Aiming at capacity optimization of an isolated microgrid, this paper establishes a bi-level capacity optimization model that considers load demand management (LDM) while comprehensively considering load and renewable generation uncertainties. The uncertainties in this paper are brought by the source and load on the same timescale, as well as by the different characteristics of uncertainty presented over different timescales. For long timescales, the problem of source/load random uncertainty is solved using the stochastic network calculus theory to meet the energy balance constraints. For short timescales, we primarily aim to resolve the problem of power balance at the operation level, considering the uncertainty of source/load prediction errors and the impact of LDM. Particularly, by controlling the interruptible and shiftable loads, LDM can optimize load characteristics, reduce operation costs and increase system stability. The bi-level optimization model established in this paper is analyzed with regard to energy and power balance constraints, and the proposed mixed integer linear programming (MILP) model is solved by utilizing the CPLEX solver to minimize the investment cost. A typical microgrid, comprising a wind turbine (WT), a photovoltaic panel (PV), a controllable micro generator (CMG) and an energy storage system (ESS), is taken as an example to study capacity optimization problems. The simulation results verify the rationality and effectiveness of the proposed model and method.

To support the autonomy and economy of grid-connected microgrid (MG), we propose an energy storage system (ESS) capacity optimization model considering the internal energy autonomy indicator and grid supply point (GSP) resilience management method to quantitatively characterize the energy balance and power stability characteristics. Based on these, we establish a three-stage coupled model including investment decision, day-ahead operation strategy, and real-time power fluctuation smoothing control. Investment processes solve the balance of ESS investment and internal energy autonomy. Day-ahead operational scheduling mainly solves unit commitment problems in MG, which are constrained by the ESS capacities. For real-time power fluctuation smoothing, we propose a power fluctuation smoothing control strategy, coordinated by ESS and direct load control (DLC), to achieve GSP resilience management. Furthermore, we use Monte Carlo Simulations, stochastic scenario combinations, and uncertainty set to characterize the fluctuations of both supply and demand at the above different stages, respectively. To solve the established model with multi-objective, multi-uncertainties, and multi-stage coupling, a robust counterpart method has been used to convert the uncertainty problem to the deterministic one, and then decomposition-based multi-objective evolutionary algorithm (MOEA/D) to efficiently used to solve the problem. Finally, simulations are conducted to verify the effectiveness of the proposed model.

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.

Variable renewable energy (VRE) is known to be highly influenced by weather events and may ramp up or down abruptly. Therefore, increasing penetration of solar power will cause fundamental and structural changes to power system operation regimes. In this letter, we conduct an analysis of a 1-minute temporal resolution dataset from 6 Regional Transmission Organizations (RTOs) in the US during 2018 and 2019. By examining day-ahead power exchange and real-time power imbalance time series characteristics, distinct patterns can be observed: the day-ahead power exchange time series curve looks like the canyon, while the real-time power imbalance time series curve looks like a butterfly shape, which becomes bigger as solar penetration level increases. This letter will provide some scientific evidence for the reform of the day-ahead power exchange and real-time power balance operation in response to high solar power penetration.