Energy Informatics最新文献

The widespread integration of high-penetration distributed photovoltaic (PV) systems presents multiple challenges to distribution networks, particularly under extreme weather conditions such as short-term heavy rain, where issues like voltage violations and increased network losses become more severe. To address these challenges, this paper proposes a multi-objective coordinated optimization strategy for flexible interconnected distribution networks. The strategy establishes a multi-objective optimization model that comprehensively considers network losses, voltage deviations, PV output fluctuations, and regulation costs. It fully leverages the precise power flow regulation capability of the Soft Open Points (SOPs), coordinated with the fluctuation-smoothing effect of energy storage systems (ESS) and the voltage support function of multiple types of reactive power compensation devices. By introducing a special crowding distance (SCD) ordering mechanism, the traditional four-vector intelligent metaheuristic (FVIM) algorithm was upgraded to a multi-objective optimization algorithm for solution, significantly enhancing the quality of the Pareto optimal solution set. Simulation results based on the modified IEEE 33-bus and IEEE 39-bus systems show that the proposed strategy reduces the 24-hour total network loss by 19.17% and 6.37%, respectively, while keeping the minimum system voltage within the safe range throughout the day. Comparative analysis across multiple scenarios verifies the effectiveness and robustness of the strategy in smoothing PV fluctuations, optimizing system operation, and coping with extreme weather events. This research provides an efficient solution for the coordinated optimal operation of high-PV-penetration distribution networks, balancing economic efficiency, power quality, and operational stability.

High-renewable power systems are crucial for climate change mitigation and energy transition, yet their grid integration poses stability challenges. Hydrogen energy storage, with its large-scale and long-duration advantages, offers a promising solution to enhance flexibility against the volatility and intermittency of high-proportion clean energy. While recent studies have explored various electricity-synergy business models, they often lack a unified multidimensional evaluation framework. This study establishes an electricity-hydrogen-energy storage synergistic optimization model that minimizes total operational costs while comparing hydrogen transportation and electricity transmission modes. Simulation in a high-renewable demonstration area in Southwest China shows the hydrogen transportation mode outperforms, reducing wind and solar curtailment rates to 12.68% and 7.75%, respectively, cutting system costs by 57.3%, and generating additional hydrogen revenue. Sensitivity analysis identifies hydrogen selling price and production efficiency as key economic drivers, offering insights for planning and operating hydrogen storage in high-renewable systems. These findings support decision-making for electro-hydrogen system planning, business model innovation, and policy formulation.

While data sharing in a digitalized world is increasingly important, its inherent tension between data verifiability and privacy limits stakeholders’ engagement. In the course of the sustainable energy transition, this tension also hinders the integration of small-scale flexibility devices in electricity grids: Grid operators must rely on verifiable data for secure operations, while device owners often seek to ensure data protection and thus privacy. Applying an Action Design Research Approach, we design and implement the Flexibility Provision Data Flow and the digital Trust Diamond that leverages wallet-based identity management for ensuring verifiability and privacy-preserving data sharing. To that end, we also derive two fundamental design principles for systems that leverage the Trust Diamond, namely Verifiability through Delegation and Mediated Sovereignty, and show their applicability for optimized grid operations. Our research further results in proposing a Decentralized Flexibility Provision Architecture that operationalizes these findings in a laboratory and field test environment. Performance tests indicate the solution’s practical scalability. This study highlights the importance of Information Systems-based solutions to enhance data sharing and address trust concerns between stakeholders. Moreover, it provides an actionable design to realize wallet-based data sharing to enable a decentralized flexibility provision for Redispatch measures.

Industrial demand-side flexibility is essential for power systems with high shares of variable renewables, yet static grid fees and full load hours incentives can suppress flexible electrification by penalizing peaks and rewarding uniform consumption. This paper introduces an open, extensible, regulation-aware optimization platform that integrates market and emission data with a mixed-integer scheduling model of a bi-valent industrial dryer capable of using electricity and gas. The platform is demonstrated through a representative use case from the energy intensive paper manufacturing sector operating under the current reduced static grid fee pursuant. Because the reduction is contingent on meeting a minimum full load hours threshold, the scope for exploiting the bivalent flexibility potential remains limited. As an alternative, a proposed dynamic grid fee regime featuring time-varying low- and high grid fee windows is evaluated. Any reduction of power consumption from the grid during the high grid fee window does not count against meeting the full load hours threshold. With dynamic fees, the optimization concentrates electric operation within low grid fee windows, increasing electrified heat, reducing emissions, and maintaining competitive costs. Notably, during summer months, these windows align well with periods of lower grid carbon intensity, reinforcing cost–emission co-benefits. However, this alignment deteriorates in winter due to higher variability in renewable generation patterns and constrained window timing, limiting incentive effectiveness. The findings demonstrate that tariff parameterization materially shapes realizable industrial flexibility and that regulation-aware optimization can translate latent technical potential into sustained, temporally targeted electrification aligned with system conditions. The platform enables reproducible, policy-relevant scenario analysis and can aid plant operators for scheduling flexible assets and tariff designers in testing tariff parameters against realistic operational responses.

This paper presents a conceptual and architecture-centric design study for Home Energy Management Systems (HEMS), introducing a cloud-based data and software engineering approach that emphasizes the organization, indexing, processing, and analysis of IoT-generated energy data. The proposed architecture supports scalable and reliable ingestion, storage, and retrieval of heterogeneous smart home data, laying the groundwork for high-performance analytics and real-time operations. Among the contributions, a conceptual framework is presented that compiles and classifies HEMS functionalities derived from various demand-side management programs and mechanisms, organized according to their relevance to key stakeholders, including end-users, service aggregators, and utility operators. This framework aims to leverage the potential benefits of these functionalities within multi-level energy communities, while also guiding the design of the proposed architecture and aligning its components with the operational, regulatory, and informational needs of each actor, thereby fostering dynamic interactions and user-centered service delivery. The architecture is structured into three interconnected environments-Ingestion, Operational, and Analytical-each responsible for enabling specific capabilities, from real-time monitoring and control to large-scale data analysis and decision support. By explicitly linking stakeholder needs with software components and data flows, the proposed system ensures adaptability, scalability, and meaningful participation in energy management. A conceptual evaluation demonstrates how the architecture supports representative HEMS use cases and stakeholder roles, offering a structured foundation for addressing emerging challenges in demand-side energy coordination and cloud-based HEMS architectures. Finally, the work includes the practical validation of the Ingestion Environment, providing experimental results that confirm the system’s scalability, performance, and reliability under realistic IoT workloads, thereby bridging the conceptual design with empirical evidence from an implemented component of the proposed architecture.

The increasing integration of renewable energy sources has significantly decreased the effective inertia of modern power systems, thereby weakening their ability to regulate frequency. Traditional Automatic Generation Control (AGC) strategies are becoming increasingly inadequate in handling rapid frequency dynamics and stability issues, especially in the presence of communication delays. This paper proposes a novel three-layer coordinated frequency control strategy, named AGC-RTEM-P2P, which incorporates fractional-order control, a hierarchical system architecture, and delay compensation techniques. Central to the approach is a fractional-order proportional-integral-derivative (FOPID) controller, which supports a multi-timescale control framework consisting of the AGC layer, the real-time energy market (RTEM) layer, and the peer-to-peer (P2P) coordination layer. Communication delays are effectively addressed using Padé approximation and Recursive Least Squares (RLS) estimation. Control parameters are globally optimized using the lightning search algorithm (LSA). Simulation studies conducted on the IEEE 118-bus system show that the proposed strategy outperforms conventional methods by reducing frequency deviations, enhancing system stability margins, and lowering control energy consumption.

A dynamic management model is developed to improve security and operational resilience in power monitoring networks in isolated microgrid conditions. These systems are subject to frequency and voltage instabilities due to limited generation capacity, low inertia, and large shares of dynamic and pulse loads. The dynamic management model integrates isolation strategies, coordinated load balancing, and security-constrained power management strategies. The Multi-Objective Beetle Antennae Search-driven- Enhanced Pity Beetle Algorithm (MOBAS-EPB) facilitates optimizing multiple objectives concurrently and balancing system stability, operating efficiency, and risk management. The framework includes data preprocessing steps as part of the microgrid security and risk management datasets that handle missing values, Z-score normalization, and dynamically detect security threats, reconfiguration of load sharing to avoid cascading failures, and system integrity. Validation is performed on a modified IEEE 33-bus test system under simulated cyber-attacks and load disturbances using the Power Systems Computer-Aided Design (PSCAD) environment. Experimental results demonstrate that MOBAS-EPB reduces frequency deviations by 41.2%, improves voltage stability by 37.6%, decreases outage duration by 48.5%, and shortens network recovery time by 45.9%. The proposed approach enables adaptive control decisions, effectively managing uncertainties from operational and security factors, and demonstrates the potential of combining intelligent optimization with isolation and load coordination to achieve reliable, secure, and efficient operation of decentralized power monitoring networks.

With the quick incorporation of distributed photovoltaic (PV) systems into the contemporary energy grid, guaranteeing consistent power output and system reliability has become critical. These systems are susceptible to a variety of operational anomalies, including dust accumulation, and shading which can reduce energy efficiency without being detected. Traditional anomaly detection methods frequently fail to capture the internal dynamics and latent factors that affect PV system performance over time. There is an urgent need for a robust, real-time, and dynamic monitoring strategy that can accurately detect abnormal behavior in distributed PV systems. The goal of this research is to create and execute a state space model (SSM)-based anomaly detection framework that dynamically estimates system conditions and alerts to deviations from expected power output in real time. A dataset of 5000 records and 10 attributes was used, which contained time-series data from distributed PV systems, such as actual and expected power output, irradiance, temperature, and other operational parameters. Model matrices (A, B, and C) were estimated using linear regression and enhanced by Kalman filtering. The system estimated power output at each time step and calculated the deviation from actual values. A threshold-based approach was employed to classify anomalies. The model was trained on data labeled as normal and validated against both normal and abnormal entries. The proposed SSM-based anomaly detection model had an accuracy of 94.70%, a precision of 93.85%, a recall of 94.30%, an F1-score of 94.07%, and a Matthews Correlation Coefficient (MCC) of 89.32%, indicating strong predictive capability in detecting abnormal performance in PV systems.

To address the impact of faults in the planning process of AC/DC hybrid microgrids and achieve integrated and flexible planning, an integrated flexible planning method based on a Nonlinear Extended State Observer (NLESO) is proposed for AC/DC hybrid microgrids. The general structure of the AC/DC hybrid microgrid is analyzed, a mathematical model of the bidirectional AC/DC converter is established, and an optimization model for the AC/DC hybrid microgrid is formulated. With the minimum sum of voltage fluctuations in the hybrid microgrid as the objective function, constraints are defined, and the objective function is solved using particle swarm optimization. Experimental results demonstrate that the proposed method maintains the voltage amplitude at each node above 0.96 p.u. at all times, ensuring local voltage stability and reducing the microgrid’s operating cost. The annual overall cost is reduced by 318,600 yuan, representing a decrease of approximately 2.37%.

Solar and wind energy’s popularity seems to be on the rise and does not show signs of stopping. However, renewable energy resources tend to be unreliable due to unexpected weather. This may lead to an unstable electrical grid, which causes blackouts and additional problems. These issues could be dealt with using multiple energy management systems that are available to help reduce the risk and stabilize the electrical grid. This study aims to examine the capability of these Smart Grids to become smarter and more accurate and whether energy consumption could be forecast in order to use it in small and big renewable energy plants. This study found that to be easily achievable and with great accuracy. In fact, the minimum amount of training data required to achieve good results across all horizon sizes (1, 2, and 7 days) is one year as concluded by the results. However, a small period of two months is enough to forecast small periods from an hour up to 24 h. Additionally, the LSTM network was found to be most suitable.