Risk trading and endogenous probabilities in investment equilibria

A risky design equilibrium problem is an equilibrium system that involves N designers who invest in risky assets, such as production plants, evaluate these using convex or coherent risk measures, and also trade financial securities in order to manage their risk. Our main finding is that in a complete risk market - when all uncertainties can be replicated by financial products - a risky design equilibrium problem collapses to what we call a risky design game, i.e., a stochastic Nash game in which the original design agents act as risk neutral and there emerges an additional system risk agent. The system risk agent simultaneously prices risk and determines the probability density used by the other agents for their risk neutral evaluations. This situation is stochastic-endogenous: the probability density used by agents to value uncertain investments is endogenous to the risky design equilibrium problem. This result is most striking when design agents use coherent risk measures in which case the intersection of their risk sets turns out to be a risk set for the system risk agent, thereby extending existing results for risk markets. We also investigate existence of equilibria in both the complete and incomplete cases

Stochastic Equilibrium Models for Generation Capacity Expansion

Capacity expansion models in the power sector were among the first applications of operations research to the industry. We introduce stochastic equilibrium versions of these models that we believe provide a relevant context for looking at the current very risky market where the power industry invests and operates. We then look at the insertion of risk related investment practices that developed with the new environment and may not be easy to accommodate in an optimization context. Specifically we consider the use of plant specific discount rates that we derive by including stochastic discount rates in the equilibrium model. Linear discount factors only price systematic risk. We therefore complete the discussion by inserting different risk functions (for different agents) in order to account for additional unpriced idiosyncratic risk in investments. These different models can be cast in a single mathematical representation but they do not have the same mathematical properties. We illustrate the impact of these phenomena on a small but realistic example

Risk trading and endogenous probabilities in investment equilibria

A risky design equilibrium problem is an equilibrium system that involves N designers who invest in risky assets, such as production plants, evaluate these using convex or coherent risk measures, and also trade financial securities in order to manage their risk. Our main finding is that in a complete risk market - when all uncertainties can be replicated by financial products - a risky design equilibrium problem collapses to what we call a risky design game, i.e., a stochastic Nash game in which the original design agents act as risk neutral and there emerges an additional system risk agent. The system risk agent simultaneously prices risk and determines the probability density used by the other agents for their risk neutral evaluations. This situation is stochastic-endogenous: the probability density used by agents to value uncertain investments is endogenous to the risky design equilibrium problem. This result is most striking when design agents use coherent risk measures in which case the intersection of their risk sets turns out to be a risk set for the system risk agent, thereby extending existing results for risk markets. We also investigate existence of equilibria in both the complete and incomplete cases

Generation Capacity Investments in Electricity Markets:Perfect Competition

Abstract: In competitive electricity markets, markets designs based on power exchanges where supply bidding (barring demand-side bidding) is at the sole short run marginal cost may not guarantee resource adequacy. As alternative ways to remedy the resource adequacy problem, we focus on three different market designs in detail when demand is inelastic, namely an energy-only market with VOLL pricing (or a price cap), an additional capacity market, and operating-reserve pricing. We also discuss demand-side bidding (i.e., a price responsive demand) which can be seen as a categorically different alternative to remedy the resource adequacy problem. We consider a perfectly competitive market consisting of three types of agents: generators, a transmission system operator, and consumers; all agents are assumed to have no market power. For each market design, we model and analyze capacity investment choices of firms using a two-stage game where generation capacities are installed in the first stage and generation takes place in future spot markets at the second stage. When future spot market conditions are assumed to be known a priori (i.e., deterministic demand case), we show that all of these two-stage models with different market mechanisms, except operating-reserve pricing, can be cast as single optimization problems. When future spot market conditions are not known in advance (i.e., under demand uncertainty), we essentially have a two-stage stochastic game. Interestingly, an equilibrium point of this stochastic game can be found by solving a two-stage stochastic program, in case of all of the market mechanisms except operating-reserve pricing. In case of operatingreserve pricing, while the formulation of an equivalent deterministic or stochastic optimization problem is possible when operating-reserves are based on observed demand, this simplicity is lost when operatingreserves are based on installed capacities. We generalize these results for other uncertain parameters in spot markets such as fuel costs and transmission capacities. Finally, we illustrate how all these models can be numerically tackled and present numerical experiments. In our numerical experiments, we observe that uncertainty of demand leads to higher total generation capacity expansion and a broader mix of technologies compared to the investment decisions assuming average demand levels. Furthermore for the same VOLL (or price cap) level and under the assumptions of random demand with finite support and no forced outages, energy-onlymarkets with VOLL pricing tend to lead to total generation capacity below the peak load with a certain probability whereas energy markets with a forward capacity market or operating-reserve pricing result in higher investments. Finally, the regulator decisions (e.g., reserve capacity target) in capacity markets and operating-reserve pricing can be chosen in such a way that results in very similar investment levels and fuel mix of generation capacities in b

Risk trading in capacity equilibrium models

We present a set of power investment models, the class of risky capacity equilibrium problems, reflecting different assumptions of perfect and imperfect markets. The models are structured in a unified stochastic Nash game framework. Each model is the concatenation of a model of the short-term market operations (perfect competition or Cournot), with a long-term model of investment behavior (risk neutral and risk averse behavior under different assumptions of risk trading). The models can all be formulated as complementarity problems, some of them having an optimization equivalent. We prove existence of solutions and report numerical results to illustrate the relevance of market imperfections on welfare and investment behavior. The models are constructed and discussed as two stage problems but we show that the extension to multistage is achieved by a change of notation and a standard assumption on multistage risk functions. We also treat a large multistage industrial model to illustrate the computational feasibility of the approach