Levelised Cost of Demand Response: Estimating the Cost-Competitiveness of Flexible Demand

TL;DR

The paper introduces the levelised cost of demand response (LCODR), a lifetime cost metric analogous to LCOS but incorporating consumer reward payments and rebound costs, enabling direct comparison of demand response with energy storage. It defines four direct load control schemes (V2G, smart charging, smart heat pumps, heat pumps with thermal storage) and evaluates them against twelve storage applications within a UK context, using an availability-adjusted value factor and Monte Carlo uncertainty analysis. Results show heat pumps with thermal storage are consistently cheaper than storage for feasible applications, while EV-based DR can be competitive only in high-discharge scenarios; rewards dominate the DR cost structure. The framework, while insightful, relies on UK-centric data and stated-preference inputs, highlighting the need for revealed-preference data and expanded DR scheme coverage to improve robustness and applicability across regions.

Abstract

To make well-informed investment decisions, energy system stakeholders require reliable cost frameworks for demand response (DR) and storage technologies. While the levelised cost of storage (LCOS) permits comprehensive cost comparisons between different storage technologies, no generic cost measure for the comparison of different DR schemes exists. This paper introduces the levelised cost of demand response (LCODR) which is an analogous measure to the LCOS but crucially differs from it by considering consumer reward payments. Additionally, the value factor from cost estimations of variable renewable energy is adapted to account for the variable availability of DR. The LCODRs for four direct load control (DLC) schemes and twelve storage applications are estimated and contrasted against LCOS literature values for the most competitive storage technologies. The DLC schemes are vehicle-to-grid, smart charging, smart heat pumps, and heat pumps with thermal storage. The results show that only heat pumps with thermal storage consistently outcompete storage technologies with EV-based DR schemes being competitive for some applications. The results and the underlying methodology offer a tool for energy system stakeholders to assess the competitiveness of DR schemes even with limited user data.

Figures (10)

• Figure 1: Energy and power profiles for servicing an energy storage application with different technologies. Note that the minimum energy level for EVs ($E_{EV}^{min}$) is their energy capacity ($E_{EV}^{max}$) multiplied by the guaranteed minimum charge ($f_{GMC}$): $E^{min}_{EV} = E^{max}_{EV} \times f_{GMC}$
• Figure 2: Illustration of the components and estimation process of the availability profile-adjusted LCODR ($LCODR_{VF}$)
• Figure 3: Illustration of the components and estimation process of the availability profile-adjusted LCODR ($LCODR_{VF}$)
• Figure 4: Discharge duration limits illustration on the energy profiles of different DR assets. Note that the heat pump-only profile (3rd graph) shows an activation that reduces the heat pump's power consumption to zero ($\Delta P^{red}_{HP} = P^{act,avg}_{HP}$)
• Figure 5: Value factor results with dotted lines indicating averages. The distribution arises from a Monte-Carlo analysis that took sub-samples of 50 DR assets to assess the sensitivity of the value factor to changes in consumer behaviour.