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Vehicle-to-Grid (V2G) technology has been widely applied in recent years. Under the time-of-use pricing, users independently decide the charging and discharging behavior to maximize economic benefits, charging during low-price periods, discharging during high-electricity periods, and avoiding battery degradation. However, such behavior under inappropriate electricity prices can deviate from the grid’s goal of minimizing peak–valley load difference. Based on the basic electricity data of a community in Beijing and electricity vehicle (EV) random travel behavior obtained through Monte Carlo simulation, this study establishes a user optimal decision model that is influenced by battery degradation and electricity costs considering depth of discharge, charging rate, and charging energy loss. A mixed-integer linear programming algorithm with the objective of minimizing the cost of EV users is constructed to offer the participation power of V2G. By analyzing grid load fluctuations under different electricity pricing strategies, the study derives the formulation and adjustment rules for optimal electricity pricing that achieve ideal load stabilization. Under 30% V2G participation, the relative fluctuation of grid load is reduced from 31.81% to 5.19%. This study addresses the challenge of obtaining optimal electricity prices to guide users to participate in V2G to minimize the peak–valley load fluctuation.

Given the challenge that traditional methods in short-term power load prediction are impacted by the complexity of power load data, resulting in poor prediction accuracy, this paper presents an effective approach based on a ‘decomposition-reconstruction’ architecture. This method integrates the Variational Mode Decomposition (VMD), Dung Beetle Optimization (DBO) algorithm, Long Short-Term Memory (LSTM) network, and Attention (ATTENTION) mechanism. Firstly, to simplify the data, VMD is applied to decompose the load sequence. Subsequently, The LSTM is employed to capture the time-series attributes of each element. The Attention mechanism is integrated to accentuate crucial information. Moreover, the improved DBO algorithm is employed to fine-adjust the variables of the LSTM, thereby boosting the model’s performance. Experiments based on actual power load data show that, compared with traditional methods, this approach enables a more precise capture of load variation patterns. As a result, it greatly enhances the precision and credibility of short-term power load prediction.

The rapid development of the energy industry has made the complexity of managing Multi-Energy Systems (MES) a key challenge. Optimizing MES operations is central to enhancing energy efficiency and sustainability. This paper proposes a framework based on interactive mechanisms to achieve collaborative autonomous optimization of MES. The framework allows MES to autonomously optimize while exchanging signals with a coordinator. Through numerical simulations, we minimize operational costs and effectively manage information privacy and operational authority. The results show that the framework significantly improves the coordination of MES operations, promotes the consumption of renewable energy, and reduces dependence on the main power grid. The CAFIL model encourages local consumption of renewable energy, alleviates grid congestion, complements the power demand of the main grid, and reduces load fluctuations. As the load increases, energy efficiency decreases. This framework is applicable for assessing the evolution of MES operations.

In this work, we explore the use of Reinforcement Learning (RL) to learn Individual Pitch Control (IPC) functions directly from simulation, using only high-level turbine state as input. Our goal is to learn a parametric pitch signal that minimizes structural load variation based on the current operating condition. We treat this as a single-step control problem, trained using a physical simulator. We show that this approach learns effective IPC functions with high sample efficiency and produces interpretable, periodic control signals of arbitrary complexity that generalize across operating points. Results under uniform and turbulent inflow with RL-IPC control show a significant reduction in fatigue loads compared to baseline Collective Pitch Control (CPC) and it also performs better than classical Proportional Integral PI-IPC control. Our results suggest that reinforcement learning can serve as a practical tool for simulation-driven control design in physical systems with a solution that is unbiased by human intervention, relying solely on a raw performance evaluation.

In this paper, an electromechanical coupling method is proposed to simulate the torque load of a reciprocating compressor drive system, and the feasibility of the method is verified by carrying a reverse torque test platform. The load torque of the motor drive shaft under three different exhaust pressures of 0.6 MPa, 1 MPa, and 1.8 MPa were simulated and tested, respectively, and the results show that the load fluctuation of the 1 MPa condition has the least influence, which is the optimal condition for this compressor.

Vertical-axis wind turbines are great candidates to enable wind power extraction in urban and off-shore applications. Currently, concerns around turbine efficiency and structural integrity limit their industrial deployment. Flow control can mitigate these concerns. Here, we experimentally demonstrate the potential of individual blade pitching as a control strategy and explain the flow physics that yields the performance enhancement. We perform automated experiments using a scaled-down turbine model coupled to a genetic algorithm optimiser to identify optimal pitching kinematics at on- and off-design operating conditions. We obtain two sets of optimal pitch profiles that achieve a three-fold increase in power coefficient at both operating conditions compared to the non-actuated turbine and a 77% reduction in structure-threatening load fluctuations at off-design conditions. Based on flow field measurements, we uncover how blade pitching manipulates the flow structures to enhance performance. Our results can aid vertical-axis wind turbines increase their much-needed contribution to our energy needs. Vertical-axis wind turbines offer untapped opportunities for energy generation but suffer from dynamic stall in strong winds. Here, authors implement individual blade pitch control to benefit from stall vortices instead of suppressing them, tripling the power coefficient and reducing load transients by 70%.

A temperature-dependent dynamic landing model for bogie landing gear has been developed in this study to address the deficiency in quantitative environmental temperature considerations for landing gear performance analysis, which is validated by data from both full-scale drop tests and simulations. Analysis of dynamic landing loads and displacements under varying environmental temperatures (from −45°C to +55°C) demonstrates maximum increases of 17% in main buffer’s air spring force, 1.2% in its axial resultant force, 16% in pitch damper’s air spring force and 11% in its axial resultant force, 3.0% in tire radial forces, while total ground vertical load variation remains ≤2.0%, overload coefficient deviation ≤0.03, and damping efficiency fluctuation ≤3.0%, confirming stable landing performance across temperature extremes. Compared to the filling tolerance method, the proposed quantitative temperature analysis reduces extreme-environment vertical ground loads by 7.33%.

With the continuous increase in grid-connected new energy sources and new types of intermittent loads, the short-term fluctuation amplitude of source-load power is continuously increasing, which increasingly affects the effectiveness of tertiary voltage control decisions. To address this issue, this paper proposes a short-term efficient decision-making method for tertiary voltage control considering short-term power fluctuations. Firstly, the decision-making cycle of tertiary voltage control is shortened, effectively enhancing its adaptability to short-term source-load power fluctuations. Secondly, to address the computational burden brought by the shortened decision cycle, this paper fully considers the temporal characteristics of short-term load fluctuations in practical power grid nodes and proposes two linearization methods for tertiary voltage control. Specifically, these methods include a three-point linear estimation method for small fluctuation periods and a sensitivity linearization optimization method for large fluctuation periods. By shortening the decision cycle and linearizing decision calculations, the efficiency of tertiary voltage control decision-making is greatly improved. Based on actual data from a provincial-level power grid in the northwest region of China, the effectiveness of the proposed methods is verified through simulation analysis.

Aiming at the problems of electric vehicle disorderly charging on grid load stability and charging cost, this study considers the grid load pressure and presents a multi-faceted optimized model for electric vehicle charging. The model is addressed by utilizing a particle swarm optimization algorithm designed for multiple objectives. The outcomes demonstrate that the charging framework built upon the multi-objective particle swarm algorithm has a fast convergence speed and can avoid the limitation of the local optimal solutions. Under the premise of reducing by managing the grid load fluctuation, the model effectively curbs the expense of vehicle charging while also minimizing peak-to-valley disparities in grid load.

Urbanization increases electricity demand due to population growth and economic activity. To meet consumer’s demands at all times, it is necessary to predict the future building energy consumption. Power Engineers could exploit the enormous amount of energy-related data from smart meters to plan power sector expansion. Researchers have made many experiments to address the supply and demand imbalance by accurately predicting the energy consumption. This paper presents a comprehensive literature review of forecasting methodologies used by researchers for energy consumption in smart buildings to meet future energy requirements. Different forecasting methods are being explored in both residential and non-residential buildings. The literature is further analyzed based on the dataset, types of load, prediction accuracy, and the evaluation metrics used. This work also focuses on the main challenges in energy forecasting due to load fluctuation, variability in weather, occupant behavior, and grid planning. The identified research gaps and the suitable methodology for prediction addressing the current issues are presented with reference to the available literature. The multivariate analysis in the suggested hybrid model ensures the learning of repeating patterns and features in the data to enhance the prediction accuracy.