Short-term risk management for electricity retailers under rising shares of decentralized solar generation - Marianna Russo; Emil Kraft; Valentin Bertsch; Dogan Keles (Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP), 2021)
Electricity retailers face increasing uncertainty due to the ongoing expansion of unpredictable, distributed generation in the residential sector. We analyze how increasing levels of households' solar PV self-generation affect the short-term decisionmaking and associated risk exposure of electricity retailers in 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 twostage stochastic mixed-integer optimization model to investigate the trading portfolio and risk optimization problem faced by retailers. Through conditional value-at-risk we assess 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 affect 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 risk sharing between customer and retailer. 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.
Keywords: C10; C50; G10; Q42; Q48; 330; Electricity markets; Stochastic model; Stochastic programming; Retailer uncertainty modeling; Riskmanagement