The development of low-carbon power systems has not only elevated the investment costs of power enterprises, but also generated a vast amount of electricity data. The electricity data trading holds promising potential as a primary means to cover investment costs. However, there is a lack of research on the electricity data trading. To address this issue, this article designs an electricity data trading method based on price game and blockchain for low-carbon power systems. It encompasses a data trading framework and the corresponding trading mechanism. The proposed trading framework contains data providers, data consumers, and a blockchain-based information system that plays the role of the data servicer to handle the transactions between data providers and consumers. The proposed trading mechanism mainly consists of three parts: 1) valuation; 2) pricing; and 3) copyrights confirmation. Those parts are executed sequentially to complete the electricity data trading process from valuation to clearing. Specially, the information theory is employed to realize multidimensional electricity data valuation. Further, the data trading game pricing is formulated as a multiobjective optimization problem considering market power constraints to solve. In addition, the digital watermarking combined with blockchain is designed to protect the electricity data copyright. With those components, the designed electricity data trading method enables the power enterprises to make profit from the low-carbon smart energy systems. Finally, experiments demonstrate the effectiveness of the proposed method.

Renewable energy sources (RES) and electric vehicles (EVs) are widely recognized as primary ways to reduce carbon emissions and essential components of low-carbon power systems. However, both of them have strong uncertainties which bring great challenges to power transactions and the operation of power grids. This paper defines the uncertainty cost of wind power producer(WPP) in day-ahead(DA) market pricing based on information entropy theory for the first time and proposes a EV charging management strategy with DA contract. The constructed low-carbon emission electricity market(LCEM) quantifies the uncertainty cost and contracts the disordered charging of EVs. It reduces the uncertainty and ensures the balance of power supply and demand. In addition, the transaction between WPP and EVs is described as the Stackelberg game, and its communication network is constructed through a blockchain network to ensure transaction efficiency and privacy security. Experiments show that LCEM can accurately measure the uncertainty of wind power generation, increase the net profit for WPP by more than 2% when the prediction error is greater than 10%, and adopt EV contract charging management in the DA market to minimize charging costs.

This paper investigates the residential energy management problems in the power-heat-coupling system. For better solving this issue, based on the novel concept of We-Energy (WE) for Energy Internet, this paper proposes a residential WE (R-WE)framework faced by the terminal users who play an important role in renewable energy consumption. Inspired by the full-duplex feature of R-WE, the power and heat supplydemand balance constraints are fulfilled in a regional unit of EI, but it may be inequality constraints for corresponding consumer, producer or WE operation modes in only one R-WE. The R-WE framework can be classified into four operation modes, which is island mode, consumer mode, producer mode, and WE mode. On this basis, the R-WE models cooperate to achieve the objective of minimizing the operation costs, smoothing out the loads' variations and renewable resource fluctuations. Specifically, the R-WE framework with respect to the heat-power-coupling problem can be solved in the distributed double-consensus algorithm (DDCA), which is designed two sets of consensus algorithms by using four completely different consensus variables to calculate the power and heat multipliers, evaluate the power and heat generations and thereby further obtain the power mismatches of electricity and heat. Also, the KKT optimal conditions of the proposed DDCA is further proved. Meanwhile, a novel projection operation method for combined heat and power devices is designed to take the infeasible solutions mapped into the feasible region. Finally, the simulation results in different cases further demonstrate that the proposed R-WE frame can be an appropriate method to analyze the terminal consumers and harmonize multi-energy producers in the process of constructing EI projects. And an R-WE framework of the campus has experimented with minority loads in island mode.

This economic dispatch problem has been tended to be solved by using distributed optimization algorithms which are easier to suffer from diversified cyberattacks, e.g., the denial-of-service (DoS) attacks. It leads to enormous secure risks for the economic operation of smart grid. To address this issue, this article aims to propose a distributed secure dispatch method to effectively defend the DoS attacks. First, considering the coexistence of the attack sequence and triggering sequence, the actual affected period and actual safe period are analyzed and defined. It provides an analysis model for the subsequent algorithm design. Then, by designing switched system dynamics along with hybrid-triggering concept, a novel distributed secure dispatch strategy is presented. The proposed method can enable each distributed generator (DG) to reasonably use estimation values and switched rate of system evolution to mitigate the effect of the DoS attacks. Meanwhile, contributed by the designed hybrid-triggering communication strategy, the proposed method takes advantages of reduced communication costs, flexible execution, and fast and reliable communication recuperation among DGs. With those efforts, the proposed method is capable of high robustness to resist the DoS attacks well. Moreover, theoretically analytic results are proposed to verify the correctness of the proposed method. Finally, simulation results are provided to show the feasibility and effectiveness of the proposed method.

Although the P2P power transactions using the multiagent deep deterministic policy gradient (MADDPG) algorithm has been extensively studied, there are still challenges in privacy protection and training incentives. Furthermore, the stability and efficiency of the strategy decreases when dealing with nonindependent identically distribution (Non-IID) data from heterogeneous prosumers. Therefore, this article proposes a blockchain-enabled asynchronous federated learning-MADDPG (BEAFL-MADDPG) framework designed to enhance the training efficiency of heterogeneous prosumers while safeguarding data privacy. The framework includes a novel P2P energy trading model that facilitates energy trading amidst incomplete information while ensuring privacy assurances. In addition, a BEAFL-MADDPG algorithm is proposed, which accelerates training processes and enables parallel computation among agents. This algorithm enhances the efficiency of algorithm and empowers the training of diverse prosumers. Furthermore, a blockchain-enabled training mechanism and prosumer incentive scheme are proposed that not only encourage prosumer engagement in training but also ensure traceable transactions without the need for trust among participants. These mechanisms promote transparency and integrity, fostering a collaborative and secure environment for energy trading. Simulation results demonstrate that the framework achieves peak load reduction through optimized P2P trading, maintains computation efficiency across discount rates, and ensures secure transactions via blockchain-based incentives. These practical benefits support scalable and sustainable community microgrid operations.

In this paper, a class of time-varying output group formation containment control problem of general linear heterogeneous multiagent systems (MASs) is investigated under directed topology. The MAS is composed of a number of tracking leaders, formation leaders and followers, where two different types of leaders are used to provide reference trajectories for movement and to achieve certain formations, respectively. Firstly, compensators are designed whose states are estimations of tracking leaders, based on which, a controller is developed for each formation leader to accomplish the expected formation. Secondly, two event-triggered compensators are proposed for each follower to evaluate the state and formation information of the formation leaders in the same group, respectively. Subsequently, a control protocol is designed for each follower, utilizing the output information, to guide the output towards the convex hull generated by the formation leaders within the group. Next, the triggering sequence in this paper is decomposed into two sequences, and the inter-event intervals of these two triggering conditions are provided to rule out the Zeno behavior. Finally, a numerical simulation is introduced to confirm the validity of the proposed results.