Utilizing Device-level Demand Forecasting for Flexibility Markets - Full Version

TL;DR

This paper introduces a device-level demand response scheme that leverages flexible energy demand to reduce market imbalances, demonstrating its financial viability and analyzing the impact of forecast accuracy and data granularity.

Contribution

It presents a comprehensive demand flexibility modeling approach at the device level and evaluates its effectiveness in real market scenarios, highlighting utility over forecast accuracy.

Findings

Demand flexibility can reduce regulation costs by up to 54%.

Forecast accuracy has limited impact on financial benefits.

Optimal data granularity enhances market participant rewards.

Abstract

The uncertainty in the power supply due to fluctuating Renewable Energy Sources (RES) has severe (financial and other) implications for energy market players. In this paper, we present a device-level Demand Response (DR) scheme that captures the atomic (all available) flexibilities in energy demand and provides the largest possible solution space to generate demand/supply schedules that minimize market imbalances. We evaluate the effectiveness and feasibility of widely used forecasting models for device-level flexibility analysis. In a typical device-level flexibility forecast, a market player is more concerned with the \textit{utility} that the demand flexibility brings to the market, rather than the intrinsic forecast accuracy. In this regard, we provide comprehensive predictive modeling and scheduling of demand flexibility from household appliances to demonstrate the (financial and otherwise) viability of introducing flexibility-based DR in the Danish/Nordic market. Further, we investigate the correlation between the potential utility and the accuracy of the demand forecast model. Furthermore, we perform a number of experiments to determine the data granularity that provides the best financial reward to market players for adopting the proposed DR scheme. A cost-benefit analysis of forecast results shows that even with somewhat low forecast accuracy, market players can achieve regulation cost savings of 54% of the theoretically optimal.