Many photovoltaic and wind generation capacity owners gain access to power markets by signing up with virtual power plants. Power generation from these renewable sources of electricity is inherently uncertain and, consequently, revenue is random, which induces a risk for the owner. In this study, we investigate to what extent pooling different technologies and locations in the portfolio of a virtual power plant can reduce aggregate risk. To this end, we develop stochastic models for factors driving the assets’ underlying market and volume risks on which we base a model for risk-optimized pooling. Using the German market as an example, we demonstrate that optimal portfolios have a clearly better risk/return profile than the market portfolio. This finding holds in the case without subsidies as well as the case with feed-in tariffs.

The 1/N investment strategy, i.e. the strategy to split one’s wealth uniformly between the available investment possibilities, recently received plenty of attention in the literature. In this paper, we demonstrate that the uniform investment strategy is rational in situations where an agent is faced with a sufficiently high degree of model uncertainty in the form of ambiguous loss distributions. More specifically, we use a classical risk minimization framework to show that, for a broad class of risk measures, as the uncertainty concerning the probabilistic model increases, the optimal decisions tend to the uniform investment strategy. To illustrate the theoretical results of the paper, we investigate the Markowitz portfolio selection model as well as Conditional Value-at-Risk minimization with ambiguous loss distributions. Subsequently, we set up a numerical study using real market data to demonstrate the convergence of optimal portfolio decisions to the uniform investment strategy.