Steven E. Widergren

Modeling uncertainties in aggregated thermostatically controlled loads using a State queueing model

To study the impacts of price responsive demand on the electric power system requires better load models. This paper discusses the modeling of uncertainties in aggregated thermostatically controlled loads using a state queueing (SQ) model. The cycling times of thermostatically controlled appliances (TCAs) vary with the TCA types and sizes, as well as the ambient temperatures. The random consumption of consumers, which shortens or prolongs a specific TCA cycling period, introduces another degree of uncertainty. By modifying the state transition matrix, these random factors can be taken into account in a discrete SQ model. The impacts of considering load diversity in the SQ model while simulating TCA setpoint response are also studied.

An Orderly Transition to a Transformed Electricity System

In September 2001, the U.S. Department of Energy (DOE) sponsored a workshop to develop a roadmap on communication and control technologies for distributed energy resources. From this and subsequent activities with industry, academia, and other research institutions, the concepts and vision of an interactive and adaptable electricity system emerged; a vision that is now commonly referred to as smart grid. Supported by significant federal investment announced in 2009, the DOE, its national laboratories, and industry partners are now making significant strides in smart grid deployments, demonstrations, and research. An imperative underlying these activities is that they contribute to ensuring the steps taken in transitioning toward a smart grid retain a system with dependable performance to the economy and society it services. This paper summarizes the status of current smart grid related efforts, explains smart grid priorities, and describes the direction of research and development activities supported by the DOE.

A survey of load control programs for price and system stability

Load control and demand side load management programs have been implemented in a large number of competitive power markets. These programs can provide enhanced system security and many benefits to participants. This paper reviews and compares existing economically driven programs.

Residential energy resource models for distribution feeder simulation

Advances in information technology, ubiquitous communications, and distributed generation and storage reveal new opportunities for the participation of demand-side resources in balancing the physical and economic operation of electric power systems. To better understand the potential impact of this participation, accurate, detailed energy resource models are necessary at the distribution feeder level. This presentation describes a detailed approach to residential energy resource modeling that preserves the individual characteristics of major residential appliances and human behavior patterns so that their contribution to energy efficiency schemes and intelligent demand curtailment algorithms is properly portrayed. These models are derived from previous analyses of residential and commercial building systems supported by data collected from the end-use load and consumer assessment program (ELCAP) undertaken by the Bonneville power administration from 1983 to 1990. Preliminary results of using these models in distribution system simulations indicate that non-obvious, complex behavior patterns can emerge when consumers are confronted with varying price signals.

Simulating the dynamic coupling of market and physical system operations

As energy trading products cover shorter time periods and demand response programs move toward real-time pricing, financial market-based activity impacts ever more directly the physical operation of the system. To begin to understand the complex interactions between the market-driven operation signals, the engineered controlled schemes, and the laws of physics, new system modeling and simulation techniques must be explored. This discussion describes requirements for interactions and an approach to capture the dynamic coupling between energy markets and the physical operation of the power system appropriate for dispatcher reaction time frames.

AEP Ohio gridSMART Demonstration Project Real-Time Pricing Demonstration Analysis

This report contributes initial findings from an analysis of significant aspects of the gridSMART® Real-Time Pricing (RTP) – Double Auction demonstration project. Over the course of four years, Pacific Northwest National Laboratory (PNNL) worked with American Electric Power (AEP), Ohio and Battelle Memorial Institute to design, build, and operate an innovative system to engage residential consumers and their end-use resources in a participatory approach to electric system operations, an incentive-based approach that has the promise of providing greater efficiency under normal operating conditions and greater flexibility to react under situations of system stress. The material contained in this report supplements the findings documented by AEP Ohio in the main body of the gridSMART report. It delves into three main areas: impacts on system operations, impacts on households, and observations about the sensitivity of load to price changes.

A Framework for Addressing Interoperability Issues

Interconnected electric power systems are composed of many businesses cooperating to deliver electricity to the consumer. Todays technology offers the ability to exchange information to enhance operational and financial effectiveness beyond the automation in present use. The incentive to exchange information electronically is a result of system and business processes that span organizations such as scheduling and coordinating electric supply and demand-side resources, detecting and correcting system problems, or providing settlement and billing information to market participants. The drivers for automating these information processes are evolving as industry policies, market rules, and technical capabilities mature. To facilitate information exchange, the GridWisetrade Architecture Council (GWAC) promotes a collaborative path forward to advance interoperability so that organizations can better connect their inter-business processes as they see fit now and as they evolve in the future. The GWAC aims to frame the debate on interoperability to advance an overriding strategic perspective. This paper introduces a context-setting framework that organizes concepts and terminology to identify and debate interoperability issues, articulate improvements, and prioritize and coordinate actions across the electric power community.

Simulating price responsive distributed resources

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.

Real-time pricing demand response in operations

Dynamic pricing schemes have been implemented in commercial and industrial application settings, and recently they are getting attention for application to residential customers. Time-of-use and critical-peak-pricing rates are in place in various regions and are being piloted in many more. These programs are proving themselves useful for balancing energy during peak periods; however, real-time (5 minute) pricing signals combined with automation in end-use systems have the potential to deliver even more benefits to operators and consumers. Besides system peak shaving, a real-time pricing system can contribute demand response based on the locational marginal price of electricity, reduce load in response to a generator outage, and respond to local distribution system capacity limiting situations. The US Department of Energy (DOE) is teaming with a mid-west electricity service provider to run a distribution feeder-based retail electricity market that negotiates with residential automation equipment and clears every 5 minutes, thus providing a signal for lowering or raising electric consumption based on operational objectives of economic efficiency and reliability. This paper outlines the capability of the real-time pricing system and the operational scenarios being tested as the system is rolled-out starting in the first half of 2012.

Residential real-time price response simulation

The electric industry is gaining experience with innovative price responsive demand pilots and limited roll-outs to customers. One of these pilots is investigating real-time pricing signals to engage end-use systems and local distributed generation and storage in a distributed optimization process. Attractive aspects about the approach include strong scalability characteristics, simplified interfaces between automation devices, and the adaptability to integrate a wide variety of devices and systems. Experience in this nascent field is revealing a rich array of engineering decisions to consider along with the application of complexity theory. To test the decisions, computer simulations are used to reveal insights about design, demand elasticity, and the limits of response (including consumer fatigue). Agent-based approaches lend themselves well in the simulation to modeling the participation and interaction of each piece of equipment on a distribution feeder. This paper discusses rate design and simulation experiences at the distribution feeder level where consumers and their HVAC systems and water heaters on a feeder receive real-time pricing signals.