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AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F2: Distributed Energy System".

Deadline for manuscript submissions: closed (5 February 2026) | Viewed by 9267

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Dr. Jiaqi Ruan

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Guest Editor

College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: power systems; renewable energy integration; AI; cyber–physical security; climate resilience; risk management
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Dr. Yujia Huang

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College of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Interests: power flow control; integrated energy system; stability analysis and control
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Dr. Chao Yang

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Guest Editor

School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Interests: electricity market; power system resilience; power system operation and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the face of the accelerating integration of large-scale renewable energy sources, the operation of modern power systems must adapt to maintain stability, reliability, and economic viability. As more wind, solar, and other renewables join the grid, operators confront fluctuating power generation and limited traditional dispatchable resources. Real-time monitoring, forecasting, and advanced control have become essential for handling intermittency and ensuring power quality. Although the modernization of grid infrastructure enables flexible load management and encourages new regulatory and market frameworks, digitization introduces potential vulnerabilities, underscoring the importance of basic cyber–physical security measures.

This Special Issue addresses these evolving challenges by showcasing state-of-the-art research in AI-enhanced power system operation and management with renewable energy integration technology. Through this collection of cutting-edge studies, the Special Issue aims to foster a deeper understanding of advanced operational and managerial methodologies, thereby accelerating the global transition toward a cleaner, more efficient, and resilient energy future.

Dr. Jiaqi Ruan
Dr. Yujia Huang
Dr. Chao Yang
Guest Editors

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Keywords

advanced AI applications
AI-enhanced operation and management methods
large-scale renewable energy integration
multi-energy coupling and coordination
demand-side management and demand response
real-time monitoring and analytics
cyber–physical security and resilience
risk assessment and management
distributed generation and microgrid operation

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AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems—2nd Edition in Energies (1 article)

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19 pages, 1760 KB

Open AccessArticle

Adaptive Rolling-Horizon Optimization for Low-Carbon Operation of Coupled Transportation–Power Systems

by Zhe Zhang, Shiyan Luan, Yingli Wei, Fan Tang, Haosen Li, Pengkun Sun and Chao Yang

Energies 2026, 19(1), 227; https://doi.org/10.3390/en19010227 - 31 Dec 2025

Viewed by 680

Abstract

The rapid growth of electric vehicles (EVs) has created new challenges for the coordinated low-carbon operation of transportation and power systems. To address this issue, this paper proposes an adaptive rolling-horizon dynamic user equilibrium (DUE) optimization framework for the low-carbon operation of coupled transportation–power systems. The framework integrates transportation, power, and environmental dimensions into a unified objective. On the transportation side, a DUE-based traffic assignment formulation captures both road travel times and station-level queuing dynamics, providing a realistic representation of EV user behavior. This DUE-based traffic assignment model is coupled with an optimal AC power flow formulation to ensure grid feasibility and quantify network losses. To internalize environmental costs, a carbon emission flow module propagates generator-specific carbon intensities to charging stations, aligning charging decisions with their true emission sources. These components are coordinated within a rolling-horizon method in which the prediction window adapts its length to the variability of demand and renewable forecasts. The proposed method allows longer horizons to improve foresight in stable conditions and shorter ones to maintain robustness under volatility. Numerical case studies demonstrate the effectiveness and robustness of the proposed framework and its potential to support low-carbon, high-efficiency operation of coupled transportation–power systems. Full article

(This article belongs to the Special Issue AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems)

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21 pages, 2676 KB

Open AccessArticle

Digital Twin-Enabled Distributed Robust Scheduling for Park-Level Integrated Energy Systems

by Xiao Chang, Shengwen Li, Qiang Wang, Liang Ji and Bitian Huang

Energies 2025, 18(24), 6471; https://doi.org/10.3390/en18246471 - 10 Dec 2025

Viewed by 558

Abstract

With the deepening of multi-energy coupling and the integration of high proportions of renewable energy, the Park Integrated Energy System (PIES) 1demonstrates enhanced energy utilization flexibility. However, the random fluctuations in photovoltaic (PV) output also pose new challenges for system dispatch. Existing distributed robust scheduling approaches largely rely on offline predictive models and therefore lack dynamic correction mechanisms that incorporate real-time operational data. Moreover, the initial probability distribution of PV output is often difficult to obtain accurately, which further degrades scheduling performance. To address these limitations, this paper develops a PV digital twin model capable of providing more accurate and continuously updated initial probability distributions of PV output for distributed robust scheduling in PIESs. Building upon this foundation, this paper proposes a distributed robust scheduling method for the PIES based on digital twins. This approach aims to maximize the flexibility of energy utilization in PIESs and overcome the challenges posed by random fluctuations in PV output to PIES operational scheduling. First, a PIES model is established after investigating a park-level practical integrated energy system. To describe the uncertainty of PV output, a PV digital twin model that incorporates historical data and temporal features is developed. The long short-term memory (LSTM) neural network is employed for output prediction, and real-time data are integrated for dynamic correction. On this basis, error perturbations are introduced, and PV scenario generation and reduction are carried out using Latin hypercube sampling and k-means clustering. To achieve multi-energy cascade utilization, the objective of optimization is defined as the minimization of the sum of system operating cost and curtailment cost. To this end, a two-stage distributed robust optimization model is constructed. The optimal scheduling scheme was obtained by solving the problem using the column-and-constraint generation (CCG) algorithm. The proposed method was finally validated through a case study involving an actual industrial park. The findings indicate that the constructed digital twin model achieves a significant improvement in prediction accuracy compared to traditional models, with the root mean square error and mean absolute error reduced by 13.3% and 10.81%, respectively. Furthermore, the proposed distributed robust scheduling strategy significantly enhances the operational economics of PIESs while maintaining system robustness, compared to conventional methods, thereby demonstrating its practical application value in PIES scheduling. Full article

(This article belongs to the Special Issue AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems)

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17 pages, 8702 KB

Open AccessArticle

Data-Driven Based Dynamic State Estimation Method for Regional Integrated Energy Systems Incorporating Multi-Dimensional Generation-Grid-Load Characteristics

by Shengwen Li, Xiao Chang, Liang Ji and Junchen Mao

Energies 2025, 18(23), 6278; https://doi.org/10.3390/en18236278 - 28 Nov 2025

Cited by 1 | Viewed by 498

Abstract

The regional integrated energy system (RIES) has emerged as a critical focus in energy systems research. The comprehensive incorporation of renewable energy and inherent multi-energy flow interconnection within RIES markedly elevates the complexity of “generation-load” balance regulation. Traditional model-driven dynamic state estimation methods, however, are constrained by fundamental limitations—complex modeling, inadequate representation of multi-energy flow interdependencies, and poor computational efficiency. This study proposes a data-driven dynamic state estimation method for RIES, utilizing multi-dimensional “generation-grid-load” characteristic information as its primary input and employing a synergistic framework of Empirical Mode Decomposition-Singular Value Decomposition (EMD-SVD) alongside an enhanced Bidirectional Long Short-Term Memory (BiLSTM) network. EMD-SVD preprocesses raw data to remove noise and extract essential features, while the enhanced BiLSTM serves a dual purpose: it first attains high-precision photovoltaic output prediction and multi-energy load forecasting and subsequently evaluates the node states of the multi-energy flow coupling system. A case study on a practical coupled RIES, comprising a 33-node power system, 7-node gas system, and 6-node thermal system, demonstrates that the proposed method achieves high estimation accuracy and remarkable computational efficiency while effectively addressing the inherent limitations of conventional model-driven approaches. Full article

(This article belongs to the Special Issue AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems)

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25 pages, 5177 KB

Open AccessArticle

Process Control via Electrical Impedance Tomography for Energy-Aware Industrial Systems

by Krzysztof Król, Grzegorz Kłosowski, Tomasz Rymarczyk, Konrad Gauda, Monika Kulisz, Ewa Golec and Agnieszka Surowiec

Energies 2025, 18(22), 5956; https://doi.org/10.3390/en18225956 - 13 Nov 2025

Cited by 1 | Viewed by 1173

Abstract

Conventionally, tomography is an inspection technique in which tomographic images are intended for human perception and interpretation. In this work, we shift this paradigm by transforming tomography into an autonomous estimator of industrial reactor states, enabling fully automated process control. Alcoholic fermentation was employed as an example of a controlled process in the current study. The work presents an original concept utilizing transfer learning in conjunction with a ResNet-type artificial neural network, which converts electrical measurements into a sequence of values correlated with the conductivity of pixels constituting the cross-section of the examined biochemical reactor. The conductivity vector is transformed into a parameter determining substrate concentration, enabling dynamic process regulation in response to signals generated from EIT (Electrical Impedance Tomography). Within the scope of the described research, calibration of the conductivity vector against substrate concentrations was performed, and a Matlab/Simulink-based dynamic Monod kinetics model was developed. The obtained results demonstrate high accuracy in substrate concentration estimation relative to reference values throughout a forty-six-hour process. The same signals enable energy-efficient process control, in which cooling and mixing intensity are regulated according to energy prices and renewable energy availability. This strategy may possess particular application in facilities where fermentation installations are co-located with bioenergy production units. Full article

(This article belongs to the Special Issue AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems)

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23 pages, 1361 KB

Open AccessArticle

Differentiated Pricing-Mechanism Design for Renewable Energy with Analytical Uncertainty Representation

by Xianzhuo Liu, Xue Yuan, Qi An and Jiale Liu

Energies 2025, 18(18), 4922; https://doi.org/10.3390/en18184922 - 16 Sep 2025

Viewed by 957

Abstract

With the integration of high-penetration renewable energy, existing uniform marginal pricing mechanisms face critical challenges, including difficulty in recovering flexibility resource capacity costs and free-riding phenomena caused by renewable energy’s variability. To address these issues, this paper proposes a differentiated pricing mechanism for renewable energy based on analytical uncertainty representation to avoid marginal price distortion and promote the rational allocation of ancillary service costs. Firstly, a joint clearing model for energy and reserve ancillary service is developed, incorporating a distributional robust chance constraint based on moment information to model the uncertainty of renewable energy. Then, the composition structure of the nodal marginal price for ancillary service demand is redefined, offering clearer and more explicit price signals compared with traditional uniform marginal pricing. After that, quantification of the impact of energy storage on renewable energy forecast errors and ancillary service pricing is conducted, with a systematic analysis of its role in reducing ancillary service costs and optimizing generation revenue. Simulation results on the modified IEEE 30-bus system demonstrate significant advantages over traditional uniform pricing: the proposed mechanism ensures fair cost allocation, effectively mitigates free-riding problems, and provides clear economic signals. With energy storage units regulating renewable power output, it could lead to a 12.9% reduction in ancillary service costs while increasing total generation revenue by 6.73%. Full article

(This article belongs to the Special Issue AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems)

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19 pages, 2459 KB

Open AccessArticle

Temporal-Alignment Cluster Identification and Relevance-Driven Feature Refinement for Ultra-Short-Term Wind Power Forecasting

by Yan Yan and Yan Zhou

Energies 2025, 18(17), 4477; https://doi.org/10.3390/en18174477 - 22 Aug 2025

Viewed by 1082

Abstract

Ultra-short-term wind power forecasting is challenged by high volatility and complex temporal patterns, with traditional single-model approaches often failing to provide stable and accurate predictions under diverse operational scenarios. To address this issue, a framework based on the TCN-ELM hybrid model with temporal alignment clustering and feature refinement is proposed for ultra-short-term wind power forecasting. First, dynamic time warping (DTW)–K-means is applied to cluster historical power curves in the temporal alignment space, identifying consistent operational patterns and providing prior information for subsequent predictions. Then, a correlation-driven feature refinement method is introduced to weight and select the most representative meteorological and power sequence features within each cluster, optimizing the feature set for improved prediction accuracy. Next, a TCN-ELM hybrid model is constructed, combining the advantages of temporal convolutional networks (TCNs) in capturing sequential features and an extreme learning machine (ELM) in efficient nonlinear modelling. This hybrid approach enhances forecasting performance through their synergistic capabilities. Traditional ultra-short-term forecasting often focuses solely on historical power as input, especially with a 15 min resolution, but this study emphasizes reducing the time scale of meteorological forecasts and power samples to within one hour, aiming to improve the reliability of the forecasting model in handling sudden meteorological changes within the ultra-short-term time horizon. To validate the proposed framework, comparisons are made with several benchmark models, including traditional TCN, ELM, and long short-term memory (LSTM) networks. Experimental results demonstrate that the proposed framework achieves higher prediction accuracy and better robustness across various operational modes, particularly under high-variability scenarios, out-performing conventional models like TCN and ELM. The method provides a reliable technical solution for ultra-short-term wind power forecasting, grid scheduling, and power system stability. Full article

(This article belongs to the Special Issue AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems)

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21 pages, 985 KB

Open AccessArticle

Assessment of Grid-Tied Renewable Energy Systems’ Voltage Support Capability Under Various Reactive Power Compensation Devices

by Jie Cao, Mingshun Liu, Qinfeng Ma, Junqiu Fan, Dongkuo Song, Xia Zhou, Jianfeng Dai and Hao Wu

Energies 2025, 18(14), 3880; https://doi.org/10.3390/en18143880 - 21 Jul 2025

Cited by 4 | Viewed by 1506

Abstract

The weak grid strength in regions with large-scale renewable energy integration has emerged as a universal challenge, limiting the further expansion of renewable energy development. Currently, the short-circuit ratio (SCR) is widely used to quantify the relative strength between AC systems and renewable energy. To address this issue, this study first analyzes and compares how different reactive power compensation methods enhance the SCR. It then proposes calculation frameworks for both the SCR and critical short-circuit ratio (CSCR) in renewable energy grid-connected systems integrated with reactive power compensation. Furthermore, based on these formulations, a quantitative evaluation methodology for voltage support strength is developed to systematically assess the improvement effects of various compensation approaches on grid strength. Finally, case studies verify that reactive power compensation provided by synchronous condensers effectively strengthens grid strength and facilitates the safe expansion of the renewable energy integration scale. Full article

(This article belongs to the Special Issue AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems)

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26 pages, 5869 KB

Open AccessArticle

Dynamic Reconfiguration Method of Active Distribution Networks Based on Graph Attention Network Reinforcement Learning

by Chen Guo, Changxu Jiang and Chenxi Liu

Energies 2025, 18(8), 2080; https://doi.org/10.3390/en18082080 - 17 Apr 2025

Cited by 5 | Viewed by 1853

Abstract

The quantity of wind and photovoltaic power-based distributed generators (DGs) is continually rising within the distribution network, presenting obstacles to its safe, steady, and cost-effective functioning. Active distribution network dynamic reconfiguration (ADNDR) improves the consumption rate of renewable energy, reduces line losses, and optimizes voltage quality by optimizing the distribution network structure. Despite being formulated as a highly dimensional and combinatorial nonconvex stochastic programming task, conventional model-based solvers often suffer from computational inefficiency and approximation errors, whereas population-based search methods frequently exhibit premature convergence to suboptimal solutions. Moreover, when dealing with high-dimensional ADNDR problems, these algorithms often face modeling difficulties due to their large scale. Deep reinforcement learning algorithms can effectively solve the problems above. Therefore, by combining the graph attention network (GAT) with the deep deterministic policy gradient (DDPG) algorithm, a method based on the graph attention network deep deterministic policy gradient (GATDDPG) algorithm is proposed to online solve the ADNDR problem with the uncertain outputs of DGs and loads. Firstly, considering the uncertainty in distributed power generation outputs and loads, a nonlinear stochastic optimization mathematical model for ADNDR is constructed. Secondly, to mitigate the dimensionality of the decision space in ADNDR, a cyclic topology encoding mechanism is implemented, which leverages graph-theoretic principles to reformulate the grid infrastructure as an adaptive structural mapping characterized by time-varying node–edge interactions Furthermore, the GATDDPG method proposed in this paper is used to solve the ADNDR problem. The GAT is employed to extract characteristics pertaining to the distribution network state, while the DDPG serves the purpose of enhancing the process of reconfiguration decision-making. This collaboration aims to ensure the safe, stable, and cost-effective operation of the distribution network. Finally, we verified the effectiveness of our method using an enhanced IEEE 33-bus power system model. The outcomes of the simulations demonstrate its capacity to significantly enhance the economic performance and stability of the distribution network, thereby affirming the proposed method’s effectiveness in this study. Full article

(This article belongs to the Special Issue AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems)

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