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1. Adaptive Deep Reinforcement Learning for VVC in High DER-Integrated Distribution Grids

   https://doi.org/10.1109/TPEC63981.2025.10906926  

   Vallam_Kondu, Sai Chandana; Ravikumar, Gelli (February 2025, IEEE)

   The increasing penetration of Distributed Energy Resources (DERs) in modern distribution grids has created sig-nificant challenges in maintaining voltage profiles and minimizing reactive power losses. While Deep Reinforcement Learning (DRL) approaches have been explored for Volt/VAR Control (VVC), existing methods often rely on single-agent frameworks or lack adaptive mechanisms to balance competing grid objectives dy-namically. Multi-Agent (MA) DRL frameworks have emerged but typically require explicit inter-agent communication, limiting scalability and practicality. To address these limitations, we propose a novel MA-DRL framework that incorporates adaptive reward computation to implicitly coordinate special-ized agents, including a Voltage Control Agent for maintaining voltage profiles and a Reactive Power Agent for optimizing reactive power dispatch. Unlike prior approaches, our method dynamically adjusts local and global rewards based on real-time grid conditions, enabling scalable and efficient grid control without explicit inter-agent communication. We evaluated the framework on the IEEE 123-bus test system under high DER penetration. The results demonstrated that the proposed MA-DRL approach successfully eliminated voltage violations, reduced power losses through optimized reactive power dispatch, and decreased transformer tap changes and capacitor operations, thereby extending equipment lifespan. These results demonstrate the scalability and practicality of adaptive reward-based MA-DRL for addressing modern grid management challenges. 

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2. TE-SAT: Transactive Energy Simulation and Analysis Toolsuite

   https://doi.org/10.1109/DESTION50928.2020.00009  

   Neema, Himanshu; Sztipanovits, Janos; Hess, David J.; Lee, Dasom (April 2020, 2020 IEEE Workshop on Design Automation for CPS and IoT (DESTION))

   Transactive Energy (TE) is an emerging discipline that utilizes economic and control techniques for operating and managing the power grid effectively. Distributed Energy Resources (DERs) represent a fundamental shift away from traditionally centrally managed energy generation and storage to one that is rather distributed. However, integrating and managing DERs into the power grid is highly challenging owing to the TE implementation issues such as privacy, equity, efficiency, reliability, and security. The TE market structures allow utilities to transact (i.e., buy and sell) power services (production, distribution, and storage) from/to DER providers integrated as part of the grid. Flexible power pricing in TE enables power services transactions to dynamically adjust power generation and storage in a way that continuously balances power supply and demand as well as minimize cost of grid operations. Therefore, it has become important to analyze various market models utilized in different TE applications for their impact on above implementation issues.In this demo, we show-case the Transactive Energy Simulation and Analysis Toolsuite (TE-SAT) with its three publicly available design studios for experimenting with TE markets. All three design studios are built using metamodeling tool called the Web-based Graphical Modeling Environment (WebGME). Using a Git-like storage and tracking backend server, WebGME enables multi-user editing on models and experiments using simply a web-browser. This directly facilitates collaboration among different TE stakeholders for developing and analyzing grid operations and market models. Additionally, these design studios provide an integrated and scalable cloud backend for running corresponding simulation experiments. 

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3. Distributed Volt-Var Curve Optimization Using a Cellular Computational Network Representation of an Electric Power Distribution System

   https://doi.org/10.3390/en15124438  

   Dharmawardena, Hasala; Kumar Venayagamoorthy, Ganesh (June 2022, Energies)

   Voltage control in modern electric power distribution systems has become challenging due to the increasing penetration of distributed energy resources (DER). The current state-of-the-art voltage control is based on static/pre-determined DER volt-var curves. Static volt-var curves do not provide sufficient flexibility to address the temporal and spatial aspects of the voltage control problem in a power system with a large number of DER. This paper presents a simple, scalable, and robust distributed optimization framework (DOF) for optimizing voltage control. The proposed framework allows for data-driven distributed voltage optimization in a power distribution system. This method enhances voltage control by optimizing volt-var curve parameters of inverters in a distributed manner based on a cellular computational network (CCN) representation of the power distribution system. The cellular optimization approach enables the system-wide optimization. The cells to be optimized may be prioritized and two methods namely, graph and impact-based methods, are studied. The impact-based method requires extra initial computational efforts but thereafter provides better computational throughput than the graph-based method. The DOF is illustrated on a modified standard distribution test case with several DERs. The results from the test case demonstrate that the DOF based volt-var optimization results in consistently better performance than the state-of-the-art volt-var control. 

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4. CPS-DERMS: Cyber-Physical Security and Impact Analysis of DERMS Against MITM Attacks

   https://doi.org/10.1109/GreenTech62170.2025.10977697  

   Vallam_Kondu, Sai Chandana; Ravikumar, Gelli; Mohan, Sathya Narayana (March 2025, IEEE)

   The growing integration of Distributed Energy Resources (DERs) into modern power grids, managed via DER Management Systems (DERMS), has introduced significant cybersecurity challenges. Communication vulnerabilities in DERMS architecture create opportunities for adversarial attacks, such as Man-in-the-Middle (MITM), that intercept and manipulate critical data flows, disrupting grid operations and compromising reliability. This paper investigates MITM attacks targeting three critical nodes in DER-DERMS communication: (i) DER gateway routers, (ii) inter-router pathways, and (iii) DERMS gateway routers. To analyze these vulnerabilities, we developed CPS-DERMS, a multi-co-simulation hardware-in-the-loop testbed integrating grid-side simulations with OPAL-RT, large-scale cyber simulations using NS-3, and an indige-nous Python-based DERMS platform. The study demonstrates innovative strategies for executing MITM attacks, including maintaining payload integrity and bypassing detection mechanisms, while highlighting the critical vulnerabilities within DER-DERMS communication systems. We considered a real utility's DER-integrated distribution grid as a case study. Experimental results reveal that MITM attacks can cause overvoltages, reactive power imbalances, and increased tap-changer operations, significantly degrading power quality and grid reliability. 

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5. DL-Based Distributed Anomaly Detection for Attack Resilient DER Monitoring and Control Applications

   https://doi.org/10.1109/KPEC65465.2025.11045010  

   Kondu, Sai_Chandana Vallam; Ravikumar, Gelli (April 2025, IEEE)

   The proliferation of distributed energy resources (DERs) within modern cyber-physical power systems has transformed grid operations, while simultaneously introducing sophis-ticated cybersecurity vulnerabilities. Communication protocols such as Modbus, SunSpec Modbus, and DNP3, widely used for DER coordination, were not originally designed with robust security, rendering them susceptible to confidentiality, integrity, and availability (CIA) threats. In this paper, we propose a scalable, distributed intrusion and anomaly detection system (D-IADS) that integrates protocol-specific rule generation with deep learning (DL)-based temporal pattern recognition. Our framework employs physics-informed thresholding, rule-based mitigation logic, and a lightweight LSTM-based anomaly detection module deployed across edge-intelligent devices (EIDs) and control centers. We present a rule taxonomy for IT/OT-based attacks on SunSpec Modbus and DNP3, along with a hybrid Snort/Suricata-compatible rule engine. Case studies on the IEEE 123-bus feeder demonstrate the proposed system's ability to achieve 97.32% anomaly detection accuracy and 97.30% macro-precision against stealthy cyberattacks. This research offers a modular and deployment-ready solution for securing DER-integrated smart grids. 

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