Aggregated demand flexibility prediction of residential thermostatically controlled loads and participation in electricity balance markets

TL;DR

This paper introduces a probabilistic method combining Monte Carlo simulation and bisection to accurately predict the maximum demand flexibility of residential thermostatic loads for market participation.

Contribution

It presents a novel probabilistic approach to predict demand flexibility of TCLs using a combined Monte Carlo and extremum search method, enabling reliable market bids.

Findings

Method accurately predicts maximum flexibility power with high confidence.

Validated in three case studies with Spanish electricity markets.

Improves reliability of demand response participation in balancing markets.

Abstract

The aggregate demand flexibility of a set of thermostatically controlled residential loads (TCLs) can be represented by a virtual battery (VB) in order to manage their participation in the electricity markets. For this purpose, it is necessary to know in advance and with a high level of reliability the maximum power that can be supplied by the aggregation of TCLs. A probability function of the power that can be supplied by a VB is introduced. This probability function is used to predict the demand flexibility using a new experimental probabilistic method based on a combination of Monte Carlo simulation and extremum search by bisection algorithm (MC&ESB). As a result, the maximum flexibility power that a VB can provide with a certain guaranteed probability is obtained. The performance and validity of the proposed method are demonstrated in three different case studies where a VB bids its aggregate power in the Spanish electricity balancing markets (SEBM).