As power to gas (P2G) technology gradually matures, the coupling between electricity networks and natural gas networks should ideally evolve synergistically. With the intent of characterizing market behaviors of integrated electric power and natural gas networks (IPGNs) with P2G facilities, this paper establishes a steady-state model of P2G and constructs optimal dispatch models of an electricity network and a natural gas network separately. In addition, a concept of slack energy flow (SEF) is proposed as a tool for coordinated optimal dispatch between the two networks. To study how the market pricing mechanism affects coordinated optimal dispatch in an IPGN, a market equilibrium-solving model for an IPGN is constructed according to game theory, with a solution based on the Nikaido-Isoda function. Case studies are conducted on a joint model that combines the modified IEEE 118-node electricity network and the Belgian 20-node gas network. The results show that if the game between an electric power company and a natural gas company reaches market equilibrium, not only can both companies maximize their profits, but also the coordinated operation of the coupling units, i.e., gas turbines and P2G facilities, will contribute more to renewable energy utilization and carbon emission reduction.

Static security assessment (SSA) is an important procedure to ensure the static security of the power system. Researches recently show that cyber-attacks might be a critical hazard to the secure and economic operations of the power system. In this paper, the influences of false data injection attack (FDIA) on the power system SSA are studied. FDIA is a major kind of cyber-attacks that can inject malicious data into meters, cause false state estimation results, and evade being detected by bad data detection. It is firstly shown that the SSA results could be manipulated by launching a successful FDIA, which can lead to incorrect or unnecessary corrective actions. Then, two kinds of targeted scenarios are proposed, i.e., fake secure signal attack and fake insecure signal attack. The former attack will deceive the system operator to believe that the system operates in a secure condition when it is actually not. The latter attack will deceive the system operator to make corrective actions, such as generator rescheduling, load shedding, etc. when it is unnecessary and costly. The implementation of the proposed analysis is validated with the IEEE-39 benchmark system.

In view of the ineluctable uncertainties induced by renewables and load demand, it becomes challenging to realize reliable online energy scheduling for microgrid clusters. To overcome this challenge, this paper proposes a novel energy management framework based on tube-based model predictive control for off-grid microgrid clusters, which enables the robustness against system uncertainties in the energy scheduling strategy with less sacrifice in economic performance and computational efficiency. The proposed energy management framework adopts a mixed-stage optimization structure, where the concerned problem is progressively optimized along with diverse energy management scales and various prediction horizons. A novel decentralized decomposition and coordination algorithm based on the alternating direction method of multipliers is developed, which enhances privacy preserving and overall convergency. Case studies in presence of high system uncertainties on a typical microgrid cluster demonstrate the effectiveness and computational efficiency of the proposed framework and solving algorithm.

In this paper, we propose an energy storage capacity optimization (ESCO) method for grid-connected microgrid systems (MSs) considering multiple time scale uncertainty coupling. First, an envelope model and a box uncertainty model are, respectively, employed to characterize the stochastic uncertainties and the forecasting error uncertainty of the renewable energy (RE) power supply and load demand. Based on the above two uncertainty characterization models, the energy balance ability indicator and the robustness coordination cost indicator are proposed, which are used to quantitatively analyze the MS energy balance state probability for different ESCO schemes and the economic cost of improving the robustness of the MS optimal operation, respectively. Then, an ESCO model, where the stochastic uncertainty and forecasting error uncertainty of the RE power supply and load demand are, respectively, considered in the long-time-scale investment decision of the energy storage capacity and the short-time-scale operation optimization of the energy storage system, is established, with the goal of realizing an MS that operates in a grid-friendly and economical manner. Furthermore, a new multi-objective compound differential evolution (MO-CDE) algorithm is designed to solve the energy storage capacity collaborative optimization model efficiently. Finally, simulations are conducted to verify the rationality and effectiveness of the proposed model and method.

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.