Modeling, Analysis, and Control of Demand Response Resources

Abstract

While the traditional goal of an electric power system has been to control supply to fulfill demand, the demand-side can plan an active role in power systems via Demand Response (DR), defined by the Department of Energy (DOE) as "a tariff or program established to motivate changes in electric use by end-use customers in response to changes in the price of electricity over time, or to give incentive payments designed to induce lower electricity use at times of high market prices or when grid reliability is jeopardized" [29]. DR can provide a variety of benefits including reducing peak electric loads when the power system is stressed and fast timescale energy balancing. Therefore, DR can improve grid reliability and reduce wholesale energy prices and their volatility.This dissertation focuses on analyzing both recent and emerging DR paradigms. Recent DR programs have focused on peak load reduction in commercial buildings and industrial facilities (C&I facilities). We present methods for using 15-minute-interval electric load data, commonly available from C&I facilities, to help building managers understand building energy consumption and 'ask the right questions' to discover opportunities for DR. Additionally, we present a regression-based model of whole building electric load, i.e., a baseline model, which allows us to quantify DR performance. We use this baseline model to understand the performance of 38 C&I facilities participating in an automated dynamic pricing DR program in California. In this program, facilities are expected to exhibit the same response each DR event. We find that baseline model error makes it difficult to precisely quantify changes in electricity consumption and understand if C&I facilities exhibit event-to-event variability in their response to DR signals. Therefore, we present a method to compute baseline model error and a metric to determine how much observed DR variability results from baseline model error rather than real variability in response. We find that, in general, baseline model error is large. Though some facilities exhibit real DR variability, most observed variability results from baseline model error. In some cases, however, aggregations of C&I facilities exhibit real DR variability, which could create challenges for power system operation. These results have implications for DR program design and deployment. 2Emerging DR paradigms focus on faster timescale DR. Here, we investigate methods to coordinate aggregations of residential thermostatically controlled loads (TCLs), including air conditioners and refrigerators, to manage frequency and energy imbalances in power systems. We focus on opportunities to centrally control loads with high accuracy but low requirements for sensing and communications infrastructure. Specifically, we compare cases when measured load state information (e.g., power consumption and temperature) is 1) available in real time; 2) available, but not in real time; and 3) not available. We develop Markov Chain models to describe the temperature state e...

Distributed Control Design for Balancing the Grid Using Flexible Loads

“…On the academic side, Matheiu's dissertation [32] and references [33,34] were highly influential, motivating in part the research surveyed in this chapter and others [16,26,46,48]. The control model in [32] is based on the mean-field setting of [29], with the introduction of a control signal from a central authority: at each time slot, a BA or aggregator broadcasts probability values {p ⊕ τ , p τ : τ ∈ R} where p ⊕ τ (p τ ) denotes the probability of turning the device on (off) when the temperature of the device is τ.…”

“…The control model in [32] is based on the mean-field setting of [29], with the introduction of a control signal from a central authority: at each time slot, a BA or aggregator broadcasts probability values {p ⊕ τ , p τ : τ ∈ R} where p ⊕ τ (p τ ) denotes the probability of turning the device on (off) when the temperature of the device is τ. The temperatures are binned to obtain a finite state-space aggregate model.…”

Spectral Decomposition of Demand-Side Flexibility for Reliable Ancillary Services in a Smart Grid

“…Loads that have been considered include commercial building HVAC fans in the time-scale of seconds to nearly one hour [10,13,18], thermostatic devices that can provide ancillary service in the time-scale of a few minutes [4,21,22] (and refs. therein), electric vehicle charging that can provide ancillary service in the time scale of a few hours [9,19,21,27], and pool pumps in the states of Florida or California can provide ancillary service on longer time scales [8,23] (these loads are also used for peak-shaving [1,5]).…”

“…Many BAs employ demand response (DR) programs that use controllable loads to reduce peak demand and manage emergency situations [22]. Florida Power and Light (FPL), for example, has 780,000 residential customers enrolled in their OnCall Savings Program which allows FPL to remotely turn off select equipment -such as pool pumps -when needed [1].…”

Automated Measurement and Signaling Systems for the Transactional Network

“…Ideally, a baseline prediction will account for all of the scheduled and routine uses of electricity in the building as well as electric load that varies with outdoor air temperature. The agents described here build upon baseline models that are described in detail by Mathieu et al (2011aMathieu et al ( , 2011b, Mathieu (2012), and Price (2010).…”