Open AccessOptimal day‐ahead demand response contract for congestion management in the deregulated power market considering wind power
Author(s) -
Wu Jiasi,
Zhang Buhan,
Jiang Yazhou
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.1063
Subject(s) - demand response , electricity market , electric power system , computer science , market clearing , peak demand , mathematical optimization , operations research , load management , incentive , electricity , reliability engineering , power (physics) , economics , engineering , microeconomics , electrical engineering , mathematics , physics , quantum mechanics
In the liberalised electricity market, congestion management (CM) with a high penetration of wind energy is a challenging task for Independent System Operators (ISOs). Even though demand response (DR) provides an opportunity to alleviate transmission congestion, strategic selection of aggregated loads to contract with for DR in the day‐ahead economic dispatch is still under‐investigated. To solve this problem, this study proposes a bi‐level optimisation model to determine the optimal DR buses for CM in the day‐ahead market considering the uncertainty of wind power. The upper model serves to compute the available transfer capability (ATC), while the lower model is to calculate the stochastic dynamic optimal power flow. Through converting the stochastic ATC values to a summation of load supply capability of each load node, the loads which will deteriorate transmission congestion if the corresponding demand grows are determined. The corresponding loads with DR are selected as the optimal candidates. Furthermore, this study constructs a two‐stage optimisation model for optimal load dispatch by incorporating both price‐based DR and incentive‐based DR. The result can be used to assist system operators in decision‐making of electricity biddings from DRs for load curtailment and shift in the market clearing. As a result, the difference of peak and valley loads is reduced; ISOs as well as DR participators can both get economic benefits. Simulation results from the PJM 5‐bus training system and the modified IEEE 30‐bus system demonstrate the effectiveness of the proposed algorithm for DR contracting in CM.