Variability in automated responses of commercial buildings and industrial facilities to dynamic electricity prices

Changes in the electricity consumption of commercial buildings and industrial facilities (C&I facilities) during Demand Response (DR) events are usually estimated using counterfactual baseline models. Model error makes it difficult to precisely quantify these changes in consumption and understand if C&I facilities exhibit event-to-event variability in their response to DR signals. This paper seeks to understand baseline model error and DR variability in C&I facilities facing dynamic electricity prices. Using a regression-based baseline model, we present a method to compute the error associated with estimates of several DR parameters. We also develop a metric to determine how much observed DR variability results from baseline model error rather than real variability in response. We analyze 38 C&I facilities participating in an automated DR program and find that DR parameter errors are large. Though some facilities exhibit real DR variability, most observed variability results from baseline model error. Therefore, facilities with variable DR parameters may actually respond consistently from event to event. Consequently, in DR programs in which repeatability is valued, individual buildings may be performing better than previously thought. In some cases, however, aggregations of C&I facilities exhibit real DR variability, which could create challenges for power system operation

Probabilistic Determination of Consumers Response and Consumption Managing Strategies in Demand Side Management

Il documento punta a risolvere il problema dell'incertezza che affligge il gestore del sistema elettrico quando deve chiedere la variazione dell'assorbimento elettrico da parte degli utenti in caso di congestione della rete o picco nel prezzo dell'elettricità. Il metodo prevedere una caratterizzazione da un punto di vista probabilistico dalla capacità di variazione di carico da parte dei consumatori ed illustra come il gestore può usufruire degli utenti stessi per massimizzare il risultato

Dynamic Pricing, Advanced Metering, and Demand Response in Electricity Markets

Presents an overview and analysis of the possible approaches to bringing an active demand side into electricity markets. Part of a series of research reports that examines energy issues facing California

Scenario-based Economic Dispatch with Uncertain Demand Response

This paper introduces a new computational framework to account for uncertainties in day-ahead electricity market clearing process in the presence of demand response providers. A central challenge when dealing with many demand response providers is the uncertainty of its realization. In this paper, a new economic dispatch framework that is based on the recent theoretical development of the scenario approach is introduced. By removing samples from a finite uncertainty set, this approach improves dispatch performance while guaranteeing a quantifiable risk level with respect to the probability of violating the constraints. The theoretical bound on the level of risk is shown to be a function of the number of scenarios removed. This is appealing to the system operator for the following reasons: (1) the improvement of performance comes at the cost of a quantifiable level of violation probability in the constraints; (2) the violation upper bound does not depend on the probability distribution assumption of the uncertainty in demand response. Numerical simulations on (1) 3-bus and (2) IEEE 14-bus system (3) IEEE 118-bus system suggest that this approach could be a promising alternative in future electricity markets with multiple demand response providers

Information Processing view of Electricity Demand Response Systems: A Comparative Study Between India and Australia

Background: In recent years, demand response (DR) has gained increased attention from utilities, regulators, and market aggregators to meet the growing demands of electricity. The key aspect of a successful DR program is the effective processing of data and information to gain critical insights. This study aims to identify information processing needs and capacity that interact to improve energy DR effectiveness. To this end, organizational information processing theory (OIPT) is employed to understand the role of Information Systems (IS) resources in achieving desired DR program performance. This study also investigates how information processing for DR systems differ between developing (India) and developed (Australia) countries. Method: This work adopts a case study methodology to propose a theoretical framework using OIPT for information processing in DR systems. The study further employs a comparative case data analyses between Australian and Indian DR initiatives. Results: Our cross case analysis identifies variables of value creation in designing DR programs - pricing structure for demand side participation, renewable integration at supply side, reforms in the regulatory instruments, and emergent technology. This research posits that the degree of information processing capacity mediates the influence of information processing needs on energy DR effectiveness. Further, we develop five propositions on the interaction between task based information processing needs and capacity, and their influence on DR effectiveness. Conclusions: The study generates insights on the role of IS resources that can help stakeholders in the electricity value chain to take informed and intelligent decisions for improved performance of DR programs