Residential microgrid is widely considered as a new paradigm of the home energy management system. The complexity of Microgrid Energy Scheduling (MES) is increasing with the integration of Electric Vehicles (EVs) and Renewable Generations (RGs). Moreover, it is challenging to determine optimal scheduling strategies to guarantee the efficiency of the microgrid market and to balance all market participants’ benefits. In this paper, a Multi-Agent Reinforcement Learning (MARL) approach for residential MES is proposed to promote the autonomy and fairness of microgrid market operation. First, a multi-agent based residential microgrid model including Vehicle-to-Grid (V2G) and RGs is constructed and an auction-based microgrid market is built. Then, distinguish from Single-Agent Reinforcement Learning (SARL), MARL can achieve distributed autonomous learning for each agent and realize the equilibrium of all agents’ benefits, therefore, we formulate an equilibrium-based MARL framework according to each participant’ market orientation. Finally, to guarantee the fairness and privacy of the MARL process, we proposed an improved optimal Equilibrium Selection-MARL (ES-MARL) algorithm based on two mechanisms, private negotiation and maximum average reward. Simulation results demonstrate the overall performance and efficiency of proposed MARL are superior to that of SARL. Besides, it is verified that the improved ES-MARL can get higher average profit to balance all agents.

Non-intrusive load monitoring (NILM) provides fine-grained consumption information at the appliance level by analyzing the terminal voltage and total current measured. It shows prospective applications in demand side management, such as demand response, energy efficiency, and home energy management system. However, most cutting-edge NILM models have a critical assumption that switching events are triggered by known appliances in the training set, which may be unrealistic. In reality, new appliances are constantly being added, reducing the effectiveness of current methods for load monitoring. In this paper, we propose a conditional generative adversarial network (CGAN) to correctly classify all the known appliances while simultaneously detecting unknown ones using the V-I trajectory features. We integrate variational autoencoder and capsule networks in the generator network, the capsule features of the same known appliance class are forced to match a pre-defined Gaussian and a group of Gaussian priors (one for each appliance class) as the posterior distribution approximately. Furthermore, using an additional encoder network maps the generated V-I trajectory to its latent representation, minimizing the distance between the latent representations and the feature vectors of the generator aids in learning the data distribution for known appliance samples during the training process. In these ways, we can improve the ability of the model to detect unknown appliances. Experimental results on two public datasets demonstrate the effectiveness and superiority of our method.

As the global warming crisis becomes increasingly serious, decarbonized integrated electricity-gas system (IEGS) which can reduce CO ₂ emissions are gradually developed. However, with high proportion of renewable energy access, the inherent randomness and volatility bring great difficulties to energy scheduling optimization of decarbonized IEGS. In this article, for reducing CO ₂ emissions, meanwhile improving the utilization efficiency of wind power, a novel IEGS architecture with collaborative operation of power-to-gas (P2G), carbon capture system (CCS) and electric vehicles (EVs) is constructed. P2G is operated in a refined model combined with H ₂ storage and captured CO ₂ by CCS can be further consumed in reaction of P2G. Besides, EVs are innovatively adopted in IEGS as flexible energy resource to reduce the impact of wind power fluctuations. Additionally, a multi-step day ahead-intraday collaborative optimization framework is proposed to handle with the uncertainty of wind power, more accurate predicted wind power can be adopted under this framework. The objective function of constructed IEGS is minimize the total operating costs, which takes the CO ₂ processing costs and penalty costs of wind power deviations into consideration. Numerical studies are conducted with different cases, with the constructed structure and proposed multi-step optimization framework, the emissions of CO ₂ can be efficiently reduced and wind power utilization can be significantly improved, the total operating costs of IEGS can be reduced more than 20% compared with other cases, which demonstrates that the research of this article has better economic benefits and environmental friendliness.

Distribution topology is oftentimes changed to cope with the development of local power load. Therefore, connectivity verification has become a critical task for optimal grid operation. In this paper, a novel cloud-edge collaboration approach is presented to identify outlier users and correct connections. Based on the smart meter voltage analytics, an affinity propagation clustering-based local outlier factor (AP-LOF) algorithm is proposed for the voltage outlier identification and verification of the edge transformer. Compared to traditional methods, it can effectively identify the outlier user groups with high internal voltage correlation. Besides, a recommendation mechanism is developed in the cloud center, which repositions the identified outlier users by coordinating the information exchange between the edge transformers and the cloud center. Numerical tests are conducted using the actual smart meter voltage data. The results show that the proposed AP-LOF algorithm exhibits a better performance, which is suitable for the identification of various outlier users. Compared to a centralized architecture, 66% savings in calculation time is achieved by the cloud-edge collaboration approach. It further demonstrates the effectiveness and practicability of the proposed method in terms of identification accuracy and verification efficiency.

False data injection attacks (FDIAs) are an important network attack threatening the security of power systems to tamper with instruments and measurements. Conventional FDIAs detection approaches are limited to processing the high-dimensional non-Euclidean correlation of grid data. Inspired by the recent advances in deep learning, we propose a novel unsupervised method for FDIAs detection by combining the complementary strengths of dual graph-convolutional autoencoder (DAE) and generative adversarial network (GAN). The technique first proposes the design of adversarial DAE to represent the data dimension reasonably in a low dimension. Among them, the application of GAN establishes not only practical constraints for reconstruction but also adds new strong support for the score calculation of the detection model. Second, the proposal of the graph convolutional strengthens the reasonable representation of the non-Euclidean data of the power system. Finally, considering the nonstationarity of power system performance, we use dynamic thresholds to adaptively fit the detection scores obtained by the model to improve the overall performance of the model comprehensively. We verify the effectiveness of our proposed unsupervised algorithm by performing on IEEE 14-bus and IEEE 118-bus systems. Furthermore, robustness tests in different noise environments demonstrate the excellent generality of the algorithm.

Demand response (DR) programs aim to improve grid reliability, reduce customer costs, and manage building energy demand. However, coordinating autonomous demand response in grid-interactive buildings faces challenges due to uncertainties in system model parameters, complex energy demands, and interdependent operational constraints. Specifically, the energy management problem in grid-interactive buildings is formulated as a Partially Observable Markov Decision Process (POMDP), where each building has only local information and no knowledge of other buildings’ behavior. This makes achieving consistent coordinated energy management challenging. To address these issues, this paper proposes a novel approach called Sequential Update-based Multi-Agent Deep Reinforcement Learning (SUMADRL). This approach updates the policy of each building sequentially, considering the updates already made by previous buildings. This ensures that the joint policy update of buildings is always highly aligned with the overall energy optimization goal and improves coordinated energy management among buildings. Additionally, the sequential update scheme provides theoretical guarantees for achieving monotonically improving rewards and converging to Nash equilibrium, enhancing credibility and stability. Experimental results show that the proposed method significantly reduces energy costs, achieves efficient load shaping, and demonstrates superior effectiveness and stability in demand response compared to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm.