gain parameter and an adaptive threshold are derived. Then, via adaptive thresholding, a delicate three-step multi-fault detection scheme is developed to detect the occurrence of battery various faults, including short-circuit faults, current and voltage sensor faults. Finally, the efficacy of the proposed scheme is validated under several experimental case studies involving a variety of faults with their different levels of severity and erroneous SOC initialization. • A general battery model that incorporates both multi-faults and disturbances. • A multi-objective nonlinear observer with formal stability and performance analysis. • A three-step multi-fault detection scheme with an adaptive threshold. • Extensive experimental studies involving a variety of faults. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) are promising solutions to improve power distribution system (PDS) resilience against natural disasters. However, the existing MG formation approaches based on the linearized Distflow (LinDistflow) model always demand MG roots and their corresponding topologies. This can result in an increased number of variables and constraints in the optimization problem, and deteriorate their computational performance. To this end, an adaptive LinDistflow model is proposed based on the single commodity flow model in graph theory in this paper. Specifically, we show that active and reactive powers can be represented as commodities, which are sent from one node to each of its adjacent nodes in the graph. Then, the power flow and nodal voltage calculation based on the commodity flow only requires adjacent node information of the original topology rather than various MG topologies caused by the dynamic deployment of mobile energy resources (MERs). Furthermore, by incorporating the adaptive LinDistflow model as constraints, a dynamic MG formation approach is proposed for resilient load restoration considering large-scale MER deployment. The problem is formulated as a mixed-integer nonlinear programming problem (MINLP). A linearization technique is proposed based on the propositional logic constraints. It employs the propositional logic that partitions the solution space into two separated regions. Accordingly, the region that the solution lies in can be selected linearly. The effectiveness of the proposed approach is demonstrated based on the IEEE 37-Node, 123-Node and 8500-Node Test Feeders. • An adaptive Linearized Distflow model is proposed based on single commodity flow. • Dynamic microgrid formation for large-scale deployment of mobile energy resources. • The propositional logic constraint is employed to address the problem nonlinearity. • Computational complexity is analyzed to show the efficiency improvement. [ABSTRACT FROM AUTHOR]

Aiming at a large number of decentralized resources such as rural biomass, rooftop photovoltaic, and decentralized wind power, a novel rural virtual power plant (VPP) has been conceptualized. The VPP is integrated by biomass waste conversion system (BWC), carbon to energy (C2P), demand response aggregator (DRA), and distributed renewable energy (DRE), namely, BDCD-VPP. Then, this paper proposes a data-driven two-stage robust optimization dispatching model for BDCD-VPP considering carbon-green certificates equivalent conversion mechanism. This model aims to address the uncertainty of wind power plants (WPP) and photovoltaic (PV) power generation. Strong duality theorem (SDT) and C&CG algorithm are applied to construct this model. Thirdly, the Aumann-Shapley method (A-S) was enhanced by incorporating risk factor, cost factor, and carbon reduction factor. This refinement results in the development of a synergistic scheduling revenue allocation method for electricity‑carbon-green certificates. Finally, the Lankao rural energy revolution pilot program in China is selected as the case study, the results showed: (1) The BDCD-VPP aggregates distributed energy sources such as the rural WPP and the PV to realize electricity‑carbon-electricity cycle effect. The BDCD-VPP generates a green certificate revenue of 47.17¥/MWh and exhibited carbon emissions of 0.37 t/MWh. The proposed Carbon-Green Certificates Equivalent Conversion Mechanism increases BDCD-VPP benefit ratio by 3.52%. (2) The proposed two-stage robust optimization dispatching model enhances the ability of BDCD-VPP to adapt to uncertainty. Compared to the day-ahead stage, biomass power generation (BPG) and waste power generation (WPG) increase upward peaking power by 24.05% and 9.99% in intra-day stage. Flue gas treatment system (FG-TS), gas-power plant carbon capture (GPPCC) and power to gas (P2G) increase downward peaking power by 65.15%, 70.99% and 25.94%. (3) Utilizing the proposed benefit allocation methodology, BPG and WPG need to concede 38.25¥/MWh and 65.02¥/MWh for carbon emissions associated with electricity. WPP and PV need to concede 64.25¥/MWh and 33.71¥/MWh for power generation uncertainty. GPPCC, P2G, DRA, and small hydropower station (SHS) obtain revenues of 24.48 ¥/MWh, 55.07 ¥/MWh, 70.38 ¥/MWh, and 51.30 ¥/MWh. Compared to the A-S, the proposed benefit allocation methodology exhibits 6.92% improvement in satisfaction. Overall, the proposed data-driven two-stage robust optimization dispatching model and benefit allocation strategy facilitates the optimal aggregation and utilization of rural distributed energy resources, considering the interests of various stakeholders. This is conducive to achieving a clean and low-carbon transformation of the overall energy structure. • Proposed a novel rural virtual power plant considering biomass waste conversion and carbon to power. • Designed carbon-green certificate equivalence conversion mechanism. • Proposed a data-driven scenarios generation and reduction strategy for uncertainty factors. • Construct a two-stage robust optimization dispatching model and strong duality C&CG algorithm. • Construct an improved Aumann-Shapley-based benefit allocation strategy [ABSTRACT FROM AUTHOR]

As the integration of renewable energy sources (RES) progresses and energy technologies advance, integrated energy systems (IES) have emerged as a practical solution for leveraging RES and balancing local demand–supply dynamics. This paper investigates the planning and optimization of operations for an electricity-hydrogen integrated energy system (EH-IES), considering degradation and multi-timescale operations of storage devices to minimize the life cycle costs. To mitigate computational challenges, the planning model adopts a Representative Time Period (RTP) framework, and a novel intertemporal operation model is developed to coordinate the operations of battery and hydrogen storage. Furthermore, we propose a two-stage approach to derive representative weeks and hours from meteorological and load data using fewer data points, ensuring chronological coherence and preserving long-term patterns to validate storage device constraints and improve planning accuracy. Case studies in southeastern China's IES verify the effectiveness of our planning approach, which lowers equivalent annual costs by 4.5% and reduces battery capacity needs by 12.8%, thereby highlighting its significant economic benefits. Additionally, numerical analyses reveal that our two-stage method surpasses conventional RTP selection techniques, delivering at least 10% less average error with an equal number of operating points. • A novel planning and intertemporal model of EH-IES based on representative time periods is established. • The multi-timescale operations of the battery and hydrogen storage system is considered. • An efficient and accurate representative time-period selection method is developed. [ABSTRACT FROM AUTHOR]

Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model's parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms. • A new multiobjective optimisation method proposed and enhanced hybrid wave-wind platform. • A new combination of chaotic adaptive exploration and dynamic archive is introduced. • Effective and practical discretisation method improved performance of hybrid platform. • The performance of the proposed optimiser is validated on four real sea studies. [ABSTRACT FROM AUTHOR]

This paper proposes a predictive energy management strategy of FC PHEV based on PMP and co-state boundaries. The model predictive control (MPC) problem is established and transformed as a two-point-boundary-value one by PMP theory, and the physical constraints of FC power, FC power varying rate, and battery current, are merged by methodical derivatives. To gain the accurate co-state boundaries, the Karush-Kuhn-Tucker condition, for the first time, is employed to derive the general expressions, and a correction method is developed to modify the co-state boundaries for effectiveness guarantees. By inputting the feedback SOC, power demand, and the unified constraint, the shrunken enclosed range between the developed co-state boundaries can be determined in real time, thereby benefiting the efficient co-state calibration. Based on the co-state bounds, the concise but highly effective heuristic rules are proposed to calibrate the co-state online iteratively, and an analytical method is proposed to fast find the optimal solution of Hamilton function. The global optimality of the proposed strategy for the addressed MPC problem is also strictly proved. The validations and sensitivity analysis for the initial state of charge (SOC), the SOC reference, the predictive velocity accuracy, and the horizon length, are implemented under simulations, and the hardware-in-the-loop (HIL) experiments are carried out to verify the effectiveness of the proposed strategy under on-board environment. The results yield that, the proposed strategy can implement the timely and high-efficiency co-state updates, the smooth control commands, the expected SOC tracking effects, and improve the fuel economy. Additionally, <0.5 ms per sample is spent for the predictive horizon length 40 under HIL tests, indicating the real-time applicability of proposed strategy. • General expressions of co-state bounds are theoretically derived by KKT condition. • Designed analytical method fast minimizes Hamilton function with global optimality. • Heuristic rules by modified co-state bounds can efficiently decide co-state online. • Good co-state calibration stability favors robustness to reference and predicted speed. • HIL tests of designed method show desired fuel savings and high computing efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a techno-economic model based on experimental and market data able to evaluate the profitability of vanadium flow batteries, which are emerging as a promising technology for specific stationary energy services. Models like this are very informative on the present and perspective competitivity of industrial flow batteries in operating specific services, but they have not yet been developed to an accurate grade. This model uses technical parameters which are taken from large-area multi-cell stacks, rather than from small single cell experiments, to better characterize the behavior of real industrial reactors, and from real financial market and economic patterns of some major manufacturers. The model yields economic performance indicators as the capital cost, the operative cost, the levelized cost of storage and the net present value. A prudential present-state and a perspective analysis are elaborated to show where the economic indicators are heading, and which parameters affect more the investment profitability, thus tracking a possible roadmap for system optimization. Perspective estimations indicate that technological and market evolutions are heading to much more competitive systems, with capital costs down to 260 € kWh−1 at a energy/power duration of 10 h, to be compared with a break-even point in the net present value of 400 € kWh−1, which suggests that flow batteries may play a major role in some expanding markets, notably the long duration energy storage. [Display omitted] • A techno-economic model for vanadium redox flow battery is presented. • The method uses experimental data from a kW-kWh-class pilot plant. • A market analysis is developed to determine economic parameters. • Capital cost and profitability of different battery sizes are assessed. • The results of prudential and perspective analyses are presented. [ABSTRACT FROM AUTHOR]

Few-Shot Short-Term Wind Power Forecasting (FS-STWPF) is designed to develop accurate short-term wind power forecasting models with limited training data, reducing the losses suffered by wind farms and power systems due to the data scarcity. Based on the idea of extracting valuable knowledge from the source wind farms and then applying it to the target wind farm, a novel Meta-Learning approach (WG-Reptile) has been proposed in this paper. Building on the existing Reptile algorithm, two specific designs have been made in WG-Reptile for FS-STWPF: (1) Within-Task Samples Assignment method based on Operational Scenario (WTSAOS) has been proposed to improve the adaptability of the models to changing conditions. (2) Gradients Conflict Attenuation method based on Cosine Similarity (GCACS) has been proposed to enhance the effect of knowledge fusion from different source wind farms. Two open wind power forecasting datasets and three deep learning models have been used to implement 24-h-ahead FS-STWPF experiments with different amounts of training data. The results illustrate that the proposed WG-Reptile is able to outperform the other few-shot learning approaches. Intuitively, with only 30-day training data, the accuracy of the proposed WG-Reptile can be equivalent to the conventional supervised learning approaches trained on 6-month. • A novel Meta-Learning approach, WG-Reptile, has been proposed to perform FS-STWPF, which is able to obtain accurate STWPF models with a limited amount of training data. • WTSAOS (Within-Task Samples Assignment method based on Operational Scenario) has been devised to reasonably assign within-task training samples to different updating steps in Inner Loop , which is able to improve the applicability of the models to the changing conditions. • GCACS (Gradients Conflict Attenuation method based on Cosine Similarity) has been devised to alleviate gradient conflicts by scaling the outer learning rates for different layers of the models, which is able to filter valuable gradients to enhance the effectiveness of the knowledge extraction. [ABSTRACT FROM AUTHOR]

The heat injection during depressurization is a crucial technique for sustaining the long-term gas production rate of natural gas hydrate. To analyze the production patterns and mechanisms of hydrate dissociation under various heat injection conditions and to further optimize heat injection strategies, this study establishes a reservoir-scale three-dimensional heterogeneous computational model using a self-developed hydrate simulator. A novel in-situ heating method utilizing bottom seawater is proposed for heat injection, and comprehensive analyses of heating power and injection rates are conducted. The results indicate that increasing heating power at a constant injection rate enhances gas production rates while reducing thermal efficiency. Simultaneously, under constant heating power, gas production rates initially increase and then decrease with increasing injection rates, revealing the presence of an optimal injection rate. This paper introduces the concepts of temperature driving force and temperature modification ratio, analyzes the mechanisms of heat injection development, and derives an optimized strategy for hot water injection through curve fitting. This research enhances our understanding of the integrated approach involving thermal stimulation and depressurization, providing significant guidance for future production endeavors. • Developed a 3D heterogeneous model using a custom simulator, revealing hydrate production limits. • Analyzed and summarized optimization modes for thermal stimulation in hydrate development. • Introduced TDF and TMR concepts, enabling mechanistic interpretation of production capacity. [ABSTRACT FROM AUTHOR]

Recently, with the development of new energy technologies, all-electric ships (AESs) with hybrid energy storage system (HESS) are becoming a promising solution to reduce fuel consumption and emissions. However, the high maneuverability of ships during the actual navigation places higher performance requirements on the HESS, which presents a nonlinear and multi-objective challenge for the HESS design. Therefore, it is necessary to consider the coupling between HESS sizing and energy management strategy (EMS). In this paper, a self-adaptive joint optimization framework (SJOF) for marine HESS design considering load fluctuation characteristics is proposed, which can find the optimal decision solution with excellent system economic and battery life performance for AES HESS design. Based on the rain flow counting (RFC) method, a multi-objective joint optimization method considering life cycle cost (LCC) and battery degradation index (BDI) is introduced into SJOF. Besides, a novel EMS including the self-adaptive segmentation mechanism (SSM) and power allocation is proposed, which can achieve the most efficient energy scheduling. • A self-adaptive working condition classification approach is proposed. • A power allocation strategy with optimal filter matching is proposed. • A reconstruction method for obtaining optimal solution set is proposed. [ABSTRACT FROM AUTHOR]

Cooling for domestic applications using low/medium-grade heat sources is essential to reduce CO 2 emissions and promote environmental friendliness. Thermoacoustic technology is quite promising to meet such demand. This paper proposes a compact and highly efficient single-stage thermoacoustic refrigerator for utilizing low/medium-grade heat source. It is different from conventional design by containing only one thermoacoustic core unit and enhancing cooling efficiency by improved acoustic matching incorporating a cavity structure with no less cooling performance than a multi-unit thermoacoustic refrigerators. For this, a matrix transfer method based on thermoacoustic theory is applied to investigate the onset characterizes of the thermoacoustic cooling system. The present results reveal that the lowest onset temperature difference of 62 K can be achieved when using nitrogen as the working gases. Steady-state characteristics of the system are then investigated, including the exergy loss analysis, the axial distributions of key parameters, cooling performance under different working conditions and mixed working gases. The analysis based on the introduced cavity is subsequently done in terms of the position, volume, and quantity of cavity. Further comparisons are made between the single-cavity and multi-cavity structures, demonstrating a superior performance of the single-cavity structure. Finally, the proposed thermoacoustic refrigerator is compared with existing heat-driven cooling technologies. The results demonstrate that the thermoacoustic refrigerator can compensate the deficiencies of the existing heat-driven cooling technologies at low cooling temperatures. • A thermoacoustic refrigerator using low/medium-grade heat is proposed for domestic cooling. • The increased mean pressure plays a favorable role in reducing the onset temperature difference. • The refrigerator achieves a cooling efficiency 0.24 to 0.61 at cooling temperatures of −23 °C to 7 °C. • Single-cavity structure outperforms multi-cavity structures in terms of cooling performance. • The refrigerator compensates deficiencies of current heat-driven cooling at low temperatures. [ABSTRACT FROM AUTHOR]

In a context of massive renovation of residential buildings, stakeholders need decision-support models based on knowledge of the current building stock and accurate simulation of energy demand. This paper presents a new strategy for reducing energy consumption in the building sector, a key factor in combating climate change and promoting sustainability. We introduce an approach to (1) plan retrofits at community level, with a building resolution, for different years of an optimization period and (2) assist local authorities in selecting effective measures to improve the environmental performance of their building stock. The focus is on creating trajectory retrofit plans creation for a building stock with three main retrofit options: improving insulation, heating systems and hot water systems. We adapt a complex but linear approach, a type of problem-solving structure known as a multidimensional multiple-choice knapsack problem, which manages to handle a large number of possible retrofit combinations without becoming unwieldy. The planning process is streamlined as a single-objective optimization task that aims to reduce the total cost of retrofits by reducing their net present value. The efficiency of the model is demonstrated by simulating retrofit scenarios for 4,000 buildings in a French region to prove its ability to tackle large problems. France's targets for decarbonizing the residential sector are taken into account, with a target of reducing GHG emissions by a factor of 10 and a building stock consuming 80kWhEP/m2/year. The results show that these plans are feasible, but that they will require 50% of all buildings to undergo major renovation with abatement costs of around €200/tGES. Our practical application to an actual community demonstrates the model's ability to identify appropriate retrofitting measures and compile building data. • Development of an energy optimization framework integrating a linear simulation model. • Multi-stage optimization generalizing the knapsack problem for building renovation dynamics. • Bridging the gap in energy retrofit strategies by comparing decision-maker and model proposals. [ABSTRACT FROM AUTHOR]

Load shapes derived from smart meter data are frequently employed to analyze daily energy consumption patterns, particularly in the context of applications like Demand Response (DR). Nevertheless, one of the most important challenges to this endeavor lies in identifying the most suitable consumer clusters with similar consumption behaviors. In this paper, we present a novel machine learning based framework in order to achieve optimal load profiling through a real case study, utilizing data from almost 5000 households in London. Four widely used clustering algorithms are applied specifically K-means, K-medoids, Hierarchical Agglomerative Clustering and Density-based Spatial Clustering. An empirical analysis as well as multiple evaluation metrics are leveraged to assess those algorithms. Following that, we redefine the problem as a probabilistic classification one, with the classifier emulating the behavior of a clustering algorithm, leveraging Explainable AI (xAI) to enhance the interpretability of our solution. According to the clustering algorithm analysis the optimal number of clusters for this case is seven. Despite that, our methodology shows that two of the clusters, almost 10% of the dataset, exhibit significant internal dissimilarity. As a result, these clusters have been excluded from consideration for DR programs. The scalability and versatility of our solution makes it an ideal choice for power utility companies aiming to segment their users for creating more targeted DR programs. • Assessment of four ML clustering algorithms of different nature. • Re-definition of the problem using probabilistic classification and xAI. • Optimization of demand-side management clusters combining ML and empirical knowledge. • Experimental application on historical data from 4438 households in London. [ABSTRACT FROM AUTHOR]

In the transition from fossil fuel to electrified heating, a concerning trend is emerging in certain regions of the US. Owners of buildings with gas-based systems leave them in place after adding heat pumps (HPs). Existing control solutions for these hybrid (dual fuel) systems are rudimentary and fall short of realizing the full carbon reduction potential of these systems. Model predictive control (MPC) is often regarded as the benchmark for achieving optimal control in integrated systems. However, in the case of small-medium commercial buildings (SMCBs), the control and communication infrastructure required to facilitate the implementation of such advanced controls is often lacking. This paper presents a field implementation of easy-to-deploy MPC for a dual fuel heating system consisting of HPs and a gas-fired furnace (GF) for SMCBs. The control system is deployed on an open-source middleware platform and utilizes low-cost sensor devices to be used for real SMCBs without major retrofits. We demonstrated this MPC in a real office building with 5 HPs and 1 GF for 2 months. The test results showed that MPC reduced 27% of cost while completely eliminating GF usage by shifting 23% of the thermal load from occupied-peak time to non-occupied-non-peak times. • Implementation of a practical MPC for a heat pump-based dual fuel system in a small commercial building. • Experimental demonstration of a practical MPC with low-cost sensor retrofits for 2 winter months. • Achievement of 27% energy cost saving and 23% load shifting from occupied-peak time to non-occupied-non-peak time. • Elimination of natural gas usage with GHG emissions reduction of ∼ 52 kgCO2/month. [ABSTRACT FROM AUTHOR]

The Danish government plans two energy islands to collect offshore wind power for power distribution and green fuel production. Wind power is often criticized for lacking stability, which challenges downstream fuel synthesis processes. Solid oxide electrolysis cells (SOEC) are promising for green hydrogen production on a commercial scale, but the impact of fluctuating power on SOEC remains uncertain. This paper explores the feasibility of a Wind-SOEC coupled system by conducting a 2104-h durability test with the state-of-the-art Topsoe TSP-1 stack. Three periods of steady operation and two periods of dynamic operation were conducted. Wind power fluctuation was simulated during the dynamic period, and two control strategies were used to handle it. The constant flow (CF) and constant conversion (CC) strategies maintain the feedstock flow rate and conversion ratio of steam-to‑hydrogen, respectively. Compared to steady operation, the stack shows no signs of additional degradation in dynamic operation. Thus, the TSP-1 stack has been proven robust and flexible enough to handle fluctuating wind power supplies under both operation strategies. Further, stack performance during dynamic periods was compared and analyzed by removing degradation effects. Accordingly, SOEC stacks with CC control will consume less external heat than CF to maintain a heat balance. Nevertheless, SOEC systems with CF and CC control strategies may have different efficiency or hydrogen production costs. Tech-economic analyses will be needed to investigate control strategies at the system level. • A 2104 h SOEC stack test with simulated fluctuating wind power input was conducted. • Deployed constant flow and constant conversion strategies in the dynamic period. • The Topsoe stack is robust and flexible for harsh and varying working environments. • Fluctuations didn't introduce extra degradation in the current test. [ABSTRACT FROM AUTHOR]

Distribution networks (DNs) are under severe requirements of security and ecology, such as maintaining continuous power supply for critical loads under extreme disasters and contributing to the carbon neutrality by 2060. However, the investment of infrastructures in DNs, such as electric vehicle charging stations (EVCSs), are hardly balanced as a result of the coupled relationship between the resilience, economics and ecology with different magnitudes and dimensions. This paper proposes to avoid an overinvestment of the equilibrium configuration strategy of EVCSs in low-carbon resilient urban DNs. Firstly, a bi-level optimization model is established to configurate EVCSs. Multiple objectives, such as the security, economics and ecology of DNs, are enhanced simultaneously in the upper level. Indexes of these objectives are formulated by generating typical scenarios in the lower level, such as economic operation, fault recovery and extreme disaster restoration. Then, the generalized Nash equilibrium (GNE) model is utilized to deal with the equilibrium and coupled relationship between multiple objectives in the upper level, which is solved by the incremental penalty function algorithm. Finally, simulation tests of a 3-feeder 62-node 10 kV DN verify the superiority of the proposed GNE-based equilibrium configuration model of EVCSs compared with other approaches, and the resilience and carbon emission reduction can be improved with an equilibrium configuration scheme of EVCSs under a limitation of investment. • Resilience, economics and ecology indexes in DNs are described accurately considering the V2G-based charging / discharging behaviors of EVs. • Multiple typical operation scenarios are simulated to improve the evaluation of charging / discharging capabilities of V2G-based EVCSs in the equilibrium configuration. • The configuration strategy of V2G-based EV charging stations is proposed balancing the resilience, economics and ecology via generalized Nash equilibrium, in which the shared decision variables and the coupling relationship between multiple objectives and constraints are considered. • The optimal the amount and locations of EVCSs are obtained from a resilience and decarburization oriented perspective with limited investment. [ABSTRACT FROM AUTHOR]

The increasing incorporation of solar and wind energy sources into the power system results in a diminishing power system inertia, which compromises the security of power system operation. This paper introduces a new scheduling method for hybrid power systems (HyPS), focusing on frequency security. It considers the time-varying characteristics related to frequency security requirements and the interdependence of components, such as generation units and the delivery network. Firstly, the optimal objective is to minimize overall operational costs and the pressure on energy delivery, ensuring the reserve capacities for fast frequency regulation remained within the predefined limits. Secondly, the frequency constrained unit commitment (FCUC) problem is formulated as a Markov decision process (MDP), and a deep reinforcement learning (DRL) framework is integrated to correct the advanced scheduling scheme in real-time. Thirdly, the coordinated scheduling scheme dynamically adjusts to operational events like fluctuations and line failures in the HyPS by employing the reward function of a comprehensive assessment. Finally, numerical results demonstrate the practicality of the novel coordinated scheduling approach for hybrid power systems through simulations in the Grid2Op environment. As a result, the frequency security margin of the HyPS is capable of increasing the daily average level of the frequency security margin by 3% across diverse scenarios, including fluctuations in power demand, renewable energy variations, and power line failures. PE-interfaced renewable energy sources are thereby able to integrate with conventional synchronous units as a hybrid power system to support frequency regulation services. • A coordinated scheduling method improves frequency security by utilizing variable renewable energy sources. • The optimal scheduling method manages physical network stress while redistributing the controlled units. • A dynamic regulation module is designed to improve the real-time frequency security margin. • The dynamic regulation module is integrated with SAC for energy and reserve scheduling. [ABSTRACT FROM AUTHOR]