Table of Contents
Fetching ...

Linear energy storage and flexibility model with ramp rate, ramping, deadline and capacity constraints

Md Umar Hashmi, Dirk Van Hertem, Aleen van der Meer, Andrew Keane

Abstract

The power networks are evolving with increased active components such as energy storage and flexibility derived from loads such as electric vehicles, heat pumps, industrial processes, etc. Better models are needed to accurately represent these assets; otherwise, their true capabilities might be over or under-estimated. In this work, we propose a new energy storage and flexibility arbitrage model that accounts for both ramp (power) and capacity (energy) limits, while accurately modelling the ramp rate constraint. The proposed models are linear in structure and efficiently solved using off-the-shelf solvers as a linear programming problem. We also provide an online repository for wider application and benchmarking. Finally, numerical case studies are performed to quantify the sensitivity of ramp rate constraint on the operational goal of profit maximization for energy storage and flexibility. The results are encouraging for assets with a slow ramp rate limit. We observe that for resources with a ramp rate limit of 10% of the maximum ramp limit, the marginal value of performing energy arbitrage using such resources exceeds 65% and up to 90% of the maximum profit compared to the case with no ramp rate limitations.

Linear energy storage and flexibility model with ramp rate, ramping, deadline and capacity constraints

Abstract

The power networks are evolving with increased active components such as energy storage and flexibility derived from loads such as electric vehicles, heat pumps, industrial processes, etc. Better models are needed to accurately represent these assets; otherwise, their true capabilities might be over or under-estimated. In this work, we propose a new energy storage and flexibility arbitrage model that accounts for both ramp (power) and capacity (energy) limits, while accurately modelling the ramp rate constraint. The proposed models are linear in structure and efficiently solved using off-the-shelf solvers as a linear programming problem. We also provide an online repository for wider application and benchmarking. Finally, numerical case studies are performed to quantify the sensitivity of ramp rate constraint on the operational goal of profit maximization for energy storage and flexibility. The results are encouraging for assets with a slow ramp rate limit. We observe that for resources with a ramp rate limit of 10% of the maximum ramp limit, the marginal value of performing energy arbitrage using such resources exceeds 65% and up to 90% of the maximum profit compared to the case with no ramp rate limitations.
Paper Structure (13 sections, 15 equations, 8 figures, 1 table)

This paper contains 13 sections, 15 equations, 8 figures, 1 table.

Figures (8)

  • Figure 1: Real-time electricity price signal.
  • Figure 2: Marginal arbitrage gain plot for $b_0=b_{\min}$ shown in blue and $b_0=b_{\max}$ shown in red.
  • Figure 3: Sensitivity of ramp rate constraint on (a) number of cycles of operation and (b) arbitrage gains per cycle for $b_0=b_{\min}$ shown in blue and $b_0=b_{\max}$ shown in red.
  • Figure 4: Energy storage power output for max ramp rate set at 0.5 kW for $b_0=b_{\max}$.
  • Figure 5: Marginal arbitrage gains with different ramp levels of energy storage devices along with varying ramp rate limit.
  • ...and 3 more figures