LOLE Fundamentals Loss of Load Expectation (LOLE) Fundamentals

Transcription

1 Loss of Load Expectation (LOLE) Fundamentals March 13, 2014 Last material update: 03/12/2014

2 LOLE Agenda 2 Loss of Load Expectation (LOLE) Fundamentals Sections LOLE Background LOLE Study Connections to other MISO Processes LOLE Software & Modeling GE MARS Takeaways Reference Materials

3 LOLE Background 3 Historical Generation & Transmission Adequacy Issues

4 LOLE Background 4 Loss of Load Expectation (LOLE) Definition LOLE is the measure of how long, on average, the available generation capacity is likely to fall short of the load demand Loss of Load Probability (LOLP) is the probability in a given hour Sum of the Daily Peak LOLP values is an expectation (LOLE) Sum of all LOLP values is called Loss of Load Hours (LOLH) LOLE is used to study Generation (Resource) Adequacy Generally considered to be the existence of sufficient resources, within a system, to satisfy consumer demand. A product of unit availability, perfect storm

5 LOLE Background 5 1-day in 10-years LOLE Criteria MISO Resource Adequacy criteria for Planning Reserve target is the industry standard LOLE objective: <1-day in 10-years NERC Standard BAL-502-RFC-02 Calculate a planning reserve margin that will result in the sum of the probabilities for loss of Load for the integrated peak hour for all days of each planning year analyzed being equal to 0.1. (This is comparable to a one day in 10 year criterion).

6 LOLE Background 6 Common Terminology Misconceptions 1 day in 10 years LOLE 24 hours in 10 years LOLH. Example: 2 hours of firm load shed = 2 loss of load hours and 1 day of loss of load. By definition 1 day/ 10 years LOLE 24 hours / 10 years LOLH. Cannot calculate Loss of Energy Expectation (LOEE) from LOLH without running complete analysis.

7 LOLE Background 7 Deterministic vs. Probabilistic Deterministic: to determine, fix, resolve, settle, regulate, limit, define Examples: % Reserve, (N-1), Worst case Condition Probability: likelihood of an event, the expected relative frequency of occurrence of a specified event in a very large collection of possible outcomes. A quantitative measure of the likelihood of an event A quantitative measure of the uncertainty associated with the event occurring A quantitative indicator of uncertainty Probability concepts provide the ability to quantitatively incorporate uncertainty in the planning of power systems, which cannot be done using deterministic methods and criteria.

8 LOLE Study Connections to other MISO Processes 8 LOLE Connections to Various MISO Processes LOLE Planning Reserve Margin (PRM) Study Assessments Resource Adequacy Tariff Module E-1 Capacity Market NERC Assessments MISO Informational Forums Planning Studies Resource Forecasting MTEP Study

9 LOLE Study Connections to other MISO Processes 9 MISO Value Based Planning STEP 1: MULTI-FUTURE REGIONAL RESOURCE FORECASTING STEP 7: COST ALLOCATION ANALYSIS STEP 2: SITE-GENERATION AND PLACE IN POWERFLOW MODEL STEP 6: EVALUATE CONCEPTUAL TRANSMISSION FOR RELIABILITY STEP 3: DESIGN CONCEPTUAL TRANSMISSION OVERLAYS BY FUTURE IF NECESSARY STEP 4: TEST CONCEPTUAL TRANSMISSION FOR ROBUSTNESS STEP 5: CONSOLIDATE & SEQUENCE TRANSMISSION PLANS

10 LOLE Study Connections to other MISO Processes 10 Resource Adequacy Impact on Transmission Service Request (TSR) Before the Resource Adequacy construct Point-to-Point TSRs were the predominant way Capacity was designated to serve Load (TSR) With the implementation of the Resource Adequacy construct Reduced TSR requests Allows any market deliverable capacity to be designated by load to meet Planning Reserve Margin Requirements (PRMR) PRMR = Load + PRM

11 LOLE Study Connections to other MISO Processes 11 Resource Adequacy Overview Achieving reliability in the bulk electric systems requires that the amount of resources exceeds customer demand by an adequate margin Margins necessary to promote Resource Adequacy need to be assessed on: Longer-term planning basis Near-term operational basis Focus of MISO s RA Construct is on the longerterm planning margins used to provide sufficient resources to reliably serve Load on a forward-looking basis Resources dedicated to meet Demand have an obligation to be available to meet real-time customer demand and contingencies

12 LOLE Study Connections to other MISO Processes 12 Planning Reserve Margins (PRMs) Planned maintenance Variations in customer demands or forecast demand uncertainty Planning Reserve Margins must be sufficient to cover: Unplanned or forced outages of generating equipment System effects due to reasonably anticipated variations in weather Deratings in the capabilities of Generation resources and Demand Response Resources

13 LOLE Study Connections to other MISO Processes 13 Overview of MISO Resource Adequacy Requirements Peak Demand and Planning Reserve Margin Generation, Load, and Resource Credits PRMR LSE Obligation Planning resources

14 LOLE Study Connections to other MISO Processes 14 Reserve Margin Analysis and Determination of PRM Annual Technical Analysis Results published by November 1 st Considers several factors Common industry reliability standard Utilize probabilistic LOLE study Uses MISO planning area zones Coordinate with LSEs Only entity other than MISO that can establish a PRM Technical Analysis MISO LOLE Analysis + LOLEWG Coordination State Authority

15 LOLE Software & Modeling LOLE Model Inputs Include 15 Study System Zone & Pool definition Zone Tie Limits (Import & Export) External System Model Generation Resources Operating Parameters Unit Forced Outage Rates Planned Maintenance Schedules & Rates Energy Limits for DR and Interruptible Load Load Demand and Energy Forecast Load Shape/Profile Load Forecast Uncertainty (LFU) LOLE Model Inputs

16 LOLE Software & Modeling 16 Source of LOLE Model Input Data Generation Resources Generator Availability Data System (GADS) Unit perform statistics used to calculate forced outage rates Data is uploaded into the MISO system one month after each quarter end Generation Verification Test Capacity (GVTC) Units need to demonstration maximum output level Load Monthly Peak Demand, MISO Coincident Demand and Energy Forecast are uploaded by Load Serving Entities (LSEs) into the Module-E Capacity Tracking (MECT) Tool (deadline Nov. 1st) MISO reviews Forecast and Finalize review by March

17 LOLE Software & Modeling 17 Planning Reserve Margin LOLE Study Analysis Flowchart GADS Max Capacity Ratings GVTC EFORd Unforced Capacity(UCAP) XEFORd MECT tool Peak Load & Demand data Load Modifying Resources Behind the Meter Gen Demand Response Interruptible Load External Designated Resources Transfer Analysis Capacity Import Limit (CIL) Capacity Export Limit (CEL) MISO System LOLE Model Non-constrained MISO system copper-sheet External System Equivalent Historic Load Analysis Load Forecast Uncertainty(LFU) NERC Bandwidth Method Historic Hourly Load Profiles typical load pattern/shape Local Resource Zone LOLE Model Individually Isolated LRZs NO LOLE=0. 1? YES YES LOLE=0. 1? NO Resource Adequacy Planning Reserve Margin MISO System PRM Target at LOLE = 0.1 day/year Local Reliability Requirement Local Clearing Requirement LCR = LRR CIL

18 LOLE Software & Modeling 18 LOLE Models Utilize an Equivalized Transportation Model Detailed Transmission Model Typical Load/Power Flow model used in transmission studies Retains transmission system details with a system of voltage specific buses and voltage specific branches Loads are modeled at bus level Equivalized Transportation LOLE Model Retains individual generating unit details System of interconnected zones/areas Aggregated load at a zonal level model representation Zones are grouped to represent electrical systems to help establish assistance priority

19 LOLE Software & Modeling 19 MISO System LOLE Model LRZ-2 External LRZ-1 LRZ-3 LRZ-9 MISO- Hub LRZ-4 LRZ-8 LRZ-5 LRZ-7 LRZ-6

20 LOLE Software & Modeling 20 Local Resource Zone LOLE Model LRZ-2 LRZ-1 LRZ-3 LRZ-9 LRZ-4 LRZ-8 LRZ-5 LRZ-7 LRZ-6

21 LOLE Software & Modeling 21 MISO Wind Capacity Credit Analysis Two-step Process Step-1 Probabilistic approach Calculates MISO system-wide Effective Load Carrying Capability (ELCC) value for all wind resources in the MISO footprint Step-2 Deterministic Period Metric Results in a value for each wind resource on the MISO system

22 LOLE Software & Modeling 22 MISO Wind Capacity Credit Analysis Examples of Probabilistic Approach Example System With & Without New Resource Base System LOLE = 0.15 days/yr (or 1½ days in 10 yrs) Base System LOLE = 0.08 days/yr (or 0.8 days in 10 yrs) + New Resource (Wind) ELCC Example System at the same LOLE Decreased Load -200 MW Load Increased +100 MW Base System LOLE = 0.1 days/yr (or 1 day in 10 yrs) Base System LOLE = 0.1 days/yr (or 1 day in 10 yrs) + New Resource (Wind) 1000 MW Nameplate

23 LOLE Software & Modeling 23 Planning Year ELCC

24 24 MISO Uses Multi-Area Reliability Simulation Program (MARS) Software General Electric Company product Originally developed for New York State Uses a sequential Monte Carlo simulation Steps through time chronologically and randomly drawing unit availability Replicating simulation with different sets of random events until statistical convergence is obtained Ability to analyze reliability of interconnected generation systems Benefits of diversity Tie-line effectiveness Utilizes a flat file input format and multiple text file outputs Most widely used LOLE software in the Power Systems Industry

25 25 Analytical vs. Monte Carlo approach to analysis Analytical methods work well for small systems and represent a system using mathematical model (A direct mathematical solution). Monte Carlo methods simulate the actual process and repeat simulation until convergence criteria is met. For complex systems, a Monte Carlo brute force approach is more appropriate.

26 26 Types of Monte Carlo Analysis Non-Sequential Monte Carlo Simulation Each hour is independent of every other hour. Inability to model time-correlated issues. Inability to calculate frequency and duration indices. Sequential Monte Carlo Simulation Steps through time chronologically. Ability to model time correlated issues and calculate frequency and duration indices. Requires more detailed system data.

27 27 Monte Carlo Simulation Overview Initialize Data for Study Year Read Monte Carlo Interface File Determine Capacity State of Units Schedule Firm Contracts Replication All Months All Hours Determine Area Capacities and Margins Collect Isolated Statistics Schedule Curtailable Contracts Calculate Emergency Assistance and Resulting Area Margins Collect Interconnected Statistics Calculate Annual Indices Test for Convergence

28 28 Load Modeling LOLE model uses forecasts to adjust historical load shapes. Historical shape as well as forecasts directly impact the resulting reserve margin target. Use 50/50 load as well as load forecast uncertainty to capture uncertainty. Diversity captured is product of forecasts and historical load.

29 29 MISO Load Shape Progression 35 Historical LBA Load Shapes Apply Module-E Monthly Forecasts to Historic LBA Load 35 Forecasted LBA Load Shapes 9 LRZ Forecasted Load Shapes

30 30 Historical MISO LBA Load Shape 35 hourly historical load shapes by LBA for MISO ** 2005 historical load shape..

31 31 Application of Module-E Monthly Forecasts to Historic LBA Load Load shapes are adjusted by Module-E submitted monthly non-coincident peak forecasts

32 Load [MW] GE MARS 32 Application of Module-E Monthly Forecasts to Historic LBA Load Example LBA July Daily Peak Load Historical Forecasted Hour

33 LRZ Forecasted Load Shapes 33 LBA forecasted load shapes are summed up by LRZ 35 LBA load shapes 9 LRZ load shapes LRZ load shapes input into MARs Each LRZ has individual load forecast uncertainty

34 34 Introduction to Load Forecast Uncertainty A standard deviation statistical coefficient, applied to base 50/50 load forecast to represent the various probabilistic load levels. MISO adapted the NERC Bandwidth Methodology to perform Load Forecast Uncertainty study. A univariate time series model in which the projection of demand is modeled as a function of past demand. MISO developed upper and lower 80 percent confidence bands. 80 percent chance of future demand occurring within these bands, a 10 percent chance of occurring below the lower band, and 10 percent chance of occurring above the upper band.

35 35 Prior to PY 14/15 LFU Modeling Methodology One MISO LFU was calculated to determine the MISO wide PRM and seven zonal LFU values were used to calculate the LRZ LRRs. Historically, LFU for MISO Midwest region had been around 4%. This year, an LFU of 3.8% was calculated for Midwest region. Adding South Region companies, MISO wide LFU for was reduced to about 3%. o Aggregation inherently dampened uncertainty effects. o Assumes available transmission capability to socialize uncertainty. Latest LFU values calculated by NERC for RFC, SERC, and MRO- US were 5%, 3.4%, and 4.6%.

36 LFU Modeling Methodology for PY Zonal construct was aligned with the MISO system PRM Nine individual LRZ LFU values were modeled as part of the MISO PRM analysis. More granular zonal LFU values appropriately applies each LRZ s LFU to that LRZ s hourly load. This application of LFU more accurately reflects the uncertainty impacts of each LRZ s geographic area.

37 37 Modeling Methodologies of PY vs. Previous Years 1. Zonal vs. Overall LFUs in MARS One common LFU value was applied to every LRZ in the zonal method. The same LFU value to MISO overall system in the old method. 50/50 loads were driven away by three standard deviations in both methods. Results of both methods were the same With Similar LFU values, MARS does not treat a model with zonal LFU any different from a model with a single system wide LFU

38 38 Modeling Methodologies of PY vs. Previous Years 2. System wide LFU equivalent to the zonal methodology =3.9%. 50/50 hourly load of each LRZ was increased by its LFU(one σ). Aggregated up to one hourly load for MISO footprint. Resulted hourly load less 50/50 MISO hourly load= Hourly σ for MISO overall Average of hourly σs was 3.9% o Effective LFU for MISO Midwest close to the 3.8% calculated in the old method. o Validated that MISO Midwest LFU should not reduce as a result of MISO South integration, knowing the existing transmission limits between the two regions.

39 Effect of LFU in PRM Calculation PRMs calculated for MISO with varying LFUs. 39 MISO system was treated as an island with no capacity transaction. Results show that for LFU ranges of 3% to 4% a 1 percent increase in LFU contributes to an increase of about 2 percent in PRM UCAP.

40 LFU Modeling in MARS 40 7 levels of uncertainty are modeled with a corresponding annual probability of occurrence. 7 LFU levels are applied to every hour. Reliability indices are calculated for each load level (LFU). Weighted average value used for system/zone indices and to test convergence.

41 41 LFU Probability Distribution

42 42 LRZ Forecasted Load with LFU Bands

43 43 Inherent MISO LFU in MARS Mean : 3.922% Median: 3.925% Std. Dev:.027% Max: Min: 3.819%

44 44 Load Forecast Uncertainty Expected Value =.1 Days/Year

45 45 Unit Data Unit Name Unit Physical Local Resource Zone (LRZ) Installation Date Retirement Date Type ( Thermal, Energy Limited, Demand Side) Unit Summary Type Thermal (Combustion Turbine, Combined Cycle, Pump Storage, Fluidized Bed, Hydro, Nuclear, Steam Turbine, Diesel) Energy Limited (Demand Response and Energy Efficiency) Demand Side (Intermittent Resources such as Wind, Run-of-River Hydro, and Biomass) Thermal Units Utilize the Generator Verification Test Capacity (GVTC) for a peak capacity and each unit s monthly Net Dependable Capacity (NDC) submitted in PowerGADS determines each unit s monthly capacity profile

46 46 Forced Outage Rates & Unit Maintenance Thermal Units Only Forced Outage Rates Unit Name Transition Matrix EFORd (5 year) Unit Maintenance Unit Name Maintenance Cycle # Fixed Maintenance Typically Nuclear Units Month & Year Unit Name Begin Date Stop Date Planned Outage Rates Total Number of Cycles for that Unit Planned Outage Rate (Planned Outage Factor + Maintenance Outage Factor from PowerGADS)

47 47 Maintenance Scheduling Levelize Reserves

48 48 Energy-Limited Units Demand Response (DR) & Energy Efficiency (EE) Month & Year Unit Name Minimum Megawatt (MW) Zero Maximum Megawatt (MW) Monthly Profile Energy (MWh) Maximum Annual Energy Maximum Interruptions * Maximum Duration * Maximum Monthly Capacity Storage (MWh)

49 49 Demand Side Management (DSM) Net Hourly Load Modification Unit Name Specify net hourly load modification for a typical week 168 (# of Hours in Week) * MW Positive values decrease load

50 50 External System Modeling 7 external areas with their own generation and load. Each external area represents one or more 1 st tier MISO external Balancing Areas. External areas reserve margin targets are adjusted by purchases, sales, and DSM. Each area has tie line to MISO hub. Tie line limit set using previous years hourly NSI Import limit to MISO capped at summer on-peak hours NSI maximum. Export limit out of MISO set to zero. Simultaneous flow on multiple interfaces also set using NSI.

51 51 External System and Ties Model

52 52 Firm Imports/ Purchases External purchases claimed in MECT are included in LOLE modeling. External purchases are modeled as firm non-curtailable contracts. Modeled from external region to MISO. Firm imports are only modeled in MISO PRM model and not zonal LRR model. Firm imports do effect tie line capacity.

53 53 Firm Exports/ Sales Capacity that is ineligible for MISO PRA is excluded from MISO and zonal models. Currently, only units that clear in PJM Reliability Pricing Model (RPM) are designated as sold in the LOLE model. Units are removed from physical LRZ and capacity is added to external area where sold.

54 54 Simulation Stopping Criterion Index Daily LOLE Hourly LOLE LOEE Isolated or Interconnected for an Area, Pool or Area Group MISO LRZ s Convergence tolerance (Standard error of the mean) days/year LOLE Can set minimum and maximum number of replications Typically, ~ 5,000 replications to minimize the standard error

55 55 Output Summaries Summary of Input Data Used in calculations of PRM and Local Reliability Requirements (LRR) Includes: Monthly load data by Area Installed Capacity by type, month and Area Unit data Weekly maintenance schedules LOLE and LOEE for each replication year

56 56 GE MARS Capacity Adjustment Flowchart

57 57 PRM Calculation PRM ICAP = [Installed capacity + ICAP Adjustment to meet 0.1 days / year LOLE + Firm Contracts] MISO Peak Demand MISO Peak Demand PRM UCAP = [Unforced capacity + UCAP Adjustment to meet 0.1 days / year LOLE + Firm Contracts] MISO Peak Demand MISO Peak Demand

58 Takeaways 58 Important Takeaways LOLE is the measure of how long, on average, the available generation capacity is likely to fall short of the load demand LOLE is used to study Generation(Resource) Adequacy Probabilistic analysis accurately captures uncertainty risk MISO Resource Adequacy criteria for Planning Reserve target is the industry standard LOLE objective: <1-day in 10-years Aligns with NERC standards Achieving reliability in the bulk electric systems requires that the amount of resources exceeds customer demand by an adequate margin (Planning Reserve Margin) LOLE models utilize an Equivalized Transportation Model to determine Planning Reserve Margin and Local Reliability Requirements All Market Participants are encouraged to participate in the stakeholder process through LOLEWG

59 Reference Materials 59 Reference Materials Loss of Load Expectation Reports Loss of Load Expectation (LOLE) Study Reports s/resourceadequacystudies.aspx Loss of Load Expectation Working Group (LOLEWG) staskforces/lolewg/pages/home.aspx 2014 Wind Capacity Report ind%20capacity%20report.pdf Resource Adequacy Documents BPM usinesspracticesmanuals.aspx BPM Resource Adequacy MISO Tariff: Module E NERC Standard BAL-502-RFC-02