Energy Informatics最新文献

This study proposes a two-level multi-objective particle swarm optimization (MPSO) framework, enhanced by a novel mutation mechanism, to optimize energy management in stochastic dynamic distribution network reconfiguration (DDNR). The hierarchical model addresses real-time decision-making under uncertainty by minimizing power losses at Level 1 through optimal switching configurations, and simultaneously reducing operating costs and Energy Not Supplied (ENS) at Level 2 by leveraging distributed generation (DG) and electric vehicles (EV) with the Eliminating Zone method to manage uncertainties in demand and market prices. The three objectives—losses, costs, and ENS—are integrated into a non-dominated solution set to balance trade-offs. Simulation on a 95-node test network shows that the proposed MPSO outperforms conventional methods (PSO, SFLA, GWO), achieving a 25% reduction in static distribution network reconfiguration losses (from 540 kW to 449.51 kW), a 21% reduction in losses (from 39,695.45 kWh to 32,823.36 kWh), and a 35% decrease in ENS under dynamic reconfiguration. These quantitative results demonstrate the effectiveness of the proposed approach in enhancing energy efficiency, reducing costs, and improving reliability, supporting the development of sustainable and resilient smart grids.

With the widespread integration of renewable energy sources, power systems increasingly require enhanced flexibility and economic efficiency. To address the constraints imposed by high costs of conventional physical energy storage in virtual power plant planning, a bi-level expansion planning model incorporating virtual energy storage systems is proposed. Initially, a user behavior model for virtual energy storage is developed, where incentive and discount signal mechanisms are integrated to characterize charge-discharge response characteristics. Subsequently, a bi-level optimization model is established, wherein the upper level minimizes energy storage configuration costs through capacity allocation optimization, while the lower level maximizes operational revenue through energy storage scheduling strategy determination. To improve computational efficiency, a hybrid Grey Wolf Optimization algorithm is employed for model solution. The effectiveness of the proposed methodology is evaluated using an industrial park located in the southeast coastal region as a test case. Experimental results indicate that the virtual energy storage system achieved an equivalent storage capacity of 10.4 MWh, reducing total storage investment costs by 18.9% compared to physical-storage-only solutions. The proposed bi-level optimization model improves annual operational revenue by 97.9% and 55.9% compared to the baseline and single-level models, respectively. This approach effectively reduces energy storage investment costs while enhancing operational revenue of virtual power plants and system dispatch flexibility.

Positioning, coverage, and energy efficiency are essential for developing next-generation intelligent sensor networks. In wireless sensor networks (WSNs), the random deployment of sensor nodes (SNs) frequently results in suboptimal area coverage and excessive energy consumption, primarily due to overlapping sensing regions and redundant data transmissions. This research presents a Particle Swarm Optimization (PSO) algorithm to optimize the deployment of electronic information sensing nodes. The focus is on maximizing the monitored area while minimizing energy usage. A Scalable coverage-based particle swarm optimization (SCPSO) algorithm integrates a probabilistic coverage model based on Euclidean distance to detect coverage gaps and guide the optimal positioning of nodes, ensuring that each target within the region of interest is covered by at least one sensor. Data preprocessing, including Z-score normalization and Independent Component Analysis (ICA), ensures feature scaling and dimensionality reduction for improved model performance, enabling effective optimization. Experimental results under different key metrics included coverage rate (CR) for various numbers of nodes (0.9971) with 50 nodes, deployment (99.95%) with the best coverage, and computation time (0.008s), indicating significant performance improvements under optimized deployment configurations. These results highlight the effectiveness of swarm intelligence methods in enabling energy-efficient, performance-optimized deployment of electronic information sensing systems in intelligent WSNs.

At present, the electricity market reform has entered a deep area, electricity consumption forecasting has become increasingly important, accurate electricity consumption forecasting provides a reference basis and decision-making support for power dispatching and market transactions, and residential power consumption prediction can help users choose appropriate power suppliers and power supply programs according to the market situation, and provide the reliability and economy of power consumption. Residential electricity consumption is complex, affected by many factors and prone to significant noise disturbances, which results in electricity consumption data that is characterized by non-stationary, intermittent and erratic fluctuations. Therefore, using a single model is challenging to accurately predict household electricity consumption. To meet this challenge, this paper designs the structure of this three-step residential electricity consumption forecasting. At the first stage, we first analyzed cumulative effects of temperature on residential electricity consumption, and an hourly temperature correction model combining meteorological factors is constructed, the adjusted hourly temperature data are then entered into the predictive model. We have developed a Variable Modal Decomposition (VMD) data decomposition technique optimized for non-governmental organizational models, which improves the problem of subjectivity in parameter setting in traditional VMD, thus enhancing the performance and accuracy in data decomposition. In the second stage, developed a BiLSTM-AM based integrated deep learning model and dynamically adjusted the weights of the influencing factors by introducing an Attention Mechanism (AM) to enhance the stability of the model, also predict multiple IMF components obtained after the NGO-VMD decomposition, respectively. In the third stage, the training residuals of the BiLSTM-AM model are used as target variables to correct the prediction error in BiLSTM-AM using the XGBoost regression model. A variety of model configurations were constructed using actual data from a coastal province in southern China, and the computational results show that the integrated prediction model exhibits excellent stability and accuracy.

This study proposes an intelligent energy management system for electric vehicles. This system uses multi-objective optimization to overcome the limitations of existing electric vehicles, including limited range, battery life degradation, and low energy utilization efficiency. The research aims to comprehensively optimize the vehicle’s power, battery life, and energy utilization efficiency. The method involves creating an energy management strategy based on multi-objective optimization that incorporates the Pontryagin minimum principle and deep Q-Network. This method uses the Pontryagin minimum principle to create an initial optimization framework and adjusts it in real time using a deep Q-network to address the complex, dynamic characteristics of an electric vehicle’s energy management system. The simulation results demonstrated that the proposed system achieved significant improvements. Compared to mainstream energy management systems, it had the lowest fuel cell and power cell degradation rates of 19.21% and 40.28%, respectively. Additionally, the system exhibited an average acceleration time of 5.38 s and an average hill climbing ability of 25.91%. These outcomes demonstrate the effectiveness of the proposed EMS in optimizing power, extending battery life, and improving energy utilization efficiency. This makes it an innovative solution for developing electric vehicle energy management systems.

To address the challenges of high carbon emissions in traditional power systems, which conflict with China’s “dual carbon” strategy, and the difficulty of integrating wind power into the grid, this study proposes a novel gas turbine capacity planning method that integrates a tiered carbon trading mechanism, two-stage power-to-gas (P2G) devices, and Carbon capture power plants (CCPP). First, a joint operation model is developed, integrating gas turbines, two-stage P2G devices, CCPP, and wind turbines while accounting for wind power output uncertainty. Then, a tiered carbon trading mechanism is introduced. Unlike conventional models that apply a uniform carbon price, the proposed framework adopts a differentiated carbon cost structure to better reflect emission levels and incentivize cleaner energy deployment. The objective is to minimize the total investment and operational costs of the system, subject to standard operational constraints and transmission security limits. Finally, case studies based on a modified IEEE 30-bus system are conducted to quantitatively evaluate the impact of the proposed mechanism, gas turbines, and P2G devices on economic performance, wind power utilization, and carbon emissions. The results confirm the feasibility and effectiveness of the planning model, highlight the roles of carbon trading policy, natural gas prices, and hydrogen storage efficiency, and offer valuable insights for investment decision-making under carbon and energy market uncertainties.

Hydrogen is the key to decarbonising heavy-duty transport. Understanding the distribution of hydrogen demand is crucial for effective planning and development of infrastructure. However, current data on future hydrogen demand is often coarse and aggregated, limiting its utility for detailed analysis and decision-making. This study developed a spatial disaggregation approach to estimating hydrogen demand for heavy-duty trucks and mapping the spatial distribution of hydrogen demand across multiple scales in Australia. By integrating spatial datasets with economic factors, market penetration rates, and technical specifications of hydrogen fuel cell vehicles, the approach disaggregates the projected demand into specific demand centres, allowing for the mapping of regional hydrogen demand patterns and the identification of key centres of hydrogen demand based on heavy-duty truck traffic flow projections under different scenarios. This approach was applied to Australia, and the findings offered valuable insights that can help policymakers and stakeholders plan and develop hydrogen infrastructure, such as optimising hydrogen refuelling station locations, and support the transition to a low-carbon, heavy-duty transport sector.

With the global building energy consumption accounting for nearly 40% and the housing demand rising sharply due to population growth and accelerated urbanization, small green housing has attracted much attention as a key model for sustainable development. This study focuses on the design of small green housing for the future, aiming to comprehensively evaluate its energy efficiency and adaptability. By constructing an innovative comprehensive evaluation model SGH-EAM, integrating energy efficiency evaluation components, adaptability evaluation components and fusion decision components, the model is derived using multidisciplinary theories such as thermodynamics, heat transfer, and ergonomics. Experiments were conducted on small green housing cases in 100 different regions, and compared with models such as EEM-GH and SA-HM. The results show that the SGH-EAM model performs well in energy efficiency, with an average annual heating energy consumption reduction rate of 30%, cooling energy consumption reduction of 25%, and lighting energy consumption reduction of 35%. In terms of adaptability, the spatial adjustable flexibility has a comprehensive score of 80 points. The comprehensive evaluation score is 83 points, which is significantly higher than other models. Research shows that the SGH-EAM model can effectively improve the accuracy of small green housing assessment, provide a comprehensive theoretical basis for its design, and promote the development of housing construction towards a green, intelligent and sustainable direction.

Aiming at the critical challenges of fragmented environmental-economic value tracking and inefficient multi-stakeholder coordination in green electricity trading, this study proposes a blockchain-based collaborative management method integrating environmental attributes (e.g., carbon offsets) with economic transactions. Leveraging blockchain’s decentralized, tamper-proof distributed ledger, the method ensures transaction transparency, automates settlement via smart contracts, and establishes a verifiable audit trail for environmental benefits. Experimental comparisons demonstrate that the blockchain platform ​reduces transaction costs by 30%, shortens settlement time by 75%, and significantly enhances market liquidity and transparency versus traditional modes. This approach optimizes resource allocation, minimizes intermediary dependencies, and provides a robust technical pathway for scaling green power adoption. Key implementation barriers include blockchain’s energy consumption, smart contract vulnerabilities, and regulatory fragmentation across jurisdictions. Future work will focus on enhancing blockchain energy efficiency and developing cross-regional regulatory frameworks for green power markets.