Open AccessTwo‐stage nested bilevel model for generation expansion planning in combined electricity and gas markets
Author(s) -
Cong Hao,
Wang Xu,
Jiang Chuanwen
Publication year - 2019
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.2019.0293
Subject(s) - time horizon , electricity market , electricity , renewable energy , clearance , environmental economics , electricity generation , bilevel optimization , natural gas , profit (economics) , flexibility (engineering) , energy market , operational planning , operations research , microeconomics , economics , operations management , business , computer science , mathematical optimization , engineering , optimization problem , power (physics) , waste management , mathematics , electrical engineering , medicine , physics , management , finance , quantum mechanics , urology
The growing utilisation of natural gas and renewable energy resources brings more challenges to generation expansion planning problems. Short‐term operational constraints are equally important for long‐term capacity planning. This work studies the interdependence between electricity and natural gas systems and presents a combined market mechanism that allows two‐stage energy trading and planning based on asynchronous electricity and gas markets. In the first stage, the gas market is cleared with the objective of maximum social welfare (lower level), at the same time obtaining the optimal strategies offered by gas producers and gas‐fired units (upper level). In the second stage, generation companies and consumer companies aim to maximise their corresponding profits in the planning horizon (upper level), and electricity market is cleared on principle of maximum social welfare (lower level). Then, the authors develop a modified alternative direction method of multiplier algorithm to solve the two‐stage nested bilevel model. To improve operational flexibility of expansion plans, uncertainties of renewable energy generation and integrated demand response are also included and analysed in different risk scenarios. Case studies validate the effectiveness of the proposed methodology.