PNNL-SA-41618 1 Simulating Price Responsive Distributed Resources N. Lu Member IEEE, D. P. Chassin, Member IEEE, and S. E. Widergren, Sr. Member, IEEE Given a large penetration of price responsive DER with its associated market transactions, a new approach is also proposed, based upon more holistic statistical mechanic techniques. Such an approach holds promise in revealing the fundamentals of complex system behavior by elevating analysis from the overwhelming details to higher levels of abstract behavior. Abstract — Distributed energy resources (DER) include distributed generation, storage, and responsive demand. The integration of DER into the power system control framework is part of the evolutionary advances that allow these resources to actively participate in the energy balance equation. Price can provide a powerful signal for independent decision-making in distributed control strategies. To study the impact of price responsive DER on the electric power system requires generation and load models that can capture the dynamic coupling between the energy market and the physical operation of the power system in appropriate time frames. This paper presents modeling approaches for simulating electricity market price responsive DER, and introduces a statistical mechanics approach to modeling the aggregated response of a transformed electric system of pervasive, transacting DER. II. DEMAND RESPONSE MODEL FOR RESIDENTIAL LOADS As one example of price responsive DER, we investigated the models for appliances calibrated against a database of extensively monitored residential distribution feeders. The simulation of the dynamic response of residential loads for use in demand response programs requires models that can interact with price, voltage, and frequency input signals. The diversity of the appliances over time must be taken into account in the load models. Factors of diversity can be divided into two types: spatial and temporal. Spatial diversity is caused by differences in household characteristics, such as the size and type of the appliances. Temporal diversity is the result of the random behavioral variations among different households. Such variations may include occupancy schedule, likelihood and frequencies of opening doors and windows, thermostat settings, and other lifestyle-related factors [1]. Physically based load modeling methodologies have been widely used because they are able to predict the individual load dynamic response and allow one to obtain the aggregated response of these loads with reasonable accuracy [2]. Index Terms—demand response, load model, load synthesis, thermostatically controlled appliance, system simulation, distributed energy resources. I. INTRODUCTION s market-based approaches have dominated changes in the operation of the bulk power system, initiatives have also begun to involve distributed energy resources (DER) in this process. Controllable load, distributed generation, and storage at the finger tips of the system hold promise to enhance cost effective operation and reliability. These resources represent half of the energy balance equation that traditionally exhibits uncontrolled, but generally predictable behavior. Though individually small contributors, by using appropriate signals or incentives the control of vast numbers of DER can have significant operational impact. A Demand management programs have been implemented for some time, but more recently, initiatives that provide price incentives to DER operation have appeared. As these programs become more prevalent, the need to understand their impact on reliable system operation becomes important. The following discussion reviews work done to model price responsive load from the details of individual electric appliances interacting at the distribution feeder level, to price responsive feeder equivalent models appropriate for regional system level studies. Inherent in the price responsive DER modeling is the simulation of market-based systems that allow these resources to interact with bulk energy markets to drive decision-making in economically efficient directions. Fig. 1: A bottom-up approach The load model we used for price responsive residential load simulation takes a bottom-up approach in which detailed physically-based models of each type of appliance are developed, as shown in Fig.1. An aggregated load model using a state queuing model (SQ) approach [4] was developed based on modeling group appliance behaviors to improve the computation speed. The SQ model serves as a simplified reduced order feeder equivalent model to the detailed household-based load model, as shown in Fig. 2. This work is supported by the Pacific Northwest National Laboratory operated for the U.S. Department of Energy by Battelle under Contract DE- AC06-76RL01830. N. Lu, D. P. Chassin, and S. E. Widergren are with the Energy Science and Technology Division, Pacific Northwest National Laboratory, P.O. Box 999, MSIN: K5-20, Richland, WA - 99352, USA (e-mail: ning.lu@pnl.gov , david.chassin@pnl.gov , steve.widergren@pnl.gov ) © 2004 IEEE. Reprinted from Proceedings of 2004 PES PSCE Meeting