Classical lossless compression algorithm highly relies on artificially designed encoding and quantification strategies for general purposes. With the rapid development of deep learning, data-driven methods based on the neural network can learn features and show better performance on specific data domains. We propose an efficient deep lossless compression algorithm, which uses arithmetic coding to quantify the network output. This scheme compares the training effects of Bi-directional Long Short-Term Memory (Bi-LSTM) and Transformers on minute-level power data that are not sparse in the time-frequency domain. The model can automatically extract features and adapt to the quantification of the probability distribution. The results of minute-level power data show that the average compression ratio (CR) is 4.06, which has a higher compression ratio than the classical entropy coding method.

To meet the challenge of large-scale renewable energy penetration and take full advantage of existing AC infrastructure, the bipolar DC distribution system is of interest. In this article, the system structure and characteristics of the bipolar DC distribution network are proposed. The three-level Neural Point Clamped Converter (NPC) is used in the proposed system to construct the bipolar DC system. To optimize the DC system performance, an improved cooperative control and energy management strategy is proposed mainly to mitigate DC voltage fluctuation and balance the positive and negative phase voltage. The improved strategy consists of (1) 2-degree of freedom (2DOF) PID controller in traditional voltage control loop; (2) cooperative controller to take full advantage of storage system; (3) voltage equalization controller to balance two-phase voltages; and (4) the energy management system to dispatch the response job to batteries and supercapacitors. Experiments and simulations are performed to validate the effectiveness of the proposed strategy.

Lightweight service identification models are very important for resource-constrained distribution grid systems. To address the increasingly larger deep learning models, we provide a method for the lightweight identification of complex power services based on knowledge distillation and network pruning. Specifically, a pruning method based on Taylor expansion is first used to rank the importance of the parameters of the small-scale network and delete some of the parameters, compressing the model parameters and reducing the amount of operation and complexity. Then, knowledge distillation is used to migrate the knowledge from the large-scale network ResNet50 to the small-scale network so that the small-scale network can fit the soft-label information output from the large-scale neural network through the loss function to complete the knowledge migration of the large-scale neural network. Experimental results show that this method can compress the model size of the small network and improve the recognition accuracy. Compared with the original small network, the model accuracy is improved by 2.24 percentage points to 97.24%. The number of model parameters is compressed by 81.9% and the number of floating-point operations is compressed by 92.1%, making it more suitable for deployment in resource-constrained devices.

Household loads are becoming dominant in virtual power plants (VPP). However, their dispatch potential has not yet been explored due to the lack of detailed user power management. To solve this issue, a novel two-layer user energy management strategy based on HG-multi-agent reinforcement learning has been proposed in this paper. Firstly, a novel two-layer optimization framework is established, where the upper layer is applied to coordinate the scheduling and benefit allocation among various stakeholders and the lower layer is applied to execute intelligent decision-making for users. Secondly, the mathematical model for the framework is established, where a detailed household power management model is proposed in the lower layer, and the generated predicted power demands are used to replace the conventional aggregate model in the upper layer. As a result, the energy consumption behaviors of household users can be precisely described in the scheduling scheme. Furthermore, an HG-multi-agent reinforcement-based method is applied to accelerate the game-solving process. Case study results indicate that the proposed method leads to a reduction in user costs and an increase in VPP profit.

Constrained by current market mechanisms, small-scale virtual power plants (SS-VPPs) on the distribution network side struggle to exert their market characteristics. To address this, this paper proposes a trading framework and operational strategy for distribution-side SS-VPPs to participate in the day-ahead energy market. First, an operation and trading framework for distribution networks involving SS-VPPs is proposed. This framework comprehensively considers the clearing process of the electricity energy market, the operation mechanism of the distribution network, and the cost structures of various stakeholders, while clarifying the day-ahead market clearing mechanism at the distribution network level. Next, accounting for energy balance constraints and distribution network congestion constraints, this paper establishes a collaborative optimization model between SS-VPPs and active distribution networks. After obtaining the energy optimization results for all stakeholders, distribution locational marginal pricing (DLMP) is determined based on the dual problem solution to achieve multi-stakeholder market clearing. Finally, simulations using a modified IEEE 33-node test system demonstrate the rationality and feasibility of the proposed strategy. The framework fully exploits the market characteristics and dispatch potential of SS-VPPs, significantly reduces overall system operating costs, and ensures the economic benefits of all participants.

In medium-voltage DC distribution systems (MVDC-DSs), feeder congestion may occur when multiple converter terminals sharing the same AC feeder experience fast source–load variations. Limited communication further challenges real-time converter coordination and secure system operation. To address these issues, this paper has proposed an adaptive online management strategy for multi-terminal feeder congestion in MVDC-DSs under limited communication. First, the economic operation objective and practical system constraints are formulated within a distributed optimization framework, where feeder congestion limits, voltage security, and load supply requirements are explicitly incorporated. Then, an adaptive online regulation mechanism is embedded into local converter controllers, enabling converter power to be coordinated in real time using only neighboring information. In this way, feeder congestion can be mitigated while reliable load supply and economic operation are maintained. Simulation studies and hardware-in-the-loop experimental results demonstrate the effectiveness, scalability, and real-time applicability of the proposed strategy under dynamic operating conditions.