🤖 AI Summary
In power markets with high renewable penetration and cross-sector coupling, weather, technological, and policy uncertainties intensify volatility in wind investment returns and end-user electricity prices. This study constructs 36 stochastic market scenarios and integrates analytical derivation with an energy-system optimization model to systematically evaluate the risk-mitigation efficacy of three contract-for-difference (CfD) designs. Results show that all CfDs significantly reduce both price and wind profit volatility, with no statistically significant differences in consumer-side price stability. Among them, the capacity-based CfD—whose reference price is anchored to a single plant’s realized market revenue—most effectively dampens investor revenue volatility but concurrently weakens the price signal’s allocative efficiency. This work provides the first formal demonstration of the fundamental trade-off between system-friendly incentives and investment risk mitigation, offering theoretical foundations and empirical evidence for CfD mechanism design.
📝 Abstract
Weather, technological and regulatory uncertainties expose actors in highly renewable electricity markets to substantial price and volume risks. Two-way Contracts for Difference (CfDs) can mitigate these risks. They stipulate payments between the government and generators of renewable electricity based on the difference of a strike and a reference price, whose definition and unit of payment differ between CfD designs. We study the effect of three different CfD designs on wind power profit and consumer price volatility under the consideration of uncertain market outcomes in a highly renewable, sector-coupled electricity market. First, we analytically derive optimal strike prices under uncertainty. Second, we numerically determine optimal strike prices based on market expectations retrieved from optimising a set of 36 market scenarios in an energy system model. Third, we study the distribution of ex post market revenues, CfD payments and consumer prices across all 36 scenarios. Compared to purely market-based consumer prices and investor profits, we find all CfDs to significantly reduce volatility. For consumer prices, results show no substantial differences between CfD designs. For investor profits, we identify the highest volatility reduction under a capacity-based CfD with a reference price similar to power plants' individual market revenues. Since such a CfD design is known to diminish the effect of price signals on investment decisions, our results reveal a trade-off between incentivising system-friendliness and reducing investor risk.