Keskeiset käsitteet
A model predictive control framework that optimally allocates distributed energy resource aggregations to provide ramping and regulation-type services, reducing the requirements from bulk generation.
Tiivistelmä
The paper presents a model predictive control (MPC) framework for the optimal allocation of distributed energy resource (DER) aggregations to provide ramping and regulation-type services to the power grid.
Key highlights:
- The DER aggregations are modeled using generalized linear battery models that capture the dynamics and capacity constraints of different DER classes.
- An optimal control problem is formulated to minimize the costs associated with bulk generation and the state of charge of the DER aggregations.
- The infinite-horizon optimal control problem is converted to a sequence of finite-horizon constrained optimization problems using the MPC approach.
- This allows the framework to leverage more accurate short-term forecasts of net demand while ensuring long-term optimality and constraint satisfaction.
- Simulations using real-world data demonstrate that the MPC-based DER allocation can significantly reduce the ramping and regulation requirements from bulk generation, thereby improving grid stability and efficiency.
Tilastot
The net-demand forecast is from the California Independent System Operator (CAISO) dataset for September 2023.
The forecast disturbances are modeled using the balancing reserves deployed (BRD) by the Bonneville Power Administration (BPA) in September 2023.
Lainaukset
"The grid operator can tackle these challenges by intelligently allocating distributed energy resources (DERs), such as solar photovoltaic, wind turbines, battery storage, and flexible loads."
"MPC has strong guarantees for transient operations and ensures constraint satisfaction, while also accounting for long-run optimality."