Spatial Electric Load Analysis for Substation Siting and Load Balancing

Transcription

1 Spatial Electric Load Analysis for Substation Siting and Load Balancing At United Power, the engineering and GIS groups were tasked with answering the following question, Will we have the infrastructure to support future demand in 5 or 10 years? We turned to spatial technologies to provide management with an accurate and detailed GIS-generated load density forecast. Demand and energy readings from CIS were integrated with GIS to produce base grids for summer and winter peaks. The analysts combined base load grids with 2 forecast sources to produce long-range forecast raster grids. The complex analysis process was performed with multiple Model Builder models for consistency and repeatability. ESRI s Spatial Analyst extension performed raster analysis. By maintaining a spatial history of power consumption, accurate data is readily available for a plethora of statistical studies and testing whatif scenarios. Using GIS technologies for planning purposes increases forecast accuracy and efficiency and creates a roadmap for future land and ROW acquisitions.

2 Spatial Electric Load Analysis for Substation Siting and Load Balancing David Hollema GIS Analyst Jared Weeks Electrical Engineer United Power, Inc. Brighton, Colorado ESRI EGUG 2008

3 Today s agenda Who we are Long range forecast goals Spatial load analysis basics Components of spatial load forecasting Load center prediction Results and looking ahead

4 United Power Facts Rural electric cooperative headquartered in Brighton, CO Incorporated in October of 1938 Wires hot in 1940 to 750 customers Nearly 65,000 customers today covering 900 square miles Historically fast growing up to 5000 new accounts per year Among the top 10 fastest growing coops nationwide

5 Long range forecast goals and tasks Spatially project coincidental peak load over the next 10 years Define substation influence areas Forecast and locate future load centers for substation placement Determine substation transformer upgrades Update every 3 years modular)

6 What is spatial load analysis? A process of looking at historical electric power consumption with a spatial e.g. mapping) component Seasonal peak focus Demand kw) or energy kwh) Typically includes forecasting for substation siting Change detection analysis Temporal study Used to optimize current electric distribution system

7 , , , , , , , , , , , ,000 50,000 50,000 0 Monthly Demand to to Summer Peak NO SPATIAL COMPONENT Winter Peak Monthly Monthly Coincident Coincident Peak Peak Demand Demand kw) kw)

8 Summer Peak Demand Linear Forecast Summer Peak Demand Linear Forecast Peak Demand kw) Peak Demand kw) NO SPATIAL COMPONENT 5yr trend 5yr trend Historic Historic

9 Spatial Load Forecast Approach 3 components 1. Base Map Historical snapshot of peak seasonal load Peak load density map Metrostudy Data Provider of housing data Used to forecast residential growth 3. Point Loads Internal knowledge of future large commercial loads from district reps

10 Base map preparation need to know where you ve been to know where you re going Meter Reading Extraction Load Table Preparation Must be repeatable and modular! Spatial Join Rasterization Base Map Preparation

11 Meter Reading Extraction Primary input for entire analysis Interested in 1 moment in time with coincident peak demand, settle for 30 day data billed monthly) 10% Meter Read-type Breakdown Oracle view used to extract CIS data to SDE instance 35% 55% Manual Read Carrier Line AMR Drive-by AMR Revolving billing cycle makes capturing monthly peak difficult Base Map Preparation

12 SDE Oracle view into CIS database SELECT MAXBI_CONSUMER.BI_ACCT) AS ACCT_NBR, MAXBI_TYPE_SERVICE.BI_SRV_STAT_CD) AS SRV_STAT_CD, /* BI_CONSUMER_VIEW_1.BI_ADDR_TYPE,*/ MAXBI_CONSUMER_VIEW_1.BI_SORT_NAME) AS NAME, MAXBI_CONSUMER_VIEW_1.BI_LNAME) AS LAST_NAME, MAXBI_CONSUMER_VIEW_1.BI_FNAME) AS FIRST_NAME, BI_SRV_LOC.BI_SRV_MAP_LOC AS SERVLOC, /* BI_HIST_USAGE.BI_CUR_HIST_SW, */ MINBI_HIST_USAGE.BI_RATE_SCHED), MAXBI_HIST_USAGE.BI_PRES_READ_DT) AS READ_DATE, SUMBI_HIST_USAGE.BI_USAGE) AS KWH, MAXBI_HIST_USAGE.BI_BILL_DMD_HIST) AS DEMAND_KW, MAXBI_HIST_USAGE.BI_REV_YRMO) /* BI_HIST_USAGE.BI_PRES_MTR_RDG AS KWH */ CIS GIS FROM BI_CONSUMER INNER JOIN BI_AR ON BI_CONSUMER.BI_ACCT=BI_AR.BI_ACCT) INNER JOIN BI_CONSUMER_VIEW_1 ON BI_CONSUMER.BI_ACCT=BI_CONSUMER_VIEW_1.BI_VWN_CO_ACCT) INNER JOIN BI_TYPE_SERVICE ON BI_AR.BI_ACCT=BI_TYPE_SERVICE.BI_ACCT) AND BI_AR.BI_TYPE_SRV=BI_TYPE_SERVICE.BI_TYPE_SRV)) INNER JOIN BI_HISTORY ON BI_TYPE_SERVICE.BI_ACCT=BI_HISTORY.BI_ACCT) AND BI_TYPE_SERVICE.BI_TYPE_SRV=BI_HISTORY.BI_TYPE_SRV)) AND BI_TYPE_SERVICE.BI_SRV_LOC_NBR=BI_HISTORY.BI_SRV_LOC_NBR)) INNER JOIN BI_SRV_LOC ON BI_TYPE_SERVICE.BI_SRV_LOC_NBR=BI_SRV_LOC.BI_SRV_LOC_NBR) INNER JOIN BI_HIST_USAGE ON BI_HISTORY.BI_ACCT=BI_HIST_USAGE.BI_ACCT) AND BI_HISTORY.BI_TYPE_SRV=BI_HIST_USAGE.BI_TYPE_SRV)) AND BI_HISTORY.BI_SRV_LOC_NBR=BI_HIST_USAGE.BI_SRV_LOC_NBR)) AND BI_HISTORY.BI_HIST_CD=BI_HIST_USAGE.BI_HIST_CD)) AND BI_HISTORY.BI_BILL_DT_TM=BI_HIST_USAGE.BI_BILL_DT_TM) WHERE BI_CONSUMER_VIEW_1.BI_ADDR_TYPE=N' ' AND BI_HIST_USAGE.BI_CUR_HIST_SW=N'Y' AND BI_HIST_USAGE.BI_REV_YRMO= AND BI_RATE_SCHED NOT IN 'SC0','SC1','SC5') --removes Golden Aluminum fed off transmission) and Frederick/Evanston area primary meters --such as and double-counted load) AND SUBSTRBI_SRV_LOC.BI_SRV_MAP_LOC,5,1)!='6' AND BI_SRV_LOC.BI_SRV_MAP_LOC!=' ' --removes Spindle Hill Energy Peak Plant fed off transmission GROUP BY BI_SRV_LOC.BI_SRV_MAP_LOC Base Map Preparation

13 Base Map Preparation

14 Load table preparation Engineering calculation fields added Some meters billed by usage kwh), others by usage and demand kw) Estimate coincident peak kw using kwh Monthly Demand & Energy- July 1998 to July 2008 Monthly Coincident Peak Demand kw) Thousands Demand kw) Millions Monthly Coincident Energy kwh) Energy kwh) 0 Base Map Preparation

15 Energy kwh) to Coincidental Power kw) 1 2 Energy Average Power Peak Power 3 Peak Power - Coincidental Peak Load - Noncoincidental Average_ Load Peak _ Power = Noncoincidental kwh LF Biling _ Period Average _ Power = Peak _ Power = CF * Hours Peak _ Power Coincidental Biling _ PeriodNoncoincidental Energy Power kw) Average Power 20 Peak Load - Coincidental Energy kwh) :00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 Typical United Power System Peak 16:00 18:00 20:00 22: Base Map Preparation

16 Understanding Load Factors and Coincident Factors C oincident Factor CF ) =.5 C oincident Factor CF ) =.5 Power kw) Power kw) Load Factor LF) =.32 Load Factor LF) = A verage Power A verage Power :00 0:00 2:00 2:00 4:00 4:00 6:00 6:00 8:00 8:00 10:00 10:00 12:00 12:00 Tim e hr) Tim e hr) 14:00 14:00 16:00 16:00 18:00 18:00 20:00 20:00 22:00 22:00 Base Map Preparation

17 Use the most accurate sources of information Residential Peak _ PowerCoincident al R = LF * Average _ Power * CF Industrial Peak _ PowerCoincident al I = Peak _ Power * CF LF Energy Average Power Peak Power Peak Power - Coincidental CF Base Map Preparation

18 Load table preparation ModelBuilder model Base Map Preparation

19 Load table preparation ModelBuilder model continued Base Map Preparation

20 Base Map Preparation

21 Spatial Join GIS + ivue load data + vector grid Summarizes for each grid cell, number of customers, usage kwh), demand kw), and coincident peak demand kw) SAINT VRAIN RANCH BOULEVARD SAINT VRAIN RANCH BOULEVARD SANDY RIDGE COURT SANDY RIDGE COURT SAGE STREET SHENANDOAH STREET SHENANDOAH STREET SAGE STREET , 500, and 1000 ft grid cells Base Map Preparation

22 Spatial join ModelBuilder model Base Map Preparation

23 Spatial join ModelBuilder model continued Base Map Preparation

24 Rasterization ArcToolbox tool Polygon to Raster converts vector polygon feature class to geotif Raster files are much easier to analyze, manipulate, and perform algebraic computations ESRI s Spatial Analyst used for raster manipulation vector raster Base Map Preparation

25 Raster Results Berthoud Summer - July Gilcrest Mead Platteville Hygiene 2007 Peak Demand Longmont Niwot Firestone Frederick Dacono Fort Lupton Hudson Keenesburg Erie Valmont Wattenberg Winter - January East Portal Eldora Nederland Rollinsville Pinecliffe Wondervu Crescent Boulder Marshall Eldorado Springs Lafayette Louisville Superior Broomfield Thornton Eastlake Henderson Brighton Lochbuie Northglenn Empire Central City Miles N Golden Leyden Coincident Arvada Demand kw) Wheat Ridge Westminster Western Hills Edgewater Federal Heights Dupont Adams City Commerce City ,000 1,001-20,000 20,001-40,000 Denver International Airport Watkins Bennett Base Map Preparation

26 coincident peak timeline Base Map Preparation

27 2002 versus 2008 coincident peak Plains territory Base Map Preparation

28 Metrostudy Data Number Number of of Closings Closings 5,000 5,000 4,500 4,500 4,000 4,000 3,500 3,500 3,000 3,000 2,500 2,500 Function Function fit fit to to Annual 2,000 Annual 2,000 averages 1,500 1,500 Rolling averages Rolling Annual Annual Closings Closings 1,000 1, Q03 4Q03 4Q04 4Q04 4Q05 4Q05 4Q06 4Q06 4Q07 4Q07 4Q08 4Q08 4Q09 4Q09 4Q10 4Q10 4Q11 4Q11 4Q12 4Q12 4Q13 4Q13 4Q14 4Q14 4Q15 4Q15 Quarter Quarter Q) Q) Survey of subdivision data Updated quarterly Spatial reference Annual closings fit to a 5 year sine wave Metrostudy Forecast

29 Metrostudy 2017 forecast Berthoud Berthoud Æ 60 Æ 60 Gilcrest Gilcrest MetroStudy provieds point data ene ene wot wot en en Longmont Longmont Erie Erie Lafayet te Lafayet te Louisville Louisville É 42 Æ 470 É 42 Æ 470 uperior uperior Æ Broomfield Broomfield Nort hglenn Nort hglenn É 44 Æ 121 É 44 Æ 121 Federal Heights Æ 2 Æ 95 Federal Heights Æ 95 Westminster Dupont Westminster Dupont Metrostudy Forecast Mead Mead Platteville Platteville Æ 66 Æ Firestone 25 Firestone Frederick Dacono Frederick Dacono Fort Lupton Æ 52 Fort Lupton Æ 52 Æ 7 Æ 7 Thornton Thornton Eastl ake Eastl ake Æ 2 Wattenberg Wattenberg Brighton Brighton Henderson Henderson Commerce City Commerce City Lochbuie Lochbuie Hudson Hudson Keenesburg Keenesburg Denver International Airport Denver International Airport Circular shapes are from multiplying the estimated area of a lot by the number of lots and converting it into the area of a circle. Future platted subdivisions forecasted to go online based on onsite activity. Qtr Lot Future Prelim Record Vacant Survey Equip Active Front Lot Land Stakes on Site Excavation Street Paving Future 0'-80' 4,800 4, ,257 0 Future 0'-0' Future 70'-80' Future 0'-0' Future 0'-0'

30 Point Loads Information gleaned by the in-house staff about large commercial/industrial customers 3-5 year accuracy Spatially represented Forecasted annually Point Load Forecast

31 Point loads 2007 forecast 36 Valmont É 15 7 Boulder Marshall Eldorado Springs Æ 93 Louisville É 42 Superior Leyden Berthoud Waste Water Treatment Plant 500kW Erie Palisade Park 2000kW WCR 34 & HW LLC 0kW Mead High Rise Semcrude 500kW Aurora Dairy Platteville 2000kW 500kW Mead H.S. Æ 66 Hygiene Life Bridge 750kW 500k W Hotels Longmont 750k W Warehouse 85 Anadarko No. 2 Pioneer Village 300kW 40000kW 1748kW Suncor Energy Upgrade THF Firestone AVA Solar 0kW Anadarko No kW 10000kW 10000kW Firestone 25 Keenesburg Niwot Frederick Boulder Scientific Hudson Correctional Facility Æ Dacono 3000kW 1600kW Fort Lupton Hudson Æ 52 Æ 7 Æ 72 Æ 128 Æ 121 Lafayette Æ 470 Broomfield Mc Whinney 7000kW Denver Water Pump Facility 2000kW Northland Village PUD 5000kW Vestas Wattenberg 18000kW LEED's Manufacturing 2500kW Brighton Thornton Aspen Reserve Adams Crossing 372kW 2375kW Eastl ake Henderson Nort hglenn É 44 Æ 60 Otho Stuart 0kW Federal Heights South Adams Water - Pumping Station Æ kW Westminster Dupont Æ 2 Gilcrest Adams County Qt House 1800kW Walmart 540kW Brighton High school No kW Denver International Airport 34 Roggen Prospect Valley Æ 79 Point Load Forecast

32 Map Algebra Forecast Process 2007 Base Map Metrostudy + Point Loads x scale factor dependent on year) +

33 Explanation of scale factor Scale factor based of a linear trend of historic data. No double counting! Commercial - Industrial Point loads Metro Study Load MW) Load MW) Linearly Trended Grow th Linearly Trended Grow th Hybrid Forcast Plains) Hybrid Forcast Plains) Metrostudy - pre-intersect w ith grid Metrostudy - pre-intersect w ith grid Forecasted Com/Ind Loads Forecasted Com/Ind Loads Historic Data Plains) Historic Data Plains) =

34 2017 Plains Forecast Summer - July Berthoud Mead Platteville Gi lcrest 2017 Peak Demand Hygiene Longmont Niwot Firestone Frederick Dacono Fort Lupton Hudson Keenesburg Erie Valmont Wattenberg Boulder Lafayette Lochbuie Louisvi lle Brighton Marshall rado Springs Superior Broomfield Thornton Eastlake Henderson Northglenn Leyden Federal Heights Westminster Western Hills Dupont Adams City Commerce City Denver International Airport Arvada

35 Load Centers: Substation influence areas Heavily loaded feeders no more than 4 miles in length Physical Barriers Interstates Railroads Rivers Utilize existing infrastructure Lincoln Hills Lincoln Hills Æ 119 Æ 119 Ralston Creek Ralston Creek É É Del Camino Del Camino Slater Æ 66 Slater Æ Rinn Valley Rinn Valley Æ 128 Silver Saddle Æ 79 Silver Saddle Æ 93 É Æ 72 Æ Reunion Æ 60 Æ 60 É É É Æ 470 Æ 470 Æ 121 Æ 121 É 224 É É É 35 Davis Davis Homestead Homestead Hudson Hudson Dacono Fulton Coal Mine Dacono Fulton Coal Mine Æ 52 Brighton-Dist Æ 52 Brighton-Dist Henry Lake/Lochbuie Henry Lake/Lochbuie É 157 Erie Storm Chaser 157 Erie Storm Chaser Xylophone Xylophone Parkway Parkway Platte Valley Æ 7 É 42 Platte Valley Bromley Æ 7 42 Green Bromley Green É Henderson Henderson 76 Barr Lake 76 Barr Lake É 22 Prairie Center 22 Prairie Center Crescent Æ 79 Crescent Æ 128 Æ 72 Blue Mountain Blue Mountain Reunion É 53 É Load Center Forecast Æ 58 Æ 58 Æ 95 Æ É É Æ 2 Æ 2 É 30 30

36 Current feeder extents 34 Current switching 36 verses optimal switching configurations É 402 É 60 É 56 Æ 257 Æ 60 É Switching changes as substations come online Æ 66 Del Camino Platteville Rinn Valley Frederick Dacono Æ 52 Fulton Hudson Æ 119 Lincoln Hills Crescent Æ 72 É 157 É 170 Æ 93 Æ 128 É 42 Parkway Green Æ 7 Brighton Dist Henderson Platte Valley É 22 Bromley 76 Riverdale Silver Saddle Reunion É 44 Lochbuie Barr Lake Ralston Creek É 46 É 270 É 224 É 265 Æ 470 Æ 121 É 53 É Load Center Forecast Æ 58 Æ É

37 Find Load Center ModelBuilder Model

38 Influence Areas and Load Centers Slater Slater Coal Mine Coal Mine Rinn Valley Rinn Valley Parkway Parkway Green Green Dacono Dacono Erie Erie Del Camino Del Camino Brighton-Dist Brighton-Dist Henderson Henderson Platte Valley Platte Valley Fulton Fulton Davis Davis Storm Chaser Storm Chaser Bromley Bromley Prairie Center Prairie Center Henry Lake/Lochbuie Henry Lake/Lochbuie Hudson Hudson Legend Legend Plains Dist. Load Center Plains Dist. Load Center Substation - Existing Substation - Existing Homestead Homestead Xylophone Xylophone Barr Lake Barr Lake Legen Legen Pl Su Pl Su Substation influence boundaries defined Heavily loaded feeders 4 miles max Physical Barriers Interstates Railroads Rivers Silver Saddle Silver Saddle Reunion Reunion Load Center Forecast

39 2017 forecasted load centers versus substations ^ Load Center Forecast Slater Slater ^ ^ ^ ^ ^ Coal Mine Coal Mine Parkway Parkway ^ ^ ^ ^ ^ ^ ^ Brighton Dist Erie Brighton Dist Erie Storm Chaser Storm Chaser ^ ^ ^ ^ Henry Lake/Lochbuie Henry Lake/Lochbuie Platte Valley Xylopho ne Platte ValleyBromley Xylopho ne Bromley ^ ^ Green ^ Green Barr Lake Barr Lake Henderson Henderson ^ ^ Prairie Center Prairie Center ^ Silver Saddle Silver Saddle Del Camino Del Camino Rinn Valley Rinn Valley Dacono Dacono Fulton Fulton Davis Davis Reunion Reunion Hudson Hudson Homestead Homestead Plains video South video

40 Load Statistics by Influence Area Barr Lake Brighton-Dist Bromley Coal Mine Dacono Davis Del Camino Erie Fulton Green Henderson Henry Lake/Lochbuie Homestead Hudson Power Used MVA) Parkway Platte Valley Prairie Center Reunion Rinn Valley Silver Saddle Slater Storm Chaser Xylophone 2007 Influence Area Load Center Forecast

41 NOAA DMSP Nighttime Lights

42 NOAA DMSP Nighttime Lights Berthoud Gilcrest Mead Platteville Hygiene Longmont Niwot Firestone Frederick Dacono Fort Lupton Hudson Keenesburg Prospect Valley Va lmont Erie Wattenberg East Portal Eldora Nederland Pinecliffe Wondervu Rollinsville Crescent Boulder Lafayette Louisville Marshall Superior Eldorado Springs Broomfield Thornton Eastlake Northglenn Brighton Henderson Lochbuie Leyden Federal Heights Commerce City Westminster Dupont Western Hills Adams City Denver International Airport Central City Arvada Empire Idaho Springs Mountain View Wheat Ridge Golden Edgewater Pleasant View Denver Edgemont Aurora Watkins Bennett

43 Looking ahead Make better use of ArcInfo level tools and Spatial Analyst to simplify the process Use Point to Raster tool to convert service points with load directly to raster Interested in spatial electric load forecasting class for formal education Explore other forecasting methods including land use classification Create more empirical models independent of rate structure. take advantage of data loggers

44 Acknowledgements and References Various folks at United Power Software systems: ArcGIS 9.2, ArcGIS Spatial Analyst, Oracle 10g with ArcSDE 9.2, CIS NISC ivue with Oracle back-end Valenti, Jessica. Spatial Load Forecasting, presented at ESRI EGUG, October 2006, Albuquerque, NM. Willis, Lee. Spatial Electric Load Forecasting. Second Edition. New York: Marcel Dekker, Image and Data processing by NOAA's National Geophysical Data Center. DMSP data collected by the US Air Force Weather Agency.