A Data-Driven Bidding Model for a Cluster of Price-Responsive Consumers of Electricity

Abstract

This paper deals with the market-bidding problem of a cluster of price-responsive consumers of electricity. We develop an inverse optimization scheme that, recast as a bilevel programming problem, uses price-consumption data to estimate the complex market bid that best captures the price-response of the cluster. The complex market bid is defined as a series of marginal utility functions plus some constraints on demand, such as maximum pick-up and drop-off rates. The proposed modeling approach also leverages information on exogenous factors that may influence the consumption behavior of the cluster, e.g., weather conditions and calendar effects. We test the proposed methodology for a particular application: forecasting the power consumption of a small aggregation of households that took part in the Olympic Peninsula project. Results show that the pricesensitive consumption of the cluster of flexible loads can be largely captured in the form of a complex market bid, so that this could be ultimately used for the cluster to participate in the wholesale electricity market.

Inverse optimization for the recovery of constraint parameters

“…In this setting, each market participant solves an optimization problem to determine a bid which they submit to a facilitator, who in turn incorporates the bids in a market clearing optimization problem that determines prices and the consumption or production allocated to each bidder. In this context, the facilitator may impute bidders' right-hand-side constraint parameters, such as bounds on consumption, which can then be used to inform a pricing strategy that aims to maximize profit or control peak demand (Saez-Gallego et al, 2016;Saez-Gallego & Morales, 2018;Xu et al, 2018;Lu et al, 2019). Similarly, a bidder may seek to impute several unknown parameters which can be used in the process of deciding her bid.…”

Demand Response Resource Allocation Method Using Mean-Variance Portfolio Theory for Load Aggregators in the Korean Demand Response Market

“…On the other hand, owners of DRRs with higher shutdown cost would preferably participate in demand response whenever the price is relatively high enough to recover its shutdown cost. Despite the fact that there have been former studies dealing with consumers' responses to electricity prices [10], the participation rate is considered in this study to be a dependent variable of the electricity price and participation rate in the future is estimated through regression model as follows:…”

“…The market-bidding problem of a pool of price-responsive consumers for the aggregator or the retailer is studied in [10]. The complex market bid, consisting of a series of price-energy bidding curves, consumption limits, and maximum pike-up and drop-off rates, can largely capture the price-sensitive consumption of the cluster of flexible loads.…”

Learning From Past Bids to Participate Strategically in Day-Ahead Electricity Markets

“…In [24], inverse optimization is used to identify the bids of marginal suppliers in a multi-period network-constrained electricity pool. In [25], it is employed to address the market-bidding problem of a cluster of price-responsive consumers of electricity. In [26], it is used to determine market structure from commodity and transportation prices; the methodology is applied to data from the MISO electricity market.…”