Smart Grid Energy Management

Latest papers

• Questionnaire-Guided Disaggregation of Energy Appliance Use for Domestic Smart Meter Data

  Achal Nanjundamurthy, Rupam Misra, Suzanne Little, Alan F. Smeaton · 2 October 2026

  Ireland's smart metering programme records electricity use at 30-minute resolution, with smart meters installed in over 80\% of households as of late 2025. While this is useful for billing of smart, time-of-use tariffs, it is too coarse to capture use of domestic appliances. We present a label-free …

• Finite-Sample Probabilistic Safety Certification for AI-Based Grid-Edge Coordination

  Yihong Zhou, Hanbin Yang, Thomas Morstyn · 24 September 2026

  Coordinating large population of flexible grid-edge devices can alleviate the need for time-consuming and capital-intensive network upgrades, and AI-based control methods such as multi-agent reinforcement learning or imitation learning are promising in their real-time decision scalability. However, …

• Learning Defensive Policies against Diverse Inference Attacks for Smart Meter Privacy

  Ruichang Zhang, Mustafa A. Mustafa · 23 September 2026

  Smart meter (SM) data provides fine-grained visibility into household energy consumption, but also exposes users to privacy risks. Inference attacks, known as non-intrusive load monitoring (NILM), can perform appliance-level inference from aggregate signals and recover sensitive behavioral patterns.…

• Cost-Aware Reinforcement Learning with Action Masking and Projection for Battery Energy Storage Dispatch under Suppressed-Spread Market Shifts

  Kuanlin Chen, Chen-Wei Kuo, Cheng-En Ou · 22 September 2026

  Battery energy storage system (BESS) dispatch must preserve operational feasibility while declining price spreads reduce the margin available to pay for cycling. We study a proximal policy optimization (PPO) controller whose pre-selection physical action mask and emergency projection are separated f…

• CityLearn v3: A Configurable Simulation and Evaluation Framework for Realistic Control Studies of Renewable Energy Communities

  Tiago Fonseca, Luis Lino Ferreira, Armando Sousa, Ava Mohammadi, Zoltan Nagy · 21 September 2026

  Renewable energy communities (RECs) coordinate buildings, photovoltaic generation, batteries, electric vehicles and flexible loads. Controller studies often simplify changing participation, equipment availability, service deadlines and data quality, so lower cost or peak demand can conceal missed se…

• Towards Sustainable Hydrogen Systems: Supply Chain Optimization with Model Predictive Control and Reinforcement Learning

  Mahammad Valiyev · 14 September 2026

  Hydrogen supply chains are expected to play a central role in future low-carbon energy systems by enabling renewable energy integration, long-duration storage, and decarbonization of industrial and transportation sectors. However, their operation is challenged by renewable generation variability, el…

• Reinforcement Learning for Sequential Solar PV Policy Design under Uncertainty: An Agent-Based Approach

  Iias Faiud, Jonaid Shianifar, Michael Schukat, Karl Mason · 7 September 2026

  Designing effective and fiscally sustainable policies for solar photovoltaic (PV) adoption requires balancing adoption gains against public expenditure under uncertainty and heterogeneous decision-making. This study formulates PV policy design as a sequential decision problem and integrates reinforc…

• LLM-Assisted Behavioural and Scenario Augmentation for Agent-Based Energy Adoption Models

  Iias Faiud, Hossein Khaleghy, Michael Schukat, Karl Mason · 7 September 2026

  Recent advances in large language models (LLMs) create opportunities to enrich simulation-based energy policy analysis, particularly by supporting structured behavioural assumptions and exploratory techno-economic scenarios. However, directly replacing adoption models with LLM reasoning raises conce…

• Reinforcement Learning and Rule-Based Peer-to-Peer Pricing in Residential PV-BES Communities

  Pablo Benalcazar, Maciej Kalka, Wilian Guam\'an, Jacek Kami\'nski · 3 September 2026

  This paper compares rule-based and learning-based pricing mechanisms for peer-to-peer (P2P) electricity trading in residential photovoltaic communities. The rule-based benchmarks comprise bill-sharing as an ex post allocation mechanism, the mid-market rate, and supply-demand-ratio pricing. The reinf…

• Accelerating the Adoption of Residential Solar Power Systems: Policy Analysis using a Dynamic Structural Model

  Sebasti\'an Souyris, Jason A. Duan, Anantaram Balakrishnan, Varun Rai · 26 August 2026

  Problem definition: Solar electricity generation is a strategic component of energy portfolios designed to meet growing demand and reduce carbon emissions. Governments and municipalities encourage household photovoltaic (PV) adoption through upfront rebates and tax credits. Limited budgets require p…

• LLMs are Few-Shot Decision-Makers: Generalized Context-Aware Microgrid Frequency Control through Prompt Decision Transformer

  Xu Yang, Chenhui Lin, Haotian Liu, Kaihang Deng, Yunhe Li, Wenchuan Wu · 25 August 2026

  The rapid evolution of energy structures has positioned microgrids as pivotal components of next-generation power systems, offering enhanced resilience and renewable energy integration. However, the inherent low inertia, complex dynamics, and poor model conditions of microgrids necessitate advanced …

• A-CPES: A Reference Framework for Agentic AI in Cyber-Physical Energy Systems

  Xiaoyu Zhang, Qiuye Sun, Jiachen Xu, Zhongming Yao, Yushuai Li · 25 August 2026

  Energy system operation contains a loop of work that automation has never taken over: posing the optimization problem the current cycle should solve, disposing of infeasibility, sequencing a solution into interlocked switching orders, assembling evidence no single model holds, negotiating adjustable…

• A Real-Time Tsetlin Machine-based Non-intrusive Load Monitoring System on MCUs

  Tianhang Tan, Han Wu, Tousif Rahman, Shengyu Duan, Alex Yakovlev, Rishad Shafik · 20 August 2026

  Non-Intrusive Load Monitoring (NILM) systems estimate individual appliance energy consumption from a single aggregate meter, without requiring separate sensors for each device. By installing a single meter that measures a building's total electricity consumption, NILM algorithms can determine the ac…

• Large Language Model Assisted Operational Monitoring for Battery Energy Storage System Integrated Power Distribution Networks

  Azmeer Akhtar, Md Fazley Rafy, Anurag K. Srivastava · 18 August 2026

  Battery energy storage systems (BESS) are increasingly used in distribution networks for voltage regulation and demand response, which increases the volume and complexity of operational telemetry available to grid operators. This paper presents an AI-enabled monitoring framework that connects a larg…

• From Caveman to Expert Analyst: Energy Consumption of Variable LLM Tasks

  Diego Manya, Ethan I. Thorpe, Ji Zhang, Myranda Shirk, Jiamian He, Angel Hsu, Michael P. Vandenbergh · 14 August 2026

  The energy demand growth and environmental impacts of artificial intelligence (AI) have generated substantial interest in supplying sufficient low-cost electricity for AI-driven data center development. Research on the ability of demand-side management to address these challenges has been more limit…

• EnergyBridge: Benchmarking Household Energy Management, User Participation, and Grid Flexibility

  Xudong Wu, Zeqing Wu, Jiarui Zhang, Xuhao Fan, Ziang Ding, Yuming Zhuang, Mingqi Yuan, Yilun Du, Hongjie Jia, Yunfei Mu, Jiayu Chen · 11 August 2026

  Residential virtual power plants (VPPs) can provide grid flexibility by shifting household demand, but physical flexibility becomes dependable capacity only when residents authorize a plan and the promised response is delivered. Existing benchmarks evaluate control but omit event-specific authorizat…

• SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation

  Lin Jiang, Dahai Yu, Ravikumar Gelli, Guang Wang · 5 August 2026

  Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment. However, access to such data is often restricted by privacy concerns and data-sharing constraints, motivating growing interest in synthetic energ…

• DRP-FLR: Data-Driven Assessment of Demand Response Potential for Flexible Load Regulation in Smart Grids

  Yunhao Yao, Siyu Jing, Yang Yang, Qiang Xu, Changqi Weng, Xiang-Yang Li · 28 July 2026

  The rapid growth of AI workloads and renewable energy resources exacerbates supply-demand imbalance in power systems, making traditional load regulation designed for efficient allocation inadequate and motivating demand response (DR) mechanisms to enable load controllability in smart grids. However,…

• Market Strategy Evaluation for Prosumers in Local Electricity Markets

  Lukas Peter Wagner, Raoul Bisson, Felix Gehlhoff · 22 July 2026

  Prosumers equipped with distributed generation and flexible loads form autonomous cyber-physical energy systems that control local resources and participate in local energy markets with minimal human intervention. This work develops and evaluates an agent-based simulation platform in which agents, r…

• WattCouncil: Context-Aware Household Energy Scenario Generation With Governed LLMs

  Mohannad Takrouri, Nicolas M. Cuadrado A., Martin Tak\'a\v{c} · 14 July 2026

  The accelerating shift toward low-carbon power systems, together with the widespread adoption of behind-the-meter technologies such as rooftop solar and electric vehicles, is placing new operational and analytical demands on electricity grids. At the same time, smart-grid research increasingly relie…

• Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms

  Marcos Eduardo Cruz Victorio, Karl Mason · 8 July 2026

  The dairy industry in Ireland has a large potential for the integration of renewable energy and the reduction of carbon emissions. However, researchers of distributed generation control are mainly focused on residential and commercial applications. To contribute to the effective integration of renew…

• Understanding electricity consumption behaviour through Inverse Reinforcement Learning

  Enrico Cofler, Carlos Rodriguez-Pardo, Matteo Giuliani, Andrea Castelletti, Massimo Tavoni · 7 July 2026

  Understanding how households consume electricity in response to socioeconomic and climatic drivers is important for decision-makers designing energy policies in a changing climate and under geopolitical tensions. Consumers respond differently to thermal stress depending on income, consumption habits…

• State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading

  Jesper Klicks, Sander Vr\v{z}ina, Vincent Fran\c{c}ois-Lavet · 26 June 2026

  Energy trading decisions depend not only on current market prices, but also on expected future market conditions, and operational constraints. This makes the state representation given to a reinforcement learning agent an important design choice. We study this in HydroDam, a pumped-storage arbitrage…

• How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?

  David Akinpelu, Akintonde Abbas, Rereloluwa Alimi, Ayodeji Lana · 26 June 2026

  Agentic benchmarks have emerged across general-purpose and domain-specific settings, including finance, coding, law, and drug discovery, yet energy-domain evaluations remain largely limited to static knowledge recall. This is a critical gap for a sector that requires live data retrieval, specialized…

• Hybrid Sequence Modeling and Reinforced Verification for Controllable Target-Conditioned Decision Making

  Yue Pei, Hongming Zhang, Chao Gao, Martin M\"uller, Yingying Zhang, Mengxiao Zhu, Hao Sheng, Ziliang Chen, Liang Lin, Haogang Zhu · 24 June 2026

  Target-conditioned sequence models provide a simple interface for controllable offline decision making, but the requested target return can be an unreliable control signal, especially when the target return lies in underrepresented regions of the dataset. This paper proposes Doctor, a hybrid sequenc…