This article proposes an interpretable deep reinforcement learning (DRL) method for energy management of low-carbon community energy systems (LCCES), which effectively addresses the transparency limitations caused by the black-box nature of traditional DRL neural network structures, thereby overcoming a key constraint in energy system applications. First, we develop a hybrid integer dynamic decision DRL algorithm to solve the low-carbon scheduling problem in community energy systems with continuous-discrete hybrid action spaces. Second, we construct an interpretable artificial intelligence framework, where the temporal attention mechanism is used to process and extract features to provide macro-level decision contribution analysis. These features are input into the decision tree for extracting device-level rules. Building upon this, we design an ensemble decision tree architecture with temporal attention mechanism to effectively identify critical time periods influenced by system inertia and energy fluctuations, thereby achieving interpretable optimization strategies while enhancing decision robustness under state fluctuations. Simulation results based on the independent test set demonstrate that, in comparison with alternative methods, the proposed approach yields a 22.1% cost reduction and a 32.4% carbon emission reduction rate relative to twin delayed deep deterministic policy gradient (TD3), and a 14.7% improvement in explanation accuracy compared with static decision trees.

An effective coordination strategy between charging stations (CSs) and the distribution system operator (DSO) can fully exploit the charging flexibility of electric vehicles to improve the flexibility of power systems. Multiagent deep reinforcement learning (MADRL) has proven its effectiveness in coordination problems. However, existing MADRL-based approaches face the problems of incentive inequity and sample efficiency. To address these challenges, this article formulates the CSs-DSO coordination problem as a novel Stackelberg partially observable Markov decision process, where the involved agents, the DSO as leader and the CSs as followers, are rewarded according to their respective contributions. Furthermore, a knowledge-informed Stackelberg multiagent deep reinforcement learning (KI-SMADRL) strategy is proposed to solve this model, which integrates aggregation-allocation knowledge modules into CS agents’ learning loop to improve sample efficiency, and can coordinate CSs-DSO in a distributed manner. The effectiveness of the proposed strategy is verified by comparing its performance with multiple benchmarks.

This article proposes a multienergy trading market model based on price matching, aiming to foster multienergy collaboration and enhance energy utilization through individual participation. With the ongoing advancements in energy distribution and marketization, the energy Internet necessitates improved applicability and efficiency for personalized energy responses. To address these requirements, a multienergy trading market model is proposed, which enables the avoidance of user information disclosure and guarantees user trading autonomy. In addition, a joint trading mechanism is designed that accounts for multiple time scales and energy types, consequently reducing trading failures caused by overlooking energy transmission processes. By performing the proposed trading mechanism, the market operator can match various energy types using conversion devices, thereby augmenting matching efficiency. An income mechanism is also established to deter the operator from purposefully evading potential trading opportunities for personal gain. To address the proposed model, an improved hierarchical reinforcement learning algorithm is employed, which effectively overcomes challenges associated with large state action spaces and sparse rewards. Numerical examples are provided to confirm the efficacy of the proposed approach.

For the integrated energy system (IES) formed by a cluster of energy hubs (EHs), the outputs of EHs and system parameters, which greatly influence the security performance, should be properly adjusted. In this article, an event-triggered distributed hybrid control scheme is proposed to achieve security and economic operation for the IES. First, the EH output control is designed based on the features of energy networks as well as the containment and consensus algorithms. According to the control of outputs, the electricity and heat load power can be accurately shared without knowing the network parameters. Second, the pressure is bounded within an acceptable range, and the frequency is reverted to the reference value by implementing the proposed control method. Third, an event-triggered communication strategy is employed to design the corresponding protocols, resulting in reduced communication cost. Finally, the control of devices based on the equal incremental principle is proposed to achieve the minimal economic cost for each EH with considering energy prices. The results of numerical case studies are presented to validate the performance of the proposed control method.

This article investigates the energy operation problem based on We-Energy (WE), a novel full-duplex model in Energy Internet (EI). A dual-objective optimal energy operation model of WE is formulated with the consideration of economical benefit and security operation under different time scenarios. Due to the inaccurate model of distributed generation devices and loads, a multipolicy convex hull reinforcement learning (MCRL) algorithm is proposed. It can find the multiobjective strategy set with model-free feature. Moreover, considering the limitations of artificial intelligence technology and the human advantages in information processing for complex task, a two-channel Human-in-the-loop (HITL) method is designed to combine with MCRL to avoid decision-making risks. The one channel of HITL can evaluate the operation strategy by human under normal conditions so that the understanding of human for complex operating conditions can be incorporated into the machine learning algorithms to improve the confidence of intelligent systems. The other channel of HITL can allow human to participate in real-time adjustment under abnormal conditions to avoid system out of control. Simulation studies of modified EI are confirmed that the proposed algorithm can improve system performance effectively.

This article investigates a distributed adaptive dual control that employs both consensus algorithm and improved equal incremental principle (IEIP), to guarantee the security operation while reducing the energy consumption of the energy Internet (EI). Since the security operation is a critical factor of the EI with the energy hub (EH), it is necessary for the EI to adjust the outputs of EHs and the system parameters, which have a great influence on the performance. The consensus-based control of the EI can resolve the energy-coupling issue and accurately share the electricity and heat loads power without requiring the information of the network parameters, which is hard to know. Meanwhile, the variations of system parameters caused by the droop action greatly impact the security operation of the EI. It can be adaptively recovered by executing the proposed control strategy. Furthermore, in order to reduce the energy consumption, the equipment in hubs should also be managed in view of the features of EH. The minimal loss of the energy can be achieved by utilizing the control of devices via the IEIP by considering the coupling characteristics of devices. The performance of the proposed method is demonstrated through numerical case studies.