Climate change mitigation requires governmental intervention, but different choices are at hand. While economists in general advocate for first-best instruments, reality looks quite different, with especially many subsidy schemes for renewable energies being used. Supporters of these schemes often argue that investment risk reduction is essential to achieve ambitious environmental targets. In this paper we compare four different instruments (cap, tax, minimum quota and feedin tariffs/renewable auctions) in terms of efficacy and efficiency and also quantify investment risks, assuming an uncertain investment environment, represented by different information shocks on demand, investment and fuel cost. We use a long-term electricity market equilibrium model (generalized peak load pricing model) of the future German electricity market implemented as a linear optimization problem. Starting from an equilibrium, single input parameters are varied to simulate the arrival of new information. Running the model again with partly fixed capacities then allows us to analyze the adjustment of the power plant portfolio towards the new equilibrium over time. As expected quantity-based instruments are effective in assuring achievement of quantitative goals, notably a certain emission level. Yet risks for investors are rather high in that furthermore that first-best instruments are the most efficient. Risks are lower with price solutions, especially feed-in tariffs or renewable auctions provide the possibility to limit risks extremely by diversification only inside the electricity market.
Investments in power generation assets are multi-year projects with high costs and multi-decade lifetimes. Since market circumstances can significantly change over time, investments into such assets are risky and require structured decision-support systems. Investment decisions and dispatch in electricity spot markets are connected, thus requiring anticipation of expected market outcomes. This strategic situation can be described as a bilevel optimization problem. At the upper level, an investor decides on investments while anticipating the market results. At the lower level, a market operator maximizes welfare given consumer demand and installed generation assets as well as producer price bids. In this paper, we formulate this problem as a mathematical program with equilibrium constraints (MPEC). We consider this model to include a dynamic, rolling-horizon optimization. This structure splits the investment process into multiple stages, allowing the modification of wait-and-see decisions. This is a realistic representation of actors making their decision under imperfect information and has the advantage of allowing the players to adjust their data in between rolls. This more closely models real-world decision-making and allows for learning and other feedback in between rolls. The rolling-horizon formulation also has the beneficial byproduct of computational advantage over a fixed-horizon stochastic optimization formulation since smaller problems are solved and we provide supporting numerical results to this point.
Climate change mitigation requires governmental intervention, but different choices are at hand. While economists in general advocate for first-best instruments, reality looks quite different, with especially many subsidy schemes for renewable energies being used. Supporters of these schemes often argue that investment risk reduction is essential to achieve ambitious environmental targets. In this paper we compare four different instruments (cap, tax, minimum quota and feedin tariffs/renewable auctions) in terms of efficacy and efficiency and also quantify investment risks, assuming an uncertain investment environment, represented by different information shocks on demand, investment and fuel cost. We use a long-term electricity market equilibrium model (generalized peak load pricing model) of the future German electricity market implemented as a linear optimization problem. Starting from an equilibrium, single input parameters are varied to simulate the arrival of new information. Running the model again with partly fixed capacities then allows us to analyze the adjustment of the power plant portfolio towards the new equilibrium over time. As expected quantity-based instruments are effective in assuring achievement of quantitative goals, notably a certain emission level. Yet risks for investors are rather high in that furthermore that first-best instruments are the most efficient. Risks are lower with price solutions, especially feed-in tariffs or renewable auctions provide the possibility to limit risks extremely by diversification only inside the electricity market.
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