Distributionally Robust Chance-Constrained Optimal Power Flow With Uncertain Renewables and Uncertain Reserves Provided by Loads

摘要: Aggregations of electric loads can provide reserves to power systems, but their available reserve capacities are time-varying and not perfectly known when the system operator computes optimal generation schedule. In this paper, we formulate a chance constrained flow problem procure minimum cost energy, generator reserves, load given uncertainty in renewable energy production, consumption, capacities. Assuming that distributions known, solve with distributionally robust optimization, which ensures constraints satisfied for any distribution an ambiguity set built upon first two moments. We use sets reformulate model as semidefinite program second-order cone run computational experiments on IEEE 9-bus, 39-bus, 118-bus systems. compare solutions those by benchmark reformulations; assumes normally distributed second uses large numbers samples. find even uncertain, reduces operational costs. Also, approach is able meet reliability requirements, unlike lower computation times than approach.