Moth fly optimization;
Distributed energy resources;
Fuel cell;
Active distribution network;
Renewable;
Optimization;
Bi-layer optimization;
OPTIMAL PLACEMENT;
OPTIMAL LOCATION;
GENERATION;
ALGORITHM;
SYSTEMS;
UNITS;
SIZE;
D O I:
10.1016/j.epsr.2021.107323
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
In this article, a novel bi-layer optimization model is proposed for strategic accommodation of fuel cell (FC) in an active distribution network (ADN) that consists of wind turbines and photovoltaic modules. The foremost objective of the proposed model is to maximize the renewable power hosting potential of the distribution system. In outward-layer of the bi-layer optimization model, a vital multi-objective function is broached to assist the daily challenges of ancillary services. The objectives considered are minimizing the annual energy loss of the network, node voltage deviation, inverse power flow, demand deviation and conversion losses for battery energy storage system (BESS) connected with FC. Further, a modified version of moth flame optimization (MFO) is also proposed by overcoming a few limitations observed in its traditional variant. The proposed modification has enhanced the exploration and exploitation potential of MFO such that, their correct balance seeks the global optima. The presented modification is validated before applying it to the proposed simulation model. The proposed modified MFO is deployed to determine the optimization elements of the outward-layer. These are the details related to DERs accommodation. Whereas, a heuristic approach is proposed to solve the inward-layer optimization model designed to determine the optimal hourly power dispatch by BESS connected to FC, as per size suggested by outward-layer. The approach is designed to minimize the considered objectives i.e. annual energy losses and inverse power flow. To exhibit the competency of the proposed model, it is implemented on 33 and 108-bus balance distribution systems for numerous test cases. The resemblance of simulation obtain results reveals the inspirational dominance of the proposed optimization model. Further, the modified variant of MFO is found very effective to enhance the performance of ADN.
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页数:18
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