An Introduction to Gas-Electric Modeling in the Context of MISO s Study of the Clean Power Plan. Gas-Electric Modeling Workshop June10, 2015

Transcription

1 An Introduction to Gas-Electric Modeling in the Context of MISO s Study of the Clean Power Plan Gas-Electric Modeling Workshop June10, 2015

2 The purpose of today s workshop To provide gas industry context for gas-electric modeling in MISO s study of Clean Power Plan (CPP) impacts To walk through the details of the PLEXOS gas-electric model To discuss gas modeling assumptions and data sources for the CPP Phase III study To discuss next steps MISO s Gas-Electric Modeling Workshop

3 Background Gas industry developments and proposed environmental regulations drove MISO s early efforts to understand the interdependence of the gas and electric systems. Efforts included formation of the ENGCTF 1 and MISO-commissioned Phase I, II and III gas infrastructure studies MISO continues to investigate gas-electric interdependency in its study of the Clean Power Plan With in-house analysis, using integrated gas-electric model The overarching goal of these efforts is to inform stakeholders on the potential impacts of a transitioning generation fleet. Aim to answer question of how changing utilization of gas infrastructure will impact the electric transmission system, resource adequacy, and the cost of electricity to the consumer 1 ENGCTF: Electric and Natural Gas Coordination Task Force MISO s Gas-Electric Modeling Workshop

4 MISO s understanding of gas-electric system interactions is evolving The addition of the integrated gas-electric model to MISO s economic planning toolkit offers a new way to address a complex question. Considering gas and electric system interactions simultaneously, eliminating hand-offs between models Allowing for customized inputs and outputs, based on stakeholder interests and study needs This model is in the early stages of development along with our education process on its application for the CPP study. MISO s Gas-Electric Modeling Workshop

5 The incorporation of gas-electric modeling in MISO s CPP study aims to provide indicative answers to key questions, including What are the infrastructure impacts, both electric and gas, of a range of potential compliance strategies? How much will compliance cost, given electric and gas infrastructure expansion needs? In order to answer these questions, gas industry context is needed, for example How is natural gas infrastructure currently utilized and what are projected trends? What are the drivers for new gas infrastructure? How long does it take and how much does it cost to build new gas infrastructure? MISO s Gas-Electric Modeling Workshop

6 Gas Industry Background & Context (ANGA)

7 Integrated Gas- Electric Model Overview (Energy Exemplar)

8 Gas-Electric Modeling Scope & Assumptions (MISO)

9 How does integrated gas-electric modeling fit into the scope of MISO s Phase III CPP study? Jan 2014 Nov 2014 Dec 2014 mid 2015 mid 2015 mid Phase I & II Preliminary economic analyses showing indicative compliance costs Phase III Indicative gas and electric infrastructure with estimated costs and schedules Phase IV Impacts of Final Rule Phase V Plan for implementation, including tariff/process changes (Completed) Based on draft rule Phase III builds upon preliminary (2014) analyses, capturing CPP ramifications for not only generation, but also electric transmission and natural gas infrastructure, under a range of compliance scenarios and sensitivities. Establishes framework for analysis of the final rule, including process for identifying potential infrastructure needs and solutions Integrated gas-electric model allows for more comprehensive estimation of the cost to achieve compliance MISO s Gas-Electric Modeling Workshop

10 Integrated gas-electric modeling will be performed for each of six scenarios CPP Phase III Scenarios Business-as- Usual (BAU) CPP Constraints (CPP) Coal-to-Gas Conversions (C2G) Gas Build-Out (GBO) Gas, Wind, Solar Build-Out (GWS) High EE, Wind, Solar Build-Out (EWS) Evaluated under state-level compliance paradigm, in which each state has to rely upon its own resources to meet EPA CO 2 targets Production cost analysis performed for 2020 and 2030 Regional and sub-regional compliance modeled for select scenarios C2G and GBO scenarios represent bookend analyses for gas and electric infrastructure expansion needs MISO s Gas-Electric Modeling Workshop

11 Gas-electric modeling results will help inform identification of infrastructure needs Gas system outputs from co-optimization, per scenario Gas spot prices Duration, location & magnitude of pipeline congestion (parallels to electric side metrics of shadow prices and binding hours) Stakeholder review and input Review gas pipeline utilization/congestion trends Provide input on criteria for what constitutes an infrastructure need Based on identified needs, help develop gas infrastructure solutions, including timing and cost of implementation All Phase III study outputs will be considered indicative, along with compliance cost calculations and infrastructure solutions they inform. Phase IV analysis will evaluate the CPP in final form. MISO s Gas-Electric Modeling Workshop

12 Underlying the process of needs identification are modeling and cost estimation assumptions Modeling assumptions: Gas field production/supply volumes and cost Representation of gas pipelines and gas storage facilities Representation of gas demand (non-power sectors) Representation of gas demand from electric generators Infrastructure needs identification, along with estimation of cost to achieve compliance will require assumptions regarding: What constitutes a need for gas infrastructure build-out? Which infrastructure option/solution is most appropriate for the need? How much does it cost and how long does it take to build new gas infrastructure? MISO s Gas-Electric Modeling Workshop

13 Gas production/supply modeling assumptions for MISO s Phase III CPP study Gas production/supply is represented with ~1,000 individual gas injection points across the pipeline network Consider as proxies for pooling points/liquid gas hubs vs physical gas fields Key properties, definitions and CPP study assumptions: Initial volume (MMcf), or the volume of gas at the pooling point at start of the simulation, will be set to the vendor s generic value (1x10^16) Production cost ($/MMBtu), or the cost to supply gas at the pooling point, will be based on proprietary IHS CERA forecasts* Max production (MMcf), or the max level of supply per pooling point, defined for user-selected time interval (daily), will be based on proprietary IHS CERA forecasts* Production cost curves applied to individual supply points are based on breakeven price and production forecasts* for the closest gas basin *See Appendix for details on IHS CERA forecasts, as well as definitions for breakeven prices and productive capacity MISO s Gas-Electric Modeling Workshop

14 Non-power gas demand modeling assumptions for MISO s Phase III CPP study Non-power includes residential, commercial, industrial sectors (RCI), as well as liquefied natural gas (LNG) exports Demand profile The shape of the historical RCI demand profile from the vendor will be normalized and applied to proprietary (monthly) demand forecasts from IHS CERA to produce an 8760-hourly profile. LNG demand profiles will be static across all hours of the month Load distribution State-level RCI gas demand will be distributed amongst gas nodes within the corresponding state, per LDC gas demand forecasts gathered for EIPC study* Gas demand shortage price The vendor s generic assumption of $100/MMBtu will be used. * See the Eastern Interconnection Planning Collaborative (EIPC) Gas-Electric System Interface Study Target 2 Final Report Exhibit 15 for LDC forecast source documentation, at MISO s Gas-Electric Modeling Workshop

15 Gas pipeline modeling assumptions for MISO s Phase III CPP study Natural gas pipeline network is represented via hundreds of pipeline segments, based on EIPC study definitions* E.g. partial representation of Panhandle Eastern Pipeline (PEPL) in PLEXOS (below), compared to system map (left) Source: Energy Transfer, 2015 * See Target 2 Final Report Exhibit 7 MISO s Gas-Electric Modeling Workshop

16 Gas pipeline modeling assumptions cont. Initial volume (MMcf), or the volume of gas in the pipeline at the start of the simulation, will be set to max volume Initial volume, along with min/max volume, are approximations for linepack Max flow (MMcf), or the maximum quantity of gas that can be extracted from the pipeline in any interval, is based on EIPC study data* Flow charge ($/MMBtu), or the incremental cost of extracting gas from the pipeline in each user-defined interval (e.g. hourly, daily), is not defined in the current version of the model Flow charge represents transport cost, i.e. a significant driver for cost differentials across the gas network Future iterations of the model (Phase IV) will attempt to capture this key metric * See Target 2 Final Report Exhibit 7 MISO s Gas-Electric Modeling Workshop

17 Gas storage modeling assumptions for MISO s Phase III CPP study Storage capacity (MDth), and maximum injection/withdrawal rates (MDth/d) are defined for >200 gas storage fields, per EIPC study definition* Initial volume The volume of gas in the storage field at the start of the simulation will be set to the max volume Withdrawal/injection charges Withdrawal charge set by the vendor at 10% of the cost to produce gas in a nearby gas field Injection charges not currently modeled For Phase III study, MISO will retain 10% storage charge-to-production cost assumption, and replace generic production costs with IHS CERA breakeven costs for 10% calculation * See Target 2 Final Report Exhibit 7 MISO s Gas-Electric Modeling Workshop

18 MISO requests stakeholder input on gas modeling assumptions Including the representation of Gas transport cost Gas transportation contracts Gas field production cost curves Gas storage withdrawal/injection charges Gas demand shortage cost Generic assumptions will be updated with better information, if available, for the Phase III CPP study model MISO s Gas-Electric Modeling Workshop

19 Next steps Continue to engage with stakeholders on gas-electric modeling results at August PAC and ENGCTF Review preliminary gas-electric production cost modeling results, as available, and discuss the process for gas infrastructure expansion needs identification Apply stakeholder assumptions and model enhancements for Phase IV study gas-electric modeling MISO s Gas-Electric Modeling Workshop

20 Contact Info EPA regulations webpage Additional questions? Please contact: Tessa Haagenson at Durgesh Manjure at MISO s Gas-Electric Modeling Workshop

21 Appendix

22 Units & Acronyms MMBtu = Million British Thermal Units Mcf = Thousand cubic feet MMcf = Million cubic feet MDth = Thousand Decatherms (1 MDth = 1 MMcf) ANGA = America s Natural Gas Alliance EIPC = Eastern Interconnection Planning Collaborative ENGCTF = MISO s Electric and Natural Gas Coordination Task Force LDC = Local Distribution Company PAC = MISO s Planning Advisory Committee 22

23 IHS CERA breakeven price details Breakeven prices (USD per Mcf) are forecasted by IHS CERA for 2015 for eight US regions, as well as portions of Canada. Full-cycle unit breakeven prices are not normalized to Henry Hub but reflect economics for a play at the point of entry into the pipeline grid. Break-even prices (without and with natural gas liquids [NGLs] credits) are calculated at the play level for the typical well and include leasehold, finding and development (F&D), operating expenses (opex), royalty, taxes, and return. Capital costs are success-weighted and based on equipment needed for the typical well. Weighted-average cost of capital (WACC) is assumed to be 10%. Taxes are based on tax benefits available to all producers. Well useful life is assumed to be 20 years. 23

24 IHS CERA productive capacity details IHS CERA forecasts gas production in the form of productive capacity (Mcf/d), defined as an estimate of the pipeline-grade dry natural gas that a given play, basin or region can produce in a given year and that can be carried to market on the infrastructure assumed to exist at that time. 24

25 What is PLEXOS and why is it being used for the Phase III analysis? PLEXOS is A power market modeling and simulation software tool A flexible optimization platform with production cost modeling functionality Selection for Phase III modeling based on: Ability to represent rate-based emissions targets Ability to address stakeholder requests for state-level CO 2 modeling, consideration of electric transmission impacts and natural gas infrastructure needs Ability to simultaneously dispatch gas and electric systems Intro to Gas-Electric Modeling in MISO s CPP Phase III Study PAC 25

26 At a high level, what is the integrated gaselectric model? It is the PLEXOS (electric) production cost model with built-in gas infrastructure. It can simultaneously optimize gas and electric system operations in an hourly chronological dispatch. It is an approximation of real-world gas and electric markets clearing in the same timeframe. It does not currently account for pipeline dynamics or contractual rights. Intro to Gas-Electric Modeling in MISO s CPP Phase III Study PAC 26

27 What s the value of simulating co-optimized dispatch of the gas and electric systems? Production cost modeling requires assumptions about gas system operations E.g. static cost for fuel and fuel transport Gas network modeling (e.g. GPCM*) requires assumptions about electric system operations E.g. static demand profile for electric generators Co-optimized dispatch simultaneously considers electric demand and gas demand from all sectors Provides insight into gas-electric system interactions Eliminates hand-offs or iterations between stand-alone gas and electric models Gas Pipeline Competition Model is an RBAC, Inc. product. See: Intro to Gas-Electric Modeling in MISO s CPP Phase III Study PAC 27

28 High-Level Representation of PLEXOS Gas- Electric Modeling Electric Demand Gas Demand Electric Generation Electric Transmission Gas Pipeline Topology Gas Production 1 PLEXOS Co-optimized Gas/Electric Dispatch The tie between the gas and electric systems in PLEXOS is gas-fired electric generation. 2 Electric LMP and Production Cost Information Gas Spot Prices and Cost to Produce/ Deliver Gas Generation Dispatch Results Transmission Flows and Congestion Pipeline Flows and Congestion Intro to Gas-Electric Modeling in MISO s CPP Phase III Study PAC 28

29 Co-Optimization of Gas and Electric Dispatch in PLEXOS Minimize: the cost to serve gas and electric load [PCe + PCg + DSe + DSg + ASc] Subject to: Electric transmission constraints Pipeline constraints (design capacity) Feasible gas production Feasible electric generation and feasible ancillary services provision (per generator) Electric generation = Electric demand + Electric losses Unserved electric load Gas production = Gas demand + Gas generation demand Unserved gas load Ancillary service provision Ancillary Services Requirements (for the pool) Where: PCe = electric production cost PCg = gas production cost ASc = ancillary services cost DSe = electric demand shortage cost DSg = gas demand shortage cost This is a high-level representation of the objective function for integrated simulation of gas and electric (incl. ancillary services) system operations, via production cost modeling in PLEXOS. Intro to Gas-Electric Modeling in MISO s CPP Phase III Study PAC 29

30 Representation of Gas and Electric System Topologies in PLEXOS GAS INFRASTRUCTURE In PLEXOS ELECTRIC INFRASTRUCTURE 1 Gas pipelines (incl. interstate, intrastate, laterals) Lines Electric transmission lines Min/max volume (MMcf), max flow (MMcf/d) Min/max/overload ratings (MW), reactance (p.u.), resistance (p.u.) Pipeline/pipeline or pipeline/load interconnects Nodes Electric buses Gas basins/plays; gas storage fields Min/max production (MMcf); production price ($/MMBtu) Residential (R), commercial (C), industrial (I), power (gas-fired electric gen) loads; LNG exports Hourly profile per state for RCI; separate hourly profile for LNG; power load determined via PLEXOS (MMcf) Production/ Source Demand/Sink Electric generators, demand-side management (DSM) Min/max capacity (MW); O&M ($/MWh) and fuel price ($/MMBtu) Residential, commercial, industrial loads Hourly profile per company (MWh) Note: This list is not exhaustive; additional characteristics are modeled for both the gas and electric systems. Intro to Gas-Electric Modeling in MISO s CPP Phase III Study PAC 30

31 PLEXOS Gas-Electric Modeling Inputs 1 Gas system topology developed from publically available sources Underlying dataset vetted by electric and gas industry stakeholders as part of the EIPC gas-electric study* Gas productive capacity and demand (non-power) forecasts sourced from IHS CERA (proprietary) Electric system representation is from the MTEP15 BAU economic planning model With updates per Phase III scenarios * Eastern Interconnection Planning Collaborative Gas-Electric System Interface Study, see Intro to Gas-Electric Modeling in MISO s CPP Phase III Study PAC 31

32 Breakdown of PLEXOS Data Inputs Input Source Details 1 Generator characteristics (Ventyx) PowerBase database capacity, heat rate, max ramp capability, O&M cost, capital cost, etc. Electric transmission topology (powerflow) Gas production and transmission characteristics Load profiles, demand and energy (D&E) Renewable energy profiles Electric system contingencies Gas system contingencies (MISO) most recent MTEP study cycle (Energy Exemplar) gathered from EIPC study, public sources Load profiles (historical/actual) D&E (Load Serving Entity forecast) External to MISO (Ventyx); MISO (Module E) (NREL) simulated, site-specific data (NERC, MISO, stakeholders) historical contingencies, stakeholder-submitted contingencies min/max/overload rating, resistance, reactance, etc. gas nodes, fields, storage, pipelines, demand 8760 load profile per company, D&E varies by scenario 8760 load profile per site (site could be grouping of turbines at given development) Bus name and number, min/max rating, winter/summer ratings, overflow penalty Under development Note: Data inputs are consistent with MISO s MTEP15 BAU economic planning models, with the exception of natural gas production and transmission infrastructure, which is unique to PLEXOS. Intro to Gas-Electric Modeling in MISO s CPP Phase III Study PAC 32

33 Breakdown of PLEXOS Data Outputs Output Generator dispatch, outages, emissions, etc. Transmission flows, binding hours, shadow price Locational Marginal Prices (LMP) Cost to load, cost of production, total system costs What can we learn from the outputs? How market operations, such as which units runs, fuel type usage, generator cycling, etc., may change over time under various scenarios How transmission flows change; indicates future transmission expansion needs based on system congestion How LMP prices may change under various scenarios; indicates areas of congestions The economic impacts of various scenarios, e.g. What would be the cost to implement the Clean Power Plan? 2 Gas system flows, binding hours and price formation from cooptimal gas/electric system operation Future generator capacity, type and timing Shows how the natural gas system will react to changes in the electric system and vice versa, e.g. Can a generator get gas? Shows future capacity needs based on various scenarios, e.g. If a large number of gas generators are built, will there be enough gas supply? Intro to Gas-Electric Modeling in MISO s CPP Phase III Study PAC 33

34 PLEXOS Gas-Electric Modeling Outputs and Applications for Phase III 2 Electric System Outputs Locational Marginal Prices (LMPs) Shadow prices and binding hours Production cost Gas System Outputs Gas spot prices Parallels to shadow prices, binding hours; showing duration, location & magnitude of pipeline congestion Stakeholder review and input Quantify transmission congestion and cost to produce electricity Quantify gas pipeline utilization/congestion trends Inform identification of electric transmission expansion needs Inform identification of gas infrastructure implications/needs All Phase III study outputs are indicative, along with the compliance costs and any infrastructure solutions they inform. Intro to Gas-Electric Modeling in MISO s CPP Phase III Study PAC 34