A Minimal Incentive-Based Demand Response Program With Self Reported Baseline Mechanism

Abstract

In this paper, we propose a novel incentive based Demand Response (DR) program with a self reported baseline mechanism. The System Operator (SO) managing the DR program recruits consumers or aggregators of DR resources. The recruited consumers are required to only report their baseline, which is the minimal information necessary for any DR program. During a DR event, a set of consumers, from this pool of recruited consumers, are randomly selected. The consumers are selected such that the required load reduction is delivered. The selected consumers, who reduce their load, are rewarded for their services and other recruited consumers, who deviate from their reported baseline, are penalized. The randomization in selection and penalty ensure that the baseline inflation is controlled. We also justify that the selection probability can be simultaneously used to control SO's cost. This allows the SO to design the mechanism such that its cost is almost optimal when there are no recruitment costs or atleast significantly reduced otherwise. Finally, we also show that the proposed method of selfreported baseline outperforms other baseline estimation methods commonly used in practice.

Baselining Flexibility from PV on the DSO-Aggregator Interface

“…The universal smart energy framework (USEF) foundation identifies gaming as one of the issues with their (self-reported) baseline model, in which deviations from the reported baseline are not penalised [7,8]. In [9], a DR program is implemented, taking the system operator's perspective. Consumers report their baseline and to ensure truth-telling, consumers deviating from the baseline-while not providing promised flexibility-are penalised with a random penalty.…”

Optimal Portfolio Selection Methodology for a Demand Response Aggregator

“…The coordinated participation of DR allows for the improvement of the economic efficiency in electricity markets since DR can reduce peak demand and price volatility [3]. Additionally, in a scenario with a high penetration of stochastic renewable sources, DR promises to be a better alternative than using polluting and costly reserves to balance the variability of renewable generation [4]. Countries such as the United States have prescribed that DR resource owners can offer this service as a resource supply for greater market transparency [5].…”

A generalized economic model for optimally selecting forecasted load profiles for measuring demand response in residential energy management system

“…For example, Reference 16 outlined the fact that demand response programs go through three basic steps of development; (a) establishing a baseline against which demand reduction is measured, (b) designing the payment scheme for agents who reduce their consumption from this baseline, and (c) the selection scheme for selecting the customers that would yield the maximum demand response. Researchers 17 went one step further and examined the merits of using different self‐reported baselines to measure the performance of a demand response program. According to their research, the system operator recruits consumers for a demand response program.…”

“…Recently published literature has mainly focused on creating new baselines 15 . These new baselines are supposedly more accurate or have less bias than existing baselines 17 . Unfortunately, creating new baselines would not significantly benefit the current state of the art as all baselines are inherently and inevitably imperfect 16 .…”