A linearized multi-objective Bi-level approach for operation of smart distribution systems encompassing demand response

被引:17
作者
Rawat, Tanuj [1 ]
Niazi, K. R. [1 ]
Gupta, Nikhil [1 ]
Sharma, Sachin [2 ]
机构
[1] Malaviya Natl Inst Technol, Dept Elect Engn, Jaipur, Rajasthan, India
[2] Sobhasaria Grp Inst, Sikar, India
关键词
Demand response; Multi-objective; Bi-level; Distributed energy resources; Distribution systems; DISTRIBUTION NETWORKS; RECONFIGURATION; OPTIMIZATION; MANAGEMENT; PROGRAMS; ENERGY; MODEL;
D O I
10.1016/j.energy.2021.121991
中图分类号
O414.1 [热力学];
学科分类号
摘要
In smart grid parlance, the demand response (DR) creates an opportunity to enhance techno-economic metrics of distribution system while concurrently benefitting the customers. These advantages can be obtained only when DR is incorporated and managed optimally in coordination with other smart technologies. In this paper, a multi-objective bi-level optimization model is proposed to study the coordination of distribution system operator (DSO) and DR aggregators. The DSO at the upper level aims to determine the pricing policy for DR participants, dispatch of distributed generations and battery storage to maximize both economic and technical objectives. The DR aggregators at the lower level respond to the price signals by scheduling flexible loads to maximize profit. The DR aggregators are acting on the behalf of customers. The multi-objective problem at the upper level is solved using epsilon-constraint method and thereafter, best compromising solution is decided through fuzzy criteria. The resulting bi-level model is converted into a single level model using Karush-Kuhn-Tucker conditions and strong duality theorem. Moreover, a linearized power flow is developed to remove the complexity of non-linear AC load flow equations. The effectiveness and efficacy of the proposed model is assessed on 33-bus distribution system under different scenarios. (c) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页数:12
相关论文
共 36 条
  • [31] Study on day-ahead optimal economic operation of active distribution networks based on Kriging model assisted particle swarm optimization with constraint handling techniques
    Tang, Jia
    Wang, Dan
    Wang, Xuyang
    Jia, Hongjie
    Wang, Chengshan
    Huang, Renle
    Yang, Zhanyong
    Fan, Menghua
    [J]. APPLIED ENERGY, 2017, 204 : 143 - 162
  • [32] A hybrid demand response mechanism based on real-time incentive and real-time pricing
    Xu, Bo
    Wang, Jiexin
    Guo, Mengyuan
    Lu, Jiayu
    Li, Gehui
    Han, Liang
    [J]. ENERGY, 2021, 231
  • [33] Robust Coordination of Distributed Generation and Price-Based Demand Response in Microgrids
    Zhang, Cuo
    Xu, Yan
    Dong, Zhao Yang
    Wong, Kit Po
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2018, 9 (05) : 4236 - 4247
  • [34] Robust Operation of Microgrids via Two-Stage Coordinated Energy Storage and Direct Load Control
    Zhang, Cuo
    Xu, Yan
    Dong, Zhao Yang
    Ma, Jin
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2017, 32 (04) : 2858 - 2868
  • [35] Distribution feeder reconfiguration for service restoration and load balancing
    Zhou, Q
    Shirmohammadi, D
    Liu, WHE
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 1997, 12 (02) : 724 - 729
  • [36] ε-Constraint and Fuzzy Logic-Based Optimization of Hazardous Material Transportation via Lane Reservation
    Zhou, Zhen
    Chu, Feng
    Che, Ada
    Zhou, MengChu
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2013, 14 (02) : 847 - 857