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Adversarial Constraint Learning for Robust Dispatch of distributed energy resources in Distribution Systems

IEEE TRANSACTIONS ON SUSTAINABLE energy 2025年第2期16卷 1139-1152页

作者： Chen, Ge Zhang, Hongcai Song, Yonghua Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA Univ Macau State Key Lab Internet Things Smart City Macau 999078 Peoples R China Univ Macau Dept Elect & Comp Engn Macau 999078 Peoples R China

The variability of renewables and power demands poses significant challenges for the dispatch of distributed energy resources (DERs) in distribution networks, as they often introduce uncertainties that may lead to power flow constraint violations. Robust optimization (RO) is a powerful tool for managing the operational risks caused by these uncertainties. However, solving robust DER dispatch problems is nontrivial since the non-convex AC power flow constraints prevent the use of strong duality to find deterministic counterparts. To this end, this paper proposes adversarial constraint learning that can provide linear surrogates for robust dispatch problems. This method begins by designing a gradient-based adversarial attack process to identify the worst-case constraint violations. A "teacher" model is trained in advance to enable rapid gradient calculations during this attack process. Under the teacher's supervision, two "student" models are then trained to predict the worst-case violation from candidate dispatch decisions and nominal operating conditions (i.e., renewable generation and power demands). These student models are further reformulated into equivalent mixed-integer linear programming (MILP) forms and serve as computationally efficient surrogates for the original robust dispatch problems. Simulations across various operating conditions and test systems demonstrate that our method can achieve desirable feasibility, low suboptimality, and high online computational efficiency.

关键词： Uncertainty Load flow Training Voltage Indexes distributed power generation Computational modeling Power demand Artificial neural networks Upper bound distributed energy resources robust optimization adversarial attack neural networks distribution networks

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Barriers to Balcony Solar and Plug-In distributed energy resources in the United States

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ENERGIES 2025年第8期18卷

作者： Gerber, Daniel L. Ginsberg-Klemmt, Achim Stoler, Lyn Shackelford, Jordan Meier, Alan Bldg Technol Urban Syst Dept Lawrence Berkeley Natl Lab Berkeley CA 94720 USA GismoPower LLC Sarasota FL 34239 USA Impulse Labs San Francisco CA 94103 USA

Plug-in distributed energy resources (DERs), such as balcony solar, backfeed power to the home through a standard plug. These systems may represent the future of residential solar and storage, particularly as recent net metering policies have reduced the economic appeal of rooftop solar. While plug-in DERs have seen widespread success in Europe, their U.S. market is stagnant. This paper reviews the technical, interconnection, and regulatory barriers hindering the adoption of plug-in DERs. We first discuss the technical barriers, which include touch safety, breaker masking, and bidirectional ground-fault circuit interrupters. We then examine utility perspectives on plug-in DERs and strategies for navigating interconnection challenges. Finally, we discuss regulatory hurdles related to UL standards and the National Electrical Code.

关键词： balcony solar plug-in distributed energy resources battery storage National Electrical Code utility

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Adaptive Hosting Capacity Forecasting in Distribution Networks with distributed energy resources

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ENERGIES 2025年第2期18卷

作者： Islam, Md Tariqul Hossain, M. Jahangir Habib, Md. Ahasan Zamee, Muhammad Ahsan Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia

The sustainable integration of distributed energy resources (DER) into distribution networks requires accurate forecasting of hosting capacity. The network and DER variables alone do not capture the full range of external influences on DER integration. Traditional models often overlook the dynamic impacts of these exogenous factors, leading to suboptimal predictions. This study introduces a Sensitivity-Enhanced Recurrent Neural Network (SERNN) model, featuring a sensitivity gate within the neural network's memory cell architecture to enhance responsiveness to time-varying variables. The sensitivity gate dynamically adjusts the model's response based on external conditions, allowing for improved capture of input variability and temporal characteristics of the distribution network and DER. Additionally, a feedback mechanism within the model provides inputs from previous cell states into the forget gate, allowing for refined control over input selection and enhancing forecasting precision. Through case studies, the model demonstrates superior accuracy in hosting capacity predictions compared to baseline models like LSTM, ConvLSTM, Bidirectional LSTM, Stacked LSTM, and GRU. Study shows that the SERNN achieves a mean absolute error (MAE) of 0.2030, a root mean square error (RMSE) of 0.3884 and an R-squared value of 0.9854, outperforming the best baseline model by 48 per cent in MAE and 71 per cent in RMSE. Additionally, Feature engineering enhances the model's performance, improving the R-squared value from 0.9145 to 0.9854. The sensitivity gate also impacts the model's performance, lowering MAE to 0.2030 compared to 0.2283 without the sensitivity gate, and increasing the R-squared value from 0.9152 to 0.9854. Incorporating exogenous factors such as the time of day as a sensitivity gate input, further improves responsiveness, making the model more adaptable to real-world conditions. This advanced SERNN model offers a reliable framework for distribution network operators,

关键词： artificial intelligence hosting capacity distributed energy resources PV EVs battery energy storage systems sensitivity gate contextual input sensitivity-enhanced recurrent neural network (SERNN)

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Coordinated control for distributed energy resources in Islanded microgrids with improved frequency regulation capability

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RENEWABLE energy 2025年 244卷

作者： Zhao, Teyang Liu, Hui Su, Jinshuo Wang, Ni Luo, Zhiqiang Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tech Nanning 530004 Peoples R China

With the increasing prevalence of renewable energies in islanded microgrids, wind turbine generators are becoming one of the main resources that provide frequency ancillary services by rapid and significant active power increments. Nevertheless, the substantial power increment will inevitably lead to a significant secondary frequency dip for the rotor speed restoration period and frequency overshoot for minor disturbances. This paper proposes an improved deloaded scheme to avoid the secondary frequency dip by increasing the frequency regulation reserve, which can also reduce the pitch angle adjustments. In the deloaded mode, based on the quantified frequency regulation reserve and operational limitations of wind turbine generators, an active power enhancement strategy is proposed to improve the frequency nadir by enhancing the active power contribution. The stable operation of the wind turbine generator can be ensured by varying the active power with the rotor speed. Moreover, to eliminate the frequency overshoot and improve the frequency response, a switching-based coordinated control strategy is proposed to flexibly regulate the active power outputs for wind turbine generators and distributed energy resources by switching different control strategies according to the frequency deviation. Finally, simulation results on an islanded microgrid validate the effectiveness of the proposed strategies. For instance, compared with active power reserve control, the proposed switching-based coordinated control can reduce the maximum frequency deviation by 32.2 % and 14.5 % in large load increase and decrease scenarios, respectively.

关键词： Frequency ancillary service distributed energy resources Frequency regulation reserve Switching controller Islanded microgrids

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Enhancing Distribution System Flexibility through Network Restructuring and Optimal Planning of distributed energy resources

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EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY 2025年第2期27卷

作者： Ma, Rui Zhao, Yuhao Li, Jianfeng State Grid HEBEI Elect Power Co Ltd Elect Power Res Inst Shijiazhuang 050021 Hebei Peoples R China State Grid Hebei Energy Technol Serv Co Ltd Shijiazhuang 050021 Hebei Peoples R China

This research introduces a novel outage management strategy (OMS) designed to enhance the resilience of distribution systems (DiSs) during severe weather-induced power outages. The approach incorporates an innovative network restructuring technique that utilizes an updated matrix to efficiently identify and reconfigure the radial network's topology following line faults. This restructuring optimizes the use of distributed energy resources (DERs) by strategically replacing connection and cross-section lines, formulated as an optimization problem solved through the social spider optimization algorithm. The key contributions include a unique matrix rank-based topology modeling method that avoids traditional empirical topological searches, proving effective in N-1, N-2, and N-3 scenarios. Comprehensive planning of DERs, encompassing both dispatchable and non-dispatchable resources, ensures optimal power supply during crises, even in systems with low to moderate DER penetration. Extensive simulations on IEEE 69-bus and IEEE 123-bus systems show that the method can sustain up to 85% of the load after line failures, significantly outperforming traditional microgrid techniques. Additionally, the strategy reduces energy resource exploitation costs by over 20% compared to conventional approaches, offering a robust solution for enhancing DiS resilience.

关键词： distribution system flexibility outage management strategy network restructuring distributed energy resources line fault recovery

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Secure Voltage-Modulated Vector Current Control of distributed energy resources Using Delayed DSOGI Under Distorted and Unbalanced Grid Conditions

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第1期61卷 1091-1101页

作者： Bamigbade, Abdullahi de Leon, Francisco NYU Dept Elect & Comp Engn Brooklyn NY 11201 USA GE Vernova Energy Consulting Schenectady NY 12345 USA

This article presents a vector current control (VCC) method for distributed energy resources (DERs) to achieve secure control and enhanced power quality under distorted and unbalanced grid conditions. We consider the impact of voltage distortion and unbalance on DER control and the role of phase-locked loop (PLL) when employing the dual second-order generalized integrator (DSOGI) approach to achieve high quality positive sequence voltage extraction under distorted and unbalanced conditions. Revealing the vulnerability of DSOGI PLL to adversarial parameter manipulation attack, we propose a delayed DSOGI voltage-modulated VCC (VM-VCC) scheme for DERs to mitigate the effect of distorted and unbalanced voltage on current injection by the DER. We further analyze the vulnerability of the delayed DSOGI and employ the concept of digital twin to achieve secure DER contol. Additionally, we present the implementation of the proposed delayed DSOGI-based VM-VCC for achieving balanced DER current injection during voltage unbalance. Comparative performance analysis conducted between the proposed approach and its DSOGI PLL counterpart demonstrates that, in addition to providing enhanced estimation, the proposed approach ensures robust and secure DER control performances.

关键词： Phase locked loops Power harmonic filters Voltage control Harmonic analysis distributed power generation Vectors Estimation Power quality Voltage measurement Reactive power Digital twin distributed energy resources harmonics power quality secure control second-order generalized integrator vector current control voltage unbalance and voltage modulation

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Low and medium voltage distribution network planning with distributed energy resources: a survey

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ELECTRICAL ENGINEERING 2025年第2期107卷 1797-1828页

作者： Pham, Tan Nhat Shah, Rakibuzzaman Dao, Minh N. Sultanova, Nargiz Islam, Syed Federat Univ Australia Ctr New Energy Transit Res Ballarat Vic 3350 Australia RMIT Univ Sch Sci Melbourne Vic 3000 Australia Federat Univ Australia Ctr Smart Analyt Ballarat Vic 3350 Australia

The penetration of distributed energy resources (DERs) such as photovoltaic systems, energy storage systems, and electric vehicles is increasing in the distribution system. The distinct characteristics of these resources, e.g., volatility and intermittency, introduce complexity in operation and planning of the distribution system. This paper first summarized the physical characteristics and morphological evaluation of the current and future distribution networks. Then, the impact of these changes on system operation and planning is outlined. Next, the tools, methods, and techniques for energy forecasting, optimal planning, and distribution system state estimation are reviewed and discussed, along with the challenges. As the main contributions, this research systematically organized the published works and assessed the relevant milestones regarding distribution system planning with DERs and emerging technologies. Finally, the key research directions in this domain are outlined. [GRAPHICS] .

关键词： distributed energy resources Distribution network Forecasting Low voltage network Optimization Planning State estimation

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Multi-Objective Approach for Distribution System Planning Considering Stochastic Customer-Owned distributed energy resources

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IEEE ACCESS 2025年 13卷 40561-40576页

作者： Ferraz, Renato S. F. Ferraz, Rafael S. F. Junior, Vanio F. S. Rueda-Medina, Augusto C. Univ Fed Espirito Santo Dept Elect Engn BR-29075910 Vitoria Brazil Univ Fed Piaui Dept Renewable Energy Technol BR-64049550 Teresina Brazil Univ Estadual Santa Cruz Dept Engn & Comp BR-45662900 Ilheus Brazil

The installation of distributed energy resources (DERs) by end-customers has increased significantly in recent years. While customer-owned DERs offer various benefits, their integration adds complexity to distribution system planning due to the unpredictability of their quantity, sizes, and locations. Consequently, they are referred to as stochastic customer-owned DERs in this study. In the specialized literature, optimization-based DER allocation is often explored;however, these allocation problems typically assume that the distribution system operator has control over the allocation of DERs within the grid, whereas real-world investments are predominantly driven by end-customers. Therefore, in this article, a multi-objective planning approach is proposed considering stochastic customer-owned DERs. This approach simultaneously addresses dynamic network reconfiguration, capacitors allocation, and dynamic adjustment of on-load tap changer transformer. The main objective is to minimize power loss costs and improve the system's voltage profile by minimizing a novel voltage consistency indicator (VCI) introduced in this article. The optimization problem is solved using the Multi-objective Cuckoo Search, with the final solution selected through the Fuzzy Decision-making Method. Additionally, the Monte Carlo Method is employed to address uncertainties related to load, DER generation, and the quantity, sizes, and locations of customer-owned DERs. Finally, the results highlighted the importance of conducting a multi-period analysis, as it led to improvements compared to the static analysis. The proposed approach effectively reduced the VCI by 72.41% and minimized the power loss costs by 68.56% compared to the original system.

关键词： Capacitors Planning Optimization Transformers Resource management Uncertainty Switches distributed power generation Reactive power Photovoltaic systems Capacitor allocation distributed energy resources multi-objective optimization network reconfiguration on-load tap changer transformer voltage consistency indicator

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A novel deep learning approach for estimating and classifying short-term voltage stability events in modern power systems with composite load and distributed energy resources

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ELECTRICAL ENGINEERING 2025年第2期107卷 1783-1795页

作者： Vineeth, V. V. Vijayalakshmi, V. J. Karpagam Acad Higher Educ Dept Elect & Elect Engn Coimbatore 641021 India

Short-term voltage stability in modern power systems is greatly impacted by the existence of composite loads with dispersed distributed energy resources (DERs) in the low- and medium-voltage power networks, as well as by their interactions. With composite loads and dispersed distributer energy resources (DERs) in low- and medium-voltage power networks, this study presents a novel way to estimate voltage breakdown in current power systems. In order to avoid voltage collapse, it also attempts to categorize the severity of such incidents well in advance. Combining long short-term memory (LSTM) and Convolutional Neural Network (CNN) based on deep learning, one can forecast and categorize the severity of short-term voltage stability (STVS) incidents. The CNN-LSTM model uses the post-failure voltage time series data as input. Using time domain modeling, these voltage time series data were produced for a range of composite load and distributed energy resource combinations during sporadic three-phase fault scenarios in the IEEE Bus14. The random permutations of these generated data are used during training for the introduced model in order to maximize the diversity of the three-phase short circuit fault, DERs, and composite load. The Kerala Grid at 220 kV is used to assess the created model. The outcomes produced for the actual system, 220 kV Kerala Grid, confirm the applicability of the proposed method for real-time STVS analytics and show a greater accuracy of 97.93% together with superior efficiency. Additionally, this aids in identifying and averting the likely voltage breakdown in contemporary power networks.

关键词： Composite loads distributed energy resources Short-term voltage stability Post-fault voltage series data Deep learning

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Analysis of Impacts in Electric Power Grids Due to the Integration of distributed energy resources

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ENERGIES 2025年第3期18卷

作者： Marles-Saenz, Eduardo Gomez-Luna, Eduardo Guerrero, Josep M. Vasquez, Juan C. Univ Valle Grp Invest Alta Tens GRALTA Cali 760015 Colombia Aalborg Univ Ctr Res Microgrids CROM AAU Energy DK-9220 Aalborg Denmark

In the present article, the impacts that arise in electrical grids due to the integration of distributed energy resources (DER) are identified and analyzed, aiming to provide a basis from which the effects of these new technologies can be considered. To conduct this research, information was collected and analyzed, which was classified according to each of the impacts evidenced in the literature, such as technical, economic, social, environmental, sectoral, and political. Considering the classification of impacts by category, the corresponding advantages and disadvantages were highlighted, and based on this, a qualitative evaluation of the information found was conducted along with respective analyses. Thus, based on the development of this article, it can be concluded that DER influences many aspects, and according to the qualitative evaluation, clarity is provided regarding the contribution of each impact within electrical grids. It was found that, out of 100% of the impacts identified, those with the highest percentage of contribution are the technical impacts.

关键词： distributed energy resources identification of technical environmental economic social political and sectoral impacts advantages and disadvantages of the impacts qualitative assessment analysis

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