Yafeng Peng Jedidiah Shirey Contents Context Problem Statement - - PowerPoint PPT Presentation

Abram Gross Yafeng Peng Jedidiah Shirey

Contents

 Context  Problem Statement  Method of Analysis  Forecasting Model  Way Forward  Earned Value

NOVEC Background (1 of 2)

 Northern Virginia Electric Cooperative (NOVEC) is a

distributor of energy to 6 counties in Northern Virginia.

 Nearly 150,000 customers.  Mandated to meet all energy requests in pre-specified

zones of obligation.

 Primary means to service communities is through bulk

energy market purchases.

NOVEC Background (2 of 2)

 Energy Purchases:

 1) Bulk purchases via contracts

1 month to 3 years prior to delivery.

 2) Spot purchases as needed to

meet peak demand up to one day prior to delivery.

Problem Statement

 Warming trends have caused NOVEC to question

whether the current weather-normalization methodology is still the best available model.

 NOVEC needs a new weather-normalization method

that accounts for changing weather trends or a recommendation that the existing model is sufficient.

To what extent is the climate and its impact on energy demands changing?

Purpose & Objectives

 Purpose:

 NOVEC needs to remove weather-effects from energy consumption to

more accurately inform bulk purchases.

 Explore and recommend a methodology to normalize monthly energy

purchases.

 Objectives:

 Characterize the relationship between economic

variables, weather, customer-base, and energy consumption.

 Develop a normalization procedure to remove weather effects from

energy demand.

Recommendation for improvement to current weather-normalization methodology.

Scope

 Data:

 NOVEC monthly energy purchases data since 1983.  Dulles weather data since 1963.  Historic economic factors data since 1980s.  30 years of forecasted economic factors.

 Model:

 Parameters: historic energy purchases, weather data, economics, and

customer-base.

 Predictions for energy consumption over a 30-year horizon.  Regression: characterize dynamics between parameters.  Weather-normalization: remove seasonal weather impacts on

NOVEC’s load.

 Forecast: facilitate testing of varied normalization methodologies.  Ensure synergy with NOVEC’s existing suite of models

(regression, weather-normalization, forecast).

Key Definitions

 CDD: Cooling Degree-Day = MAX(ActualDegree – UpperBound, 0) * Duration.  HDD: Heating Degree-Day = MAX(LowerBound – ActualDegree, 0) * Duration.  Neutral zone: Upper and lower bounds for temperatures that do not impact load.

* Analysis will explore varied upper/lower bounds for temperature

30 40 50 60 70 80

CDD HDD

Actual_Temperature Upper_Bound Lower_Bound

t

Weather Data Visualization (1 of 2)

 Historic weather data was

analyzed by average temperature per month to view trends.

 Average temperature

trending upwards from 1960 to the present for each month (graph is for July).

Weather Data Visualization (2 of 2)

 Variance was also evaluated and no change is seen.

Essential Elements of Analysis & Measures of Effectiveness

 EEA 1: What is the rate of climate change?

 MoE 1.1: Seasonal trends and variability.  MoE 1.2: Rate of climate change.

 EEA2: What econ-model best predicts NOVEC’s customer-base?

 MOE 2.1: Best subset of econometrics to gauge service demand.  MOE 2.2: Goodness of fit test for selected model.

 EEA3: What impact does weather have on energy demand?

 MOE 3.1: Relationship between weather and load beyond base demand.  MOE 3.2: Changes in climate compared to per capita consumption.  MOE3.3: Goodness of fit test for selected model.

• III. Forecast Model
• II. Develop normalization methodology
• I. Relate model parameters

Methodology

Residential Commercial Composite Customer-Base GDP Housing Stocks Employment Historic Power Demand Monthly Demand HDD Impact CDD Impact Remaining Power Demand

Weather- Normalization Forecast Assess Model Basic Load Economic Variables Customer Combination

Per Capita Demand

* Model forecast will be verified against 2012-2013 power demand and compared to existing model’s accuracy. *

EEA1: Rate of Climate Change

 Historic weather data was analyzed to determine trends:

 Seasonal fluctuations.  Overall rate of change.  Characterize uncertainty/variability.

200 400 600 800 1000 1200 1400 1600 1000 2000 3000 4000 5000 6000 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 Degree-Days Below 65 °F

Heating Degree-Days - 1963 to 2011

Total HDD Average HDD Max HDD 200 400 600 800 1000 1200 1400 1600 1000 2000 3000 4000 5000 6000 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 Degree-Days Above 65 °F

Cooling Degree-Days - 1963 to 2011

Total CDD Average CDD Max CDD

EEA2: Economics Model

 Model to associate economic factors to customer-base

in under development.

20 40 60 80 100 120 140 10 20 30 40 50 60 70 80 Aug-89 Aug-90 Aug-91 Aug-92 Aug-93 Aug-94 Demand (Millions - kWH) Customers & Housring Stocks (Thousands)

Residential Demand & Metro-D.C. Econometrics

Housing Stocks Customers Demand

EEA3: Impact of Weather

 Changes to per capita energy demand by customer

type: residential and non-residential.

2000 4000 6000 8000 10000 12000 14000 16000 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10

Energy Consumption (kWhr)

Monthly Customer per Capita Demand (2005 - 2010)

Non- Residential Residential HDD CDD

Model Development

 Pre-processing: Excel Workbook with VBA Macros.

 Automated computations of HDD and CDD.

 Allows user-defined “neutral” boundaries for calculating.  Ingests data as currently maintained by sponsor.

 Automated linear transformations for regression models.

 GUI design facilitates some simple regression models germane to

VBA; limited capability.

 Launch and export conditioned data to R; expanded capability.

 Statistical Modeling in R: Executable file for regression

analysis and improved visualization.

Preliminary Results

 Variability in yearly weather patters doesn’t appear to change.  Climate change is more apparent in winter and summer than fall and spring.

20 40 60 80 100 120 140 160 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 Degree-Days Below 65 °F

Summer Heating Degree Days

JUN JUL AUG 1966 1969 1972 1975 1981 1984 1990 1993 1996 1999 2005 2008 200 400 600 800 1000 1200 1400 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 Degree-Days Exceeding 65 °F

Summer Cooling Degree Days

JUN JUL AUG 1966 1969 1975 1978 1981 1984 1987 1990 1996 1999 2005 2008 2011 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 200 400 600 800 1000 1200 1400 1600 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 Degree-Days Below 65 °F

Spring Heating Degree Days

MAR APR MAY 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 50 100 150 200 250 300 350 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 Degree-Days Exceeding 65 °F

Spring Cooling Degree Days

MAR APR MAY 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011

Way Forward & Risks

 Data visualization and preliminary analysis completed.  Website design completed. Still need to finalize

content.

 Model design/creation under construction. Major

focus over next couple of weeks.

 Excel calling R needs to be completed.  Customer transformation  Final model fitting  Forecast

Earned Value

0.00 10000.00 20000.00 30000.00 40000.00 50000.00 60000.00

Dollars

Earned Value

Earned Value Planned Value Actual Cost

CPI & SPI

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8

Ratio

CPI and SPI

CPI SPI

References



www.novec.com



NOVEC 25th Anniversary History Film http://www.youtube.com/watch?v=2qfeOKnPPGg



“Improving Load Management Control for NOVEC”; Kozera, Lohr, McInerney, and Pane. http://seor.gmu.edu/projects/SEOR-Spring12/NOVECLoadManagement/team.html