Residential Electricity Rates and DR Practices

The way residential consumers are charged for electricity hasn‟t changed that much ever since the modern electricity distribution system came into operation in the early 1900‟s. The total amount of energy in kWh is measured at the end of every billing cycle (typically a month), and a fixed unit cost per kWh is applied to this amount. This

has been the norms of electric billing systems since the beginning, and it is the most prevalent form of billing method even today. The steady decline of natural resources and over population has led to a shift in the balance of energy economics. As the issue of global energy crisis becomes increasingly urgent, the importance of grid stability and energy efficiency grows too. The cost of peak power production as well as loss of unused off-peak energy has a much greater impact in energy economics today than before. Much interest has been generated in demand side management (DSM) in the past few decades to control load imbalance and improve grid stability by providing incentives to the consumers willing to change their demand. Various pricing structures with variable rates, both static and dynamic, have been studied and proposed as demand response (DR) programs. However, DR programs are only provided as an optional service and most utilities still enroll customers under flat rate billing structure by default. A brief review of existing electricity rates and DR programs are reviewed here.

The national electric grid in the United States is a vast network of generators, substations, transmission lines and distribution feeders. The power regulation is done by various independent system operators (ISO) or regional transmission organizations (RTO) based on the designated service areas. The distribution level authority for smaller regions lies on service providers or the utility companies. There are different types of service providers in different states and regions. Most cities operate under single providers such as ComEd (Chicago), San Diego Gas and Electric and Louisville Gas and Electric where residents are only served by a single entity. Some municipality services are also present where residents living in a particular municipality are served only by the municipality operated utilities. For example the city of Los Angeles is serviced by the

Los Angeles Department of Water and Power (LADWP). However, in some states like Texas, consumers are able to choose from multiple utility companies to buy power from, for example TXU, First Choice, Green Mountain, Reliant, etc. This is called deregulated multiple competing providers, and competing utilities try to offer attractive pricing schemes to enroll consumers.

The standard residential electric rates vary between 5 ¢/kWh to 15 ¢/kWh depending on the service territory (Figure 6). Most electric utilities have a standard flat rate for all customers. Some utilities like LADWP, however, provide variable unit charges depending on different contracts. Although base charges, transmission charges, etc. vary along with the contract, the main purpose is to enroll different customer types based on their usage pattern to different contracts. The unit rates for these contracts vary between 13.92 ¢ and 21.63 ¢ per kWh (www.ladwp.com). Other companies make use of variable block charges where different rates are charged depending on the total amount of energy used in a billing period. For example, Austin Energy (Austin, TX) provides standard rates of 3.3 ¢/kwh for the first 500 kWh, 8 ¢/kWh if total energy is 501 – 1000 kWh, 9.1 ¢/kWh if total energy is 1001 – 1500 kWh, 11 ¢/kWh if total energy is 1501 – 2500 kWh, and 11.4 ¢/kWh for consumption of greater than 2500 kWh for summer period (www.austinenergy.com). This is a blocked system of fixed rates, which tries to motivate consumers to lower their total consumption.

Some utility companies also use a demand charge on top of the regular standard rate in order to encourage peak load reduction. CPS Energy in San Antonio, TX for example charges a peak capacity charge of 1.98 ¢/kWh for all consumption greater than 600 kWh on top of the standard 6.91 ¢/kWh for total energy consumed

(www.cpsenergy.com). A different approach to demand charge that penalizes peak power levels instead of energy consumption is also used. Intermountain Rural Electric in Denver, CO charges consumers with $6.94/kW on-peak and $3.56/kW off-peak demand charges in addition to a standard rate of 6.47 ¢/kWh (www.irea.coop). The demand charge is applied to the maximum energy drawn in any hour within the pre-determined on-peak and off-peak windows.

Figure 6: Residential electric rates by service territory in 2013 (NREL, 2016)

Aside from cost incentives using standard fixed rates, there are variable pricing mechanisms that are also used as optional DR programs. Variable pricing mechanisms can be divided into two categories: static and dynamic variable rates. The most widely used pricing structure with variable rates is the time of use (TOU) rates. Many utilities

provide their customers a rate structure in which different times of the day are designated with different rates. Hours of maximum expected consumption are usually designated as peak hours and a higher rate assigned. Similarly, hours of minimum expected consumption are designated as off-peak hours and a much lower rate is provided. This encourages consumers to shift their load to off-peak hours and benefit from the lower cost. A variation of the TOU rates is the seasonal TOU, where summer and winter period are assigned either different rates or different on/off peak windows. Some utilities also attach demand charge on top of TOU rates. For example Duke Energy in Charlotte, NC offers a rate structure with 6.93 ¢/kWh peak and 5.7 ¢/kWh off-peak rates. In addition to that, a $7.77/kW for summer and $3.88/kW for winter demand charge is applied to the peak load (www.duke-energy.com).

In contrast to static variable pricing, dynamic variable pricing attempts to change unit rates dynamically throughout the day based on various factors. These factors may include real-time fluctuation of market prices, variation of weather, fluctuation of total demand, etc. Although, true price of energy can be charged to the consumer by using this type of mechanism, it is not widely used right now due to the complexity in understanding stochastic behavior of consumers in response to such price fluctuations.

Pricing methods like real time pricing (RTP) and critical peak pricing (CPP) could be considered as dynamic variable pricing structures. Customers of ComEd in Chicago, IL are provided with an option of subscribing to Residential Real Time Pricing (RRTP) (www.comed.com). RRTP program participants agree to pay the wholesale real-time market price, which is usually much lower except during peak hours. Participants have access to hourly prices online and also receive high price notifications and alerts. A

slightly different method of real time pricing was tested by AEP Columbus Southern Power (Columbus, OH) in 2014 as a pilot study (www.aep.com). This mechanism used a double auction system with real time pricing data. CPP subscribers on the other hand enjoy lower rates that the standard rate in exchange for extremely high rates during a few peak hours during a month. These peak hours are not known in advance and thus we can consider this model as dynamic. When the utility predicts a high demand, it notifies the customer of this period, in hopes that they will reduce their energy draw to avoid high cost. Pacific Gas and Electric (San Francisco, CA) and Arizona Public Service (Phoenix, AZ) are a few companies that provide CPP.

The current state of DR practices clearly indicates an opportunity of research in dynamic variable pricing mechanisms. Out of the 25 largest cities in the US, only two offer RTP and six offer CPP, however 21 of them have programs for TOU. The study of consumer behavior to dynamic pricing models will surely help in developing better DR programs. In this dissertation, we will study existing pricing structures as well as some innovative dynamic pricing mechanism to find optimal methods for peak reduction.