Stochastically Optimized, Carbon-Reducing Dispatch of Storage, Generation, and Loads

We present a new formulation of a hybrid stochastic-robust optimization and use it to calculate a look-ahead, security-constrained optimal power flow. It is designed to reduce carbon dioxide (
${\rm CO}_{2}$
) emissions by efficiently accommodating renewable energy sources and by realistically evaluating system changes that could reduce emissions. It takes into account ramping costs,
${\rm CO}_{2}$
damages, demand functions, reserve needs, contingencies, and the temporally linked probability distributions of stochastic variables such as wind generation. The inter-temporal trade-offs and transversality of energy storage systems are a focus of our formulation. We use it as part of a new method to comprehensively estimate the operational net benefits of system changes. Aside from the optimization formulation, our method has four other innovations. First, it statistically estimates the cost and
${\rm CO}_{2}$
impacts of each generator's electricity output and ramping decisions. Second, it produces a comprehensive measure of net operating benefit, and disaggregates that into the effects on consumers, producers, system operators, government, and
${\rm CO}_{2}$
damage. Third and fourth, our method includes creating a novel, modified Ward reduction of the grid and a thorough generator dataset from publicly available information sources. We then apply this method to estimating the impacts of wind power, energy storage, and operational policies.