Electricity Peak Load Demand Management: Overview
We analyze direct load control contracts (DLCCs) that utilities use to curtail electricity consumption of the participating customers during peak load periods. These contracts stipulate a limit on the number of times (calls) and the total number of hours of power reduction as well as the duration of each call. The stochastic dynamic optimization problem that determines how many customers to call and the timing and duration of each call for each day is a provably difficult (NP-hard) optimization problem. We develop an approximation scheme and analyze its asymptotic behavior. We show that the relative error approaches zero as problem size (length of the horizon) approaches infinity. We apply our solution approach to the data provided by three major utility companies in California. Our data set contains hourly energy consumption profiles from 2009 to 2014. Our analysis indicates that applying our approach can potentially reduce the peak load consumption from 36 to 32 Gigawatt hours in a hot summer day, which corresponds to 50% cost reduction during the peak hours. Overall, our experiment shows a potential for 5%-7% saving in the energy generation cost.
The energy consumption varies over a day and it may reach a peak during the afternoon in summer and during the night in winter. Energy consumption profile (ECP) is a graph where its X-axis is time of day, and Y-axis is energy consumption rate (megawatts/hour). ECP depends on the type of days—e.g., low usage days v.s. high usage days. We are mostly concerned with reducing the energy consumption during the high usage days.
Power is generated using different sources. Base power generators have the least amount of environmental impacts. Let the capacity of the base power generators be W megawatts/hour. As long as the power consumption rate is below W, no other generator is turned on. If at any time in a day, the ECP goes above W then a secondary generator has to be turned on. This could be say Diesel powered generator (as an example). The secondary sources are significantly more harmful to the environment than the base generators. In this project, we aim to minimize the use of secondary power generators.
A solution that avoids the high cost and environmental impacts of power generation is to ask consumers to participate in load shedding programs which are refer to as calling contracts. Based on these programs, energy companies ask some users to reduce power consumption during peak periods. The consumers that participate in these programs get some incentives such as lower power rate. There are several such programs but the one that we study in this project has the following structure.
All those who want to participate are told that they will be asked to shed load (reduce consumption) during certain hours. This notice is say given one day in advance. The amount by which the power consumption is to be reduced is also specified (may be turn down the air conditioner for example). The participants are partitioned into identical groups. There are other restrictions in the load shedding program that we study in this project: (i) each group of customers will be asked to shed load no more than K times in a year, (ii) duration of each episode will be less than L hours, (iii) power reduction occurs over a continuous interval, (iv) total number of hours of load reduction for a group in a year cannot exceed H hours.
For energy firms, matching supply and demand is challenging due to variability in demand, large fluctuations in cost among alternative power generation methods, and the inability to store power. Power consumption is often weather dependent and varies by hour of day. High demand can cause grid failure, which is estimated to cost tens of billions of dollars per year. Inability to meet demand, besides being unacceptable, results in significant direct and indirect financial penalties for the utilities.
The U.S. Government Accountability Office finds that the cost of generating electricity during hot summer days is about 10 times higher than at night. The top 100-highest priced hours in a year account for nearly 20% of the total energy cost. Therefore, dynamic demand management during peak periods is central to matching supply and demand in this industry. In this project, we study Direct Load Control Contracts (DLCCs), which are popular (According to the Federal Energy Regulatory Commission, in the U.S., “as of 2012, more than 200 utilities across the country offered some type of direct load control program for residential customers”). The goal of these contracts is to help customers reduce their energy use and utility bills, ensure the reliability of the electric grid, reduce greenhouse gas emissions and carbon footprint, and reduce the need for building new power infrastructure. DLCCs permit utility companies to directly reduce a customer’s energy usage using a remote control device that is installed on site. It is also anticipated that the implementation of DLCCs will rapidly expand due to the Internet of Things.
We design a method for answering the following questions on a daily basis: which groups should be called the next day? how many hours should each group reduce their consumption? and when should each group who are called begin reducing their consumption? We apply our method to the data provided by three major utility companies in California, which offer this kind of programs. Our data set contains hourly energy consumption profiles from 2009 to 2014. Our analysis indicates that applying our approach can potentially reduce the peak load consumption from 36 to 32 Gigawatt hours in a hot summer day, which corresponds to 50% cost reduction during the peak hours. Overall, our experiment shows a potential for 5%-8% saving in the energy generation cost.
- Fattahi, A., Dasu, S., Ahmadi, R. (2018) Peak Load Energy Management Problem by Direct Load Control Contracts. 2nd Major revision submitted to Operations Research. Download.
- 2nd Place: POMS College of Sustainable Operations 2018 Best Student Paper Competition.
- Totty, M (2018) A More Efficient Way to Help Utilities Share the Inconvenience of Power Outages: A model by Reza Ahmadi and Ali Fattahi could enable power companies to lower the cost of peak electricity. UCLA Anderson Review, January 17, 2018. Link