# VPP Pricing I: Stochastic Processes & Partial Integro Differential Equation

The basis for the virtual power plant (VPP) pricing algorithm is a joint stochastic process for the power price and the gas price. Instead of defining a  stochastic differential equation for the spark spread directly the spark spread is then given by the difference between the power price and the gas price times the VPP heating rate.

The Kluge model will be used to define the power price process [1], an exponential Ornstein-Uhlenbeck will describe the gas price [2]



where $N_t$ is a Poisson process with jump intensity $\lambda$. To match the power forward curve $F_0^t$ the seasonal function $p_t$ is given by [1]:



To be consistent with the gas forward curve $H_0^t$ the seasonal function $g_t$ is defined by



The Feynman-Kac theorem can be applied to derive the corresponding three-dimensional  partial integro differential equation



In general at least one further dimension is needed to keep track of the state of the virtual power plant. Therefore solving this model using finite difference methods will lead to a four-dimensional PIDE problem.

The following diagram shows an one year example path for the power and the gas price based on the freely available Kyos example forward curves. The sample parameterization is affected by [1] for the power process and [2] for the gas process.

$\beta=200, \eta=2.5, \lambda=4, \alpha=7, \sigma_x=1.4, \kappa=4.45, \sigma_u=\sqrt{1.3}, \rho=70\%$

The code is available here. It depends on the latest QuantLib version from the SVN trunk. If you want to generate the plot directly out of the C++ program you also need R, RCPP and RInside.

[2] G. Fusai, A. Roncoroni, Implementing Models in Quantitative Finance: Models and Cases, Chapter 19, ISBN: 978-3-540-22348-1

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