Risk Limiting Dispatch with Ramping Constraints

TL;DR

This paper develops an optimal dispatch framework for power systems with ramping constraints, balancing system reliability and cost under renewable energy uncertainty, using stochastic control and chance constraints.

Contribution

It introduces a novel stochastic control model incorporating ramping constraints and compares two computational methods for optimal dispatch in renewable-rich power systems.

Findings

Chance constrained dispatch outperforms MPC controller.

The approach is robust to changes in renewable energy distribution.

Explicit modeling of ramping constraints improves reliability.

Abstract

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.