TY - JOUR
T1 - Stochastic bidding for VPPs enabled ancillary services
T2 - A case study
AU - Wang, Zheng
AU - Li, Chaojie
AU - Zhou, Xiaojun
AU - Xie, Renyou
AU - Li, Xiangyu
AU - Dong, Zhaoyang
PY - 2023/12/15
Y1 - 2023/12/15
N2 - Strategic bidding which aims to optimally harvest the price difference in the wholesale electricity market can efficiently allocate VPPs’ aggregated resources to provide large flexibility for energy and frequency regulation (FR), aiding in the real-time rebalancing of supply and demand. However, the uncertainty of renewable energy results in substantial imbalances between supply and demand, which causes high randomness of system frequency deviation and significant price fluctuations in the electricity market, making bidding challenging. To tackle these issues, a risk-averse optimal bidding strategy is proposed for VPPs to participate in both energy and FR markets. Moreover, a payment recovery mechanism is designed to recover the cost of FR undersupply. Specifically, each VPP submits bids to maximize profit, while the market operator clears the market and penalizes undersupply through the payment recovery mechanism. The existence of Nash equilibrium is proved, and a distributed best response algorithm is implemented to calculate the risk-averse optimal bidding strategy, taking into account Denial-of-Service attacks. The mean-convergence and variance-convergence of our proposed algorithm are derived. A case study of Australian National Electricity Market (NEM) validates the effectiveness of the proposed method in reducing overbidding risk, strengthening FR service reliability and improving profits for VPPs. © 2023 Elsevier Ltd.
AB - Strategic bidding which aims to optimally harvest the price difference in the wholesale electricity market can efficiently allocate VPPs’ aggregated resources to provide large flexibility for energy and frequency regulation (FR), aiding in the real-time rebalancing of supply and demand. However, the uncertainty of renewable energy results in substantial imbalances between supply and demand, which causes high randomness of system frequency deviation and significant price fluctuations in the electricity market, making bidding challenging. To tackle these issues, a risk-averse optimal bidding strategy is proposed for VPPs to participate in both energy and FR markets. Moreover, a payment recovery mechanism is designed to recover the cost of FR undersupply. Specifically, each VPP submits bids to maximize profit, while the market operator clears the market and penalizes undersupply through the payment recovery mechanism. The existence of Nash equilibrium is proved, and a distributed best response algorithm is implemented to calculate the risk-averse optimal bidding strategy, taking into account Denial-of-Service attacks. The mean-convergence and variance-convergence of our proposed algorithm are derived. A case study of Australian National Electricity Market (NEM) validates the effectiveness of the proposed method in reducing overbidding risk, strengthening FR service reliability and improving profits for VPPs. © 2023 Elsevier Ltd.
KW - Frequency regulation
KW - Payment recovery
KW - Risk management
KW - Risk-averse bidding
KW - Stochastic game
UR - http://www.scopus.com/inward/record.url?scp=85171430871&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85171430871&origin=recordpage
U2 - 10.1016/j.apenergy.2023.121918
DO - 10.1016/j.apenergy.2023.121918
M3 - RGC 21 - Publication in refereed journal
SN - 0306-2619
VL - 352
JO - Applied Energy
JF - Applied Energy
M1 - 121918
ER -
