Energy Informatics最新文献

Employing fossil fuels in electricity generation increases carbon emissions and worsens global warming. Relying solely on fossil fuels is not a sustainable solution, which is making renewable energy sources (RESs) an essential part of a sustainable power system. However, high RES integration into power systems poses stability challenges, primarily due to issues with balancing supply and demand. One technical solution is to transfer RES unpredictability to the natural gas network, thereby enhancing power system stability. Other complementary solutions include energy storage systems (ESS), such as power-to-gas (P2G) conversion and battery energy storage systems (BESS), which provide additional balancing capabilities. Peer-to-peer (P2P) energy trading facilitates the integration of distributed energy resources (DERs), while microgrid architectures enable their implementation in medium-voltage (MV) distribution systems. However, neglecting grid constraints—particularly active power losses and voltage stability limits—may compromise the sustainability and cost-effectiveness of RES integration. This paper proposes a multi-objective decentralized P2P energy transactive market framework for the integrated multi-energy microgrid (IEM). The proposed framework employs a double-objective optimization (DOO) approach, minimizing both operating costs and active power losses while incorporating AC power flow constraints and gas pipeline dynamics. A Continuous Double Auction (CDA) mechanism facilitates dynamic energy trading among various energy resources (ERs), containing gas-fired generators (GFGs), prosumers (PRs), P2G systems, and RES-based energy producers. The DOO P2P market outperforms single-objective approaches, achieving 40% lower energy losses and 47% lower peak power demand. Additionally, since the DOO framework reduced energy losses and peak demand significantly, the loss-aware energy dispatch improves the average nodal voltage by 1.5%.

This paper proposes an innovative congestion management method for Active Distribution Networks (ADNs) by integrating a Pre-trained Large Language Model (PLLM) with the Goose Optimization (GO) algorithm to tackle challenges posed by the uncertainty of flexible resources. Initially, a short-term power forecasting method is developed using PLLM enhanced with Low-Rank Adaptation (LoRA). This approach precisely captures the dynamic fluctuations of power sources and loads, achieving R2 of 0.96 and 0.95 in wind and load forecasting, respectively. Compared to traditional algorithms (BP, LSTM, BiLSTM), it reduces MAE and RMSE by at least 39.08% and 19.68% for wind forecasting, and 15.82% and 21.80% for load forecasting, respectively, ensuring high-accuracy inputs for the subsequent optimization process. Subsequently, to address the bottlenecks of slow convergence and inefficiency inherent in conventional algorithms for large-scale scheduling, the novel GO algorithm is utilized as the core engine to solve the complex optimal power flow problem. Multi-scenario simulations on the IEEE 33-bus system validate that the proposed framework effectively alleviates line overloads and voltage violations, with the GO algorithm improving scheduling efficiency by at least 65.50% compared to the improved PSO. These findings highlight the exceptional performance and considerable potential of the combined PLLM and GO approach for improving the operational security, economic viability, and scheduling efficiency of ADNs.

Under the large-scale integration of wind turbine and photovoltaic into the grid, the power system faces the challenge of insufficient flexibility for regulation. Coordinated planning of hydro-wind-solar-storage systems can effectively mitigate the output volatility of renewable energy sources. This paper proposes a distributionally robust planning method for hydro-wind-solar-storage systems based on the Wasserstein distance. First, taking into account the spatiotemporal correlations of factors such as wind speed and solar irradiance, an auxiliary classifier generative adversarial network (AC-GAN) is employed to generate a set of wind turbine and photovoltaic output scenarios. Then, a bilevel capacity planning model is constructed for the integrated system. The upper level aims to minimize investment costs by determining the optimal energy storage capacity, while the lower level focuses on minimizing operational costs through optimizing storage operation states and the output of various devices. Subsequently, an improved proximal policy optimization (PPO) algorithm, grounded in the Markov decision process framework, is used to solve the model. Finally, an actual case study based on a hydro-wind-solar system in Qinghai China is conducted to validate the effectiveness of the proposed method.

Accurate prediction of domestic electricity consumption, particularly in intelligent buildings, is crucial for optimal resource utilization and allowing data-driven energy management. The comparison of Long Short-Term Memory (LSTM), Seq2Seq, and Prophet time-series models for electricity consumption prediction using the Low Carbon London dataset is carried out in this study. Preprocessing tasks involving the removal of outliers and missing value replacement were conducted, and Optuna was utilized to optimize model performance by tuning hyperparameters. Evaluation reveals that the Prophet model did the best in accuracy, then the LSTM model, which was remarkably improved via hyperparameter tuning, followed by Seq2Seq, which improved but performed slightly less effectively than LSTM. This article demonstrates the capability of deep learning models to recognize complex temporal patterns and provides a foundation for scalable, data-driven solutions in sustainable energy management. The study also sets the stage for future research on household-specific predictions and cluster-based optimization.

The data management method for transmission line defects and hidden dangers enables timely identification and resolution of safety risks in transmission lines, thereby reducing the probability of failures. However, existing data on defects and hidden dangers are often affected by redundant interference, resulting in low mining accuracy. To address this issue, this paper proposes a data management approach for transmission line defects based on an improved isolation forest algorithm. The types of transmission line hidden dangers are analyzed, and a data governance framework for such hidden dangers is established. This framework collects basic data of transmission lines through multiple channels, performs denoising and normalization processing, and constructs a sample dataset for transmission lines. The isolation forest algorithm is selected as the method for detecting hidden trouble data in transmission lines. The algorithm is enhanced using binary particle swarm optimization to improve the detection of hidden trouble data. The detected defect data are applied to the early warning of transmission lines, thereby completing the defect data management process. Experimental results demonstrate that the proposed method can quickly and accurately detect defect data in transmission lines, and the detection results can effectively facilitate risk warning for transmission lines.

In response to the increasingly concealed and sophisticated methods of electricity theft, which are difficult to comprehensively cover and detect in a timely manner, a method for identifying high-risk electricity theft behaviors in low-voltage distribution station areas based on density-based clustering of IoT sensing data is investigated. An intelligent IoT power distribution terminal is deployed at the distribution transformer side within the station area to collect IoT sensor data reflecting electricity consumption behavior. The density-based clustering algorithm is employed to achieve comprehensive clustering of the IoT sensing data by determining the initial cluster centers and iteratively searching and updating these centers. The clustering results of the IoT sensing data are used as input to an LM-BP neural network, which classifies the electricity consumption behavior data in the station area into normal and abnormal categories. Based on optimal matching values, a feature matching approach is applied to determine whether abnormal electricity consumption samples correspond to high-risk theft behaviors, thereby enabling the detection of such behaviors in low-voltage distribution station areas. Experimental results demonstrate that the proposed method can accurately identify high-risk electricity theft behaviors, such as meter bypassing, by leveraging the density-based clustering results of IoT sensing data.

The need for predictive maintenance methods has arisen as a key element in improving operational efficiency, reliability, and life expectancy of photovoltaic (PV) systems and the future complex renewable energy infrastructure sets. The Machine learning (ML) technique is sub part of Artificial Intelligence (AI) technology which has widened their adoption in energy analytics, resulting in numerous studies proposing different algorithms for monitoring, prediction, and prevention of system failures. The overview of these approaches is yet to be exhaustive in the existing literature regarding a metric-based evaluation. In addressing this gap, the article undertakes a structured review of the state-of-the-art recent peer-reviewed literature on predictive maintenance in solar PV systems. Each work will, therefore, be appraised against standardized performance metrics models, which include aspects such as accuracy, precision, recall, F1-score, area under the curve (AUC), and model-specific indicators- Root Mean Square Error (RMSE), latency, and execution delays. A numerical analysis table summarizes and compares the predictive capabilities of techniques such as Random Forest, CatBoost, Convolutional Neural Network (CNN) ensembles, Long Short-Term Memory (LSTM) autoencoders, Supervisory Control and Data Acquisition (SCADA) IoT frameworks, and Digital Twins. High-performing models, such as CatBoost and custom CNN architectures, indicate the effectiveness of hybrid deep learning strategies in fault diagnostics. The review establishes a new benchmark for evaluating PdM systems, readying the bar between academic innovation and real-world deployment. It outlines future research directions including model generalization, real-time edge AI deployment, and integration with climate-aware forecasting systems. This work complements an important entry point for other works by researchers and industry stakeholders’ intent on deploying scalable and resilient predictive maintenance solutions in renewable energy networks.

The increasing volatility of electricity prices, driven by the growing share of renewable energy, calls for new approaches. This paper proposes a dynamic Bayesian network (DBN) method for electricity price interval forecasting. The model uses predicted values of wind power generation, total power generation, and total electricity consumption, along with historical electricity prices, as inputs. The network structure is determined using a greedy search algorithm, and the model parameters are estimated through maximum likelihood estimation (MLE). By treating the predictions of wind power, total generation, and total consumption as reasoning evidence, the method employs joint tree inference to generate discrete states and posterior probabilities for electricity prices, thereby enabling interval forecasting. The DBN-based interval predictions achieve a prediction interval coverage probability (PICP) of 95.24%, a normalized average width (PINAW) of 9.25%, and an accumulated width deviation (AWD) of 0.56%. The effectiveness of the proposed method was evaluated by comparing its predictions with actual electricity prices and with results from both particle swarm optimization-kernel extreme learning machine (PSO-KELM) and long short-term memory (LSTM)-based methods. This innovative approach not only provides prediction intervals but also associates them with corresponding probabilities, offering significant potential to enhance market participants’ decision-making and mitigate price risks.

As a supporting device for electric vehicles, DC charging piles are widely distributed and in large quantities, involving a huge emerging electricity trading market. Ensuring metering accuracy of charging pile is critical to maintaining fair electricity trading. The traditional on-site verification method for charging pile involves high personnel input and low verification efficiency, making it difficult to meet the massive metering verification demand. In this paper, based on knowledge-assisted modal decomposition, the metering error prediction method for charging pile is proposed to remotely locate the charging pile whose metering error is about to exceed the threshold in advance. First, the trend and multi-period characteristics of metering error data—driven by factors such as temperature, humidity, electrical stresses, and user behavior—are analyzed. With an adaptive data imputation method, high-ratio continuous missing values in metering data time series are completed. Then, the error data time series is decomposed into trend, multi-level periodic, and residual terms with the improved seasonal-trend decomposition method. Finally, the trend and multiple periodic terms are predicted based on the support vector regression model, and they are combined to form the error prediction. The effectiveness and superiority of the proposed method are validated through practical application.

In electricity markets, evaluating collusion and market power is a critical challenge for network operators, as such behaviors can disrupt fair competition, induce price volatility, and reduce market efficiency. Effective methods are therefore required to identify and quantify the influence of each market participant on the profits of others. This study aims to assess collusion and market power in large-scale power systems through structural analysis, addressing gaps left by previous research. The proposed methodology relies on two lemmas to model market behavior. Lemma 1 quantifies the effects of various factors on local price changes and generation capacities, while Lemma 2 evaluates their impact on the profit variations of generation units. Using the matrix derived from Lemma 2, which captures profit responses to marginal unit price changes, collusion and market power across the network are assessed. Additionally, three new indicators are introduced to measure market power and collusion in large networks. The approach is applied to a 300-bus system, and detailed analysis demonstrates that changes in generation pricing strategies can substantially influence market power and collusive behavior, providing regulators with a tool for proactive market monitoring and intervention.