2021· International Journal of Applied Data Science & Modern Computing· 1 citation
Abstract
Frequent network of renewable energy sources, electric cars, and distributed generation stations has changed traditional power systems into complicated smart grids. This change puts in place considerable uncertainty, non-linear and dynamic decision-making problems regarding energy management. Conventional optimization methods are usually unable to adapt effectively to the stochastic and time sensitive nature of contemporary smart grids. A recent development in machine learning has been presented as a means of solving these problems by use or Reinforcement Learning (RL), a branch of machine learning, which allows intelligent agents to acquire an optimal control policy by interacting with the environment. The paper will be a detailed report on the implementation of reinforcement learning to solve smart grid optimized energy. The framework proposed is based on the demand-side control, scheduling of energy storage and integration of renewable energy to reduce the operational cost without affecting the grid stability and reliability. The different RL paradigms such as Q-learning, Deep Q-networks (DQN) as well as Policy Gradients are discussed in their applications in the context of the smart grid. An elaborated methodology is constructed, with its system modelling, the design of state space, design of reward functions, and processes of training. The simulated experiments prove that the RL-based management strategy is much more effective in terms of minimization of costs and peak loads and its use of renewable energy sources in comparison with traditional rule-based and optimization-based strategies. The findings indicate the versatility and scability of reinforcement learning techniques in complex power system settings. The study concludes that reinforcement learning will be a highly robust and versatile solution to next-generation optimization of cyber grids with regard to data-based and autonomous, data-based grid management systems.
The goal of this research is to evaluate energy management issues in renewable-connected smart grids, especially when the power generation produced by solar and wind fluctuates, making it difficult to maintain balanced electricity supply. There is a need for battery energy storage systems (BESS) to operate optimally in order to minimize the dependency on electrical utilities, smooth out fluctuations, and increase the amount of energy generated from renewable sources. As such, we proposed a framework using reinforcement learning (RL) to create an intelligent BESS charge-discharge schedule that can adapt to the dynamic nature of the grid. Specifically, the model uses the state of the grid (the level of renewable power produced, how much electricity is being consumed, and the battery’s state of charge) to learn the best control actions to improve BESS operation. Our simulation results indicate that using RL to optimize BESS operation will improve the efficiency of dispatching energy, increase the percentage of renewable energy used, and decrease operating costs compared to traditional ways of controlling BESS. RL shows potential for use in adapting to energy management issues in smart grids.
Akhtam Uralov, Akmaljon Aliboyev, Nargiza Nazarova et al.· EPJ Web of Conferences· 0 citations
A comprehensive review of RL-based control applications across RES-integrated energy domains, including power grids, microgrids, and building energy systems, identifies emerging trends and highlights dominant design patterns across power grid, microgrid, and building-level applications.
P. Michailidis, F. Minelli, H. Coban et al.· Infrastructures· 0 citations
The increasing integration of renewable energy sources and distributed energy resources has made smart microgrid energy management more complex due to renewable intermittency and dynamic operating conditions. This paper proposes a Multi-Agent Reinforcement Learning (MARL)-based energy management framework in which intelligent agents coordinate renewable generation, energy storage, controllable loads, and grid interaction. Through distributed learning and real-time decision-making, the framework optimizes energy scheduling, improves system reliability, reduces operational cost and carbon emissions, enhances renewable energy utilization, and provides a scalable, autonomous, and resilient solution for next-generation smart microgrids.
Anatoly Kitov, M. Kartsev· International Journal of Mod...· 0 citations
To enhance power grid adaptability amid rising renewable energy integration, this paper proposes M3-PPO, a meta-reinforcement learning algorithm that enables efficient the strategy transfer and rapid adaptation across tasks with varying energy mixes. Built upon a base framework (M-PPO) that integrates PPO and MAML, M3-PPO introduces two key innovations to overcome MAML’s training instability: a Mamba-based context encoder for richer task representation in the inner loop, and a global-local momentum update mechanism for smoother meta-parameter optimization in the outer loop. Experiments on the Grid2Op platform demonstrate that M3-PPO significantly outperforms baseline algorithms in generalization and scheduling efficiency, achieving robust performance even when simulating complex energy environments. The approach is particularly suitable for integration with antenna-enabled smart grid monitoring, wireless data acquisition, and edge-computing platforms, providing an engineering-oriented solution for adaptive, real-time, and robust power grid scheduling in modern renewable-rich energy systems.
Q. Dai, X. Hu, J. Li et al.· Advanced Electromagnetics· 0 citations
The integration of photovoltaic generation, battery storage, electric vehicles, smart appliances, and dynamic electricity pricing has made residential energy management a challenging real-time optimization problem. Conventional demand-side management methods often depend on fixed rules and are less effective under uncertain solar generation, changing tariffs, and variable user demand. To address this issue, this paper proposes a Proximal Policy Optimization-based deep reinforcement learning framework for smart home energy management. The proposed PPO controller learns adaptive scheduling decisions using real-time PV output, electricity price, battery state of charge, EV charging status, and appliance operating conditions. The controller coordinates shiftable, controllable, and non-shiftable loads while reducing electricity cost and maintaining user comfort. The proposed method is compared with DDPG and TRPO. Simulation results show that PPO reduces the average daily energy cost by 4.7% compared with TRPO and 8.3% compared with DDPG. The results confirm that PPO is an effective and stable approach for real-time residential demand-side management.
Chittemma Yerra, Kiran Teeparthi, R. Naik et al.· Energies· 0 citations
With the increasing proportion of renewable energy integration in modern power systems, grid fault recovery faces greater complexity and uncertainty, posing significant challenges for intelligent energy transmission infrastructures and reliable operation in electromagnetic power networks. To address these issues, this paper proposes a collaborative decision-making model (CDM-RL) that combines diffusion models (DM) and reinforcement learning (RL) for intelligent power grid restoration. The proposed framework generates physically feasible fault scenarios through a denoising diffusion probabilistic model (DDPM) and integrates graph neural networks (GNN) with proximal policy optimization (PPO) to achieve efficient restoration control under complex operating conditions. An alternating training mechanism is further introduced to enhance the generalization capability of the model across diverse fault scenarios. Experimental evaluation on the IEEE 33-bus system demonstrates that CDM-RL significantly outperforms conventional approaches, achieving an initial power restoration time of 8.9 s, a load recovery rate of 93.1%, and an average of only 3.9 switching operations while maintaining superior cross-scenario stability of 85.3%. The proposed AI-driven framework improves grid self-healing capability and provides an effective technical pathway for enhancing the resilience and operational reliability of intelligent electromagnetic energy transmission and distribution systems.