On the capacity value of renewable energy sources in the presence of energy storage and ramping constraints

The objective of this paper is to analyze the value that renewable energy sources (RES) have in providing capacity, and specifically study the interactions that take place in the presence of energy storage systems (ESS) and ramping constraints. To examine this question, we use a new analytical framework that optimizes over different high and low probability scenarios using a stochastic, security constrained Optimal Power Flow (S-SC-OPF). We are interested in the effect of adding a significant amount of RES and analyze the individual generator response and the consequences for overall system metrics. Past studies have shown that, while higher RES penetrations are usually associated to lower system costs, including the provision of ancillary services, the most common direct collateral consequence is the increase in the total generating capacity needed to reliably operate the system [1]. Our model determines the amount of reserves as an endogenous variable, given a set of credible contingencies and a characterization of the uncertainty coming from the renewable energy sources. The main advantage of this model is the explicit inclusion of the cost for the capacity required, both for contingency reserve and for ramping transitions between periods of analysis, as well as a valuation of the wear-and-tear incurred by the generators in these transitions. Our approach simulates several periods applying a S-SC-OPF that minimizes the total system costs including the procurement of energy and ancillary services. We applied this model to a reduction of the Northeastern Power Coordinating Council (NPCC) and calibrate the demand to a day similar to the loading conditions in a hot summer. The system results show reduction of system costs adding wind and storage into the system while helping reduce capacity.