Optimal Residential Demand Response in Distribution Networks

被引:131
|
作者
Shi, Wenbo [1 ]
Li, Na [2 ]
Xie, Xiaorong [3 ]
Chu, Chi-Cheng [1 ]
Gadh, Rajit [1 ]
机构
[1] Univ Calif Los Angeles, Smart Grid Energy Res Ctr, Los Angeles, CA 90095 USA
[2] MIT, Lab Informat & Decis Syst, Cambridge, MA 02139 USA
[3] Tsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China
关键词
Demand response (DR); distributed algorithms; distribution networks; optimal power flow (OPF); smart grid; CONVEX RELAXATION; FLOW; MANAGEMENT; ALGORITHM; MODEL;
D O I
10.1109/JSAC.2014.2332131
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
Demand response (DR) enables customers to adjust their electricity usage to balance supply and demand. Most previous works on DR consider the supply-demand matching in an abstract way without taking into account the underlying power distribution network and the associated power flow and system operational constraints. As a result, the schemes proposed by those works may end up with electricity consumption/shedding decisions that violate those constraints and thus are not feasible. In this paper, we study residential DR with consideration of the power distribution network and the associated constraints. We formulate residential DR as an optimal power flow problem and propose a distributed scheme where the load service entity and the households interactively communicate to compute an optimal demand schedule. To complement our theoretical results, we also simulate an IEEE test distribution system. The simulation results demonstrate two interesting effects of DR. One is the location effect, meaning that the households far away from the feeder tend to reduce more demands in DR. The other is the rebound effect, meaning that DR may create a new peak after the DR event ends if the DR parameters are not chosen carefully. The two effects suggest certain rules we should follow when designing a DR program.
引用
收藏
页码:1441 / 1450
页数:10
相关论文
共 50 条
  • [41] Incorporation of Demand Response Programs and Wind Turbines in Optimal Scheduling of Smart Distribution Networks: A Case Study
    Ghahramani, Mehrdad
    Heris, Morteza Nazari
    Zare, Kazem
    Ivatloo, Behnam Mohammadi
    2017 CONFERENCE ON ELECTRICAL POWER DISTRIBUTION NETWORKS CONFERENCE (EPDC), 2017, : 25 - 32
  • [42] Optimal thermostatically-controlled residential demand response for retail electric providers
    Moglen, Rachel L.
    Chanpiwat, Pattanun
    Gabriel, Steven A.
    Blohm, Andrew
    ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS, 2023, 14 (03): : 641 - 661
  • [43] Active distribution networks planning with integration of demand response
    Mokryani, Geev
    SOLAR ENERGY, 2015, 122 : 1362 - 1370
  • [44] Quantitative Risk Management by Demand Response in Distribution Networks
    Sgouras, Kallisthenis I.
    Dimitrelos, Dimitrios I.
    Bakirtzis, Anastasios G.
    Labridis, Dimitris P.
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2018, 33 (02) : 1496 - 1506
  • [45] Demand Response in Radial Distribution Networks: Distributed Algorithm
    Li, Na
    Chen, Lijun
    Low, Steven H.
    2012 CONFERENCE RECORD OF THE FORTY SIXTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS (ASILOMAR), 2012, : 1549 - 1553
  • [46] Impact of Demand Response on Reliability Enhancement in Distribution Networks
    Mansouri, Mohammad Reza
    Simab, Mohsen
    Bahmani Firouzi, Bahman
    SUSTAINABILITY, 2021, 13 (23)
  • [47] Opportunities and challenges of demand response in active distribution networks
    Ponnaganti, Pavani
    Pillai, Jayakrishnan R.
    Bak-Jensen, Birgitte
    WILEY INTERDISCIPLINARY REVIEWS-ENERGY AND ENVIRONMENT, 2018, 7 (01)
  • [48] Deep Reinforcement Learning for Demand Response in Distribution Networks
    Bahrami, Shahab
    Chen, Yu Christine
    Wong, Vincent W. S.
    IEEE TRANSACTIONS ON SMART GRID, 2021, 12 (02) : 1496 - 1506
  • [49] Optimal Operation of District Heating Networks Through Demand Response
    Capone, M.
    Guelpa, E.
    Verda, V.
    INTERNATIONAL JOURNAL OF THERMODYNAMICS, 2019, 22 (01) : 35 - 43
  • [50] Optimal Demand Response Based on Utility Maximization in Power Networks
    Li, Na
    Chen, Lijun
    Low, Steven H.
    2011 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING, 2011,