skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Trading strategies for distribution company with stochastic distributed energy resources

Abstract

This paper proposes a methodology to address the trading strategies of a proactive distribution company (PDISCO) engaged in the transmission-level (TL) markets. A one-leader multi-follower bilevel model is presented to formulate the gaming framework between the PDISCO and markets. The lower-level (LL) problems include the TL day-ahead market and scenario-based real-time markets, respectively with the objectives of maximizing social welfare and minimizing operation cost. The upper-level (UL) problem is to maximize the PDISCO’s profit across these markets. The PDISCO’s strategic offers/bids interactively influence the outcomes of each market. Since the LL problems are linear and convex, while the UL problem is non-linear and non-convex, an equivalent primal–dual approach is used to reformulate this bilevel model to a solvable mathematical program with equilibrium constraints (MPEC). The effectiveness of the proposed model is verified by case studies.

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1340688
DOE Contract Number:
AC02-06CH11357
Resource Type:
Journal Article
Resource Relation:
Journal Name: Applied Energy; Journal Volume: 177; Journal Issue: C
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; Distributed energy resources (DERs); Proactive distribution company (PDISCO); Electricity markets; Bilevel game-thoretic model; Multi-period AC power flow; Mathmatical program with equilibrium constraints (MPEC); Mathematical program with primal and dual constraints (MPPDC)

Citation Formats

Zhang, Chunyu, Wang, Qi, Wang, Jianhui, Korpås, Magnus, Pinson, Pierre, Østergaard, Jacob, and Khodayar, Mohammad E. Trading strategies for distribution company with stochastic distributed energy resources. United States: N. p., 2016. Web. doi:10.1016/j.apenergy.2016.05.143.
Zhang, Chunyu, Wang, Qi, Wang, Jianhui, Korpås, Magnus, Pinson, Pierre, Østergaard, Jacob, & Khodayar, Mohammad E. Trading strategies for distribution company with stochastic distributed energy resources. United States. doi:10.1016/j.apenergy.2016.05.143.
Zhang, Chunyu, Wang, Qi, Wang, Jianhui, Korpås, Magnus, Pinson, Pierre, Østergaard, Jacob, and Khodayar, Mohammad E. Thu . "Trading strategies for distribution company with stochastic distributed energy resources". United States. doi:10.1016/j.apenergy.2016.05.143.
@article{osti_1340688,
title = {Trading strategies for distribution company with stochastic distributed energy resources},
author = {Zhang, Chunyu and Wang, Qi and Wang, Jianhui and Korpås, Magnus and Pinson, Pierre and Østergaard, Jacob and Khodayar, Mohammad E.},
abstractNote = {This paper proposes a methodology to address the trading strategies of a proactive distribution company (PDISCO) engaged in the transmission-level (TL) markets. A one-leader multi-follower bilevel model is presented to formulate the gaming framework between the PDISCO and markets. The lower-level (LL) problems include the TL day-ahead market and scenario-based real-time markets, respectively with the objectives of maximizing social welfare and minimizing operation cost. The upper-level (UL) problem is to maximize the PDISCO’s profit across these markets. The PDISCO’s strategic offers/bids interactively influence the outcomes of each market. Since the LL problems are linear and convex, while the UL problem is non-linear and non-convex, an equivalent primal–dual approach is used to reformulate this bilevel model to a solvable mathematical program with equilibrium constraints (MPEC). The effectiveness of the proposed model is verified by case studies.},
doi = {10.1016/j.apenergy.2016.05.143},
journal = {Applied Energy},
number = C,
volume = 177,
place = {United States},
year = {Thu Sep 01 00:00:00 EDT 2016},
month = {Thu Sep 01 00:00:00 EDT 2016}
}