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Electrification of transport offers opportunities to increase energy security, reduce carbon emissions, and improve local air quality. Plug-in electric vehicles (PEVs) are creating new connections between the transportation and electric sectors, and PEV charging will create opportunities and challenges in a system of growing complexity. Here, I use highly resolved models of residential power demand and PEV use to assess the impact of uncoordinated in-home PEV charging on residential power demand. While the increase in aggregate demand might be minimal even for high levels of PEV adoption, uncoordinated PEV charging could significantly change the shape of the aggregate residential demand, with impacts for electricity infrastructure, even at low adoption levels. Clustering effects in vehicle adoption at the local level might lead to high PEV concentrations even if overall adoption remains low, significantly increasing peak demand and requiring upgrades to the electricity distribution infrastructure. This effect is exacerbated when adopting higher in-home power charging. Electrification of transport offers many benefits for the energy transition but introduces a number of complexities around the electric system. This study undertakes modelling of residential power demand and electric vehicle use to understand the impact of uncoordinated vehicle charging on the electricity load.

Existing power forecasting models struggle to simultaneously handle high-dimensional, noisy load data while capturing long-term dependencies. This critical limitation necessitates an integrated approach combining dimensionality reduction, temporal modeling, and robust prediction, especially for multi-day forecasting. A novel hybrid model, SLHS-TCN-XGBoost, is proposed for power demand forecasting, leveraging SLHS (dimensionality reduction), TCN (temporal feature learning), and XGBoost (ensemble prediction). Applied to the three-year electricity load dataset of Seoul, South Korea, the model’s MAE, RMSE, and MAPE reached 112.08, 148.39, and 2%, respectively, which are significantly reduced in MAE, RMSE, and MAPE by 87.37%, 87.35%, and 87.43% relative to the baseline XGBoost model. Performance validation across nine forecast days demonstrates superior accuracy, with MAPE as low as 0.35% and 0.21% on key dates. Statistical Significance tests confirm significant improvements (p < 0.05), with the highest MAPE reduction of 98.17% on critical days. Seasonal and temporal error analyses reveal stable performance, particularly in Quarter 3 and Quarter 4 (0.5%, 0.3%) and nighttime hours (<1%). Robustness tests, including 5-fold cross-validation and Various noise perturbations, confirm the model’s stability and resilience. The SLHS-TCN-XGBoost model offers an efficient and reliable solution for power demand forecasting, with future optimization potential in data preprocessing, algorithm integration, and interpretability.

The supercritical CO 2 (S-CO 2 ) Brayton cycle is expected to replace steam cycle in the application of solar power tower system due to the attractive potential to improve efficiency and reduce costs. Since the concentrated solar power plant with thermal energy storage is usually located in drought area and used to provide a dispatchable power output, the S-CO 2 Brayton cycle has to operate under fluctuating ambient temperature and diverse power demand scenarios. In addition, the cycle design condition will directly affect the off-design performance. In this work, the combined effects of design condition, and distributions of ambient temperature and power demand on the cycle operating performance are analyzed, and the off-design performance maps are proposed for the first time. A cycle design method with feedback mechanism of operating performance under varied ambient temperature and power demand is introduced innovatively. Results show that the low design value of compressor inlet temperature is not conductive to efficient operation under low loads and sufficient output under high ambient temperatures. The average yearly efficiency is most affected by the average power demand, while the load cover factor is significantly influenced by the average ambient temperature. With multi-objective optimization, the optimal solution of designed compressor inlet temperature is close to the minimum value of 35°C in Delingha with low ambient temperature, while reaches 44.15°C in Daggett under the scenario of high ambient temperature, low average power demand, long duration and large value of peak load during the peak temperature period. If the cycle designed with compressor inlet temperature of 35°C instead of 44.15°C in Daggett under light industry power demand, the reduction of load cover factor will reach 0.027, but the average yearly efficiency can barely be improved.

In the process of promoting demand-side management, the core stakeholder groups are government departments, power grid companies, and electricity users. Due to the different positions and conflicting interests of the three parties in the game, intense and complex battles will occur. This paper investigates a tripartite evolutionary game involving government, power grid companies, and electricity users in the context of demand-side management (DSM) and analyzes the dynamic interactions between government departments, power grid companies, and electricity users within the framework of DSM using evolutionary game theory. Using evolutionary game theory, we explore how incentives and strategic interactions among these three stakeholders evolve over time, affecting the stability of DSM policies. The model addresses the asymmetry in the decision-making process and examines the dynamic equilibrium outcomes under various scenarios. The results provide insights into the optimal design of incentive mechanisms to enhance DSM adoption. The findings offer practical recommendations to improve DSM policies, fostering balanced interests between government, grid companies, and users. This research contributes to a deeper understanding of strategic interactions in DSM, revealing how adaptive behaviors can enhance energy efficiency. It also underscores the importance of carefully designed incentive mechanisms in achieving long-term stability and cooperation among key stakeholders.

In recent years, environmental and energy issues relating to global warming have become more serious, and there is a need to shift from conventional power generation, which emits an abundance of carbon dioxide, to renewable energy sources without emissions, such as solar and wind. However, solar power generation, which is one of the renewable energies, changes dynamically, depending on real time weather conditions. Thus, power supplied mainly by solar power generation is often unstable, and an appropriate on-demand energy management for demand-to-supply is required to ensure a stable power supply. Demand-to-supply management methods include inventory management analysis and on-demand inventory management analysis. The cumulative-control method has been used as one of the production management methods to visually manage inventory status in factories and warehouses, while the on-demand cumulative-control method is an extension of inventory management analysis. This study models a demand-to-supply management method for a solar power generation system by using the on-demand cumulative-control method in an actual case. First, a demand-to-supply management method is modeled by an on-demand cumulative-control method, using actual power data from a childcare facility in Tokyo. Next, the on-demand cumulative-control method is adopted to the case without batteries, and the amount of electricity to be purchased is estimated. Finally, the effectiveness of the maximum battery capacity and the amount of the initial charge are examined and discussed by sensitivity analysis.

The rapid growth of electric vehicles (EVs) is likely to endanger the current power system. Forecasting the demand for charging stations is one of the critical issues while mitigating challenges caused by the increased penetration of EVs. Uncovering load-affecting features of the charging station can be beneficial for improving forecasting accuracy. Existing studies mostly forecast electricity demand of charging stations based on load profiling. It is difficult for public EV charging stations to obtain features for load profiling. This paper examines the power demand of two workplace charging stations to address the above-mentioned issue. Eight different types of load-affecting features are discussed in this study without compromising user privacy. We found that the workplace EV charging station exhibits opposite characteristics to the public EV charging station for some factors. Later, the features are used to design the forecasting model. The average accuracy improvement with these features is 42.73% in terms of RMSE. Moreover, the experiments found that summer days are more predictable than winter days. Finally, a state-of-the-art interpretable machine learning technique has been used to identify top contributing features. As the study is conducted on a publicly available dataset and analyzes the root cause of demand change, it can be used as baseline for future research.

We develop a time series model to forecast weekly peak power demand for three main states of Australia for a yearly timescale, and show the crucial role of environmental factors in improving the forecasts. More precisely, we construct a seasonal autoregressive integrated moving average (SARIMA) model and reinforce it by employing the exogenous environmental variables including, maximum temperature, minimum temperature, and solar exposure. The estimated hybrid SARIMA-regression model exhibits an excellent mean absolute percentage error (MAPE) of 3.41%. Moreover, our analysis demonstrates the importance of the environmental factors by showing a remarkable improvement of 46.3% in MAPE for the hybrid model over the crude SARIMA model which merely includes the power demand variables. In order to illustrate the efficacy of our model, we compare our outcome with the state-of-the-art machine learning methods in forecasting. The results reveal that our model outperforms the latter approach.

Optimal power source sizing and energy management strategies are crucial in the problem of component sizing for hybrid electric vehicles fuel cell. Ensuring cost-effective sizing while meeting power demand necessitates consideration of these factors as well, thereby ensuring a good driving range, reduced energy-loss and consumption, and minimal degradation of fuel cells and batteries for hybrid power sources. The purpose of this work concerns the sizing and the modelling of a power source utilized in a fuel cell hybrid vehicle, the principal source of energy is a Proton Exchange Membrane Fuel Cell, while an Ultra-Capacitor bank serves as an auxiliary source. The sizing algorithm initiates by computing the power demand, which is determined by the mechanical characteristics of the vehicle. This calculation involves considering the instantaneous speed of the chosen drive cycle and the instantaneous road gradient. Subsequently, the algorithm proceeds to determine the mechanical power needed by the motor. In this article, a frequency splitting approach is employed to determine the power distribution between the SC and the fuel-cell for Worldwide Harmonised Light Vehicles Test Procedure (WLTP) driving cycles. The fuel cell operates effectively at low frequencies, whereas the supercapacitor provides power at high frequencies. The efficiencies of every power transformer, including the motor, gearbox, differential, and DC-DC converters, are considered in our work. The data analysis is conducted using the MATLAB software environment. The obtained results demonstrated that the approach outlined in this research article offers a more efficient sizing and energy management between sources in terms of simplicity and adherence to operational conditions of the fuel cell and supercapacitor.

Uncertainties in the solar photovoltaic (PV) power generation, random behaviour of consumer load power demand, and unexpected failures are the major factors for the consumer power interruption (CPI) hours, which reduce the reliable power supply rate or reliability index, of an islanded microgrid (IMG). One way to address this challenge is to complement the IMG with solar PV, energy storage system (ESS), and diesel generator (DG). This study first defines the reliability index of an IMG that equipped with solar PV, ESS, and DG. Then the authors propose an energy management strategy (EMS) for an IMG to maintain the reliability index above a given threshold limit while lowering the cost through higher utilisation of PV, lower CPI hours, and lower DG operating hours. They utilise real IMG consumers load power demand and solar radiance data for simulation studies to show how the proposed EMS achieves the desired trade‐off in reliability and cost.

In the current scenario of the deregulated Indian electricity market where the power demand and its availability vary remarkably, the factors playing a significant role in demand variations are often associated with the impact of unprecedented weather conditions and technological evolutions. To maintain grid security and discipline that yield to financial implications, there lies a great need to formulate an equilibrium between electricity supply and demand. Devising a model to anticipate the variations which are highly adaptive to such changes is the need of the hour. For this purpose, an algorithm has been proposed in this study, which is best suited for the day‐ahead load forecast. The variables selected for the forecast are one‐day‐lagged demand statistics, seasonality trend, weather, and calendar variables. The proposed algorithm outperforms the existing benchmark model, which is evaluated through various statistical performance metrics such as mean absolute percentage error, mean absolute error, root‐mean‐square error, and coefficient of variation. The performance of the proposed methodology at the seasonal level is analysed and validated through uncertainty analysis with one post‐sample year for the state of Delhi, India. This model presents its compatibility to prevalent grid regulations as well as shall hold good in the weather and demand variations possibly expected in the future.