Distributed Generation & Alternative Energy Journal

Optimisation of Emergency Power Restoration in Distribution Networks with Distributed Generation Integration

2026-02-06T21:38:49+01:00

The integration of distributed generation (DG) such as photovoltaic and wind power systems into distribution networks significantly alters power flow patterns and operational characteristics, introducing stochasticity and uncertainty into voltage and loss behavior. Higher-order semi-invariants (cumulants) yield greater accuracy and computational efficiency than traditional Monte Carlo simulations; therefore, they can be employed as the preferred and more reliable method of performing effective stochastic power flow analysis of very complex power systems. Meanwhile, wiring errors at the user side, such as neutral-to-earth misconnection, compromise residual current device (RCD) protection and elevate electric shock risks. Feeder clustering via the CFSFDP algorithm is integrated with this stochastic loss modeling framework to identify user groups and isolate faulty users, providing a cohesive methodological approach for practical distribution network analysis. An analysis performed on wiring error hazard mechanisms and the associated ground scheme’s performance against RCDs; examining the stochastic power flow and line loss distribution models by utilizing higher-order cumulative distributions for evaluating the voltage and loss statistical distributions. Cumulative distribution methodologies have been shown to have an order of magnitude less compute time than Monte Carlo simulation and equivalent performance accuracy. The user grouping algorithm developed in this study (CFSFDP) does not require the specification of input cluster(s) to create a grouping. Fault localization is accomplished through an adaptive lasso-type model. The method developed for validating the algorithms used in this study on the IEEE 34 Bus network have shown to be very accurate and efficient.

Enhancing Intelligent Fault Detection and Classification in Power Grid Engineering Using LSTM Neural Networks

2025-11-08T18:32:06+01:00

Power grid engineering is necessary for the distribution of power, but problem detection and categorization are made extremely difficult by their growing complexity, particularly with the incorporation of renewable energy sources. Decision trees and support vector machines are two examples of fault detection techniques that frequently fail to handle the dynamic and non-stationary character of power grid data. These techniques’ efficacy in real-time defect identification is limited because they are unable to capture the complex relationships and temporal dependencies present in time-series data. Furthermore, a lot of conventional models are unable to generalize to different kinds of problems, which results in errors and delays in fault identification. For improved fault detection and classification in power grids, this research suggests a hybrid approach that combines Temporal Fusion Transformers (TFT) with Long Short-Term Memory (LSTM) Neural networks. While the LSTM neural network is used to represent sequential data, the TFT model is particularly good at capturing complicated linkages in time-series data. By combining these two models, the suggested approach can improve grid efficiency and dependability by offering precise fault forecasts in real-time. The findings show that the suggested hybrid model works better than conventional techniques, with a fault classification accuracy of 98.66% as opposed to decision trees’ 97.42% and SE-CDAE’s 97.98% accuracy. The performance of the model demonstrates its potential for real-time implementation in contemporary power grids, guaranteeing improved fault identification and prompt reactions to avert system breakdowns.

Enhanced Resilience and Cost Optimization in Peer-to-Peer Energy Trading with Smart Pricing and MILP-Based Optimization for Multi-Microgrid Systems

2025-11-08T18:09:05+01:00

The transition toward decentralized energy systems has led to the development of Peer-to-Peer (P2P) energy sharing schemes, allowing prosumers to exchange surplus energy within regional networks. This paper presents an optimized energy transaction framework that improves system resilience while facilitating cost-effective energy exchange through a two-stage adaptive P2P pricing mechanism The principal objective is to develop an internal pricing structure that combines market-based initial price formation with real-time electric vehicle-aware price adjustment to support equitable energy trades, reduce dependence on centralized utilities, and enhance economic efficiency. The suggested methodology uses a mathematical optimization model that integrates supply-demand dynamics and pricing strategies. A Mixed-Integer Linear Programming (MILP) method is employed to optimize energy distribution among prosumers, while considering network limitations and variations in renewable energy. Simulation data derived from actual energy profiles are used to validate the framework across various market scenarios. Numerical results show that the P2P energy trading mechanism enhances system resilience by decreasing peak demand by 20% and reducing prosumer costs by an average of 15%. The proposed pricing strategy guarantees equitable energy distribution, reduces transaction costs, and encourages active customer engagement. The findings demonstrate that decentralized energy markets can advance sustainability while also providing modern power systems with economic advantages.

Multi-Objective Scheduling of V2G-Enabled PEVs in Local Multi-Energy Systems: Balancing Profitability and Carbon Emissions Using Time-Varying Operational Profiles

2025-11-27T19:36:56+01:00

Plug-in electric vehicles (EVs) introduce both benefits and challenges to effective energy regulation when considered in contemporary power systems. To incorporate PEVs with V2G and G2V technology into LMESs, the authors of this study propose an in-depth MOO framework, which maximizes economic profitability and minimizes CO₂ emissions through simulations of a system with gas carriers, electricity, heating, cooling, and other renewable energy sources (RES), including photovoltaics, CHP units, and thermal storage. The model relies on time-varying operational inputs, including varying electricity prices and variable RES generation profiles. Findings indicate that PEV integration progressively improves both economic and environmental performance across the examined scenarios. Compared with the No-PEV case, G2V operation increases operator profit from about 12000$ to about 18000$ while reducing CO₂ emissions from about 6000 kg to about 4800 kg. When V2G is enabled, operator profit rises further to about 22000$ and total CO₂ emissions decline to about 3500 kg. The Pareto frontier further confirms a clear trade-off between environmental and economic objectives, spanning approximately 1800-3500 kg CO₂ and 12000$–22000$ across the sampled solutions. The study of PEV behavior in clusters further shows differentiated flexibility patterns for residential, commercial, and industrial users, enabling more effective scheduling of G2V and V2G interactions and better utilization of the grid. By promoting the use of V2G and scalable optimization strategies in multi-carrier energy systems, this work provides a solid foundation for sustainable energy management.

Robust Scheduling Algorithm for Virtual Power Plants in Distributed Multi-Energy Systems Based on Generative Adversarial Reinforcement Learning

2026-02-06T20:45:27+01:00

In the scheduling of distributed multi-energy virtual power plants, this paper proposed a robust scheduling method based on Wasserstein Generative Adversarial Network with Reinforcement Learning (WGAN-RL) to address the vulnerability of scheduling strategies caused by renewable energy output fluctuations and load uncertainties. This method defined the state and action space based on physical constraints and embedded hard operating rules. Then, it designed a conditional Wasserstein GAN to generate the worst-case perturbation scenario that approximates the real distribution support boundary and covers high-risk areas, based on weather and load forecasts. On this basis, it used Proximal Policy Optimization (PPO) to train the scheduling policy in an environment with dynamically injected extreme perturbations, and improved the convergence stability by pruning probability ratios and GAE. Finally, it introduced a rolling time-domain online scheduling and a weekly fine-tuning mechanism of WGAN to achieve long-term adaptability under perturbation distribution drift. Experiments show that, in terms of economics, with a 70% renewable energy penetration rate, the average daily dispatch cost is 2680 USD ± 150 USD, and the curtailment rate is 9.6% ± 1.1%. Regarding robustness, under a perturbation of 0.7 output standard deviation, the dispatch feasibility rate remains at 90.1% ± 2.1%, and the number of strategy collapses is controlled at 9.9 ± 2.1. In terms of real-time performance, the single-step inference time is only 4.2 ms ± 0.3 ms, and the training convergence steps are only 823. This research provides a deployable, adaptive, and engineering-feasible technical path for robust dispatch of virtual power plants under high uncertainty environments.

Optimized Control of a Five-Level Transformerless Inverter Using Bayesian Techniques

2026-05-07T08:10:19+02:00

Common-ground (CG) based transformerless multilevel inverters (MLIs) are well-suited for grid-connected photovoltaic (PV) applications, because of their ability to eliminate leakage current and high efficiency. In this paper a reduced-switch CG five-level transformerless inverter (CG-5L-TLI) is presented that uses a dc source, one diode, seven switches and two capacitors. The proposed topology inherently eliminates leakage current, offers boosting of voltage without the need for any extra boost converter, and also ensures voltage balancing of the capacitors without requiring auxiliary control circuits. To improve the quality of output waveform, a selective harmonic elimination (SHE) strategy is employed, with optimal switching angles determined using a Bayesian optimization approach for the multilevel SHE-PWM scheme. The performance and effectiveness of the suggested system are validated via detailed MATLAB/Simulink simulations. Furthermore, a reduced scale laboratory setup is built to demonstrate the practical viability and operational capability of the suggested inverter.

Physics-Informed Reinforcement Learning Framework for Real-Time Coordination of EV Charging with Renewable Energy Sources

2025-07-26T07:44:27+02:00

We made a Physics-Informed Reinforcement Learning (PI-RL) framework to coordinate the charging stations for electric vehicles (EVs) in real time. These stations are powered by different renewable energy sources (RES), like wind and photovoltaic (PV). Our methodology explicitly integrates energy conservation laws, state-of-charge (SOC) dynamics, and inverter limitations into the training process, unlike previous reinforcement learning (RL)-based methodologies that are confined to single-source renewable energy systems (RES) and do not incorporate physical system constraints. We changed the Soft Actor-Critic (SAC) algorithm by adding domain-informed reward shaping and adaptive Lagrangian multipliers in order to make sure that constraints were met. We subsequently structured the EV-RES coordination issue as a physics-constrained Markov Decision Process (MDP). We evaluated the proposed PI-RL approach using actual datasets of solar irradiance, synthetic wind generation profiles, and electric vehicle arrival patterns. In terms of operational profit, safety (constraint violation rate), and use of renewable energy, our approach worked better than traditional SAC and model-based rolling optimization. Also, our model made it much less common for charge-discharge switching to happen, which led to control strategies that are easier to understand and that help the battery last longer. These results show that the PI-RL framework is a reliable and widely applicable way to manage energy in real time in EV charging infrastructures that are getting more and more complicated as they use renewable energy.

Distributed Photovoltaic Acceptance Capacity Calculation Based on Improved HEM under Uncertain Environments

2026-04-13T16:03:20+02:00

To address the low computational efficiency of traditional photovoltaic hosting capacity assessment methods under uncertain environments, this study proposes a rapid evaluation method based on scenario adaptation and application form optimization of the Holomorphic Embedding Method. Firstly, a node selection strategy based on the Lévy flight-improved particle swarm optimization algorithm is established. An objective model with constraint penalty functions is constructed. Candidate grid connection nodes with superior voltage regulation capability and potential for capacity enhancement are then efficiently screened. This greatly reduces the computational burden of subsequent stochastic evaluation. Secondly, Monte Carlo simulation is integrated with the optimized Holomorphic Embedding Method, combined with Latin hypercube sampling. This builds an assessment framework that considers the uncertainties of photovoltaic output and load fluctuation. The established model adopts the Holomorphic Embedding Method to solve deterministic subproblems efficiently. It then evaluates the overall adaptability and robustness of different integration schemes under various uncertain scenarios. Simulation validation on the IEEE-30 bus system demonstrates that the optimal scheme corresponds to nodes {3,19}. The maximum photovoltaic hosting capacity is 81.32 MW. This scheme obtains the minimum comprehensive flexibility score. It verifies optimal operational performance across annual stochastic scenarios. Meanwhile, the proposed method improves computational efficiency by approximately 82% compared with the conventional enumeration method. It maintains calculation accuracy. The results indicate that the application-oriented optimization of the Holomorphic Embedding Method, combined with stochastic scenario analysis and shared energy storage, can improve the photovoltaic accommodation capability and operational flexibility of distribution networks. It provides reliable theoretical and methodological support for the grid integration of high-penetration renewable energy.

Multi-site Wind Energy Prediction System Based on Power Decomposition and Deep Model Integration

2026-04-22T20:50:37+02:00

To address the limitations of existing wind power forecasting methods in mixed-frequency signal modeling, multi-site spatial correlation capture, and single-model generalization, this paper proposes a multi-site wind power forecasting system based on power decomposition and deep model ensemble. The system applies Variational Mode Decomposition (VMD) to separate raw power sequences into high-frequency and low-frequency components, each directed into a structurally symmetric dual-branch framework. Within each branch, three heterogeneous sub-models – ConvGAT-LSTM, Spectral Transformer, and TCN – operate in parallel; branch outputs are aggregated by simple averaging and linearly combined into a base prediction, which is subsequently refined by an XGBoost residual correction layer. Experiments on six Chinese wind farm datasets demonstrate that the proposed system achieves an average RMSE of 0.1065±0.0021 and R2 of 0.8335±0.0167 at the 24-step forecast horizon, improving over the state-of-the-art baseline TCOAT by 1.4% and 0.5%, respectively. The VMD module alone contributes an 11.6% RMSE reduction, and the system reduces local RMSE during ramp events at Site 6 by 34.9% relative to the baseline. Ablation experiments and statistical significance tests (p<0.05, Cohen’s d>0.82) confirm that each module contributes meaningfully to overall performance.

Partitioning and Voltage Control of High-Permeability Photovoltaic Distribution Network Considering Flexible Load-Side Resources

2026-03-05T01:43:01+01:00

The increasing penetration of distributed photovoltaics (PV) into distribution grids has led to a pronounced voltage over-limit issue, posing significant challenges to grid stability and power quality. Given this context, to address voltage management, a partition-based control methodology is proposed that leverages flexibility from load-side resources. Firstly, the double-layer probability fitting is carried out for photovoltaic uncertainty. Secondly, the risk indicators are innovatively proposed, and a dynamic zoning system including risk indicators is constructed. The centralized-distributed hybrid voltage control architecture suitable for dynamic partition is constructed again. Finally, the proposed model and its performance are assessed through simulations on a modified IEEE 33-bus distribution network. The proposed algorithm effectively mitigates voltage deviations and enables flexible, efficient partition-based voltage control in modernized distribution systems.

A Ground-Wire Active Current Based Fault-Location Method for Smart Monitoring of Underground Mine Cable Distribution Systems

2026-04-30T03:49:26+02:00

Underground mine cable distribution systems are critical industrial local energy infrastructures, where ground faults can lead to production interruption, difficult fault searching, and safety risks. This paper proposes a ground-wire active-current based fault-location method for smart monitoring of underground mine cable distribution systems. The method uses zero-sequence voltage as the fault trigger and phase reference, and measures ground-wire currents at both ends of each monitored cable section. By extracting the active components of the head-end and tail-end ground-wire currents, a fault judgment quantity is constructed through their algebraic summation. Theoretical analysis shows that, for non-faulted cable sections, the active ground-wire current mainly behaves as through-current and is largely cancelled by the double-ended summation. In contrast, for the faulted cable section, the fault current flows from the fault point toward both ends, producing a dominant active-current summation. Simulation results under transition resistances from 0 Ω to 1000 Ω verify that the proposed method can correctly identify internal cable-section faults and distinguish terminal busbar/switchgear faults without falsely locating a healthy cable section. A distributed smart monitoring architecture integrating ground-current sensors, zero-sequence voltage measurement, RS485/LoRa communication, and master-station processing is also presented. The proposed method provides an interpretable and practical solution for online fault location, faster fault isolation, and improved reliability of underground mine local energy distribution systems.