PowerACE - GSDP · PowerACE LAB (Genoese and Fichtner 2012) ... Reference to average, ......

KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association

Prof. Wolf Fichtner, Chair of Energy Economics, Institute for Industrial Production

www.kit.edu

PowerACEAgent-based simulation of electricity markets

MOCAP Workshop on Modelling Carbon Prices – Interacting agent networks & Strategies under riskPhilipp RinglerPotsdam, October 4, 2012

Prof. Wolf FichtnerChair of Energy Economics

2

Agenda

Project overview and motivation

PowerACE model overviewModel structureModel validation

Model applications and exemplary results

Summary and outlook on current/future research

Philipp Ringler - Agent-based simulation of electricity markets with PowerACEOctober 4, 2012


3 Philipp Ringler - Agent-based simulation of electricity markets with PowerACE

Project overview

Aim of the projectDevelopment of a simulation model of the German electricity system

Strategic behavior of market playersInterplay between several interrelated markets

Combination of the short-term perspective of daily electricity trading and long-term investment decisionsSpecial focus on the impact of emissions trading and the increased use of renewable energy sources on markets and power generation structures

2004-2007Project partners

October 4, 2012

IIPIISM


4

Methodology of PowerACE


Energy models

Top-Down ModelsBottom-Up Models

Optimizing ModelsSimulation models

System DynamicsAgent-based simulation(ABS)

Main characteristics of ABSModeling of players as agentsNo collective goal of agentsSophisticated modeling of roles and function of different players…

Analysis of existing ABS-ModelsShort-term market analyses

e.g. Day and Bunn (2001)No validation with real market dataSimplified energy systems Deficits should be overcome

Methodological requirementsConsideration of techno-economic

characteristics of the energy systemModeling of relevant playersModeling of different marketsFlexibilityInterrelation between electricity

prices and capacity expansion


5

Long-term developmentcapacities, electricity generation, emissions

Simulation power market

Graphical user interface

Overview: market simulation with capacity expansion


PowerACE

Input data Detailed power plant data (existing power plants, about 1.200 units for Germany) with techno-economic parameters

e.g. capacity, efficiency, availability, load change costs, emission factors

Investment options (e.g. coal, CCS, ...)

Fuel prices, CO2-prices (daily/monthly)

Demand profiles (hourly)

Feed-in of renewables sources (hourly)

Further model characteristicsGerman electricity marketHourly resolution: 8760h/aTime frame: up to 50 years Power plant failureImplementation in JAVACalculating time: approx. 15 min


6 Philipp Ringler - Agent-based simulation of electricity markets with PowerACEOctober 4, 2012

Generator Seller

Power plants

Investment planner

Merit Order

Households

Transport

Industry

Load profile

Grid operator

Potential savings in industry

Grid operatorRenewable

Load profile

Markets

Renewables

Demand

Supply

CO2-trader

Data base

Demand Demand (t+5)

Bid

Bid

Investment decision (new power plant)

Allocation Bid

Results

Primary reserve

Secondary reserve

Minute reserve

Demand

Agent

Demand

Spot market

Forward market

CO2 market

Reserve market

Information flow

Buyer

Model structure

Electricity supply companiesLarge utilities (E.ON, RWE, EnBW, Vattenfall, Steag) individuallyRegional and industry

Information of sellersCost data, available power plants, electricity price forecasting

Bidding their capacity on the markets



Generator Seller

Power plants

Investment planner

Merit Order

Households

Transport

Industry

Load profile

Grid operator



Load profile

Markets

Renewables

Demand

Supply

CO2-trader

Data base

Demand Demand (t+5)

Bid

Bid


Allocation Bid

Results

Primary reserve

Secondary reserve

Minute reserve

Demand

Agent

Demand

Spot market

Forward market

CO2 market

Reserve market

Information flow

Buyer

Model structure



Variable costs

Bidding price considering start-up

costs

, , ,1 , ,1 , , , ,, , , ,spot spot spot spot spoti h i h i h i h S i h Sbid p q p q

2

fuel , j ,d fuelvar, j ,d other var, j CO , d fuel

j j

p EFc c p

,var, , var, ,

,, var, , var, ,

var, ,

max( , )startup jj d h j d

u

startup jj h j d h j d

s

j d

cc 0 p c j B

tc

p c p c j Pt

c otherwise

, 1

,

0 ,

,

,

l

fix j l l l

fix j

h

h h

f sf b

markup c f b sf b

c otherwise

Scarcity-dependent price markup to

cover fixed costs

,

toth

th h

Psfdem

Scarcity

Bid

, , , , ,, ,max

0 otherwisenet j d r j d j d j

j i

P P x avq

Quantity

Indices Parameters Variables

i Agent, j unit, h hour, S step, k bid, d day, r reserved fuel

c costs, Emission factor, B plants in operation, P plants not in operation, bi threshold, fi Fixed cost share sf scarcity factor, efficiency, EF Emission factor, tu hours w/o output, ts hours w output, Reserve factor, dem demand, Ptot available capacity, av availability

p price, q quantity, x [0,1]-equal distributed RV

Bidding strategies – spot market

1.

2.

3.



Generator Seller

Power plants

Investment planner

Merit Order

Households

Transport

Industry

Load profile

Grid operator



Load profile

Markets

Renewables

Demand

Supply

CO2-trader

Data base

Demand Demand in t+5

Bid

Bid


Allocation Bid

Results

Primary reserve

Secondary reserve

Minute reserve

Demand

Agent

Demand

Spot market

Forward market

CO2 market

Reserve market

Information flow

Buyer

Model structure

planner

Long-term layer: investment plannerLarge utilities (E.ON, RWE, EnBW, Vattenfall, Steag) individuallyRegional and industry

Information of investment planner(Exogenous) Forecast of costs for fuel and emissions certificates Results of spot market and forward market

Action of investment plannerCalculation of NPVInvestment decision in case of capacity gap

Delayed availability of new power plants



Model validation - Simulation of 2001Key figure EEX 2001 PowerACE 2001

Filter FilterAv. Price [€/MWh] 24,07 22,83 21,93 21,80St. dev. [€/MWh] 24,68 11,06 9,78 9,56Correlation coeff. - - 0,44 0,69MAE [€/MWh] - - 2,52 1,39RSME [€/MWh] - - 20,43 2,86

Similarly adequate results for other years (e.g. 2004-2006; Genoese 2010)Validation necessary for deduction of hypothesis

Filtering allows better comparabilityPrice level slightly underestimated (~4%)Good indication of price trends


11

Selected completed analyses with the PowerACE model

Merit order effect of renewable energies (Sensfuß 2003)Price reduction due to feed-in of renewables

Market power on the German wholesale electricity market (Genoese2010)

Analysis of interrelated markets (Weidlich 2009)Tendered balancing power capacities influence resulting market prices

Impact of emissions trading on electricity prices (Genoese et al. 2007)

Impact of certificate allocation on power plant investment (Genoese et al. 2008)

PowerACE LAB (Genoese and Fichtner 2012)




Market power on the German electricity market?

ApproachCalibration of the model on the basis of a year with competitive prices

2001 (Müsgens 2006; von Hirschhausen et al. 2007)Simulation of the years to be analysed (2005, 2006)

Adjustment of fuel prices, CO2 prices, investmentsAll other settings from 2001 remain constant

Interpretation of simulated prices as competitive pricesCalculation and evaluation of market power indicators

Structural market power: Residual-Supply-Index (RSI) Exercised market power: Price-Cost Margin Index (PCMI)

cp. to Ellersdorfer et al. (2009); Lang (2007)

Definition market powerMarket power in general is the

ability of realizing a higher price than the competitive price.(Stoft 2002)

MotivationPrice increase on spot market (2001-2006: +113%)

Increased fuel prices (natural gas: +74%)Introduction of emissions trading (0-35€/t)

Market concentration: Oligopoly of 4 generators: 80% of capacities



Structural market power – RSI

Structural market power existentModerate structural market power under

consideration of potential for import (approx. 18 GW)

, ,,

tot h i hi h

h

S SRSI

D

Stot,h total quantity suppliedSi,h quantity supplied by company iDh electricity demand in hour h

#hours2005 2006

RSI <1,1 <1 <1,1 <1E.on 3690 2274 3983 2755RWE 3499 2032 3839 2591E.on (Imp.) 826 64 1214 92RWE (Imp.) 669 32 1068 48

Criteria for structural market power:Pivotal provider (RSI<1)RSI < 1,1 in more than 5% of the hours per year (Sheffrin 2005)



Exercised market power – PCMI

Reference to average, base and peakAverage price: max. 7% (2006), base: < 5%, peak: max. 11%

No consideration of taxes, interdependencies to minute reserve market

h hh

h

p MCPCMIMC

ph market priceMCh competitive priceh hour

Exercise of market power cannot be confirmed

averagebasepeak


15

Selected completed analyses with the PowerACE model

Merit order effect of renewable energies (Sensfuß 2003)Price reduction due to feed-in of renewables

Market power on the German wholesale electricity market (Genoese2010)

Analysis of interrelated markets (Weidlich 2009)Tendered balancing power capacities influence resulting market prices

Impact of emissions trading on electricity prices (Genoese et al. 2007)

Impact of certificate allocation on power plant investment (Genoese et al. 2008)

PowerACE LAB (Genoese and Fichtner 2012)



16

Development of PowerACE LAB


List of available power plantsList of available power plants

Hourly bid prices Hourly bid prices

Market informationMarket information



Development of an agent-based electricity market simulation model with detailed data

Simulation environment for analysis of energy systemsRealistic results compared to real market prices

Various research analyses completed

Outlook on current/future researchCapacity marketsDecisions under uncertaintySimulation of other national electricity markets European market integrationIntegration of electricity storage solutionsFlexible demand side managementFurther integration of learning algorithms Agent based participatory simulation

Summary


18

Thank you for your attention!


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