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Open research questions in Smart Grid Energy Management

129 gap statements mined from Smart Grid Energy Management papers in our 4.5M-paper local library, which holds 2,022 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • Future iterations of this system will focus on expanding the model to classify multiple appliances simultaneously. The system can be integrated into comprehensive smart home energy management solutions. Further research is needed to address the challenges of transient signal noise and electromagnetic interference (EMI).

    Non-Intrusive Load Monitoring via Edge-Based TinyML on ESP32 Microcontrollers · 2026 · DOI

  • Lack of comparison with other emerging AI/ML approaches for power factor correction or discussion of why TinyML was specifically chosen over alternative edge computing solutions.

    An Edge AI-Driven IOT Framework for Automatic Power Factor Correction in Smart Industrial Power Systems · 2026 · DOI

  • The primary challenge in current microgrids is the integration with conventional systems. The variability and uncertainty in microgrids stem from dynamic load profiles and renewable energy sources.

    Multi-agent Reinforcement Learning with Clustering and Forecasting for Optimized Energy Sharing in Microgrids · 2026 · DOI

  • Future work will focus on real-time benchmarking, safety constraint integration, and hardware-in-the-loop evaluation to enhance practical applicability. Future work will focus on validation under varying load conditions, multi-plant datasets, hardware-in-the-loop testing, multi-agent DRL extensions, digital twin integration, and safe reinforcement learning strategies to ensure robustness and practical applicability.

    Deep Reinforcement Learning-Based Intelligent Control for Efficiency Enhancement in Thermal Power Plant Fuel Management · 2026 · DOI

  • The need to respect household-level backup commitments when coordinating residential batteries.

    Residential Battery Pooling Under Backup Commitments · 2026

  • Future research can focus on testing the framework using real-world data. Future research can focus on comparing the framework with existing methods. Future research can focus on improving the scalability of the framework.

    Knowledge enhanced framework for managing electricity generation and consumption in micro smart grids using Heronian mean MCDM approach · 2026 · DOI

  • The existing methods have limitations in handling uncertain and complex decision-making problems. There is a need for a robust and efficient method for managing electricity generation and consumption in micro smart grids. The paper aims to address this research gap.

    Knowledge enhanced framework for managing electricity generation and consumption in micro smart grids using Heronian mean MCDM approach · 2026 · DOI

  • To address the three gaps identified in Section 3.5.2, this section proposes three prioritized research directions: (1) incorporating bounded rationality into behavioral models, (2) developing individualized consumer modeling with user-specific dynamic pricing, and (3) extending modeling to residential multi-energy prosumers. These directions are ranked by urgency and feasibility as follows.

    A Comprehensive Review of Consumer Models in Price-Based Demand Response and Their Applications to Electric Vehicles · 2026 · DOI

  • Existing approaches lack behavioral adaptivity and scalability under dynamic market conditions. There is a need for a novel framework that can optimize load-shifting or consumption strategies in electricity markets.

    Evolutionary game-theoretic modeling of electricity market dynamics for elastic load optimization · 2026 · DOI

  • The need to balance fairness and efficiency in real-time pricing is a technical challenge.

    Bilevel Real-Time Pricing for Tripartite Welfare Equilibrium in Smart Grids: Balancing Fairness and Efficiency · 2026 · DOI

  • Further research can be conducted to apply the proposed bilevel optimization framework to larger-scale power systems. The impact of renewable energy sources on the proposed bilevel optimization framework can be investigated.

    Bilevel Real-Time Pricing for Tripartite Welfare Equilibrium in Smart Grids: Balancing Fairness and Efficiency · 2026 · DOI

  • Future research can focus on improving the development model using more advanced artificial intelligence methods. The study's results can be used as a basis for further research on the National Power System in Poland.

    Selection of Data for Modeling the Development of the Power System Using a Recurrent Artificial Neural Network · 2026 · DOI

  • There is a research gap in obtaining development models of the National Power System using artificial intelligence methods. The gap exists between the need for development models and the possibilities of obtaining such models using artificial intelligence methods.

    Selection of Data for Modeling the Development of the Power System Using a Recurrent Artificial Neural Network · 2026 · DOI

  • The lack of effective management of energy resources in aggregated microgrids. The need for a two-tier DERMS architecture to optimize energy production and consumption.

    Situational Aspect of Power Supply Management in Aggregated Microgrids · 2026 · DOI

  • Additionally, the demonstrated system scale is limited to a small number of chargers, and further validation is required for larger deployments. This study addressed the challenge of integrating grid constraints, cost optimization, and user preferences within a unified smart EV charging framework, which is insufficiently explored in existing literature.

    DAM Price-Based Model Predictive Control for Smart EV Charging under Grid and User Constraints · 2026 · DOI

  • Home Energy Management Systems (HEMS) can reduce residential electricity costs and support demand response, but adoption is limited by the difficulty of translating household preferences into technical scheduling constraints.

    LLMs for Agentic Home Energy Management · 2026

  • Effective optimization strategies are necessary to maximize the performance of these systems.

    Optimizing residential energy management through an integrated techno-economic evaluation of PV-battery systems · 2026 · DOI

  • Future studies will address adaptive DP scheduling that varies ε based on real-time grid stress and forecast uncertainty. Future studies will investigate neural-network-based local controllers, including lightweight recurrent or graph neural networks. Future studies will explore the application of the proposed framework to other domains, such as transportation systems and communication networks.

    Neural-network-inspired federated coordination for noise-tolerant distributed optimization in cyber-physical demand-side management · 2026 · DOI

  • The study highlights the need for more efficient and scalable demand-side management systems that can handle noise and uncertainty.

    Neural-network-inspired federated coordination for noise-tolerant distributed optimization in cyber-physical demand-side management · 2026 · DOI

  • The review also identifies critical research gaps such as the lack of standardized sustainability benchmarks, limited datasets for carbon-aware optimization, insufficient integration of lifecycle assessment in RF design, AI computational energy overhead and Green AI challenges, limited real-world validation, and the absence of unified carbon accounting frameworks.

    Hybrid artificial intelligence-driven eco-energy management for carbon-neutral 6G wireless networks · 2026 · DOI

  • The lack of integrated management frameworks that synthesize neural network-based forecasting with Grey Wolf Optimization. The need for high-fidelity forecasting models to support reliable VPP operations.

    Integrated machine learning forecasting and grey wolf optimization for optimal operation of virtual power plants in smart distribution networks · 2026 · DOI

  • For instance, it does not account for revenues under multiple VPP business models; furthermore, the evaluation model does not effectively differentiate expert weights.

    Research on Comprehensive Evaluation Model of Virtual Power Plant Operational Benefits Based on DEMATEL-CRITIC-EDAS · 2026 · DOI

  • Deep Reinforcement Learning (DRL) lacks practical maturity for real-time VPP control due to 'blackbox' interpretability issues, - traditional generative frameworks like GANs deteriorate under data-scarce…

    Comprehensive Overview of Virtual Power Plants: Integration of Distributed Energy Resources into Power Systems in Terms of Aggregation, Application, and Innovation · 2026 · DOI

  • Further analysis of the impact of backup commitments on the value of pooling. Evaluation of the effectiveness of pooling in different market and regulatory environments.

    Residential Battery Pooling Under Backup Commitments · 2026

  • Operational and management challenges for REC-based systems. Technical barriers related to the integration of DERs into existing distribution networks. Regulatory and legal barriers, including uncertainty in governance structures and unclear definitions of community ownership.

    Energy Management and Optimization of Renewable Energy Communities with Flexible Load Coordination and Shared Energy Utilization · 2026 · DOI

Most-cited papers in Smart Grid Energy Management

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Open questions raised by papers listed on this page include: (1) Further research is needed to address the challenges of transient signal noise and electromagnetic interference (EMI) (“Non-Intrusive Load Monitoring via Edge-Based TinyML on ESP32 Microcontrollers”, 2026); (2) Lack of comparison with other emerging AI/ML approaches for power factor correction or discussion of why TinyML was specifically chosen over alternative edge computing solutions (“An Edge AI-Driven IOT Framework for Automatic Power Factor Correction in Smart…”, 2026); (3) Future work will focus on real-time benchmarking, safety constraint integration, and hardware-in-the-loop evaluation to enhance practical applicability (“Deep Reinforcement Learning-Based Intelligent Control for Efficiency…”, 2026); (4) Future research can focus on testing the framework using real-world data (“Knowledge enhanced framework for managing electricity generation and…”, 2026); (5) There is a need for a novel framework that can optimize load-shifting or consumption strategies in electricity markets (“Evolutionary game-theoretic modeling of electricity market dynamics for elastic…”, 2026). 129 gap statements have been mined from Smart Grid Energy Management papers in our 4.5M-paper local library, which holds 2,022 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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