Electricity retailers face increasing uncertainty due to the ongoing expansion and self-consumption of unpredictable distributed generation in the residential sector. We analyze how increasing levels of households’ solar PV self-generation affect the short-term decision-making and associated risk exposure of electricity retailers in the German day-ahead and intraday markets. First, we develop a stochastic model accounting for correlations between solar load, residual load and price in sequentially nested wholesale spot markets across seasons and type of day. Second, we develop a computationally tractable two-stage stochastic mixed-integer optimization model to investigate the trading portfolio and risk optimization problem faced by retailers. Through conditional value-at-risk we assess the retailers’ profitability and risk exposure to different levels of PV self-generation by assuming different retail tariff schemes. We find risk-hedging trading strategies and tariffs to have greater impact in Summer and with low levels of residual load in the system, i.e. when the solar generation uncertainty affects more the households’ demand to be served and the wholesale spot prices. The study is innovative in unveiling the potential of dynamic electricity tariffs, which are indexed to spot prices, to sustain a high penetration of renewable energy source while promoting a fair risk sharing between retailers, regular consumers and prosumers (consumers with self-generation). Our findings have implications for electricity retailers facing load and revenue risks in wholesale spot markets, likewise for regulators and policy-makers interested in electricity market design.
Bibliographical noteFunding Information:
We thank GET AG for kindly providing us with the retail tariff data for Germany used in this study. We thank Dr. Kai Sander (Netze BW GmbH) for the fruitful discussions and practical insights on uncertainties and associated risk exposure. This work has emanated from Marianna Russo’s research time at the Economic and Social Research Institute, Dublin. Marianna Russo acknowledges funding from the ESRI’s Energy Policy Research Center and the SFI Energy Systems Integration Partnership Programme (ESIPP) number SFI/15/SPP/E3125 . Marianna Russo also acknowledges research support by COST Action “Fintech and Artificial Intelligence in Finance - Towards a transparent financial industry” (FinAI) CA19130. The authors acknowledge the financial support of the German Federal Ministry of Education and Research under grant number 03SFK1F0-2 (ENSURE – Neue Energienetzstrukturen für die Energiewende). The opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Energy Policy Research Center and Science Foundation Ireland. All errors remain our own.
© 2022 Elsevier B.V.
- Electricity markets
- Retailers’ uncertainty modeling
- Risk management
- Stochastic modeling
- Stochastic programming