Distributed Generation & Alternative Energy Journal

Data-driven Least-squares Support Vector Machine Model for Integrating Wireless Sensor Data in Logistics Optimization and Reducing Denitrification Cost in Thermal Power Generation

Lu Liu — 2026-06-04

Flue gas denitrification of boilers in large coal-fired power stations has high operating costs, and its online optimization can reduce denitrification costs and enhance the competitiveness of power generation enterprises. In addition to the operational aspects of denitrification, the logistics of denitrification agents, such as transportation, storage, and distribution, also contribute significantly to the overall cost. This study comprehensively focuses on the online modeling and optimization of denitrification cost of thermal power units, incorporating logistics costs related to denitrification agents. The paper proposes a system that integrates wireless sensor data and real-time wireless communication from thermal power plants, aiming to construct an online denitrification and logistics integrated economic optimization system. The system establishes a boiler denitrification cost prediction model using a data-driven least-squares support vector machine (LSSVM) method combined with the BP algorithm for input variable selection. An improved genetic algorithm is applied for offline optimization of the unit’s constant operating load points and construction of an offline expert database. Additionally, a logistics cost prediction sub-model is included, analyzing historical logistics data related to denitrification agents, such as transportation distances, storage durations, and vehicle utilization rates. A fuzzy association rule mining algorithm (FARM) is utilized to extract correlations between load, logistics parameters, and optimization variables, enabling real-time optimization of both denitrification and logistics costs. The results show that the BP-LSSVM modeling method effectively reduces model complexity and improves prediction accuracy, while the GA-FARM optimization method significantly reduces comprehensive denitrification and logistics costs, providing a framework for online real-time optimization in thermal power units. Least Squares Support Vector Machine (LSSVM) is a regression-based machine learning technique known for its high accuracy and good generalization ability, especially in complex nonlinear systems. In this study, LSSVM is employed to model the denitrification cost. Meanwhile, the Back Propagation (BP) algorithm is used for effective input variable selection to reduce model complexity and enhance performance.

Development of a Mathematical and Heuristic Model for the Techno-Economic Design of Renewable Energy Systems Ensuring a Convex and Directly Optimizable Objective Function

Jorge Benjamin Wong Kcomt — 2026-06-04

This paper presents a techno-economic optimization framework for the design and sizing of a hybrid renewable energy system (HRES) integrating photovoltaic generation, wind energy, and battery storage for isolated and weak-grid coastal communities. The proposed methodology aims to minimize the annualized life cycle cost, also referred to as the equivalent annual total cost (CAET), while explicitly incorporating system reliability through the Value of Lost Load (VOLL), a concept widely adopted in power system planning and regulatory studies.

The optimization problem is formulated using an hourly energy balance over a full annual horizon of 8760 hours, allowing the explicit representation of load variability, renewable resource intermittency, and battery charge-discharge dynamics. Capital investment costs are annualized using the capital recovery factor based on established engineering economics principles, while operational costs and the economic valuation of unserved energy are jointly considered in the objective function. By embedding reliability costs directly into the cost formulation, the proposed approach modifies the mathematical structure of the optimization problem, leading to a convex cost behavior within the feasible design space.

The framework is applied to a real-world case study corresponding to the coastal community of Chérrepe, Peru, using site-specific solar irradiation, wind resource, and demand data. Simulation results demonstrate that the explicit inclusion of reliability valuation significantly influences optimal system sizing, discouraging undersized configurations with excessive unmet demand as well as oversized configurations with unnecessarily high capital costs. The resulting optimal design achieves a balanced and economically consistent trade-off between investment cost and supply reliability.

The results confirm that integrating reliability valuation directly into the techno-economic optimization process provides a transparent, robust, and replicable approach for the planning of hybrid renewable energy systems in isolated contexts. The proposed methodology can be readily adapted to other locations and technology combinations, offering a practical decision-support tool for distributed generation and alternative energy planning.

Multi-agent Reinforcement Learning-based Basic Data Collection and Dynamic Information Evaluation for Power Station Primary Frequency

Tianxiong Huang — 2026-06-04

Primary Frequency Regulation (PFR) of power stations is faced with challenges such as intensified frequency dynamic fluctuations, complex coordinated control of multiple power sources, and unbalanced operating economy. Traditional data collection methods are difficult to meet the demand for precise control of PFR. This study intends to establish a multi-dimensional data collection system to improve the accuracy of primary frequency regulation dynamic information evaluation and strategy optimization effects. It first designs a multi-source acquisition framework covering grid-side frequency indicators and power station-side equipment operation data, and combines time series interpolation and outlier detection for data preprocessing. Then, a dynamic information evaluation model based on multi-agent proximal strategy optimization is built to achieve multi-power collaborative evaluation through centralized training and decentralized execution mode. Finally, an improved particle swarm optimization algorithm is used to optimize the frequency regulation strategy. Based on the on-site measured data of a provincial-level integrated energy power station (including 4 types of power sources and continuous operation for 30 days), the research results show that the data integrity of the proposed data collection system was improved to 98.7%, and the frequency deviation prediction error of the dynamic evaluation model was controlled within ±0.02 Hz. The optimization strategy increased the lowest point of frequency by 0.03–0.05 Hz, and reduced the total cost of frequency regulation by 12.3%. The study provides accurate data support and efficient control solutions for PFR of power stations, which has important practical significance for improving the frequency stability and operating economy of the power system.

MPC-Guided Deep Reinforcement Learning for Real-Time Scheduling of Microgrid with Uncertainty

Yilu Zhang — 2026-06-04

Microgrid energy management plays a critical role in ensuring the secure and economical operation of microgrids. To address the uncertainty of renewable energy generation, this paper proposes an MPC-guided deep reinforcement learning (DRL)–based intraday scheduling strategy for microgrids. The proposed approach integrates the advantages of model predictive control (MPC) and DRL, where the optimization results of the MPC module are provided as environmental inputs to the DRL agent, and the DRL module interacts with the real microgrid environment to generate compensation actions. This framework not only mitigates the performance degradation caused by uncertainties in model-based methods, but also reduces the search space of DRL, thereby accelerating training convergence and suppressing policy fluctuations. Comparative simulations are conducted against standalone MPC and standalone DRL controllers. The results demonstrate that the proposed strategy can significantly reduce both operational security cost and economic cost, while effectively improving the utilization of renewable energy. Therefore, it provides an innovative solution for the microgrid scheduling problem.

Optimal Scheduling of Electric Vehicle Charging and Discharging in Microgrids: Analysis of the Impact on Energy Loss and Efficiency of Distribution Systems

Anjiang Liu — 2026-06-04

The current trends in electric vehicle adoption in distribution networks have brought some positives into microgrid operations. EVs can reduce greenhouse gas emissions down and create a more flexible source of energy by using V2G technology; however, uncontrolled charging of these vehicles is likely to incur losses in energy, fluctuations in voltage, and reduced efficiency to the system in total. This research proposes an optimal scheduling framework for the charging/discharging of EVs understandably connected to distribution systems embedded in microgrids. Analysis and classification of forecasted EV charging will be done based on charging data obtained from Kaggle; the charging behaviors will be classified into peak/off-peak utilization and flexible/inflexible groups, wherein energy losses are minimized, and distribution system efficiency is maximized through a multi-objective optimization model with realistic operating conditions. Simulation results on the IEEE 33-bus and 69-bus test systems demonstrate that with suitable coordination of scheduling, energy loss is reduced up to 25%, voltage stability is ensured, green energy is utilized maximally, and load relief during the peak period takes place. The results from this study highlight how smart scheduling of EVs could enhance smart grid performance in the future concerning the technology, economic, and environmental aspects.

Enhance the Performance of Solar Irradiance Deep Learning Forecasting Model Using Recursive Estimation Method with Grid Search Algorithm

Gautam Kumar — 2026-06-04

This study presents a better way to predict solar irradiance by combining the Recursive Estimation Method for Signal Decomposition with Bidirectional Long Short-Term Memory (BiLSTM) networks that are made for predictive modeling. As solar power plays a bigger and bigger role in India’s green energy strategy, it is very important to be able to accurately predict solar irradiance in order to make the best use of resources. The suggested RE-BiLSTM framework does better than standalone models like LSTM, GRU, and BiLSTM, as well as the CEEMDAN-BiLSTM model hybrid, at different times of day (15 minutes, 30 minutes, and 60 minutes) and in different seasons (summer, monsoon, autumn, and winter). RMSE, MAE, and R² are some of the evaluation metrics that show the proposed model consistently has lower error rates and higher predictive accuracy, especially at shorter time scales. Comparative analysis shows that the forecasting errors are more than 50% lower than those of the other models, which shows how strong the method is. These results suggest that the RE-BiLSTM model is a promising way to improve solar irradiance prediction and help India adapt solar power into its energy infrastructure.

Integrating Water-Based Energy Solutions in Industrial Design for Sustainable Manufacturing

Xiaoxia Lu — 2026-06-04

The research elaborates on a Water–Energy Recovery System (WERS) that has been developed as a sustainable manufacturing enhancer by extracting hydraulic energy from industrial wastewater networks. The study uses an extensive dataset of a Full-Scale Wastewater Treatment Plant from six important industrial areas of Iran, and the framework goes through extensive data preprocessing which consists of noise reduction, normalization, and extraction of the main indicators like Specific Energy Consumption (SEC) and Flow Power Ratio (FPR) for the evaluation of hydraulic–energy relationships. Hydraulic head estimation, power calculation, and multi-criteria ranking that considers flow stability, installation feasibility, and cost-effectiveness are the methods used to unearth the potential recovery locations. The WERS that comprises of a pump-as-turbine arrangement for hydraulic energy conversion is also backed by storage and smart control units, and the measurement of environmental and economic performance is done through a Life Cycle Assessment (LCA) integrated with Techno-Economic Assessment (TEA). Non-dominated Sorting Genetic Algorithm II (NSGA-II) is the one that performs the multi-objective optimization by maximizing the annual recovered energy and minimizing the total system cost. The outcomes show a total recoverable energy potential of 6.8 GWh from 613 Hm³ of the industrial wastewater, the Caspian region being the highest contributor (3.2 GWh). The configured WERS with optimization results in a power of 31,110.72 kW, CO2 reduction of 2.23 × 108 kg annually, and strong economic viability indicated by payback and cost–benefit metrics. These results accentuate the remarkable potential of water-based energy recovery as a large-scale solution of future low-carbon and resource-efficient industrial operations.

Stability Analysis of Transformer Mechanism Model Based on Finite Element Analysis Under Load Fluctuations

Li Xun — 2026-06-04

This study investigates the mechanical stability of power transformers subjected to fluctuating load currents, which induce dynamic electromagnetic forces that can lead to mechanical vibrations and potentially compromise the transformer’s structural integrity. Traditional methods often overlook time-varying loads and fully coupled electromagnetic-mechanical interactions, which limits the accuracy of stability assessments in realistic operational conditions. This method introduces a novel coupled finite element framework that simultaneously models electromagnetic and mechanical interactions under fluctuating load conditions. Unlike conventional electromechanical approaches, the proposed method captures time-dependent force variations and their direct impact on structural stability, enabling more accurate and realistic transformer stability assessment. External load profiles range from 48.8% to 120% of the rated load. The electromagnetic forces are calculated using the Maxwell stress tensor method, yielding a total integrated force of 4157.24 N. The maximum magnetic flux density is found to be 1.8458 T, well below the saturation point, and the core experiences a maximum electromagnetic stress of 95.91 Pa, which is significantly lower than the material yield strength of 350 MPa, resulting in a safety factor of over 3.6 × 106. The mechanical analysis shows a maximum displacement of 0.543 μm and a total RMS vibration amplitude of 0.291 μm. The modal analysis reveals a natural frequency of 66.69 Hz, distinct from the main electromagnetic excitation frequency of 120 Hz, indicating a low risk of resonance. Overall, the results confirm that the proposed framework accurately models the electromechanical behavior of transformers under fluctuating load conditions, ensuring their mechanical stability.

Industrial Electricity Load Forecasting Considering Periodic Features and Inter-Industry Associations

Bo Zhao — 2026-06-04

Industrial electricity load forecasting is crucial for the stable operation of power systems and energy management. However, the complex temporal patterns and dynamic interdependencies between loads from different industries make it challenging for traditional forecasting methods to model effectively. To address this, this paper proposes a forecasting model based on an inter-industry association dynamic graph neural network that integrates periodic features. The proposed method uses the Time2Vector block to adaptively capture multiple periodic fluctuations in the load sequence, and combines this with the cointegration relationship and error correction mechanism from the Vector Error Correction Model (VECM) to quantify the association strength between industries. Each industry is represented as a node, and the association strengths define the edges and their weights. Thus, this forms a Dynamic Inter-industry Association Graph (DIAG). This graph is then integrated into a Dynamic Spatial-Temporal Aware Graph Neural Network framework. As a result, the Inter-industry Association Dynamic Graph Neural Network (IADGNN) is formed. This model captures the complex dynamic characteristics of electricity loads across different industries. Test cases based on industrial load data from one province in China show that this method significantly outperforms traditional models in terms of forecasting accuracy, providing a novel solution for addressing the complex industrial load forecasting problem.

Scalable Power Dispatch Automation Methods Based on Artificial Intelligence in Distributed Energy Systems

Zhaoyuan Yin — 2026-06-04

With the high penetration of distributed energy resources (DERs), the strong uncertainty of their output and the complex interaction of network topology pose severe challenges to the real-time performance, scalability, and intelligence level of dispatching methods. This paper proposes a hierarchical distributed collaborative intelligent dispatching architecture based on deep reinforcement learning (DRL) and graph neural networks (GNNs). This architecture adopts a two-layer design of “regional agent-edge controller.” The upper-layer regional agent uses GNN to process the global power flow state and generate node marginal price signals; the lower-layer edge controllers act as independent DRL agents, making autonomous decisions based on local observations and price signals. Through a hybrid mechanism of centralized training and distributed execution, collaborative learning and plug-and-play expansion of the system are achieved. This method significantly reduces dispatch costs when dealing with uncertainty, achieving a high photovoltaic grid integration rate of up to 100%, and controlling the maximum net power deviation between day-ahead planning and real-time operation to within 0.35 MW. Even when the system is expanded to 500 DER units, the online decision-making time remains at 7.5 milliseconds, and communication overhead only slowly increases to 35.0 KB, demonstrating its significant effectiveness in improving system resilience, operational economy, and scalability.