Networked Microgrids for Self-Healing Power Systems

Abstract

This paper proposes a transformative architecture for the normal operation and self-healing of networked microgrids (MGs). MGs can support and interchange electricity with each other in the proposed infrastructure. The networked MGs are connected by a physical common bus and a designed twolayer cyber communication network. The lower layer is within each MG where the energy management system (EMS) schedules the MG operation; the upper layer links a number of EMSs for global optimization and communication. In the normal operation mode, the objective is to schedule dispatchable distributed generators (DGs), energy storage systems (ESs), and controllable loads to minimize the operation costs and maximize the supply adequacy of each MG. When a generation deficiency or fault happens in an MG, the model switches to the selfhealing mode and the local generation capacities of other MGs can be used to support the on-emergency portion of the system. A consensus algorithm is used to distribute portions of the desired power support to each individual MG in a decentralized way. The allocated portion corresponds to each MG's local power exchange target, which is used by its EMS to perform the optimal schedule. The resultant aggregated power output of networked MGs will be used to provide the requested power support. Test cases demonstrate the effectiveness of the proposed methodology. Index Terms-Consensus algorithm, distributed power generation, microgrid (MG), power distribution faults, self-healing. NOMENCLATURE Acronyms WT Wind turbine. PV Photovoltaic generator. ES Energy storage system. MT Micro turbine. MG Microgrid.

Applications of Complex Network Analysis in Electric Power Systems

“…The central controller (i.e., DSO) executes the EMA in [20]. The brute force algorithm looks for a solution that agrees with all the constraints (i.e., acceptable voltage limits, line capacities and minimum losses) and allows all the requested demands to be granted.…”

“…It was assumed that each bus controller will send a demand request, in the form of a signal that comprises four load levels with a 10% difference or more. The grant signal from the DSO will be based on a priority list in [20]. The priority has been assigned such that Area 3 has the lowest priority and Area 1 has the highest.…”

“…A single microgrid could supply several megawatts, such as UCSD MG in California, which supplies ~31.2 MW to a 4 km 2 campus; New York University MG, which has a capacity of 13.4 MW to supply 22 buildings; and Illinois Institute of Technology MG, which has a collective DERs capacity close to 9 MW [57]. The central controller (i.e., DSO) executes the EMA in [20]. The brute force algorithm looks for a solution that agrees with all the constraints (i.e., acceptable voltage limits, line capacities and minimum losses) and allows all the requested demands to be granted.…”

Fuzzy Logic-Based Operation of Battery Energy Storage Systems (BESSs) for Enhancing the Resiliency of Hybrid Microgrids

“…Various studies have been conducted to enhance the resiliency of power systems by using microgrids [3][4][5][6][7]. A system for assessing the resilience of microgrids and for rebuilding better resilient partitions was developed in [3].…”

“…The surplus of inner microgrids is reflected as a resource and deficit as a load to the outer microgrids. Networked microgrids are considered by [6,7] to enhance the resiliency of power systems. In [6], on-emergency microgrids get support from healthy microgrids and the on-outage area is sectionalized into self-adequate microgrids by [7].…”