Stochastic Simulation of Utility-Scale Storage Resources in Power Systems With Integrated Renewable Resources

We report on the extension of a general stochastic simulation approach for power systems with integrated renewable resources to also incorporate the representation of utility-scale storage resources. The extended approach deploys models of the energy storage resources to emulate their scheduling and operations in the transmission-constrained hourly day-ahead markets. To this end, we formulate a scheduling optimization problem to determine the operational schedule of the controllable storage resources in coordination with the demands and the various supply resources, including the conventional and renewable resources. The incorporation of the scheduling optimization problem into the Monte Carlo simulation framework takes full advantage of the structural characteristics in the construction of the so-called sample paths for the stochastic simulation approach and to ensure its numerical tractability. The extended methodology has the capability to quantify the power system economics, emissions and reliability variable effects over longer-term periods for power systems with the storage resources. Applications of the approach include planning and investment studies and the formulation and analysis of policy. We illustrate the capabilities and effectiveness of the simulation approach on representative study cases on modified IEEE 118 and WECC 240-bus systems. These results provide valuable insights into the impacts of energy storage resources on the performance of power systems with integrated wind resources.