Regarding the different ownerships and autonomy of microgrids (MGs) in the distributed multi-microgrid (MMG) system, this paper establishes a multi-stage energy scheduling model based on a multi-agent system (MAS). The proposed mechanism enables a microgrid agent (MGA), a central energy management agent (CEMA), and a coordination control agent (CCA) to cooperate efficiently during various stages including prescheduling, coordinated optimization, rescheduling and participation willingness analysis. Based on the limited information sharing between agents, energy scheduling models of agents and coordinated diagrams are constructed to demonstrate the different roles of agents and their interactions within the MMG system. Distributed schemes are introduced for MG internal operations considering demand response, while centralized schemes under the control of the CCA are proposed to coordinate MGAs. Participation willingness is defined to analyze the MGA’s satisfaction degree of the matchmaking. A hierarchical optimization algorithm is applied to solve the above nonlinear problem. The upper layer establishes a mixed-integer linear programming (MILP) model to optimize the internal operation problem of each MG, and the lower layer applies the particle swarm optimization (PSO) algorithm for coordination. The simulation with a three-MG system verifies the rationality and effectiveness of the proposed model and method.

Collaborative cloud–edge Power Internet of Things technology is required to support the development of smart grids, which have become intelligent, green, and regionally autonomous systems. The diversity of electricity customer behaviors and different computational intensities of energy management applications present challenges for task allocation among computing resources that belong to different agents. In this article, we propose a novel trilevel collaborative optimization model to comprehensively consider the relation among various agents, including users, edge nodes (ENs), a cloud center (CC), and a multiedge league (MEL). We first formulate a Stackelberg game between users and ENs modeled as the lower level and middle level. In addition, with the assistance of the CC, we propose a MEL cooperation scheme to analyze the collaborative task allocation problem among multiple edges, which is modeled as the upper level to maximize the social welfare of the multiedge system (MES) without damaging the interests of the various ENs. The proposed trilevel model is equivalent to a bilevel program, solved by the proposed collaborative dynamic task allocation (CDTA) algorithm. Numerical simulations are presented to verify the proposed scheme and the results show that this scheme is effective for task allocation among users, ENs, the cloud, and the MEL in a cloud-assisted MES.

Microservice-based edge computing is a key technology to support power system to adapt to the high concurrency and diversification of application in power Internet of Things. Workload modeling is not only the basis for analyzing the logical design and computing resource requirements of application, but also the premise of optimizing the computing resource configuration and scheduling of edge computing. In this paper, a workload modeling method for microservice-based edge computing is proposed. The composition of an application and the logical relationship among the microservices are elaborated. Then, the topological structure graph and model are proposed to describe the logical relationship of the microservices. Based on the established model, the workload model is formulated to calculate the computing resource demand and latency of the application. Besides, a microservice-based workload evaluation algorithm is proposed to obtain the configuration results and the latency performance. The influence of the application architecture, topological structure of microservices and virtualization technology of edge computing terminal are analyzed. Simulation results verify the validity of the proposed model and method.

With the development of smartness of power distribution system (PDS), PDS is confronted with multiple events and its operation state changes greatly and frequently. The consequent applications vary over time leading to dynamic workload, which challenges resource provision in cloud platform for PDS. In this paper, we propose a resource provision method for cloud platform in PDS via workload prediction. Starting from the causation of workload change, we forecast the operation state of DS by Markov Chain. We introduce the knowledge-based system (KBS) that contains logic relationship with operation states, events and applications, by which we predict the future workload under different operation states. We proposed a proactive dynamic computing resource provision model considering the predicted workload aiming at high-speed execution and economic operation in the cloud. The optimal solution is obtained from Pareto sets by technique for order preference by similarity to an ideal solution (TOPSIS). Results show that our proposed approach can not only improve the execution speed for DS application but also utilize the resources efficiently and economically.