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Web Based Integrated Multiple Function Customer Demand and Budget Management System Wei-Jen Lee, Ph.D., PE Director and Professor Energy Systems Research.

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Presentation on theme: "Web Based Integrated Multiple Function Customer Demand and Budget Management System Wei-Jen Lee, Ph.D., PE Director and Professor Energy Systems Research."— Presentation transcript:

1 Web Based Integrated Multiple Function Customer Demand and Budget Management System Wei-Jen Lee, Ph.D., PE Director and Professor Energy Systems Research Center University of Texas at Arlington August 28, 2008

2 Introduction Current Billing Structure –Lack of linkage between wholesale and retail markets. Most residential customers receive monthly electric bill at a static rate for the electricity consumed. It is not directly related to the true and time-varying costs of electricity provided during consumers’ actual consumptions.

3 Introduction previous crises California Day-Ahead electricity prices (PX – Southern Zone) – summer 2000

4 Energy portion of the electricity bill for San Diego’s residential customer– summer 2000 Introduction previous crises

5 Electricity Demand and Supply Curves Demand of electricity is very inelastic now The limit supply capacity can cause price spikes (small change in consumption can create large change in price) However, if the customer can respond to the priced signal and adjust his/her energy consumption, supply will be sufficient, and no price spike (new slant demand curve). Load with DR Capability Load without Elasticity Generation cost curves $/MWH Power (MW)

6 Definition of Demand Response Changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized. U.S. Department of Energy (DOE)

7 Benefits of Demand Response Enhancement of effectiveness and efficiencies in utilizing the system’s overall resources both long-term and short-term Prevent market power gaming Fairness to customers No one will ever purchase and use any merchandize without knowing the price first. Why we treat the merchandize of electricity differently

8 Energy Policy Act (EPAct) 2005 * Section 1252 (a) (14) (A) …each electric utility shall offer each of its customer classes, and provide individual customers upon customer request, a time- based rate schedule under which the rate charged by the electric utility varies during different time periods and reflects the variance, if any, in the utility's costs of generating and purchasing electricity at the wholesale level. The time-based rate schedule shall enable the electric consumer to manage energy use and cost through advanced metering and communications technology... * Passed by both House and Senate and signed into law by the President on August 8, 2005

9 Energy Policy Act (EPAct) 2005 * Section 1252 (a) (14) (B) The types of time-based rate schedules... (iii) real-time pricing whereby electricity prices are set for a specific time period on an advanced or forward basis, reflecting the utility's cost of generating and/or purchasing electricity at the wholesale level, and may change as often as hourly * Passed by both House and Senate and signed into law by the President on August 8, 2005

10 Energy Policy Act (EPAct) 2005 * Section 1252 (f) Federal Encouragement of Demand Response Devices –It is the policy of the United States that time-based pricing and other forms of demand response, whereby electricity customers are provided with electricity price signals and the ability to benefit by responding to them, shall be encouraged, the deployment of such technology and devices that enable electricity customers to participate in such pricing and demand response systems shall be facilitated, and unnecessary barriers to demand response participation in energy, capacity and ancillary service markets shall be eliminated.

11 Recent Experiences of Selected Demand Response Programs Illinois –In early 2007 Illinois Legislature enacted a bill requiring the major Illinois utilities, including the Ameren Illinois Utilities and Commonwealth Edison (ComEd) to promote the voluntary real-time hourly market-based price of electricity to statewide 4.5 million end-use residential consumers.

12 Recent Experiences of Selected Demand Response Programs Illinois –Ameren Illinois Utilities contracted the CNT Energy as its program administrator and introduced Power Smart Pricing –ComEd’s contracted the Comverge and introduced Basic Electric Service-Hourly Energy Pricing (BES-H) Day-ahead and real-time prices are posted on the website or obtained by a toll free number. From May 2007, the participants can reduce their electricity bill on an average of 16%.

13 Potential of Demand Response

14 Barriers in Implementing Demand Response Programs Lack of infrastructures to link wholesale and retail markets: advanced metering and other enabling technologies Utilities’ perspectives: –Reluctant to invest in long-term infrastructures –Revenues based on peak MW and usage MWH of their consumers: Reduce consumers’ demands = lower revenues

15 Barriers in Implementing Demand Response Programs Consumers’ initiative: –Resist to changes if the program is not simple and requires too much effort –Some peak load consumers, who benefits from the traditional flat rates, maybe reluctant to participate Price caps and some state regulations

16 Deficiencies of the Current Practice Present schemes are focused more on utility than end-use residential customers –Most demand response programs for residential consumers are largely the time-of-use electricity pricing and direct load control (not real-time pricing) –Direct load control sheds/disconnects load for network security or from generation shortage, not for consumers’ comfort Need infrastructure improvement to allow end- use residential consumers to easily modify their usage according to real time price signals

17 Motivations and Objectives We need a tool to enable the end-use residential consumers for an effective demand response. The tool should be –Simple and easy to use –Consumer-centered –Effectively balance between the consumers’ comforts and their savings

18 18 Residential Electricity Consumption Air Conditioner 16% Refrigerator 13.7% Space Heating 10.1% Water Heater 9.1% Lighting 8.8% Clothes Dryer 5.8% Range 2.8% Dishwasher 2.5% Electric Oven 1.8% Clothes Washer 0.9% Miscellaneous 28.5% Source: “Energy Information Administration: End-Use Consumption of Electricity”

19 Types of Residential Loads Reschedulable usage loads (having thermal inertia): –air conditioners and water heaters Reschedulable usage and service loads: –dishwasher, clothes washer and dryer Non-Reschedulable usage and service loads: –lights, refrigerator, TV

20 Data in the Experiments Real time pricing data –Since the electricity real time retail pricing data is not available in TX at this moment, we acquire real time price from ERCOT (www.ercot.com) and assume that ERCOT MCPE is the consumer retail prices.www.ercot.com

21 21 A Sample of ERCOT Balancing Energy Service Market Clearing Prices

22 Data in the Experiments Real time outdoor temperature data –Acquire real time local temperature data for customer demand management decision making from

23 23 Outdoor Temperature from

24 24 Air Conditioning Load Control ASHRAE Summer and Winter Comfort Zones (ASHRAE Handbook) Comfort zones have bands of approximately six degrees Fahrenheit For consumers to maintain their comfort, a four degree temperature band of thermostat setting is used in our simulation (plus or minus two degrees from the thermostat set point) ASHRAE: The American Society of Heating, Refrigerating and Air-Conditioning Engineers

25 25 Air Conditioning Load Control Strategy Temperatures settings and real time pricing

26 26 Air Conditioning Load Control Results Air Conditioner Load Consumption Energy Saving Total Cost Saving (KWH)(%)($)(%) No load control (always set at 77 degree F) base case0.970 base case Proposed strategy  Note that the amount of the total costs are low because the wholesale (not retail) electricity prices are used in the simulations, and the results are of 1 day only.

27 27 Water Heater Load Control Strategy Temperatures settings and real time pricing

28 28 Water Heater Load Control Results  Note that the amount of the total costs are low because the wholesale (not retail) electricity prices are used in the simulations, and the results are of 1 day only. Water Heater Load Consumption Energy Saving Total Cost Cost Saving (KWH)(%)($)(%) No load control (always set at 120 degree F) base case0.313 base case With Steps of Temperatures load control

29 Control of Reschedulable Loads Control re-schedulable usage and service loads: cloth washer/dryer and dish washer These loads together accounted approximately 9.2%

30 Price Naming Strategies for Reschedulable Load Control Price Naming Approach for Cloth Washer, Cloth Dryer and Dish Washer Load –Customer determines the price that the cloth washer, dryer, and dish washer should be operated. –Based upon the historical price information, the program will estimate the average waiting time. –With the option of best price estimation, it guarantees the readiness within the customer specified time frame.

31 Real-Time Pricing Data ERCOT MCPE on June 25-27, 2006

32 Hourly Load Distribution Probability: Cloth Washer Source: U.S. Department of Energy, Building America Performance Analysis Resources

33 Hourly Load Distribution Probability: Cloth Dryer Source: U.S. Department of Energy, Building America Performance Analysis Resources

34 Hourly Load Distribution Probability: Dish Washer Source: U.S. Department of Energy, Building America Performance Analysis Resources

35 Load Control Strategies A typical household (1,000-3,000 ft 2 ), the annual loads in KWH of the appliances can be approximately calculated as the functions of the number of bedrooms: –Cloth washer = (17.5 * number of bedrooms) –Cloth dryer = (139 * number of bedrooms) –Dish washer = (34.3 * number of bedrooms)

36 Load Control Strategies Assuming uniform daily load consumptions, 3 bedrooms –Daily load of a household cloth washer = KWH/day –Daily load of a household dryer = KWH/day –Daily load of a household dish washer = KWH/day

37 Aggregate Hourly Cloth Dryer Loads of Typical 1,000 Households

38 Price Naming - customers' participation in the program percentages Percentage of Participation Not participate in load control40 Named price = 2010 Named price = 3010 Named price = 4010 Named price = 5010 Named price = 6010 Named price = 7010 Total100

39 Hourly Cloth Dryer Loads of Typical 1,000 Households (different prices named) Potential savings = %

40 Aggregate Cost Savings (June 25, 06) Named Prices ($/MWH)Daily Cost ($)Savings (%) No Load Control N/A Different Levels of Participants

41 Price Naming – waiting times when named price $80/MWH (June 25-27, 06) Jun 25 - Potential savings = 4.406%

42 Price Naming – waiting times when named price $50/MWH (June 25-27, 06) Jun 25 - Potential savings = %

43 Conclusion Electric deregulations can not be effectively and efficiently accomplished if the wholesale and retail electricity markets are disconnected. –Dynamic real-time pricing information is the missing link between wholesale and retail markets for better electricity deregulation. With the enactment of Energy Policy Act 2005, we will see more supporting infrastructure of demand response for residential customers.

44 Conclusion A simple yet effective consumer-centered user interface for end-use residential consumers’ load managements in real-time electricity pricing environment is introduced. It provides possible solutions, ideas, and applications to link the wholesale utility and retail end-use residential customers


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