Open AccessBlock‐based procurement model between retailers and wind farms in medium‐long term market
Author(s) -
Zhao Tianhui,
Wang Jianxue,
Ding Tao,
Zhang Yao
Publication year - 2022
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/gtd2.12337
Subject(s) - procurement , block (permutation group theory) , term (time) , wind power , mathematical optimization , computer science , forward contract , business , operations research , industrial organization , finance , mathematics , engineering , electrical engineering , marketing , physics , geometry , quantum mechanics , futures contract
With the fast development of wind power generation, many countries actively encourage wind power to trade in the medium‐long term market. However, the traditional forward contracts from medium‐long term markets are only suitable to conventional power generation enterprises, instead of wind farms. To facilitate the accommodation of wind power in the medium‐long term market, this paper focuses on two problems: The framework of the block‐based forward contract trading method and the procurement strategy of retailers with block‐based contracts. First, a novel block‐based forward contract is proposed, where the time attributes, that is, starting and ending time, and power during each period are stipulated. Aggregating all the block‐based forward contracts will naturally form the power supply curve, which can provide boundary information for wind farms when they take part in other markets. Secondly, a chance‐constrained procurement strategy model is proposed for retailers, where the uncertainty of wind power generation, constraints of block‐based contracts, and quota obligations of retailers are considered. Furthermore, a bilinear Benders decomposition algorithm with a variant of Jensen's inequalities is applied to solve the proposed model. Finally, experimental results demonstrate the effectiveness of the proposed model and method, which is computationally efficient in solving the chance‐constrained procurement strategy problem.