Real-time electricity pricing for industrial customers: Survey and case studies in the United States

Applied Energy 195 (2017) 1023–1037

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Real-time electricity pricing for industrial customers: Survey and case studies in the United States Nasim Nezamoddini, Yong Wang ⇑ Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA

h i g h l i g h t s
The main goal is to study the reasons behind RTP’s success and failure.
We perform a detailed analysis of RTP based on the FERC and EIA survey data.
The detail information of representative RTP tariffs are summarized and analyzed.
We compare flat, TOU, and RTP using case studies in manufacturing.

a r t i c l e

i n f o

Article history: Received 5 December 2016 Received in revised form 2 March 2017 Accepted 22 March 2017 Available online 2 April 2017 Keywords: Real-time pricing Time-of-use Smart grid Demand response Manufacturing

a b s t r a c t Electricity prices change substantially over time in the wholesale markets. The fluctuations are mainly caused by power grid states, fuel price fluctuations, and market conditions. Real-time pricing (RTP) is an effective way to reduce risks utility companies face from volatile electricity prices. RTP allows participating customers to reduce electric bills by changing use patterns. This paper analyzes the pricing components and characteristics of representative RTP programs for industrial customers based on publicly available information. Our main goal is to study the reasons behind their success and failure. Knowing the details about RTP programs increases customers’ awareness of their advantages and assists them in deciding whether they should enroll. It also helps utility companies develop new programs or improve existing ones. Case studies in the manufacturing sector are presented and the savings of RTP are compared with another popular dynamic pricing program, time-of-use (TOU) pricing under different scenarios. The results show that the savings by switching from flat rates to TOU and RTP are highly program dependent. Eighteen out of the 35 base-case scenarios resulted in positive savings by switching to TOU, and 29 out of 35 base-case scenarios resulted in positive savings by switching to RTP. Ó 2017 Elsevier Ltd. All rights reserved.

1. Introduction Demand response encourages customers to change their electric consumption patterns in response to prices, incentives, or power grid contingencies [1]. It is a potential way to reduce peak loads and enhance grid reliability [2]. Peak load charges can account for as high as 30% of customers’ electric bills [3]. Reducing the peak load can result in significant savings for both customers and utility companies [4]. Although the peak periods consist of less than 1% of the operating hours, more than 10% of the capacity investment cost is spent to support those periods [5]. Even small changes in peak electricity usage can make a huge difference. For example, a 5% peak load reduction in the U.S. can eliminate the ⇑ Corresponding author at: 4400 Vestal Pkwy E, Binghamton, NY 13902-6000, USA. E-mail address: [email protected] (Y. Wang). http://dx.doi.org/10.1016/j.apenergy.2017.03.102 0306-2619/Ó 2017 Elsevier Ltd. All rights reserved.

need for 625 peak power plants and their supporting infrastructure [6]. Aside from the capacity cost savings, peak load reduction indirectly affects the operating costs of power plants and eventually wholesale electricity prices. The system operator uses cheap generation units to supply the base load and uses more expensive generation capacities to satisfy the peak load [7]. Demand response programs also support higher penetration of decentralized renewable energy with volatile power supplies [8]. Demand response can be implemented by dynamic or timevarying electric pricing programs [9]. In such programs, the power system load is managed by setting different prices at different time slots as illustrated in Fig. 1. By setting high prices during peak periods and low prices during off-peak periods, customers’ behaviors will be altered to shift consumption from on-peak to off-peak periods. Time-of-use (TOU) pricing, critical peak pricing (CPP), and real-time pricing (RTP) are the best-known time-varying demand response programs.

N. Nezamoddini, Y. Wang / Applied Energy 195 (2017) 1023–1037

35

35

30

30

25 20 15 10 5

Price (cents/kWh)

Price (cents/kWh)

1024

25 20 15 10 5

0 1

3

5

7

9 11 13 15 17 19 21 23 Hour of the day

0 1

3

5

7

9 11 13 15 17 19 21 23 Hour of the day

Fig. 1. TOU (left) and RTP (right) energy charge profile examples [4].

In TOU and CPP programs, the electricity prices are different for predetermined periods such as off-peak, mid-peak, on-peak, and critical-peak hours. In RTP programs, however, the prices vary hour-by-hour to reflect wholesale electricity prices. The RTP idea was initially presented by Boiteux in 1960s to reflect marginal costs of energy generation [10]. However, because of technological limitations, its real implementation was hindered until early 1990s, when the first RTP programs were offered to Southeast and Midwest customers [11]. The prices are announced a day ahead or an hour ahead to allow customers a short period to manage their electricity consumptions. Compared to the other two dynamic pricing schemes, RTP captures a higher range of market price variation [12]. Despite the benefits of RTP programs, there are existing difficulties that prevent its widespread acceptance among utility companies and customers [13]. Some companies are reluctant to offer RTP programs because previous consumption data for customers who adopted RTP showed most customers just reduce their electricity use in peak periods instead of moving it to off-peak hours. The main reason of the revenue loss for such utility companies is targeting the improper customers who are not able to ‘‘shift” their electricity usage. Some utility companies are concerned about the complexity of the RTP programs for customers. They believe that when there is no significant difference between the wholesale electricity prices for different periods, a complicated pricing scheme does not have any economic justification for the customers and the utilities. The main concern of customers who are reluctant to participate in the voluntary RTP programs is the risk of paying higher bills due to price volatility. Previous experiences showed that only a small portion of the customers reacted actively to the real-time prices, and once the prices spiked in certain periods, many customers dropped out. Most customers who responded to RTP are those with their own generating units, with discrete production processes, and those who had previously agreed to let the utility cut power during peak loads in exchange for lower rates [13]. The lack of customer awareness of cost saving opportunities is another barrier for customer participations in RTP programs [2]. A Lawrence Berkley National Laboratory report showed utility companies rarely helped customers balance the risk-price issues of RTP programs [11]. The need for a detailed description of RTP programs and their pricing policies is a main motivation for the present paper. Knowing the details about RTP programs increases customers’ awareness about their advantages and assists utility companies in developing new programs or improving existing ones. Previously, TOU and CPP programs have been surveyed and studied in [14,15], but to the best of our knowledge, none of the existing literature has provided such detailed analysis for RTP programs. The main goal of this research is to investigate different RTP implementations and the effects of their adoptions on the customers. This is especially important for industrial customers, who

are the main participants of the RTP programs [16]. Industrial customers are generally more energy-intensive and incur much higher electricity costs, which gives them a stronger motivation to take advantage of dynamic prices [16]. The main contributions of this work are as follow:
An extensive literature review of RTP research is conducted and we highlight the research in four different aspects, i.e., benefits, opportunities, costs, and risks (BOCR).
The data are extracted and analyzed based on the U.S. Federal Energy Regulatory Commission (FERC) and the Energy Information Administration (EIA) surveys to reveal the status quo of RTP programs in the U.S.
We collect and summarize the pricing components and characteristics of various RTP programs targeting industrial customers based on publicly available information.
We study the reasons behind the success and failure of different RTP programs using scenario analysis and the collected data. The remainder of the paper is organized as follow. Section 2 presents a literature review of the RTP research. At the national level, we perform a detailed analysis of RTP programs offered in the United States in Section 3 based on the data of the FERC and the EIA surveys. At the state level, some of these RTP programs are selected and their tariffs are studied in Section 4. Methods of calculating the monthly bill for different RTP program variants are also summarized in this section. Section 5 focuses on studying the effects of these RTP programs on industrial customers with different levels of flexibility and comparing RTP with flat and TOU rates. This is implemented by introducing a case study in the manufacturing sector. Concluding remarks and potential future works are provided in Section 6. 2. Literature review The research related to RTP has been conducted from a wide range of aspects. In Fig. 2, we summarize the research in this area from the general perspectives of benefits, opportunities, costs, and risks [17]. A great deal of the literature discussed the benefits and advantages of RTP. Some research is supported by the data collected from pilot and actual programs [18]. The peak load reduction is considered an immediate outcome of using variable electricity prices [19]. The range of benefits depends on various factors including time of the day, temperature, and humidity [20], type of customer [21], range of prices [22], type of the industry [23], and even household incomes [24]. Adopting RTP helps customers save in their electric bills if they change their consumption patterns [25]. The savings will be different depending on customer types [26], locations and the transmission system state [27], the customer’s load factor [16], and whether they own electric vehicles [28]. Models and methods were deployed for maximizing the savings under

N. Nezamoddini, Y. Wang / Applied Energy 195 (2017) 1023–1037

1025

Peak capacity reduction [18-24] Electricty bill savings [16] [25-31]

Benefits

Overall efficiency increment [32-33] Full output utilization of nonflexible generation [28] Wholesale market price reduction [34-37]

Opportunities

Renewables utilization [38-40] Environmental effect [41-45]

Real-time pricing

Back office costs [46-48] Costs

Smart appliance [46-48] Required smart metering [46-48] Communication farmeworks [46-48] Inequality and customer price risks [49-52]

Risks

No charge for future facilities [28] Income lost for utility companies [20] Improper adoptation and pricing policies [51]

Fig. 2. The BOCR framework for RTP research. (See above-mentioned references for further information.)

RTP for homes [29], manufacturing centers [30], and electric vehicles [31]. In addition to short-term savings under RTP, customers benefit from long-term efficiency improvements even with low price elasticity [32]. Some publications focused on modeling the potential opportunities that RTP may introduce. Unlike the benefits explained above, the opportunities are not the certain outcome of RTP programs and they depend on other factors and system settings. For example, one of the potential outcomes is related to altering the cleared energy market price [34]. Although RTP may cause short-term price volatility in the markets [35], its long-term outcomes eventually can provide the opportunity of electric bill saving for both participating and non-participating customers [36]. Another advantage of RTP is it creates opportunities to use renewable generation units [38]. Different models have been proposed to show how responsive load minimizes the uncertainty that comes with the costs of generation by renewable sources [39,40]. Other than potential economic opportunities, the environmental opportunities that RTP may entail were also discussed [41,42]. For example, a study on hourly electricity market data for Great Britain, Ontario, and Sweden showed the effects of dynamic pricing on carbon emission [43]. It was argued that whether RTP will lead to reduced pollutant gases highly depends on energy dispatching plans for peak periods [44,45]. The high costs of the technological requirements of RTP programs are considered a barrier for its widespread acceptance [13]. These costs include setup, maintenance, communication, and energy management expenses [46–48]. RTP requires smart metering devices to record hourly electricity use. The communication tools are needed to send the price signals to customers. To maximize gains from time-varying prices, customers must use smart appliance and energy management systems such as a home energy manager (HEM) or an enhanced programmable communicating thermostat (ePCT). The potential risks of RTP programs are also discussed in the literature. One possible risk is that there may be an equality issue about who can benefit from real time prices [49]. For example, low-income customers with no smart appliances and automated control systems may not be able to manage their electricity use [50]. The price risk of inelastic customers can be managed by proposing solutions such as hedging and price protection plans [51,52]. Utilizing storage systems is also considered as an effective solution for protecting inflexible customers against price fluctua-

tions [53]. Another concern is related to the lack of consideration of current customers in building future generation units [27]. The revenue loss for utilities is another risk of implementing these programs. The revenue loss results from load ‘‘reduction” instead of ‘‘shifting” from on-peak periods to off-peak periods [19]. Inappropriate pricing and adaptation mechanism are also threats to the success of RTP programs [54]. Offering voluntary programs may attract inappropriate customers other than the target markets, which may eventually raise doubts about its efficiency and reduce the participation willingness of utilities [11]. 3. Analysis of FERC and EIA surveys on RTP FERC evaluated the development state of the demand response in 2012 by conducting a national survey of demand response programs and advanced metering [2]. The survey used a database that contained information from 1987 entities spread around all 50 state. In 2015, the EIA surveyed demand response programs [55]. For this paper, we combed through the gigantic survey data from these two sources, looking for relevant information that reveals the current state of RTP. According to the FERC survey, 15,436 residential, commercial, and industrial customers were enrolled in RTP programs. The total potential peak reduction was 1905 MW. The survey reveals that in 2012, there were 44 RTP programs offered by 29 utility companies in 24 states. We summarize the data by state in Fig. 3. Most customers that participated in RTP programs were in Illinois, Georgia, New York, Minnesota, and New Jersey. However, the customers in Georgia, Tennessee, and Minnesota contributed the most potential peak reduction. The participants in Illinois are mainly residential customers and their contribution was small. Fig. 3 shows there was no meaningful relation between the total number of advanced meters and participation in RTP programs. For example, California and Florida had larger numbers of the advanced meters compared to other states. However, the number of participants did not exceed 131 in either state. On the other hand, Illinois had the largest number of the RTP participants, but the number of installed advanced meters is only 2.3% of that in California. Further analysis of FERC survey data shows that most RTP programs are opt-in and only 9% of the programs, mostly in New York and New Jersey, are mandatory. West Penn Power Company in Pennsylvania with 54 participants offers the only opt-out program.

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Potential Peak Reduction (MW)

Number of RTP Programs AL

1

AL

4

CA FL

2

CA

2

GA

IA

1

IL

2

IN

1

IN

NA

IN

0 40

1

10,537

6,38,995

IA

28,350

IL

2,36,424

0

IN

1,59,420

KY

6

KY

1,49,730

MI

3

MI

MN

6,98,498

MN

MO

5

MO

2,82,163

MS

0

MS

1,40,601

281

NC

14

NC

86

NC

NA

ND

1

ND

21,820

NJ

NA

NJ

NJ

16,340

NY

NA

NY

NY

25,899

OH

NA

OH

8

OH

4,87,148

OK

14

OK

37

OK

6,89,888

PA

54

PA

SC

24

SC

TN

2

TN

VA

5

VA

161

SC

2

97

TN

1

VA

443

VA

3

31

WI

1 0

5

1,82,067

ND

PA

1

450

1,03,28,076 31,98,472

GA

0

2

WI

FL 2,033

0

1

TN

27

MS

3

SC

FL GA

654

MO

NY

PA

CA

2

3

OK

131

MN

NC

OH

CA

2

1

1

14,716

IL

MI

NJ

1,35,206

AR

IA

KY

1

AL

1

6

3

ND

0

0

3

MS

50

AL

IL

MI

MO

15

Total Advanced Meters

AR

IA

KY

MN

86

FL

1

GA

0

AR

AR

Total Number of Customers

1,603

WI

12 0

421

500

1000

15,80,467 91,261 7,24,468

2,33,841

WI

1 0

5,30,950

5,000

10,000

5,36,585

0

1,00,00,000

Fig. 3. Summary of RTP programs by State.

Twenty-three percent of the RTP programs had a participation exclusion policy, preventing RTP customers from using other demand response programs. It is also worth mentioning that the market share of RTP programs is relatively small compared to the most popular dynamic pricing program, TOU. RTP programs impose greater technological requirements and hardware upgrades, and customers must bear more volatility, and thus risks, than using TOU. Like the FERC survey, the data collected through the 2015 EIA survey from 2271 utilities also provided some insights into the RTP programs. In Figs. 4 and 5 we summarize the information extracted from the EIA survey data. Fifty-nine utilities reported RTP programs. The figure shows the percentage by North American Electric Reliability Council (NERC) region and utility type. The statistics show that most RTP programs belong to SERC, RFC, NPCC regions on the East Coast and 54% are offered by investor owned utilities.

5%

2%

2%

2%

Investor Owned Retail Power Marketer Cooperative Municipal Political Subdivision Federal

14%

54% 22% Fig. 4. Percentage of utility companies offering RTP by entity type.

We extracted the information for customer types and utility company activities from the EIA survey data. The results are presented in Figs. 6 and 7. The majority are industrial customers (85%). Residential customers only account for 15% of the market. Fig. 8 shows these utilities and their affiliated Independent System Operators (ISOs) or Regional Transmission Organizations (RTOs), which operate under FERC’s recommendation to control the power grid operations (Table 1). The size of the circles in Fig. 8 reflects the population served by each ISO or RTO. As the figure indicates PJM nourishes most companies and some utilities are affiliated with more than one ISO or RTO. 4. Tariff analysis of representative RTP programs To gain deeper understanding of RTP programs, we need to analyze the detailed tariff structure and see how utilities implement them. In this paper, we extract 20 representative RTP programs from 11 utility companies across the U.S. The main criteria for the selection are the number of enrolled customers, the diversity of the offered programs, and the availability of the tariff information to the public. Table 2 summarizes the detailed information based on these companies’ tariff books as well as the FERC and EIA surveys. Table 2 shows that the RTP programs are offered in two forms: one-part programs and two-part programs. In a one-part RTP program, the hourly prices are applied to the whole electricity consumption. In a two-part RTP program, the customers are charged for hourly prices only if they deviate from the baseline usage that is defined using their historical electricity usage. These RTP programs can also be divided into day-ahead (DA) and hour-ahead (HA) programs based on the pricing notification

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3%3% 2% 8% 31% 14%

ReliabilityFirst Corporation (RFC) SERC Reliability Corporation (SERC) Northeast Power Coordinating Council (NPCC) N/A Southwest Power Pool (SPP) Texas Reliability Entity (TRE) Western Electricity Coordinating Council (WECC) Midwest Reliability Organization (MRO)

14% 25%

Percentage of Utilities offering RTP

Fig. 5. Percentage of utility companies offering RTP by NERC region.

100%

85%

80% 53%

60% 40%

15%

20%

8%

0% Industrial

Commercial

Residential

Transportation

Fig. 6. Summary of information about type of the customers.

Percentage of Utilities offering RTP

mechanism. Most of the time, DA and HA prices are very close. The eligibility in Table 2 shows the conditions that customers must satisfy to be qualified for the RTP program. In most programs eligibility is based on a customer’s maximum monthly peak load. One exception is Duke Energy, whose eligibility requirement is based on total monthly energy use. Most programs are offered to industrial and commercial customers, a few are open to residential customers. Table 2 presents additional information such as adaptation rules, notification methods, information access, additional services, complexity, and transparency of the offered programs. The column ‘‘mandatory” shows whether the program is mandatory for qualified customers. Although most RTP programs are voluntary, there are mandatory programs in New York and New Jersey. The column ‘‘notification” is an indicator of the availability of price notification systems for customers. Depending on customer preferences, the notification can be in form of phone calls, text messages, or emails. The utility companies that provide access to online pricing information are presented in the column ‘‘online access”. The column ‘‘other services” indicates whether the utilities provide additional load management services to assist the customers. The programs marked in the ‘‘complexity” column have more complicated pricing framework compared to the rest of the programs. The programs

also differ based on their transparency, i.e., whether they provide clear information regarding their RTP programs. Detailed analysis of these RTP programs and their participation data reveals that the companies that offer additional value-added services such as Load Guard and Central Air Conditioning Cycling increase the customers’ willingness to participate. One of the main reasons for lack of participation is lack of availability and transparency of information related to the offered RTP programs. Further analysis of the information of successful utilities shows that these companies provide clear, easy to access information regarding their RTP programs. Considering additional customer support services for addressing customers’ concerns is another feature of those companies. To protect their customer, these utilities also provide additional consulting services for guiding customers to select the appropriate program that completely matches with their needs and flexibilities for load management. The type of RTP programs (i.e., whether it is a one- or two-part program) does not have significant effects on participation. The extracted information shows that utility companies offer a diverse range of RTP programs to their customers. In the rest of this section, we reveal more information about implementing those programs by the derivation of a typical electric bill ðbm Þ for month m. As shown in Eq. (1), for most of the companies, the electric DeL charge of customers includes supply (C Sup ), demand m ), delivery (C

(C Dem ), and metering charges (C Metr ). Real-time prices usually affect the supply charge. In some cases, additional adjustments are made to cover potential financial risks of utility companies. The delivery or customer charge is usually a fixed monthly charge. In some special cases it is considered a variable charge that is calculated based on actual delivered electricity. Some utility companies charge cusPeak

tomers for their monthly peak demand (dm ) as the capacity or demand charge. Peak

Dem bm ¼ C Sup dm m þC

þ C Del þ C Metr

ð1Þ

70% 60%

57%

50% 40% 40%

39% 31%

29%

30%

23%

20% 5%

10% 0% Distribution

Transmission

Wholesale Marketing

Generation

Retail Marketing

Buying Transmission

Fig. 7. Summary of information about activities of utility companies.

Buying Distribution

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N. Nezamoddini, Y. Wang / Applied Energy 195 (2017) 1023–1037

City of St Marys Pennsylvania Electric Jersey Central Power Commonwealth Edison Metropolitan Edison Potomac Edison West Penn Power PECO Energy Pennsylvania Power

PJM

Entrust Energy Duke Energy Ohio TransCanada Power Marketing Central Hudson Gas & Elec Duke Energy Kentucky PPL Electric Utilities

NYISO

Energy Mark South Jersey Energy Rochester Gas & Electric UGI Utilities

ERCOT

TXU Energy Retail Constellation Energy Services Wolverine Alt. Investments Niagara Mohawk Power

ISONE

NextEra Energy Services Green Mountain Power Upper Peninsula Power Vermont Electric Cooperative Wisconsin Electric Power Northern States Power - Minnesota

MISO

Entergy Solutions Wisconsin Public Service Mint Energy Calpine Power America Duke Energy Indiana

CAISO

City of Shasta Lake Ameren Illinois Southern California Edison Prairie Land Electric Coop Kansas City Power & Light

SPP

Oklahoma Gas & Electric KCP&L Greater Missouri Operations Victory Electric Coop Assn Fig. 8. Utility companies that offer RTP and their affiliated ISOs.

Table 1 Information for ISOs operating in United States [56]. ISO Name

Installed Capacity (MW)

Miles Lines

Population (million)

CAISO ERCOT ISONE MISO NYISO PJM SPP

57,124 84,000 32,000 205,759 37,978 183,604 83,456

26,000 40,530 8130 62,250 11,056 62,566 60,944

30 23 14 48 19.5 61 18

Existing RTP programs use different approaches to calculate customers’ supply charge considering time-varying prices. Onepart programs are the simplest form of RTP programs that customers are charged for the whole electricity consumption in each hour (eh;d;m ) by real time prices (C RTP h;d;m ). ComEd is North America’s largest utility providing a one-part RTP program. Rochester Gas & Electric, Southern California Edison, and Duke Energy also offer one-part RTP programs. The supply charge for a one-part program is calculated by the following equation in which D is the set of days in month m, and H is the set of hours in a day.

Table 2 Summary of representative RTP programs (Note: Empty cells are interpreted as ‘‘not applicable”). Utility Company

State ISO

NY Rochester Gas & Electric Northern MN States Power Southern California Edison

Progress Energy Carolinas Gulf Power Company NextEra Energy Services Oklahoma Gas & Electric Duke Energy Kentucky Kansas City Power & Light

CA

NC FL TX OK

PJM

RFC

None, SERC Regulated

Participants Program Name

RTP Type One- Two- Day- Hourpart part Ahead Ahead

Residential Industrial Commercial Mandatory Notification Online Other Complexity Transparency Access Services

10,537

BESH

*

*

*

*

2033

RTP-DA-5 RTP-DAA-6 RTP-HA-5 RTP-HAA-6 Hourly Pricing

*

* * * * *

* * * * *

NYISO

NPCC

710

MISO

MRO

450

CAISO

None, Regulated None, Regulated ALL but SPP SPP

* * * * *

20 kW

*

*

*

*

*

*

200 kW

*

*

*

*

*

*

500 kW

*

*

*

*

* *

1000 kW

* *

* *

* *

*

500 kW

*

*

*

*

*

* * *

* *

* *

* *

RTPR/LMP

*

SPP

8

DAP FP RTP-M

* *

3

RTP RTP Plus

* * * *

*

N/A

SPP

* * * *

*

* * * * *

*

TRE

SPP

*

1000 kW

RTP

MO

*

* *

27

6

5000 kW

*

*

SERC

RFC

* *

*

500 kW

85

PJM

250 kW

* *

Program Offer Details

*

SERC

KY

Customer

*

A62 Firm A63 Controllable TOU-GS-1RTP TOU-GS-2RTP TOU-GS-3RTP TOU-8-RTP LGS-RTP-18

WECC 131

Eligibility

* * *

* * *

*

* *

* * * *

400 kW 5000 kWh

* * *

* *

500 kW 500 kW

* *

*

*

* *

* *

*

* *

* * *

*

*

*

* *

*

* *

N. Nezamoddini, Y. Wang / Applied Energy 195 (2017) 1023–1037

Commonwealth IL Edison Georgia Power GA

NERC

*

1029

1030

C Sup m

N. Nezamoddini, Y. Wang / Applied Energy 195 (2017) 1023–1037

XX RTP ¼ C h;d;m eh;d;m

ð2Þ

d2D h2H

In two-part programs, the real-time prices are applied only to the electricity usage over the customer baseline load (CBL). One successful example for this type is Georgia Power Company’s RTP program, which is the second largest in terms of number of RTP customers. The company offers four two-part programs, namely day-ahead, day-ahead with adjustable CBL, hour-ahead, and hour-ahead with adjustable CBL. NextEra Energy Services, Oklahoma Gas & Electric, and Kansas City Power & Light also offer similar two-part RTP programs to protect their customers from price risks. A typical monthly supply charge for the day-ahead and hour-ahead two-part programs is calculated using Eq. (3). In this formulation, ECBL h;d;m is the predefined CBL for each customer who is charged by the standard flat charge (C std ). The CBL reflects a customer’s electricity usage pattern and is established using the customer’s historical data. For new customers, the CBL is set based on the customer’s total estimated load. Any deviation from the CBL will be charged or reimbursed based on real-time prices that is reflected in the second part of the equation.

C Sup m ¼

XXh

i RTP CBL C std ECBL h;d;m þ C h;d;m ðeh;d;m  Eh;d;m Þ

ð3Þ

d2D h2H

A two-part program with an adjustable CBL allows customers to change the CBL and make their adjusted baseline (EACBL m ). The adjusted contract price (C

Adj

seasonal base load (ESCBL m ). Any deviation from the base load is charged using blocked prices (C TBlock h;d;m ) which are calculated by averaging the day-ahead prices of the block of those hours (Table 5).

C Sup m

¼

" #  X   X std CBL X Adj  ACBL RTP CBL ACBL C Sup ¼ C E þ C E  E C e  E þ h;d;m m h;d;m m m m m h2H

h2H

ð4Þ In some RTP programs, the supply charge is calculated using predefined rates instead of real-time prices. For example, in a RTP program offered by Northern States Power, the monthly bill

X

" C std ESCBL m

#  X TBlock  SCBL þ C h;d;m eh;d;m  Em

d2D

) is calculated using the expected fore-

casted RTP price in month m (C ERTP m ). In programs with adjusted baseline, the supply charge is calculated using Eq. (4).

d2D

is calculated based on the day type and predefined prices for time blocks of that day type (Table 3). The day type is announced by 4 pm of the preceding day. To get a rough estimate of the yearly rates, the expected day-type distribution is also provided for the customers. Some programs adopt a more detailed scheme for determining the supply charge. For example, the predefined rates of Southern California Edison vary depending on season, time of day, and the prior day’s temperature, as shown in Fig. 9. The rates for customers with a demand more than 500 kW vary based on their voltage range. The rates for customers with the demand less than 500 kW is determined by factors such as whether it is a weekday or a weekend, whether it is winter or summer, and the daily maximum temperature recorded by the National Weather Service in downtown Los Angeles site. A sample for these prices is presented in Table 4. For example, the electricity price for 8 am of Sunday with temperature 80 °F (27 °C) will be 0.0295 ($/kW h). The real-time prices can also be transformed to TOU blocks such as in the pilot program offered by Oklahoma Gas & Electric. In this program, the supply charge is calculated using Eq. (5) and their

ð5Þ

h2H

5. Case studies for manufacturing This section presents numerical case studies to evaluate from a manufacturer’s point of view the annual savings of different RTP programs and compares it with TOU and flat rates offered in the same utility companies. Manufacturing or industrial customers are considered as appropriate choices for managing electricity usage under real-time prices [57]. From the reviewed programs in the previous section, we selected five representative utilities

Table 3 Pricing variants for Northern States Power. Day Type

Type 1

Type 2

Type 3

Type 4

Type 5

Type 6

Type 7

Type 8

12–6 am 6–9 am 9 am to 12 pm 12–6 pm 6–9 pm 9 pm to 12 am Day-type distribution

$0.03228 $0.06563 $0.19679 $0.33090 $0.24149 $0.06563 5

$0.02460 $0.05038 $0.10738 $0.21857 $0.15209 $0.05038 5

$0.02160 $0.03834 $0.06403 $0.10873 $0.08580 $0.04282 10

$0.01965 $0.04050 $0.04908 $0.06031 $0.05080 $0.03581 35

$0.01883 $0.03889 $0.03553 $0.03553 $0.03553 $0.02765 60

$0.01719 $0.03244 $0.02692 $0.02692 $0.02692 $0.02441 110

$0.01627 $0.02430 $0.02061 $0.02061 $0.02061 $0.02006 60

$0.01463 $0.01785 $0.01892 $0.01892 $0.01892 $0.01785 80

Weekend

High cost weekend (>78 °F or 25.5 °C) °F or 25.5 °C)

Demand below 500 kW

High cost winter weekday (>90 °F or 32 °C) Winter

°F or 32 °C) °F or 35 °C)

Weekday Summer

RTP rate

Very hot summer weekday (91-94 °F or 33-34 °C) Hot summer weekday (85-90 °F or 29-32 °C)

Voltage below 2kV Demand over 500 kW

From 2kV to 50 kV Above 50 kV

Moderate summer weekday (81-84 °F or 27-29 °C) Mild summer weekday (<80 °F or 27 °C)

Fig. 9. Pricing categories for Southern California Edison.

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Extreme Hot Sum Weekday (95 °F or 35 °C)

Very Hot Sum Weekday (91–94 °F or 33–34 °C)

Hot Sum Weekday (85–90 °For 29–32 °C)

Moderate Sum Weekday (81–84 °F or 27–29 °C)

Mild Sum Weekday (80 °F or 27 °C)

High Cost Win Weekday (>90 °F or 32 °C)

Low Cost Win Weekday (90 °F or 32 °C)

High Cost Weekend (78 °F or 25.5 °C)

Low Cost Weekend (<78 °F or 25.5 °C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0.0426 0.03713 0.03139 0.02779 0.02868 0.03912 0.04036 0.04351 0.04906 0.09073 0.20954 0.46048 0.75119 1.30937 1.89547 2.66296 2.66407 1.96856 1.23238 0.86792 0.95739 0.19148 0.05298 0.04673

0.03443 0.02919 0.0241 0.02253 0.02427 0.03129 0.03346 0.03748 0.05296 0.08015 0.17131 0.27316 0.39847 0.64411 0.83372 1.07575 0.98788 0.74805 0.37991 0.25613 0.41594 0.1612 0.06784 0.03998

0.02978 0.02502 0.02066 0.01886 0.02171 0.02685 0.03 0.03427 0.03778 0.0424 0.05629 0.06411 0.08617 0.21422 0.34513 0.44855 0.45592 0.28774 0.15936 0.10867 0.10857 0.05647 0.042 0.03597

0.02797 0.02379 0.01942 0.01771 0.01959 0.02495 0.02723 0.03134 0.03615 0.04214 0.04628 0.04881 0.05076 0.05681 0.07298 0.09349 0.0852 0.06512 0.05843 0.04923 0.05129 0.04704 0.04156 0.03439

0.02664 0.02321 0.02004 0.01799 0.01989 0.02499 0.02718 0.03107 0.03566 0.04094 0.04472 0.04669 0.04855 0.05081 0.05588 0.06081 0.05948 0.05149 0.04889 0.04611 0.04843 0.04488 0.041 0.03302

0.04646 0.04343 0.03724 0.04011 0.04283 0.05351 0.06225 0.06436 0.06172 0.07054 0.10058 0.12853 0.15625 0.21604 0.27002 0.30867 0.26282 0.1711 0.14682 0.15052 0.15294 0.07894 0.05577 0.05149

0.03454 0.03085 0.02864 0.02835 0.031 0.03845 0.04436 0.04739 0.04749 0.04875 0.05037 0.05045 0.04957 0.04961 0.04906 0.04897 0.04987 0.05328 0.05544 0.05557 0.05338 0.04929 0.04466 0.03786

0.03619 0.03184 0.02829 0.02662 0.02646 0.02821 0.0267 0.0295 0.03485 0.03976 0.04333 0.04606 0.04656 0.0475 0.05037 0.0524 0.05708 0.06048 0.05858 0.05767 0.06328 0.05136 0.04382 0.03742

0.03255 0.02807 0.02582 0.02307 0.02327 0.02541 0.02321 0.02494 0.03136 0.03526 0.03928 0.04063 0.03958 0.03774 0.03817 0.03872 0.04043 0.043 0.04404 0.0467 0.04822 0.04466 0.03873 0.03233

Table 5 Time blocks of Oklahoma Gas & Electric. Blocks

Block 1

Block 2

Block 3

Block 4

Block 5

Block 6

Time period

11 pm to 3 am

3–7 am

7–11 am

11 am to 3 pm

3–7 pm

7–11 pm

for case studies and their rates cover a diverse range of pricing frameworks. The financial benefits of adopting different pricing programs are examined using a manufacturing facility. Three daily production plans that are commonly seen in manufacturing facilities are tested in this section. They are one work shift (i.e., 8 h production per day), two work shifts (i.e., 16 h production per day), and three work shifts (i.e., 24 h production per day). Seven different scenarios listed in Table 6 are examined for each of the tariffs. S1 and S4 represent the cases that the daily production always starts at 8am and continues to work until 4 pm or 12 midnight for the one-shift and two-shift production schedules, respectively. Rescheduling the production time provides flexibility for customers to experience lower electricity prices. For S2, the production may start anytime between 6am and 6 pm; for S5, the production may start any time between 6am and 10am. For S3 and S6, there is no limitation on the start time and they can be scheduled for any time. S7 represents the case that the manufacturing system operates 24 h per day without stop. So, there is no flexibility and the start time is irrelevant for S7. In fact, each scenario reflects a certain type of the customers. They include:


Partially flexible customers with different electricity usage (S2, S5).
Inflexible customers with different electricity usage (S1, S4).
Customers who have no flexibility and are experiencing the complete range of daily prices (S7). The annual electricity cost is calculated for RTP and TOU rates for year 2015 and then compared with the same system under flat rates. The information of the flat, TOU, and RTP programs are collected from the tariff books of the utilities and summarized in Table 7. The prices include the costs of power generation and supply, electricity delivery, customer and metering charges, and possible max demand or capacity charges [58–62]. The day-ahead prices in the NYISO and SPP markets used in the case studies are presented in Figs. 10 and 11. It is assumed that hourly load of the manufacturing center is fixed at 400 kW. To be able to compare different programs, it is assumed that the customer with this load level is eligible for the programs under study.

5.1. One-part RTP programs
Highly flexible customers who can completely change their load pattern (S3).
Flexible customers with a higher level of electricity usage (S6). Table 6 Scenarios for the case study. Production start time

One shift

Two shifts

Three shifts

Production starts at 8 am Production starts within time ranges Flexible production start time

S1 S2 S3

S4 S5 S6

S7

As mentioned earlier, one-part RTP program is considered the simplest form of RTP programs. To test the efficiency of these programs, the one-part RTP program offered by Rochester Gas & Electric is selected. The results are compared with flat and TOU programs offered by the same company. Fig. 12 shows the savings of the yearly bill of the RTP or TOU program compared to the flat rate. The results show that RTP programs are more efficient for cases with less flexibility (Scenarios S4-S7), while TOU programs offer more savings for customers for higher flexibility (Scenarios S2-S3).

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Table 7 Summary of flat, TOU, and RTP tariffs used in the case studies. Utility

Tariff

Southern California Edison

Flat TOU

On-peak Mid-peak Off-peak

RTP Northern States Power Company

Flat TOU

On-peak Off-peak

RTP Oklahoma Gas & Electric

Kansas City Power & Light

Flat TOU RTP Flat TOU

OnOff-

On-peak Off-peak

RTP Rochester Gas & Electric

Flat TOU RTP

On-peak Off-peak

Generation/supply ($/ kW h)

Delivery ($/kW h)

Summer

Winter

Summer

Winter

0.08542 0.23492 0.19082 0.06369 Temperature (Table 4)

0.07789 0.09169 0.09169 0.06724 based

0.02368 0.02424 0.02424 0.02424 0.02368

0.02368 0.02424 0.02424 0.02424 0.02368

Monthly customer charge ($)

Monthly demand (Capacity) ($/kW) Summer

Winter

94.65 94.65 94.65 94.65 294.75

18.62 18.62 18.62 18.62 15.44

9.51 9.51 9.51 9.51 15.44

0.08787 0.07432 0.15123 0.1228 0.03015 0.03015 Day-type based (Table 3)

10 12 12 300

3.48

3.48

10.13

10.13

0.012 0.012 0.095 0.012 0.012 0.012 SPP blocked prices 0.0596 0.04498 0.11725 0.04584 0.04758 0.03686 SPP prices (Fig. 10)

140 135 135 140 108.7 142.21 142.21 160

11.75 5.8 5.8 11.75 6.514 6.514 6.514 6.514

5.95 5.8 5.8 5.95 4.572 4.572 4.572 4.572

264.58 82.29 82.29 111.79

15.65 10.56 10.56

15.65 10.56 10.56

0.043452 0.043452 0.058409 0.058409 0.026796 0.026796 NYISO DA price (Fig. 11)

0.01408 0.01408

0.01408 0.01408

Fig. 10. SPP day ahead market prices in 2015.

Fig. 11. NYISO day ahead market prices in 2015.

The same experiment is repeated for different real-time prices, which are rescaled by shifting the original average and daily variation. For example, a 30% average change indicates a 30% price reduction. Similarly, a 30% variation change means increasing the deviations of the prices from daily average price by 30%, which will result in an increased overall price variance. The results for most

(S3) and least flexible (S4) scenarios and the scenario experiencing all 24 hours’ prices (S7) are presented in Fig. 13. The results show that increasing daily price variation provides more opportunities for highly elastic customers (S3) to change their load pattern and save money on their electricity bill. However, more hourly variations cause more financial loss for inflexible customers (S4).

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30% TOU Saving

RTP Saving

20% 10% 0% S1

S2

S3

S4

S5

S6

S7

-10% -20% Fig. 12. Results for programs offered by Rochester Gas & Electric (Note: A negative saving means a financial loss).

Savings Original saving

Savings Original saving

Savings Original saving Financial loss

S3

30% 25% 15% 10%

30% 0% -30%

5% 0% -30%

0% 30% Average change

Variation change

20%

Fig. 13. Sensitivity analyses on the RTP average and variation (Rochester Gas & Electric).

Increasing price variation for customers (S7) with a flat load who experience the whole price range (i.e., 24-h prices) will not affect their electricity charges. Overall, increasing the average price will decrease the savings for all customers and in some cases, it may lead to financial loss for inflexible customers.

savings of highly flexible customers (S3) by providing opportunities to gain more credits from reduced usage during peak hours. This is quite different from the one-part programs. The credits are calculated by multiplying the decreased electricity usage by the prices, and higher prices result in higher credits.

5.2. Basic two-part RTP programs

5.3. Day-type based RTP programs

Some utilities offer two-part RTP programs to mitigate the financial risks of one-part programs. The Kansas City Power & Light company is selected to test efficiency of a basic two-part program. The CBL is set to 400 kW for the whole day in the three-shift cases. The CBL in one- and two-shift cases is set to 400 kW from 8 am to 4 pm, and from 8 am to 12 midnight, respectively. Resulted savings under RTP and TOU programs are presented in Fig. 14. Similar to the previous section, sensitivity analyses are implemented to test the effects of price changes on the savings of S3, S4, and S7. The results presented in Fig. 15 show that the two-part program protect inflexible customers from high financial losses. Customers in S4 and S7 are insensitive to the price average and variation changes. The small loss is only because of fixed customer charges (including metering charges). It was also observed that the increases in the price average and variation will increase the

As explained earlier, in day-type based RTP programs, the day types will determine the charged prices that will be used in the customers’ contracts. Northern States Power Company is one of the utilities offering this type of RTP programs. Fig. 16 illustrates the results for comparing savings of the RTP program listed in Table 3 with the corresponding TOU program. In this experiment, it is assumed that day types follow their expected probabilities, which are obtained based on the last row of Table 3. A disadvantage of such programs is its inefficacy in reflecting real-time market prices. In order to perform the sensitivity analyses, we define four types of day-type distributions in Table 8. These distributions have the following characteristics:
High-Price Distribution: Higher probabilities for day types with higher prices.

20% TOU Saving

RTP Saving

15% 10% 5% 0% S1

S2

S3

S4

S5

S6

-5% Fig. 14. Results for programs offered by Kansas City Power & Light.

S7

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Original saving Financial loss

15%

10%

10%

5%

5%

10%

0% -30%

0% -30%

0%

30%

Average change

0%

Variation change

30%

5%

Original saving Financial loss

S4

30%

-5%

0% -30%

-10% -30%

0% 30% Average change

S7

0% 30%

-5%

0% -30%

-10% -30%

0% 30% Average change

Variation change

S3

Savings Original saving

Variation change

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Fig. 15. Sensitivity analyses on the RTP average and variation (Kansas City Power & Light).

80%

TOU Saving

RTP Saving

60% 40% 20% 0% S1

S2

S3

S4

S5

S6

S7

-20% Fig. 16. Results for programs offered by Northern States Power.

Table 8 The settings for sensitivity analyses on day types distributions. Day-Type Distribution

Type 1 (%)

Type 2 (%)

Type 3 (%)

Type 4 (%)

Type 5 (%)

Type 6 (%)

Type 7 (%)

Type 8 (%)

All (%)

High-Price Distribution Base Distribution Low-Price Distribution

20 1.4 5

20 1.4 5

15 2.8 10

15 9.6 10

10 16.4 15

10 30.1 15

5 16.4 20

5 21.9 20

100 100 100


Base Distribution: The day type distribution with expected probabilities.
Low-Price Distribution: Higher probabilities for day types with lower prices.

adjustments to protect themselves against potential risks of high market price variations.

The sensitivity analysis results for all the seven scenarios are presented in Fig. 17. Results in Fig. 17 show that the Low-Price Distribution will generally result in higher savings for all customers under all seven scenarios compared to the Base Distribution, and the High-Price Distribution will generally result in less savings (and sometimes financial losses) compared to the Base Distribution. One advantage of the day-type RTP programs is that the straightforward structure will help customers understand and estimate charges for different electricity usages patterns. On the other hand, defining prices at the beginning brings some uncertainties for utility companies and they may bear extreme financial risks. Therefore, the companies may need to consider some sorts of

Some utility companies such as Southern California Edison set the prices based on temperature. Fig. 18 shows the comparison between savings of TOU and RTP programs offered by this company. As the figure shows, setting prices only based on temperature will result in savings for customers in all scenarios regardless of their levels of flexibility to respond. Similar to daytype based RTP programs, predefined prices based on the temperature do not reflect real-time prices and may put utility companies at risk when unexpected market fluctuations occur. Another concern for adopting these prices is the potential huge financial loss for customers with fixed average daily loads. Since the prices are designed based on daily temperature, the customers who are not

100%

High-Price Distribution

5.4. Temperature based RTP programs

Base Distribution

Low-Price Distribution

50% 0% S1

S2

S3

S4

S5

S6

-50% -100% Fig. 17. Sensitivity analyses on the RTP day-type distributions (Northern States Power).

S7

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40%

TOU Saving

RTP Saving

20% 0% S1

S2

S3

S4

S5

S6

S7

-20% -40% Fig. 18. Results for programs offered by Southern California Edison.

5.5. Block-based RTP programs

transferred to TOU block prices, which are calculated by taking the average of the real-time prices of that period. The results for this program are presented in Fig. 20. The results show that RTP programs have less savings compared to TOU programs. This is mainly because the prices in this type of programs do not provide enough flexibility for customers to manage their loads and at the same time are higher compared to actual TOU programs. Moreover, the program is offered in two-part form that will limit the savings for different types of customers. Another set of experiments are implemented to test the effects of different seasonal base load

One of the block-based RTP programs is offered by Oklahoma Gas and Electricity. In these programs the real-time prices are

ESCBL on the savings. The results in Fig. 21 show that setting the m base load to a lower level (300 kW instead of 400 kW) will result in more charges (and thus less savings) compared to the base case

able to shift their loads from days with high temperature to other days will be charged more compared to customers who are able to do so. The sensitivity results for different overall temperature changes are presented in Fig. 19. The bill is not very sensitive to temperature decrease, but very sensitive to temperature increase. These programs will highly benefit customers equipped with solar power systems that can utilize their on-site power generation units in high-temperature days.

40%

20% Decrease

Base Case

20% Increase

20% 0% S1

S2

S3

S4

S5

S6

S7

-20% -40% Fig. 19. Sensitivity analyses on the RTP for different levels of temperature (Southern California Edison).

30%

TOU Saving

RTP Saving

20% 10% 0% S1

S2

S3

S4

S5

S6

S7

-10% -20% Fig. 20. Results for programs offered by Oklahoma Gas and Electricity.

30%

SCBL 300

Base Case

SCBL 500

20% 10% 0%

S1

S2

S3

S4

S5

S6

S7

-10% -20% Fig. 21. Sensitivity analyses on the RTP for different base loads (Oklahoma Gas and Electricity).

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for all the seven scenarios. On the other hand, if a customer’s actual load is less than the higher contracted base load setting (500 kW), they will be able to get higher level of savings by adopting RTP programs. These experiments highlight the importance of precisely defining the base load for customers adopting two-part programs. Moreover, the results illustrate that since loads below the contracted base load achieve higher benefits for customers, twoparts RTP may encourage customers to significantly decrease their load, which is beneficial to the overall grid stability, but may lead to revenue loss for utility companies.

fuel and environmental adjustments), surcharges, and various taxes. Because these charges vary greatly from company to company and they are usually a small proportion of customer bills, they are not included in this study. It should also be noted that the charges and prices may change from time to time because of rate updates. The research can be extended by investigating the success and failure of RTP programs for other types of customers such as residential and commercial customers.

References 6. Conclusions and future work This paper investigates different aspects of RTP programs by analyzing publicly available data from the U.S. FERC, the EIA, and representative utility companies. The inherent reasons for the success and failure of RTP programs are investigated. Numerical experiments are implemented on the data collected from representative programs and the results are compared to flat-rate and TOU programs using case studies of a typical manufacturing facility. The key findings of this research are listed below:
The financial costs and risks of RTP programs are the main concerns for implementing such programs. The complexity of RTP programs and the lack of customer awareness hinder the widespread acceptance of these programs.
The RTP programs are offered in various forms including onepart, two-part, day-type based, temperature based, and block based programs, which differ mainly in the energy supply calculation.
Overall, the comparison results in the case studies shows that the savings by switching from flat rates to dynamic pricing such as TOU and RTP are highly program dependent. Eighteen out of the 35 base-case scenarios presented in Figs. 12, 14, 16, 18, and 20 resulted in positive savings and the rest resulted in zero or negative savings by switching to TOU. Twenty-nine out of 35 base-case scenarios resulted in positive savings and the rest resulted in negative savings by switching to RTP.
The TOU programs benefit mainly the highly flexible customers (with shorter production) and customer savings decrease as longer daily production is involved. The RTP programs benefit a wider range of customers and not only highly flexible ones.
Two-parts programs may be the best choice for protecting inflexible customers from financial losses, but at the same time they limit the potential savings of these customers. Two-part programs may encourage the customers to decrease their electricity usage (to increase their savings) instead of shifting to offpeak periods. Such behaviors may eventually result in undesirable income loss for utility companies.
The day-type based and temperature based RTP programs are designed with pre-defined prices and they are more understandable for customers. However, they may cause financial risks for utility companies especially in cases that actual market prices deviate considerably from the predefined contracted prices. It should be noted that the FERC and EIA surveys were conducted on a voluntary basis and they may not reflect the programs offered by all utility companies. Moreover, this paper only selected a subset of the available programs offered by utilities in the U.S. based on publicly available sources on the Internet. These programs are representative, but they are far from comprehensive because of the highly variable nature of dynamic pricing programs. We categorize their pricing scheme and main pricing components. Some of the utilities may have additional riders, adjustments (e.g.,

[1] Siano P. Demand response and smart grids—a survey. Renew Sust Energy Rev 2014;30:461–78. [2] U.S. Federal Energy Regulatory Commission. National action plan on demand response; 2012. [retrieved February 21, 2017]. [3] Deliso R. Understanding peak demand charges; 2013. [retrieved February 21, 2017]. [4] U.S. Federal Energy Regulatory Commission. Assessment of demand response and advanced metering; 2006. [retrieved February 21, 2017]. [5] ENERNOC. Demand response: a multi-purpose resource for utilities and grid operators; 2009. [retrieved September 20, 2016]. [6] Faruqui A, Hledik R, Newell S, Pfeifenberger J. The power of five percent: how dynamic pricing can save $35 billion in electricity costs; 2007. [retrieved September 30, 2016]. [7] Ruff LE. Demand response: reality versus resource. Electr J 2002;15(10):10–23. [8] Broeer T, Fuller J, Tuffner F, Chassin D, Djilali N. Modeling framework and validation of a smart grid and demand response system for wind power integration. Appl Energy 2014;113:199–207. [9] Aghaei J, Alizadeh MI. Demand response in smart electricity grids equipped with renewable energy sources: a review. Renew Sust Energy Rev 2013;18:64–72. [10] Boiteux M. Peak-load pricing. J Bus 1960;33(2):157–79. [11] Barbose G, Goldman C, Neenan B. A survey of utility experience with real time pricing. Lawrence Berkeley National Laboratory Report; 2004. [retrieved September 30, 2016]. [12] Hogan WW. Time-of-use rates and real-time prices; 2014. [retrieved September 30]. [13] Faruqui A, Mauldin M. The barriers to real-time pricing: separating fact from fiction. Publ Util Fortnight 2002;July 15:30–40. [14] Wang Y, Li L. Time-of-use electricity pricing for industrial customers: a survey of US utilities. Appl Energy 2015;149:89–103. [15] Wang Y, Li L. Critical peak electricity pricing for sustainable manufacturing: modeling and case studies. Appl Energy 2016;175:40–53. [16] Spezia CJ. Monte Carlo analysis of real-time electricity pricing for industrial loads. J Ind Technol 2009;25(3):1–9. [17] Saaty TL. Fundamentals of the analytic network process—multiple networks with benefits, costs, opportunities and risks. J Syst Sci Syst Eng 2004;13 (3):348–79. [18] Borenstein S, Jaske M, Rosenfeld A. Dynamic pricing, advanced metering, and demand response in electricity markets. University of California Energy Institute Center for the Study of Energy Markets Report; 2002. [retrieved September 20, 2016]. [19] Wolak FA. Residential customer response to real-time pricing: the Anaheim critical peak pricing experiment. University of California Energy Institute Center for the Study of Energy Markets Report; 2007. [retrieved September 20, 2016]. [20] Allcott H. Real time pricing and electricity markets. Harvard University; 2009. [retrieved September 20, 2016]. [21] Goldman C, Hopper N, Sezgen O, Moezzi M, Boisvert R. Does real-time pricing deliver demand response? A case study of Niagara Mohawk’s large customer RTP tariff. In: Proceedings of the ACEEE 2004 summer study on energy efficiency in buildings, Pacific Grove, California. [22] Bloustein E. Assessment of customer response to real time pricing. RutgersThe State University of New Jersey; 2005. [retrieved September 20, 2016]. [23] Patrick RH, Wolak FA. Estimating the customer-level demand for electricity under real-time market prices; 2001. [retrieved September 20, 2016]. [24] Allcott H. Rethinking real-time electricity pricing. Resource Energy Econ 2011;33(4):820–42. [25] Vesterberg M. The potential for real time electricity pricing in Sweden. Center for Environmental and Resource Economics Report; 2014.
N. Nezamoddini, Y. Wang / Applied Energy 195 (2017) 1023–1037

[26] [27] [28]

[29] [30]

[31]

[32] [33] [34]

[35] [36] [37]

[38] [39]

[40] [41]

[42]

[43] [44] [45]

org/usaee2014/submissions/OnlineProceedings/wp-NYC-Submitted-Apr13. pdf> [retrieved September 20, 2016]. Hopper N, Goldman C, Neenan B. Demand response from day-ahead hourly pricing for large customers. Electr J 2006;19(3):52–63. Joskow PL, Wolfram CD. Dynamic pricing of electricity. Am Econ Rev 2012;102 (3):381–5. Asare PO, Galli AW, Lee JS, Lozano CA, O’Neill-Carillo E, Zhao RY. Real time pricing of electric power; 1995. [retrieved September 20, 2016]. Tsui KM, Chan SC. Demand response optimization for smart home scheduling under real-time pricing. IEEE Trans Smart Grid 2012;3(4):1812–21. Danandeh A, Zhao L, Zeng B, Abdollahian M. Optimal job scheduling with dayahead price and random local distributed generation: a two-stage robust approach. In: IEEE PES innovative smart grid technologies conference, Washington, DC. p. 1–9. Mohsenian-Rad AH, Leon-Garcia A. Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans Smart Grid 2010;1(2):120–33. Siano P, Sarno D. Assessing the benefits of residential demand response in a real time distribution energy market. Appl Energy 2016;161:533–51. Borenstein S. The long-run efficiency of real-time electricity pricing. Energy J 2005;26(3):93–116. Star A, Isaacson M, Kotewa L. Evaluating residential real-time pricing: connecting customer response to energy market impacts. In: Proceedings of international energy program evaluation conference, Paris, France. p. 1–11. Roozbehani M, Dahleh MA, Mitter SK. Volatility of power grids under real-time pricing. IEEE Trans Power Syst 2012;27(4):1926–40. Savolainen MK, Svento R. Real-time pricing in the Nordic power markets. Energy Econ 2012;34(4):1131–42. Alcott H. Real-time pricing and electricity market design; 2012. [retrieved September 20, 2016]. Sioshansi R. Evaluating the impacts of real-time pricing on the cost and value of wind generation. IEEE Trans Power Syst 2010;25(2):741–8. Madaeni SH, Sioshansi R. Measuring the benefits of delayed price-responsive demand in reducing wind-uncertainty costs. IEEE Trans Power Syst 2013;28 (4):4118–26. De Jonghe C, Hobbs BF, Belmans R. Value of price responsive load for wind integration in unit commitment. IEEE Trans Power Syst 2014;29(2):675–85. Gyamfi S, Krumdieck S. Price, environment and security: exploring multimodal motivation in voluntary residential peak demand response. Energy Policy 2011;39(5):2993–3004. Song M, Alvehag K, Widén J, Parisio A. Estimating the impacts of demand response by simulating household behaviours under price and CO2 signals. Electric Power Syst Res 2014;111:103–14. Stoll P, Brandt N, Nordström L. Including dynamic CO2 intensity with demand response. Energy Policy 2014;65:490–500. Holland SP, Mansur ET. Is real-time pricing green? The environmental impacts of electricity demand variance. Rev Econ Stat 2008;90(3):550–61. Nilsson A, Stoll P, Brandt N. Assessing the impact of real-time price visualization on residential electricity consumption, costs, and carbon

[46]

[47]

[48]

[49]

[50]

[51]

[52] [53] [54]

[55] [56]

[57]

[58]

[59]

[60] [61]

[62]

1037

emissions. Resour Conserv Recycl; 2015 [in press]. doi:http://dx.doi.org/10. 1016/j.resconrec.2015.10.007. Widergren S, Marinovici C, Berliner T, Graves A. Real-time pricing demand response in operations. In: IEEE power and energy society general meeting, San Diego, California. p. 1–5. Faruqui A, Palmer J. Dynamic pricing and its discontents; 2011. [Retrieved February 21, 2017]. Woolf T, Malone E, Schwartz L, Shenot J. A framework for evaluating the cost effectiveness of demand response; 2013. [retrieved February 21, 2017]. Faruqui A, Sergici S, Palmer J. The impact of dynamic pricing on low income customers. Institute for Electric Efficiency; 2010. [retrieved February 21, 2017]. Abrate G. Regulating retail electricity prices: a review of theoretical and empirical issues; 2003. [retrieved February 21, 2017]. Zhang Q, Wang X. Customer electricity purchasing risk decision under realtime pricing. In: IEEE power systems conference and exposition, Seattle, WA. p. 1–6. Borenstein S. Time-varying retail electricity prices: theory and practice. Electr Deregul: Choices Challeng 2003:317–57. Dufo-López R. Optimization of size and control of grid-connected storage under real time electricity pricing conditions. Appl Energy 2015;140:395–408. Hu Z, Kim JH, Wang J, Byrne J. Review of dynamic pricing programs in the US and Europe: status quo and policy recommendations. Renew Sust Energy Rev 2015;42:743–51. US Energy Information Administration Survey; 2015. [retrieved February 21, 2017]. ISO/RTO Council Report. Members at a glance. [retrieved February 21, 2017]. Finn P, Fitzpatrick C. Demand side management of industrial electricity consumption: promoting the use of renewable energy through real-time pricing. Appl Energy 2014;113:11–21. Southern California Edison Company. General service - industrial rate schedules. [retrieved February 21, 2017]. Oklahoma Gas & Electric Company. Oklahoma rate tariffs. [retrieved February 21, 2017]. Rochester Gas & Electric Company. Choosing your energy supply. [retrieved February 21, 2017]. Northern States Power Co (Xcel Energy). Rates and regulations. [retrieved February 21, 2017]. Kansas City Power & Light Company. Detailed tariffs. [retrieved February 21, 2017].