Residential load modeling for predicting distribution transformer load behavior, feeder load and cold load pickup

Lefebvre, S.; Desbiens, Charles

doi:10.1016/s0142-0615(01)00040-0

Cited by 33 publications

(22 citation statements)

References 19 publications

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“…We model the temperature state evolution of an individual TCL with a discrete time difference equation commonly used in the literature, e.g., [19,30], and verified against real populations of TCLs [30,81,95,37]:…”

Section: Individual Tcl Modelmentioning

confidence: 99%

Modeling, Analysis, and Control of Demand Response Resources

Mathieu

2012

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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...

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Section: Individual Tcl Modelmentioning

confidence: 99%

Modeling, Analysis, and Control of Demand Response Resources

Mathieu

2012

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“…This paper assumes that the utility functions used by agents to devise alternative plans for resource utilization, take these uncertainties into account, see [6,7]. These plans denote the options for energy consumption, i.e., the distribution of consumption, over a fixed period of time.…”

Section: The Distributed Coordination Environmentmentioning

confidence: 99%

A Distributed Agent-based Approach to Stabilization of Global Resource Utilization

Pournaras¹,

Warnier²,

Brazier³

2009

2009 International Conference on Complex, Intelligent and Software Intensive Systems

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Distributed management of complex, distributed systems is the focus of this paper. Adaptation through local deliberation by software agents within a hierarchical virtual organization is the approach taken. Global stabilization of resource utilization is the goal. Electricity networks are used to illustrate the potential of two fitness functions on the basis of which local choices for resource utilization are made: minimizing oscillations is the first function considered, reversing oscillations the second. Results reveal considerable increase in the stabilization of resource utilization compared to a system that utilizes resources in a greedy manner.

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“…Building are commonly represented by a heat transfer model that includes thermal resistances [14] - [16] (see Fig. 3).…”

Section: A Residential Building Componentmentioning

confidence: 99%

Automatic Meter Reading Provides Opportunities for New Prognosis and Simulation Methods

Wallin

Dotzauer

Thorin

et al. 2007

2007 IEEE Lausanne Power Tech

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The use of top-down models for load forecasting purposes has been a dominating method over the last few decades. However, there is now concern regarding the performance of topdown models, e.g. in situations with unusual weather conditions, due to the lack of historical data. This paper considers an alternative bottom-up approach with a stronger relation to the laws of physics. Electrical companies in Sweden are installing automatic meter reading systems for their customers, and using consumption data gives new possibilities for adapting the modeling parameters in a bottom-up model for each customer. A method for analyzing individual consumption series is suggested, where different periods in time are used to divide and identify different parts of the electricity load: base load, heat load and household loads. A review of previous work is presented, and suggestions on how to link load analysis to construction parameters for an individual building are proposed.

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Residential load modeling for predicting distribution transformer load behavior, feeder load and cold load pickup

Cited by 33 publications

References 19 publications

Modeling, Analysis, and Control of Demand Response Resources

Modeling, Analysis, and Control of Demand Response Resources

A Distributed Agent-based Approach to Stabilization of Global Resource Utilization

Automatic Meter Reading Provides Opportunities for New Prognosis and Simulation Methods

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