Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy

Demand response (DR) has an important role to play in the electricity market for maintaining the balance between supply and demand by introducing load flexibility instead of only adjusting generation levels, at almost all operational time scales. There are many players in the market who benefit from DR, like the TSO, DSOs, retailers and end-customers themselves. The recent advent of smart grid technologies advanced the integration of DR by providing the needed information and communication infrastructure to the existing grid. Available literature on DR talks about the concept and definitions of DR, possible DR models for various region-specific market structures along with few DR implementation experiences in a system with ever increasing levels of loads along with evolution of innovative technologies like renewables, micro-grids, PEVs, etc. In this paper, the available literature on DR is categorized into general concept papers and papers on DR models applicable to the wholesale or retail markets, and are presented in a precise manner.

Review of demand response under smart grid paradigm

In order to support the growing interest in demand response (DR) modeling and analysis, there is a need for physical-based residential load models. The objective of this paper is to present the development of such load models at the appliance level. These include conventional controllable loads, i.e., space cooling/space heating, water heater, clothes dryer and electric vehicle. Validation of the appliance-level load models is carried out by comparing the models' output with the real electricity consumption data for the associated appliances. The appliance-level load models are aggregated to generate load profiles for a distribution circuit, which are validated against the load profiles of an actual distribution circuit. The DR-sensitive load models can be used to study changes in electricity consumption both at the household and the distribution circuit levels, given a set of customer behaviors and/or signals from a utility.

Development of physical-based demand response-enabled residential load models

The advent of advanced sensor technology and the breakthroughs in telecommunication open up several new possibilities for demand-side management. More recently, there has been greater interest from utilities as well as system operators in utilizing load as a system resource through the application of new technologies. With the wider application of demand-side management, there is an increasing emphasis on control of loads with minimum disruption. This paper develops a new framework for designing as well as assessing such an advanced direct load control program with the objective of minimizing end-user discomfort and is formulated as an optimization problem. With a fairly general setup for demand-side management, a simulation-based framework is developed for the stochastic optimization problem. Next, using this framework, insights into the effect of different parameters and constraints in the model on load control are developed.

A Framework for Evaluation of Advanced Direct Load Control With Minimum Disruption

We investigate the potential for aggregations of residential thermostatically controlled loads (TCLs), such as air conditioners, to arbitrage intraday wholesale electricity market prices via non-disruptive load control. We present two arbitrage approaches: 1) a benchmark that gives us an optimal policy but requires local computation or real-time communication and 2) an alternative based on a thermal energy storage model, which relies on less computation/communication infrastructure, but is suboptimal. We find that the alternative approach achieves around 60%–80% of the optimal wholesale energy cost savings. We use this approach to compute practical upper bounds for savings via arbitrage with air conditioners in California's intraday energy market. We investigate six sites over four years and find that the savings range from $2–$37 per TCL per year, and depend upon outdoor temperature statistics and price volatility.

Arbitraging Intraday Wholesale Energy Market Prices With Aggregations of Thermostatic Loads

The authors review the concept of demand-side management, particularly in regard to utility intervention into the setpoint adjustment of customers' thermostats for heating and/or cooling loads. As part of a larger effort to develop a large simulation to evaluate performance of various management strategies, they have created a simple, realistic, easily implementable model for the temperature of a house having a heater/air-conditioner which is regulated by a thermostat and operates in an environment that contains random elements. Two models previously given in the literature are reviewed before giving the details of the model, the feature of which is that it is a stochastic model operating in discrete time. Computer results show good agreement with basic physical principles, and suggest that the probability distributions of on-time and off-time are negative binomial. It is concluded that this model will provide a useful tool in the evaluation of load-management strategies. >

A stochastic computer model for heating and cooling loads

Study of the recovery transient during cold load pick-up of heating and cooling loads is of concern in designing distribution systems and direct load control strategies. Examination of actual utility data reveals that this transient consists of two epochs. A description is given of five mathematical models which have been proposed in the literature for studying this transient, and a brief summary is given of results of computer experiments which show that the five models are mutually consistent, allowing the possibility of mixing or trading models where appropriate. Recommendations for monitoring new variable in load control programs are made. >

R.E. Mortensen

Papers

A stochastic computer model for heating and cooling loads

Dynamics of heating and cooling loads: models, simulation, and actual utility data