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

Joel Mathias, Rajasekhar Anguluri, Oliver Kosut, Lalitha Sankar

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.

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

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.
Paper Structure (7 sections, 10 equations, 3 figures, 1 table)

This paper contains 7 sections, 10 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem Setup
  3. Generalized linear battery models
  4. DER allocation: an optimal control approach
  5. Model Predictive Control Architecture
  6. Simulations
  7. Conclusions and Future Work

Figures (3)

  • Figure 1: Bulk generation, net-demand and the total power supplied by all classes of DER aggregations. The net-demand is from CAISO from September 1, 2023 through September 7 2023, with BPA's BRD disturbances (for the corresponding time period) coming in at 30 minute windows during each MPC update. The DER aggregations provide most of the ramping and regulation-type service required to balance the net load, thereby reducing the requirements from bulk generation.
  • Figure 2: SoC trajectories via MPC from five classes of DER aggregations that are deployed to meet net-demand forecast for CAISO from September 1 through September 7, 2023. Each optimization problem has a horizon $\tau = 24$ hours. The time shift between sucessive optimization problems is $t_s= 30$ minutes. BPA's BRD from September 1 through September 7, 2023, are added to the net-demand forecast every 30 minutes: these comprise of "near real-time" disturbances to the net-demand. The figure shows that some classes of DERs can charge to their maximum SoC capacity: particularly noticeable for ACs and bldgs.
  • Figure 3: Power trajectories for the five classes of DER aggregations obtained via the MPC solution. The DER aggregations provide both ramping and regulation-type service to the grid. The faster fluctuations are due the need to cancel the near-real time (forecast) disturbances because of the BPA BRD signal.