A Planning Framework for Optimal Partitioning of Distribution Networks Into Microgrids

Abstract

This paper proposes a novel methodology for the optimal design of microgrids in distribution systems with multiple distributed generation units. Following the IEEE Standard 1547.4-2011, the operation and control of large distribution networks can be enhanced by dividing these networks into multiple virtual microgrids. The proposed planning framework incorporates the necessary conditions for microgrids to operate efficiently in gridconnected operating mode and successfully during islanding. To obtain a robust design, the clustering process considers three objectives: maximizing the self-adequacy of the designed microgrids, maximizing microgrid islanding success probability, and a combination of both targets. For this purpose, the PG&E distribution system with 69 buses is selected as a test case. Backtracking search optimization algorithm, a probabilistic load flow approach, and graph-based theories are used to accomplish this research. Simulation results demonstrate the effectiveness of combining the self-adequacy and the islanding success probability objectives in the clustering process. Compared with other strategies present in the previous literature, the proposed framework results in more self-sufficient and successful islands assessed in terms of active and reactive power adequacy as well as voltage constraints. Next, the effects of increased penetration level of distributed generation units and installation of both distributed energy storage units and distributed reactive sources on the design process are examined. Finally, comparison with other microgrid design objectives applied in previous researches reveals that the resultant design is sensitive to the system's reliability, security, and economic requirements.

Optimal partitioning of unbalanced active distribution systems for supply‐sufficient micro‐ grids considering uncertainty

“…The objective function of this stage aims at maximizing the self-adequacy of MGs that indicates how many hours the MG can supply its total loads and losses considering the priority of the MG that has a higher total load number. So that, this objective is represented as follows 14 :…”

Chance-Constrained Optimal Distribution Network Partitioning to Enhance Power Grid Resilience

“…Both exact [9]- [11] and heuristic [12]- [15] methods have been proposed in literature to address the ODNP problem. Selfadequacy in the objective function has been surrogated by either expected load-generation imbalance within microgrids [13]- [15], or expected power flow on boundary lines [9]- [12]. Some works additionally suggest dynamic identification of boundary lines in response to faults [16], [17].…”

“…Distribution networks are usually operated in a radial fashion for protection coordination, and this radiality needs to be maintained in microgrids as well. In [11], [13]- [15], ODNP is demonstrated on an already radial feeder and radiality conditions are not explicitly enforced. This approach ignores the presence of normally open switches that allow network reconfiguration, thereby under-utilizing grid flexibility.…”

“…ODNP is further complicated by the fact that power demands and renewable energy generation are stochastic in nature. In [11], [13]- [15], [17], the uncertainty in load and generation is addressed by constructing typical daily profiles, over which optimization is performed. However, the quality of solution obtained is not evaluated.…”

Hybrid PSO-TS Based Distribution System Expansion Planning for System Performance Improvement Considering Energy Management

“…In reference [5], the authors study the power losses in the PDS by increasing the DGs penetration level, along with DG technologies mix, DG dispersion and location, and reactive power control strategy. The authors in [6] simultaneously integrate different DGs in PDS to turn the system into multi-microgrid. In their research, the objective was to maximize the self-adequacy of the designed microgrids by minimizing the active and reactive power flows in the power lines connecting the microgrids and maximize the microgrid islanding success probability.…”