Risk Limiting Dispatch with Ramping Constraints

Reliable operation in power systems is becoming more difficult as the penetration of random renewable resources increases. In particular, operators face the risk of not scheduling enough traditional generators in the times when renewable energies becomes lower than expected. In this paper we study the optimal trade-off between system and risk, and the cost of scheduling reserve generators. We explicitly model the ramping constraints on the generators. We model the problem as a multi-period stochastic control problem, and we show the structure of the optimal dispatch. We then show how to efficiently compute the dispatch using two methods: i) solving a surrogate chance constrained program, ii) a MPC-type look ahead controller. Using real world data, we show the chance constrained dispatch outperforms the MPC controller and is also robust to changes in the probability distribution of the renewables.Comment: Shorter version submitted to smartgrid comm 201

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Robust Optimal Power Flow with Wind Integration Using Conditional Value-at-Risk

Integrating renewable energy into the power grid requires intelligent risk-aware dispatch accounting for the stochastic availability of renewables. Toward achieving this goal, a robust DC optimal flow problem is developed in the present paper for power systems with a high penetration of wind energy. The optimal dispatch is obtained as the solution to a convex program with a suitable regularizer, which is able to mitigate the potentially high risk of inadequate wind power. The regularizer is constructed based on the energy transaction cost using conditional value-at-risk (CVaR). Bypassing the prohibitive high-dimensional integral, the distribution-free sample average approximation method is efficiently utilized for solving the resulting optimization problem. Case studies are reported to corroborate the efficacy of the novel model and approach tested on the IEEE 30-bus benchmark system with real operation data from seven wind farms.Comment: To Appear in Proc. of the 4th Intl. Conf. on Smart Grid Communication

Risk-limiting Dispatch with Operation Constraints

As an extension of the current theory of risk-limiting dispatch for a system with large-scale renewable integration, this paper presents a model for risk-limiting dispatch with operation constraints, such as generation limitation and network constraint. By proposing and solving four interrelated models, the problem for risk-limiting dispatch with network constraint is finally solved by using sequential optimization. Through the analysis of the model, the paper points out the feasible procedure of dispatch decision, including determining the optimal output and the generators needed to be scheduled. With this dispatch approach, the lowest dispatch cost of the whole dispatch process can be obtained.published_or_final_versio