2016 NERC Probabilistic Assessment MISO

Transcription

1 2016 NERC Probabilistic Assessment MISO 9/25/2014

2 Contents 1 Summary Probabilistic Statistics LTRA and 2016 Probabilistic Assessment Reserve Margins Software Model Description Computational Approach Transmission Modeling Approach External Modeling Approach Probabilistic Assessment vs 2016 Probabilistic Assessment Demand Modeling Load Summary Chronological Load Model Load Forecast Uncertainty Behind-the-Meter Generation Controllable Capacity Demand Response Modeling Load Modifier or a Resource Capacity Modeling Capacity Summary Generator Interconnection Queue Additions and Capacity Re-Ratings Jointly-Owned Units Sales and Purchases Intermittent and Energy-Limited Variable Resources Traditional Dispatchable Capacity Ratings Forced Outage Modeling Planned Outage Modeling Definition of Loss-of-Load Event Loss-of-Load Event

3 Tables & Figures Table 1: Internal MISO Areas... 4 Figure 1: MISO Local Resource Zones... 4 Table 2: MRA Metrics Results... 5 Table 3: Increased Demand and Energy Sensitivity... 5 Table 4: Monthly Statistics... 6 Figure 2: Monthly LOLH indices... 6 Figure 3: Monthly EUE Indices... 7 Figure 4: Model Topology... 8 Table 5: Zonal Import and Export Limits... 8 Table 6: Firm Capacity Transfers... 9 Table 7: Scenario 50/50 Peak Demand... 9 Table 8: MISO Local Resource Zone LFU Table 9: Demand Response Summary Table 10: MISO Capacity Summary Figure 5: MISO Loss-of-Load Event

4 1 Summary The Midcontinent Independent System Operator, Inc. (MISO) is a not-for-profit, member-based organization administering wholesale electricity markets that provide customers with valued service, reliable, cost-effective systems and operations, dependable and transparent prices, open access to markets, and planning for long-term efficiency. MISO as a Planning Authority operates as a single Balancing Authority and experiences its annual peak during the summer season. MISO manages energy, reliability, and operating reserves markets that consist of 36 local Balancing Authorities and 426 market participants, serving approximately 42 million customers. MISO s scope of operations covers 15 U.S. states and the Canadian province of Manitoba with 65,800 miles of transmission. MISO s membership consists of 52 Transmission Owners and 123 Non-Transmission Owners. For this analysis MISO s 10 Local Resource Zones were modeled with their respective load and generation. The 10 zones were modeled with their respective import and export limits to model the entire MISO region. External firm and non-firm support were also modeled. The internal entities modeled as part of this assessment are shown in Table 1. No. Local Balancing Area Acronym Zone 1 Dairyland Power Cooperative DPC LRZ-1 2 Great River Energy GRE LRZ-1 3 Minnesota Power MP LRZ-1 4 Montana-Dakota Utilities Co. MDU LRZ-1 5 Northern States Power Co. (Xcel) NSP/XEL LRZ-1 6 Otter Tail Power Co. OTP LRZ-1 7 Southern MN Municipal Power Agency SMP LRZ-1 8 Alliant East - Wisconsin Power and Light Co. ALTE LRZ-2 9 Madison Gas and Electric Co. MGE LRZ-2 10 Upper Peninsula Power Co. UPPC LRZ-2 11 Wisconsin Electric Power Co. WEC/MIUP 1 LRZ-2 12 Wisconsin Public Service Corp. WPS LRZ-2 13 Alliant West - Interstate Power & Light ALTW LRZ-3 14 MidAmerican Energy Co. MEC LRZ-3 15 Muscatine Power & Water MPW LRZ-3 16 Ameren Illinois AMIL LRZ-4 17 Southern Illinois Power Cooperative SIPC LRZ-4 18 Springfield Illinois - City Water Light & Power CWLP LRZ-4 19 Ameren Missouri AMMO LRZ-5 1 MIUP is a new Local Balancing Authority (LBA) that was previously a part of WEC. Since there is no change in the LRZ that the LBA resides in, the historic load collected was under WEC. If in the future, MIUP is in a different LRZ than WEC, historic breakdown of the LBAs should be collected to perform the LFU study 3

5 20 Columbia Missouri Water and Light Department CWLD LRZ-5 21 Big Rivers Electric Corp. BREC LRZ-6 22 Duke Energy Indiana DUK(IN) LRZ-6 23 Hoosier Energy Rural Elec. HE LRZ-6 24 Indianapolis Power & Light IPL LRZ-6 25 Northern Indiana Public Service NIPSCO LRZ-6 26 Southern Indiana Gas & Electric SIGE LRZ-6 27 Consumers Energy METC CONS LRZ-7 28 Detroit Edison Co. DECO LRZ-7 29 Entergy Arkansas EAI LRZ-8 30 Central Louisiana Electric Co. Inc. CLECO LRZ-9 31 Entergy Services, Inc. EES LRZ-9 32 Lafayette (City of) LAFA LRZ-9 33 Louisiana Energy and Power Authority LEPA LRZ-9 34 Louisiana Generating/Cajun Electric LAGN LRZ-9 35 South Mississippi Electric Power Association SME LRZ Entergy Mississippi EMI LRZ-10 Table 1: Internal MISO Areas Figure 1: MISO Local Resource Zones 4

6 1.1 Probabilistic Statistics The 2016 Probabilistic Assessment was performed at NERC s request as a complement to the Long- Term Reliability Assessment by providing additional probabilistic statistics of Loss of Load Hours (LOLH) and Expected Unserved Energy (EUE) for the years 2018 and The metrics calculated as part of MISO s 2016 Probabilistic Assessment are seen below in Table 2. The annual Planning Reserve Margin (PRM) study that MISO conducts determines a PRM such that all available resources are committed to meet firm load without any remaining to respond to outages and contingencies. The Base Case for the 2016 Probabilistic Assessment was run in the same manner and no resources were held aside. The metrics shown in Table 2 reflect the assumptions previously mentioned. Base Case LOLH EUE EUE (hrs/yr) (MWh/yr) (ppm) Table 2: MRA Metrics Results As part of the 2016 Probabilistic Assessment, NERC requested to perform a scenario where the metrics were calculated with an increase in demand and energy growth rates for the study years. The 2018 study year was modeled with a 2% increase in demand and energy for the 50/50 forecast. The 2020 study year was modeled with a 4% increase in demand and 2% increase in energy for the 50/50 forecast. While this sensitivity was modeled as a demand increase, for MISO it is more representable to think of it as a good proxy for increased retirement risk along with risk of increased load forecasts. The % increase is equal to 2,565 MW increase and the % increase is equal to a 5,203 MW increase. i.e. the 2018 sensitivity case could be a good proxy for increased retirement and load forecast increases that would lower our reserve margin by 2,565 MW. The results of this sensitivity are shown in Table 3. Increased Demand LOLH EUE EUE & Energy Sensitivity (hrs/yr) (MWh/yr) (ppm) Table 3: Increased Demand and Energy Sensitivity Along with the demand and energy sensitivity, the 2016 NERC ProbA includes monthly indices. These are shown in the below tables and figures. 5

7 Month LOLH (hrs/yr) 2018 Base 2020 Base 2018 Sensitivity 2020 Sensitivity EUE (MWh/yr) LOLH (hrs/yr) EUE (MWh/yr) LOLH (hrs/yr) EUE (MWh/yr) LOLH (hrs/yr) EUE (MWh/yr) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual Table 4: Monthly Statistics Figure 2: Monthly LOLH indices 6

8 Figure 3: Monthly EUE Indices LTRA and 2016 Probabilistic Assessment Reserve Margins The LTRA deterministic reserve margins decrement the capacity constrained within MISO south due to the 2,500 MW limit which reflects a decrease in reserve margin. The constraint was explicitly modeled for the probabilistic analysis and determined if sufficient capacity was available to transfer from south to north and vice versa. The modeling of this limitation produces an increase for the ProbA Forecast Planning Reserve Margin. 2 Software Model Description 2.1 Computational Approach MISO utilizes a program developed by General Electric called Multi-Area Reliability Simulation (MARS) to calculate the probabilistic indices for the applicable study years. GE MARS uses a sequential Monte Carlo simulation to model a generation system and assess the system s reliability based on any number of interconnected areas. GE MARS calculates the various indices for the MISO system by stepping through the year chronologically and taking into account generation, load, load modifying and energy efficiency resources, equipment forced outages, planned and maintenance outages, LFU and external support. The 2016 Probabilistic assessment utilized a modified model from MISO s annual Planning Reserve Margin study, which is required under Section 68A.2.1 Module E1 of the MISO Tariff. This model is built with coordination between MISO and market participants in MISO s Loss of Load Expectation Working Group. A more thorough description on model assumptions and background on MISO s Planning Reserve Margin can be found in the 2017 LOLE Study Report. 7

9 2.2 Transmission Modeling Approach For the Probabilistic Assessment transmission is modeled based on MISO s Local Resource Zones capacity import and capacity export limit. These limits reflect the First Contingency Incremental Transfer Capability (FCTTC) transfer of capacity between MISO zones. A more detailed review of this analysis and results can be found in Section 3 of the 2017 LOLE Study Report. Within GE MARS this was modeled as a hub and spoke topology. i.e. LRZ 1 s import is its import capability from all other MISO LRZ s. A simple diagram is shown below that resembles MISO methodology. Each line represents the Capacity Import and Export Limit for that zone. Within each zone is that zone s own load and generation. The MISO hub has zero load and zero generation and is just used to facilitate the capacity transfers within the model. The firm external bubble represents only the generation that is considered firm support. Figure 4: Model Topology The import and export limits for each zone are shown in table 5. Import/Export Limit LRZ 1 LRZ 2 LRZ 3 LRZ 4 LRZ 5 LRZ 6 LRZ 7 LRZ 8 LRZ 9 LRZ 10 Capacity Import Limit (CIL) (MW) 3,785 2,075 2,381 5,992 4,115 6,070 3,320 3,394 2,851 2,400 Capacity Export Limit (CEL) (MW) 686 2,290 1,772 11, ,191 1,899 2,493 2,373 1,747 Table 5: Zonal Import and Export Limits In addition to the zone specific import and export limits, a Regional Directional Limit was modeled which limits the Midwest (LRZs 1-7) to south (LRZs 8-10) flow is limited to 3,000 MWs and south to Midwest is limited to 2,500 MWs. The modeling of this limit is the main driver for the difference between the probabilistic and deterministic reserve margins. External to the MISO system, transmission constraints are determined by analysis on historical high observed summer Network Scheduled Interchange (NSI) as well as resource availability. MISO ties and interfaces with the external system are not explicitly modeled but are contained in the amount of external firm and non-firm support modeled. 8

10 2.3 External Modeling Approach The 2016 Probabilistic Assessment model included a constant 2,331 MW of external non-firm support for assistance to MISO in a time of need. This non-firm support amount is based off of historical probabilistic resource availability analysis as well historical Net Scheduled Interchange (NSI) data. A more detailed rationale can be found in section 4 of the 2017 LOLE Study Report. Firm Imports from external areas to MISO are modeled at the individual unit level. The specific external units were modeled with their specific installed capacity amount and their corresponding Equivalent Forced Outage Rate demand (EFORd). This better captures the probabilistic reliability impact of firm external imports. These units are only modeled within the External Firm hub as shown in figure 4.The external resources to include for firm imports were based off of the amount offered into the Planning Year PRA. This is, historically, an accurate indicator of future imports. For Planning Year this amount was 4,525 MW. Firm exports from MISO to external areas were also included in the analysis. Any export was decremented from the capacity available to MISO. The firm capacity transfers included in the model are shown in Table 6 and are consistent with the sales and purchases in the LTRA. Firm Capacity Transfers Winter Summer Winter Summer Imports 4,525 4,526 4,526 4,526 Exports 3,357 3,357 3,187 3,187 Table 6: Firm Capacity Transfers Probabilistic Assessment vs 2016 Probabilistic Assessment Previous results in the 2014 Probabilistic Assessment resulted in MWh EUE and 0.09 Hours/year LOLH. The results from this year s analysis resulted in a slight decrease for 2018 when compared to the analysis completed in the 2014 Probabilistic Assessment. This is largely driven by updates to load forecasts as well as changes to capacity. 3 Demand Modeling 3.1 Load Summary The 50/50 peak demands studied within the model are shown in Table 7. These are consistent with the LTRA demand numbers Expected 50/50 Peak Demand Winter Summer Winter Summer Base Case 103, , , ,076 Sensitivity 105, , , ,279 Table 7: Scenario 50/50 Peak Demand 9

11 3.2 Chronological Load Model The MISO system demand and energy forecast data used for this assessment were based on the forecasts submitted by Load Serving Entities (LSE) through the MECT tool. These non-coincident MISO peak load forecast values from the LSEs were applied to individual historic 2005 and 2006 load shapes and aggregated to form the MISO hourly load models and MISO coincident load peak created for this assessment. The historic years 2005 (MISO North/Central) & 2006 (MISO South) were chosen because they represent a typical load pattern year for MISO. 3.3 Load Forecast Uncertainty Load Forecast Uncertainty (LFU), a standard deviation statistical coefficient, is applied to a base 50/50 load forecast to represent the various probabilistic load levels. MISO connects each Local Resource Zone to a central hub with infinite ties and models each LRZ with its own LFU. MISO back-calculated the system wide LFU equivalent to MISO s current zonal methodology to be about 3.8 percent. In this calculation, the 50/50 hourly load of each LRZ was increased by one standard deviation and then aggregated up to get to one hourly load for the MISO footprint. This load was compared to the 50/50 MISO hourly load and the overall LFU for every hour was calculated. The average of these hourly MISO LFUs was about 3.8 percent. The LRZ LFU values are shown below in Table 8. Zones LFU LRZ 1 2.8% LRZ 2 4.5% LRZ 3 3.0% LRZ 4 4.8% LRZ 5 4.5% LRZ 6 3.4% LRZ 7 5.3% LRZ 8 5.1% LRZ 9 2.7% LRZ % Table 8: MISO Local Resource Zone LFU Since the North American Electric Reliability Corp. (NERC) load forecasting working group disbanded, MISO adapted the 2011 NERC bandwidth methodology to perform Load Forecast Uncertainty (LFU) analysis and developed regression models similar to NERC. MISO included historical load data ( ) to determine Local Resource Zone (LRZ) LFU. Forecasts cannot precisely predict the future. Instead, many forecasts append probabilities to the range of possible outcomes. Each demand projection, for example, represents the midpoint of possible future outcomes. This means that a future year s actual demand has a 50 percent chance of being higher and a 50 percent chance of being lower than the forecast value. 10

12 For planning and analytical purposes, it is useful to have an estimate of the midpoint of possible future outcomes, as well as the distribution of probabilities on both sides of that midpoint. Accordingly (similar to NERC), MISO developed upper and lower 80 percent confidence bands. Thus, there is an 80 percent chance of future demand occurring within these bands, a 10 percent chance of future demand occurring below the lower band, and an equal 10 percent chance of future demand occurring above the upper band. The principal features of the bandwidth methodology include: 1. A univariate time series model in which the projection of demand is modeled as a function of past demand. This approach expresses the current value of the time series as a linear function of the previous value of the series and a random shock. In equation form, the firstorder autoregressive model can be written as y t = a + y t 1 + ε t 2. The variability observed in demand is used to develop uncertainty bandwidths. Variability, represented by the variance σε of the historic data series, is combined with other model information to derive the uncertainty bandwidths. More details about the NERC methodology can be found at NERC Bandwidth Methodology. The Load Forecast Uncertainty is modeled within the GE MARS software with a normal distribution up to three sigma above and below the 50/50 demand with a corresponding weighting. 3.4 Behind-the-Meter Generation Behind-the-Meter generation is modeled as a generation resource. Many behind-the-meter generators report to the MISO PowerGADS and are required to submit a Generation Verification Test Capacity (GVTC) value annually. The Module E Capacity Tracking (MECT) pulls the GVTC and Equivalent Forced Outage Rate Demand (EFORd) from PowerGADS for each behind-the-meter generator. If there was not sufficient PowerGADS data to calculate an EFORd for a particular unit then a MISO class average value was used. MISO models each behind-the-meter generator as any other thermal generating unit with a monthly capacity and a forced outage rate. 4 Controllable Capacity Demand Response Modeling 4.1 Load Modifier or a Resource Direct Control Load Management and Interruptible Demand type of demand-response were explicitly included in the MARS model created for this assessment as energy-limited resources. These resources were limited to the number of times they could be called upon and the duration of their run time. A monthly profile for these resources is determined by the monthly values submitted in the MECT tool. This same profile was used for the 2018 and 2020 cases. These demand resources are implemented in the MARS simulation before accumulating LOLE or shedding of firm load. The LTRA utilizes these resources as a load modifier. A summary of these resources is shown in table 9. 11

13 Controllable and Dispatchable Demand Response Winter (MW) Summer (MW) Winter (MW) Summer (MW) Total 4,466 5,892 4,466 5,892 Table 9: Demand Response Summary 5 Capacity Modeling 5.1 Capacity Summary A summary of the internal MISO capacity modeled is shown in table 10. Internal MISO Capacity Expected On-Peak ( Existing Certain + Tier 1) Winter (MW) Summer (MW) Winter (MW) Summer (MW) Coal 60,831 60,417 60,831 60,417 Petroleum 1,836 1,836 1,836 1,836 Gas 62,912 61,675 63,735 61,695 Nuclear 13,096 12,904 13,096 12,904 Hydro 1,621 1,679 1,703 1,706 Pumped Storage 2,482 2,657 2,622 2,727 Geothermal Biomass Wind 1,876 1,876 1,876 1,876 Solar Other (Behind the Meter Generation) 4,286 4,269 4,286 4,269 Unknown Total 149, , , ,932 Table 10: MISO Capacity Summary 5.2 Generator Interconnection Queue Future generation was added based on unit information in the MISO Generator Interconnection Queue. Only units with a signed generator interconnection agreement were added to the MARS model used for this assessment. These new units were assigned the class-average forced outage rate based on their particular class. All future resources are considered firm deliverable capacity resources. Retirement of generation or inclusion of units in the mothballed or suspension state was based on information provided from MISO s Attachment-Y filing process. The Generation Interconnection Queue can be found on MISO s website, under the Planning tab. 12

14 5.3 Additions and Capacity Re-Ratings With new membership into MISO, generators are required to submit their GVTC to the MISO PowerGADS in order to qualify as a Planning Resource. Additionally, generation additions and capacity re-ratings are entered annually into the MISO PowerGADS. A monthly profile is determined based on the GVTC submitted and the monthly Net Dependable Capacities (NDC) entered in the MISO PowerGADS. Therefore, this assessment accounted for generation additions and capacity re-ratings. 5.4 Jointly-Owned Units Jointly-owned units (JOU) were modeled for this assessment as one unit in the LRZ that they are physically located. Typically, the majority owner is the sole entity to submit data to the MISO PowerGADS. Therefore, each unit is modeled like any other generation resource with one capacity and one forced outage rate. However, the capacity will be derated to accurately reflect the portion of a JOU that is external to the MISO footprint. 5.5 Sales and Purchases The model created for this assessment included 4,529 MW of purchases and 4,097 MW of sales. The purchases are modeled as a unit specific firm contract coming into MISO at all hours with availability based off the unit specific outage rates. The sales amount is gathered through coordination with MISO s neighboring markets and entities. 5.6 Intermittent and Energy-Limited Variable Resources Intermittent resources such as run-of-river hydro, biomass and wind were explicitly modeled as demandside resources. Non-wind intermittent resources such as run-of-river hydro and biomass provide MISO with up to 15 years of historical summer output data during hours ending 15:00 EST through 17:00 EST. This data is averaged and modeled in the LOLE analysis as UCAP for all months. Each individual unit is modeled and put in the corresponding LRZ. Each wind-generator Commercial Pricing Node (CPNode) received a capacity credit based on its historical output from MISO s top eight peak days in each past year for which data was available. The megawatt value corresponding to each CPNode s wind capacity credit was used for each month of the year. New units to the commercial model without a wind capacity credit as part of the 2016 Wind Capacity Credit analysis received the MISO-wide wind capacity credit of 15.6 percent as established by the 2016 Wind Capacity Credit Effective Load Carrying Capability (ELCC) analysis. The capacity credit established by the ELCC analysis determines the maximum percent of the wind unit that can receive credit in the PRA while the actual amount could be less due to other factors such as transmission limitations. Each wind CPNode receives its actual wind capacity credit based on the capacity eligible to participate in the PRA. Only Network Resource Interconnection Service or Energy Resource Interconnection Service with 13

15 firm point-to-point is considered an eligible capacity resource. The final value from the 2016 PRA for each wind unit was modeled at a flat capacity profile for the Planning Year. Aggregate megawatt values for wind-generating units are then determined for MISO and each LRZ. The detailed methodology for establishing the MISO-wide and individual CPNode Wind Capacity Credits can be found in the 2016 Wind Capacity Credit Report. 5.7 Traditional Dispatchable Capacity Ratings As mentioned above, only the existing resources eligible for MISO s Planning Resource Auction were included. Additionally, future units coming online were also included. The installed capacity rating from MISO s Planning Resource Auction was used as an August Capacity value since MISO typically peaks in August. A monthly profile was then determined using the Net Dependable Capacity value from PowerGADS Forced Outage Modeling The forced outage rates utilized for this assessment were established by the MISO PowerGADS. PowerGADS calculates an Equivalent Forced Outage Rate Demand (EFORd) for each generation resource. The EFORd values were calculated based off of 5 years ( ) historical data from PowerGADS and each unit was modeled individually with its unit specific EFORd value. If a unit did not have greater than 12 months of data then a class average EFORd was assigned Planned Outage Modeling Planned outages were modeled by summing the equivalent planned outage factor and equivalent maintenance outage factor produced from the MISO PowerGADS based on 5 years of historical data. Each generation resource was assigned this planned outage rate in the MARS model. The equivalent planned outage factor and equivalent maintenance outage factor accounted for the outages not included in the EFORd calculation. 6 Definition of Loss-of-Load Event 6.1 Loss-of-Load Event MISO defines a loss-of load event as anytime the amount of available system generation falls short of meeting the system s firm load. The Loss-of-Load Expectation (LOLE) is defined as the sum of the Loss-of-Load Probability for the integrated daily peak hour for each day of the year. Typically, the requirement is set such that the LOLE is no greater than one (1) day in ten (10) years. Figure 7 below shows how Real-Time Operations would step through its Emergency Operating Procedures. 14

16 5 p y Firm Load Shedding Loss-of-Load Event 4 Additional emergency steps 3 Utilize Operating Reserves 2 Demand Response, then Emergency Purchases 1 Online and Offline Emergency Only Resources Module E designated External Resources Non-Firm Exports (via curtailment) Normal Resource Utilization Figure 5: MISO Loss-of-Load Event 15