8 top-down, bottom-up electricity modeling

Top-‐Down – Bottom-‐Up Electricity Modeling Part 2: Challenges of Modeling Renewables, Detailed Electricity Models, and Hybrid Modeling

Nidhi Santen and Karen Tapia-‐Ahumada

7TH ANNUAL E PPA TRA IN ING WORKSHOP

JORDAN GRAND R ESORT HOTE L , NEWRY, ME

SEPTEMBER 3 0 – OCTOBER 1 , 2 0 16

10/1/2016 17TH ANNUAL EPPA TRAINING WORKSHOP

Agenda§ Introduction

§ Why Hybrid Modeling?§ Challenges in modeling variable-‐output renewable energy resources

§ An overview of detailed electricity models§ MIT EleMod Model (Tapia-‐Ahumada et al., 2014) § NREL Regional Energy Deployment System Model (ReEDS)

§ MITEI-‐JP Hybrid Modeling Work§ USREP-‐EleMod Integrated Modeling Framework§ Next Steps


Introduction: The Changing Electricity Sector§ Electricity generation is the largest and fastest-‐growing source of global energy related CO2 emissions

• About 40% of CO2 energy-‐related emissions come from this sector in the U.S.

• Greater deployment of wind and solar is expected if a low-‐carbon economy is anticipated (de-‐carbonization)


Source: AEO 2016

Introduction: Why Hybrid Models?§ Future energy and climate policies impact not only the electricity sector, but also the overall economy• Carbon Taxes / Energy Taxes / Emissions Cap / Technology Regulation (ex. RPS)• These policies are translated into a set of workable scenarios to foresee effects on:

Electricity prices / Electricity demand / Portfolio mix (generation, installed capacity)…Primary energy use / Energy-‐related emissions / Welfare costs / Income (regressive vs. progressive policies), Trade….

§ Top-‐down economy-‐wide models provide an important, unmatched, perspective from which to study the effects of future climate and energy policies

§ Improved simulation tools can accurately represent how the electric power sector is changing (e.g., resource mix, operations)

§ Characterize new disruptive technologies in economy-‐wide models, to correctly assess renewables deployment potential and policy costs

§ Assess sensitivity of TD models to key parameters that impact the evolution of the electricity sector


Characteristics of RE: Limited Controllable Variability of Wind

10/1/2016 7TH ANNUAL EPPA TRAINING WORKSHOP 5

§ Wind generation is variable over time, due to the fluctuations of wind speed

§ Except for curtailment or blade pitching actions, wind generation is less controllable than other technologies

Sample of wind power output for a single wind turbine, and for a group of wind plants in GermanySource: Holttinen H. , et al., 2009

Source: Vitolo et al. 2013

Characteristics of RE: Partial Unpredictability of Wind


§ Predicting wind output is difficult—much more so than predicting the output of conventional generators or load

§ Experience shows that deviations in predictions of wind output decrease with proximity to real time

Evolution of the wind forecast error, as a percentage of wind production, as a function to the distance to real time. Source: EURELECTRIC, 2010.

Characteristics of RE: Limited Controllable Variability of Solar PV


PV plant output located in Nevada on a sunny day (left) and on a partly-cloudy day (right) - Sampling time 10 seconds. Source: NERC, 2009

§ Renewable solar energy is more predictable, but still highly variable

Characteristics of RE: Local Dependency


§ Renewable resources depend heavily on geography; the best, most reliable resources are often not spatially correlated with where electricity is most needed (load centers)

Challenges in Modeling Variable Output RE: Electric Power System Impacts


§ Increased Flexibility§ Fast-‐response electric generating units (EGUs)§ Reserves requirement§ Transmission interconnections, for geographic dispersion

§ Energy storage§ Consumer load-‐shifting behavior

Impact of wind production on one-day hypothetical dispatch pattern for ERCOT in 2030. Source: (MIT, 2010).

Results for the increase in reserve requirement due to wind power. Source: (Holttinen H. , et al., 2011).

Challenges in Modeling Variable Output RE: Electric Power System Impacts§ New Market and Regulatory Structures

§ Energy service markets§ Ancillary service markets, such as:§ Reserves§ Voltage support

§ Planning/Investment in Transmission and Distribution Infrastructure and Services


Overview of Detailed Electricity Sector Models


BU Electric Power Sector Models:Overview


Hierarchical decision-‐making process in power systems (Palmintier, 2013).

BU Electric Power Sector Models:Overview§ Capacity expansion planning models (years to decades)

§ Models that determine cost-‐effective additions of electric power generating capacity and/or transmission capacity subject to various technical and policy constraints

§ Constraints, such as:§ Electricity Demand = Electricity Supply at each time interval represented and in each region represented

§ Technology-‐specific operating constraints (e.g., a coal plant takes 8 hours to start-‐up; a gas combustion turbine takes 8-‐10 minutes)

§ Resource availability (e.g., wind, solar, hydro)§ Power flow limitations (if transmission network is represented)§ Emissions limits


BU Electric Power Sector Models:Defining Features


§ High number of technology (and sub-‐technology) typically represented

§ Technologies are represented by their engineering and operating constraints § The most detailed BU electricity models represent individual real power plants and/or supporting devices, and their operating characteristics

§ The electricity market is represented by its physical reality§ Electric power flows through the transmission network are represented, and determine locational prices

§ Very good resolution in spatial and temporal dynamics, although there is a tradeoff between the two

§ LCOEs (Levelized Costs of Electricity) and the relative behavior of one technology to another are outputs of the model, not inputs!

BU Electric Power Sector Model:MITEI EleMod§ U.S. regional generation expansion power system model (Tapia-‐Ahumada and Perez-‐Arriaga, 2014**; Perez-‐Arriaga and Meseguer, 1997)• Designed to investigate system’s operation and cost recovery with large amounts of wind• LP model that minimizes the total cost of producing electricity• Deterministic / Recursive-‐dynamic structure

Optimal solutions computed in every intra-‐period of two years• Three time ranges in the decision making process:

Capacity expansion planning / Operation planning / Operation dispatch• Some details:

Regional load demands (hourly) /Regional wind profiles estimates (hourly) / Conventional technologies / Technical and environmental constraints

5/5/2014 ESRG MEETING

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US 12 Regions

Alaska California Florida New York New England South EastNorth East South Central North CentralMountain Pacific Texas

EleMod Model

Electricity Sector

𝑀𝑖𝑛 𝐶𝑜𝑠𝑡𝑠 𝑠. 𝑡.

ℎ+ = 0𝑔+ ≤ 0

Technologies:Fossil / Nuclear / Wind

Outputs:Portfolio mix(generation &installed capacity)PricesEmissionsExpenditures(fuel & capital)

Optimal Decisions

2006 2008 2010

Optimal Decisions

2050Time horizon

. . . . . .

Long-‐term scope (>40 years)


BU Electric Power Sector Model:EleMod Data and Assumptions


BU Electric Power Sector Model: EleMod Data and Assumptions


BU Electric Power Sector Model:NREL Regional Energy Deployment System (ReEDS)§ U.S. generation and transmission capacity expansion power system model (Short et al. 2011)• LP model that minimizes the total cost of producing electricity subject to a wide range of operating and system-‐level constraints

• Sequential myopic optimization structureOptimal solutions computed for every two year periods

• Time ranges in the decision making process: Capacity expansion planning / Operation dispatch

• Some details:High level of spatial detail in supply-‐demand balance and renewable resources / Several technology categories / Technical and environmental constraints / Stylized transmission network / Lower temporal resolution than EleMod (17 time segments per year)


Base Case Capacity Buildout in ReEDS. Source: NREL, 2016

Outputs:Portfolio mix(generation & transmissioninstalled capacity)PricesEmissionsExpenditures(fuel & capital)

BU Electric Power Sector Model:ReEDS Spatial Resolution


Source: NREL, 2016

ReeDS Transmission Network

BU Electric Power Sector Model:ReEDS Temporal Dynamics


Source: Short et al. 2011 Source: NREL 2016

RE Representation in TD General Equilibrium Models§ Electricity from wind – 4 key modeling choices• Nested CES structure• Elasticities of substitution• “Mark-‐up” parameter• Supply of the renewable resource fixed factor over time

§ Challenges of the TD approach• Use of LCOE to compare renewables with dispatchable generation

• Wind without and with back-‐up technologies• Results of the TD model can be sensitive to the specification of certain parameters-‐ Estimation of the mark-‐up-‐ Parameterization of the fixed factor

§ Can these challenges be assessed?

§ Can this representation be improved?


Hybrid Modeling: USREP-‐EleMod Work


Integrated Framework:Overview§ Integrated approach to model intermittent wind energy within an economy-‐wide GE framework• 2 sub-‐models coupled via an iterative algorithm

§ MIT USREP• Economy-‐energy general equilibrium model of the U.S. economy

§ BU model of the electric power sector• Capacity expansion and economic dispatch model

24

Static/Recursive-‐dynamicPartial EquilibriumNational Aggregate

Looking-‐forwardGeneral EquilibriumRegional Disaggregation

LCOE ModelTechnologyEnergy Block Segmentation

Expansion and OperationPower Plant UnitsHourly Resolution

Framework

BU model

Aggregate Sectors1 representative consumerClosed economy

Industrial DetailIndividual household detailDetailed international trade

TD model

Note: “Design choices” conceptual idea taken from EPRI


Integrated Framework:Iterative Algorithm§ Coupling between both models (Boehringer and Rutherford, 2009; Rausch and Mowers, 2013)• Information exchange using key outputs of both models• Iteration until reaching convergence/equilibrium conditions

25

TD modelwith exogenouselectricity sector

BU model ofelectricity sector

Iterative approach

Electricity DemandElectricity PriceFuel Index PricesCapital Index PricesLabor Index Prices

Generation SupplyCO2 emissionsFuel ExpensesCapital ExpensesO&M Expenses


EleMod

USREP1=USREP*

USREP0

Integrated Framework:Iterative Algorithm -‐ Implementation

Check convergence

(equilibrium in Q&P) 5/5/2014 ESRG MEETING 26

100

150

200

250

300

350

08_iter1 08_iter2 08_iter3 08_iter4 08_iter5 08_iter6 08_iter7 08_iter8

Electric dem

and [TWh-‐yr] FL NY

TX NENG

80

90

100

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Electric pric

e [$/M

Wh]

FL NY

TX NENG

0.950

0.955

0.960

0.965

0.970

0.975

0.980

0.985


NG price inde

x [p.u.]

FL NY

TX NENG


ResultsCase Description

§ Focus on competiveness of wind within an electric power system, i.e. wind-‐grid parity at wholesale level“Moment when renewable energy becomes cost competitive with the price of electricity coming from the grid”

§ Baseline scenario:• Time horizon: 2006 to 2050• Neither renewables energy mandate nor carbon emission policy• Integrated model uses a decreasing cost path trajectory for wind• TD model approximately replicates wind outcomes of integrated model• Models work with 12 U.S. regions -‐ Results shown for the New England region

§ Can both types of models capture wind-‐grid parity?• Penetration limit (optimum amount) / Effect on electricity prices


Integrated ModelReference Case Results


Integrated ModelReference Case Results

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1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

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Gen

eration [GWh]

GenerationOne week in January in year 2050

Nuclear Adv. Supercritical Coal Steam Conventional Coal SteamGas Steam Gas Combined Cycle Gas Combustion TurbineWind Wind Curtailment

(40)

8

56

104

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200

(10)

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1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

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Price [$/MWh]

Load

and

Reserves [GWh]

Operating and Capacity reserves vs. Marginal priceOne week in January in year 2050

Load Net load Wind CurtailmentMax. Connected Power Min. Connected Power Firm CapacityCapacity Requirement Marg. Price


ResultsIntegrated “Benchmark” Model vs. TD Model

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Stand-‐alone TD model

With parameters adjusted to obtain ~40% of wind generation by 2050


ResultsSensitivity TD Model: Mark-‐up & Initial Fixed Factor Endowment

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High sensitivity to mark-‐up parameter increases High sensitivity to fixed factor, and impact on the penetration pattern


Observations from Hybrid Modeling Work§ New modeling challenges brought about by intermittent renewable energy sources require careful review of and

enhancement of existing tools§ TD models need to capture key characteristics of variable output (wind and solar) energy with the necessary

temporal and spatial detail§ BU models can be enhanced to interact with and consider economy-‐wide impacts

§ Previous work introduced a benchmark model that integrates a bottom-‐up electricity sector model within an economy-‐wide general equilibrium framework§ It incorporated a relatively stylized portrayal of the electric power sector (e.g., wind only, no transmission

network, no simulated policy cases)

§ Results :• The use of an integrated model with more electricity sector details enables capturing the long-‐term adaptation of a system to the penetration of wind more realistically

• A TD approach to modeling intermittent renewable energy, if properly specified, is capable of roughly replicating the results from the benchmark model

• A TD approach is highly sensitive to key parameters which are a priori typically unknown or at least highly uncertain


Research Next Steps§ Improvement of Integrated model:• Add other technologies (e.g., solar, storage)• Add transmission across regions• Representation of policies such as Clean Power Plan and State Renewable Portfolio Standards• (Convergence when imposing economy-‐wide CO2 emissions limit**)

§ Application of Integrated model:• Climate and energy policy analysis

§ Improvement of TD equilibrium models:• Address whether or not some of the key assumptions regarding the structure and parameters used in TD models can be estimated and further refined to account for the adaptation of the electric power sector to high penetration of variable output renewable energy sources


ReferencesBöehringer, C., Rutherford, T., 2009. Integrated assessment of energy policies: Decomposing top-‐down and bottom-‐up. Journal of Economic Dynamics and Control 33 (9), 1648 – 1661.

EURELECTRIC. (2010). Integrating intermittent renewables sources into the EU electricity system by 2020: challenges and solutions. Union of the Electricity Industry.

Holttinen, H., Meibom, P., Orths, A., van Hulle, F., Lange, B., O ’Malley, M., . . . Ela, E. (2009). Design and operation of power systems with large amounts of wind power. Final report, IEA WIND Task 25, Phase one 2006-‐2008.

NERC. (2009). Accomodating High Levels of Variable Generation. North American Electric Reliability Corporation (NERC).

Perez-‐Arriaga, I., Meseguer, C., 1997. Wholesale marginal prices in competitive generation markets. IEEE Transactions on Power Systems (2), 710.

Rausch, S., Metcalf, G., Reilly, J., 2011. Distributional impacts of carbon pricing: A general equilibrium approach with micro-‐data for households. Energy Economics 33, S20 – S33.

Rausch, S., Metcalf, G., Reilly, J., Paltsev, S., 2010. Distributional implications of alternative U.S. greenhouse gas control measures. B.E. Journal of Economic Analysis & Policy: Advances in Economic Analysis & Policy 10 (2), 1 – 44.

Rausch, S., Mowers, M., 2013. Distributional and efficiency impacts of clean and renewable energy standards for electricity. Resource and Energy Economics.

Short, W., P. Sullivan, T. Mai, M. Mowers, C. Uriarte, N. Blair, D. Heimiller, and A. Martinez. 2011. Regional Energy Deployment System (ReEDS) . Golden, CO: National Renewable Energy Laboratory. NREL/TP-‐6A20-‐46534.

Tapia-‐Ahumada, K. Octaviano, C. Rausch, S. Perez-‐Arriaga, J. 2014. Modeling Intermittent Renewable Energy: Can We Trust Top-‐Down Equilibrium Approaches? MIT Center for Energy and Environmental Policy Research Working Paper Series.

Tapia-‐Ahumada, K., Perez-‐Arriaga, J., 2014. EleMod: A model for capacity expansion planning, operation planning and dispatch in electric power systems with penetration of wind, MITEI Working Paper, MIT Energy Initiative, Massachusetts Institute of Technology

Vitolo, T., G. Keith, B. Biewald, T. Comings, E. Hausman, P. Knight. 2013. Meeting Load with a Resource Mix Beyond Business as Usual: A regional examination of the hourly system operations and reliability implications for the United States electric power system with coal phased out and high penetrations of efficiency and renewable generating resources. Synapse Energy Economics for Civil Society Institute


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TOP-‐DOWN – BOTTOM-‐UP ELECTRICITY MODELING PART 27TH ANNUAL EPPA TRA IN ING WORKSHOP

SEPTEMBER 30 – OCTOBER 1 , 216


8 top-down, bottom-up electricity modeling

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