Model Predictive Control for Joint Ramping and Regulation-Type Service from Distributed Energy Resource Aggregations

TL;DR

This paper presents an MPC-based approach for optimally allocating distributed energy resources to provide grid ramping and regulation services, improving grid stability and reducing generation ramping.

Contribution

It introduces a novel MPC framework that uses short-term load forecasts for optimal DER allocation to enhance grid stability and resource utilization.

Findings

MPC reduces the need for bulk generation ramping.

The approach mitigates near-real time grid disturbances.

Simulations validate the effectiveness of the proposed method.

Abstract

Distributed energy resources (DERs) such as grid-responsive loads and batteries can be harnessed to provide ramping and regulation services across the grid. This paper concerns the problem of optimal allocation of different classes of DERs, where each class is an aggregation of similar DERs, to balance net-demand forecasts. The resulting resource allocation problem is solved using model-predictive control (MPC) that utilizes a rolling sequence of finite time-horizon constrained optimizations. This is based on the concept that we have more accurate estimates of the load forecast in the short term, so each optimization in the rolling sequence of optimization problems uses more accurate short term load forecasts while ensuring satisfaction of capacity and dynamical constraints. Simulations demonstrate that the MPC solution can indeed reduce the ramping required from bulk generation, while mitigating near-real time grid disturbances.