Load frequency control (LFC) is a critical component in power systems that is employed to stabilize frequency fluctuations and ensure power quality. As energy storage systems (ESSs) are increasingly integrated into the grid, managing additional constraints has become more challenging. To address these challenges, this paper proposes a safety reinforcement learning-based approach that incorporates ESSs into the LFC framework. By formulating a constrained Markov decision process (CMDP), this approach overcomes the limitations of conventional Markov decision processes (MDPs) by explicitly handling system constraints. Furthermore, a long short-term memory (LSTM)-based cost prediction critic network is introduced to improve the accuracy of cost predictions, and a primal-dual deep deterministic policy gradient (PD-DDPG) algorithm is employed to solve the CMDP. Simulation results demonstrate significant improvements: a 58.2% faster settling time, a 72.5% reduction in peak frequency deviation, and a 68.2% lower mean absolute error while maintaining all operational constraints.

With the extensive integration of renewable energy sources (RESs), power systems face challenges in load frequency control (LFC) due to RES intermittency. While energy storage systems (ESSs) aid frequency regulation, existing strategies are limited—single-type ESSs fail in multi-ESS scenarios, and hybrid ESSs lack state-of-charge (SoC) consistency control. This paper proposes an LFC framework combining energy storage aggregators (ESAs), leader–follower finite-time consensus control, and DDPG-RNN (Deep Deterministic Policy Gradient with Recurrent Neural Networks). ESAs aggregate small distributed ESSs for scalable regulation; consensus control ensures finite-time ESS power tracking and SoC balancing; and DDPG-RNN adaptively tunes control gains to handle RES fluctuations and load changes. Simulations on a high-RES power system with hybrid ESSs (SCES, LABES, VRFBES, LIPBES) show that the framework outperforms traditional proportional–integral–derivative (PID) control and basic leader–follower control: it reduces frequency deviation peaks, shortens recovery time, achieves SoC synchronization, and alleviates conventional generator power fluctuations.

With the increasing energy demands and concern for environmental protection, researching and optimizing multi-energy systems have become prominent issues in the energy field. To improve the overall performance of multi-energy systems, there are two main difficulties that must be overcome: the first is the issue regarding the coupling relationships between energy sources, and the second is the uncertainties related to multiple types of energy and loads. Particularly with regard to the second difficulty, it is necessary to generate a large number of effective scenarios, as many multi-energy systems have only been built recently, and operational data exhibit uncertainties. However, at the same time, the introduction of a large number of random scenarios can lead to computational difficulties, making it impossible for a model to solve this problem. To this end, in this paper, we propose an improved scheduling approach for multi-energy microgrids, balancing scenario insufficiency and computational complexity. Latin Hypercube sampling is creatively used to generate enough uncertain scenarios, and hierarchical clustering is employed to create representative scenarios to reduce the computational complexity. Then, based on these effective clustered scenarios, a multi-energy collaborative optimization method considering the coupling relationship between energy sources is proposed. The effectiveness of this method is verified through numerical tests and sensitivity analysis. The results show that the economic cost of this method is only 0.305% higher than that of the deterministic method and that it has a certain degree of robustness and a good economic performance, but it is limited by its computational efficiency. In summary, this study provides an effective solution for collaboratively optimizing the operation of multi-energy systems, aiming to provide valuable insights for research in the energy field.

Frequency stability becomes more and more important with the increase in inverter-based resources in power systems. To enhance the frequency stability, this paper proposes a novel data-driven method for frequency stability assessment and control for energy storage systems (ESSs). Leveraging the neural ordinary differential Equation (ODE) framework, the method learns a continuous-time model of the system frequency dynamics. A neural-network-based controller is then designed according to the learned neural ODE model to enhance frequency stability. The training process involves an alternating optimization algorithm that updates both the neural ODE model and the controller parameters via the adjoint sensitivity method. Simulation results demonstrate that the proposed method accurately predicts frequency trajectories. Moreover, the designed controller reduces frequency deviation by 53%.

Renewable energy systems have emerged as a crucial research area due to the escalating demand for sustainable energy solutions. With the advancement of renewable energy, the electric-thermal coupling within multi-energy systems has become more intricate. Bi-directional electric-thermal storage and conversion technologies have emerged as a potential solution to address the challenges associated with efficient energy utilization. This paper focuses on the joint planning and operation optimization of renewable energy systems considering bi-directional electric-thermal storage and conversion. The integrated framework for renewable energy systems incorporating a bi-directional electric-thermal storage and conversion unit is designed, and the joint planning and operation optimization method is proposed. Case studies are conducted based on typical annual energy demand and solar radiation characteristics in Beijing, China. Numerical results show that the proposed method can effectively handle the coupling and bi-directional conversion characteristics of electrical and thermal energy, achieving energy cost savings while fulfilling the energy demands of the system. The proposed system has a capital expenditure of USD 261,251.4 and an operating expenditure of USD 177,007.1, which shows a total cost reduction of 12.28% compared to the lithium-ion battery system, providing better economic performance while further enhancing the flexibility of energy utilization. These research findings contribute to the development of more efficient and sustainable renewable energy systems, providing a valuable reference for future research and practical applications within the energy field.

With the rapid integration of high-penetration renewable energy, its inherent uncertainty complicates power system day-ahead/intra-day scheduling, leading to challenges like wind curtailment and high operational costs. Existing methods either rely on inflexible physical models or use deep reinforcement learning (DRL) without prioritizing critical variables or synergizing multi-source energy storage and demand response (DR). This study develops a multi-time scale coordination scheduling framework to balance cost minimization and renewable energy utilization, with strong adaptability to real-time uncertainties. The framework integrates a day-ahead optimization model and an intra-day rolling model powered by an attention-enhanced DRL Actor–Critic network—where the attention mechanism dynamically focuses on critical variables to correct real-time deviations. Validated on an East China regional grid, the framework significantly enhances renewable energy absorption and system flexibility, providing a robust technical solution for the economical and stable operation of high-renewable power systems.