Power System Economics: Introduction. Daniel Kirschen

Transcription

1 Power System Economics: Introdction Daniel Kirschen D. Kirschen 2006

2 Why stdy power system economics? Generation Transmission Distribtion Cstomer D. Kirschen 2006

3 Why stdy power system economics? IPP IPP IPP IPP IPP Wholesale Market and Transmission Wires Retailer Retailer Retailer Retail Market and Distribtion Wires Cstomer Cstomer Cstomer Cstomer D. Kirschen 2006

4 Why introdce competitive electricity markets? Monopolies are inefficient No incentive to operate efficiently Costs are higher than they cold be No penalty for mistakes Unnecessary investments Benefits of introdcing competition Increase efficiency in the spply of electricity Lower the cost of electricity to consmers Foster economic growth D. Kirschen 2006

5 Changes that are reqired Privatisation Government-owned organisations become private, for-profit companies Competition Remove monopolies Wholesale level: generators compete to sell electrical energy Retail level: consmers choose from whom they by electricity Unbndling Generation, transmission, distribtion and retail fnctions are separated and performed by different companies Essential to make competition work: open access D. Kirschen 2006

6 Wholesale Competition IPP IPP IPP IPP IPP Wholesale Market and Transmission Wires Disco Disco Disco Disco Cstomer Cstomer Cstomer Cstomer D. Kirschen 2006

7 Retail Competition IPP IPP IPP IPP IPP Wholesale Market and Transmission Wires Retailer Retailer Retailer Retail Market and Distribtion Wires Cstomer Cstomer Cstomer Cstomer D. Kirschen 2006

8 Fndamental nderlying assmption Treat electricity as a commodity Examples of commodities: A ton of wheat A barrel of crde oil A cbic meter of natral gas D. Kirschen 2006

9 How do we define the electricity commodity? A Volt of electricity? An Ampere of electricity? A MW of electricity? A MWh of electricity? D. Kirschen 2006

10 Load Effect of cyclical demand Light load period Need only the most efficient generators Marginal cost is low High load period Need to rn less efficient generators Marginal cost is high 00:00 06:00 12:00 18:00 24:00 Time D. Kirschen 2006

11 Effect of cyclical demand Electrical energy cannot be stored economically Electrical energy mst be prodced when it is consmed Demand for electrical energy is cyclical Cost of prodcing electrical energy changes with the load Vale of a MWh is not constant over the corse of a day A MWh at peak time is not the same as a MWh at offpeak time Commodity shold be MWh at a given time D. Kirschen 2006

12 Effect of location 50 /MWh A 100 MW Max flow = 100 MW 100 /MWh B 100 MW 200MW Price of electricity at A = marginal cost at A = 50 /MWh Price of electricity at B = marginal cost at B = 100 /MWh Transmission constraint segments the market Commodity shold be MWh at a given time and a given location D. Kirschen 2006

13 Effect of secrity of spply A 100 MW 100 MW B 100 MW 200MW Consmers expect a continos spply of electricity Commodity shold be MWh at a given time and a given location, with a given secrity of spply Need to stdy how we can achieve this secrity of spply D. Kirschen 2006

14 Effect of the laws of physics A 50 MW π 3 =7.50 $/MWh 0 MW C B 285 MW 126 MW π 2 =11.25 $/MWh 50 MW MW 66 MW 60 MW π 3 =10.00 $/MWh 3 75 MW D Power flows from high price to low price! 300 MW D. Kirschen 2006

15 Effect of the laws of physics Exporting oranges from Norway to Spain? D. Kirschen 2006

16 Unbndling Competitive market will work only if it is fair One participant shold not be able to prevent others from competing Management of the network or system shold be done independently from sale of energy One company shold not be able to prevent others from competing sing congestion in the network Open access to the transmission network Separation of energy bsinesses from wires bsinesses Energy bsinesses become part of a competitive market Wire bsinesses remain monopolies D. Kirschen 2006

17 Conseqences Monopoly vertically-integrated tility One organisation controls the whole system Single perspective on the system Unbndled competitive electricity market Many actors, each controlling one aspect Different perspectives, different objectives How to make the system work so that all participants are satisfied (i.e. achieve their objectives)? D. Kirschen 2006

18 Generating company (GENCO) Prodces and sells electrical energy in blk Owns and operates generating plants Single plant Portfolio of plants with different technologies Often called an Independent Power Prodcer (IPP) when coexisting with a vertically integrated tility Objective: Maximize the profit it makes from the sale of energy and other services D. Kirschen 2006

19 Distribtion company (DISCO) Owns and operates distribtion network Traditional environment: Monopoly for the sale of electricity to consmers in a given geographical area Competitive environment: Network operation and development fnction separated from sale of electrical energy Remains a reglated monopoly Objective: Maximize reglated profit D. Kirschen 2006

20 Retailer (called spplier in the UK) Bys electrical energy on wholesale market Resells this energy to consmers All these consmers do not have to be connected to the same part of the distribtion network Does not own large physical assets Occasionally a sbsidiary of a DISCO Objective: Maximize profit from the difference between wholesale and retail prices D. Kirschen 2006

21 Market Operator (MO) Rns the compter system that matches bids and offers sbmitted by byers and sellers of electrical energy Rns the market settlement system Monitors delivery of energy Forwards payments from byers to sellers Market of last resort rn by the System Operator Forward markets often rn by private companies Objective: Rn an efficient market to encorage trading D. Kirschen 2006

22 Independent System Operator (ISO) Maintains the secrity of the system Shold be independent from other participants to ensre the fairness of the market Usally rns the market of last resort Balance the generation and load in real time Owns only compting and commnication assets An Independent Transmission Company (ITC) is an ISO that also owns the transmission network Objectives: Ensre the secrity of the system Maximize the se that other participants can make of the system D. Kirschen 2006

23 Reglator Government body Determines or approves market rles Investigates sspected abses of market power Sets the prices for prodcts and services provided by monopolies Objectives Make sre that the electricity sector operates in an economically efficient manner Make sre that the qality of the spply is appropriate D. Kirschen 2006

24 Small Consmer Bys electricity from a retailer Leases a connection from the local DISCO Participation in markets is sally limited to choice of retailer Objectives: Pay as little as possible for electrical energy Obtain a satisfactory qality of spply D. Kirschen 2006

25 Large Consmer Often participates actively in electricity market Bys electrical energy directly from wholesale market Sometimes connected directly to the transmission network May offer load control ability to the ISO to help control the system Objectives: Pay as little as possible for electrical energy Obtain a satisfactory qality of spply D. Kirschen 2006

26 Otline of the corse (I) Basic concepts from microeconomics Fndamentals of markets Theory of the firm Perfect and imperfect competition Contracts Organisation of electricity markets Ignore the network Participating in electricity markets D. Kirschen 2006

27 Otline of the corse (II) Secrity and ancillary services Energy services Network services System perspective Provider perspective Effect of network on prices Investing in transmission Investing in generation Take the network into consideration D. Kirschen 2006

28 Fndamentals of Markets Daniel Kirschen University of Manchester 2006 Daniel Kirschen 1

29 Let s go to the market... Opportnity for byers and sellers to: compare prices estimate demand estimate spply Achieve an eqilibrim between spply and demand 2006 Daniel Kirschen 2

30 How mch do I vale apples? Price One apple for my break Qantity 2006 Daniel Kirschen 3

31 How mch do I vale apples? Price One apple for my break Take some back for lnch Qantity 2006 Daniel Kirschen 4

32 How mch do I vale apples? Price One apple for my break Take some back for lnch Enogh for every meal Qantity 2006 Daniel Kirschen 5

33 How mch do I vale apples? Price One apple for my break Take some back for lnch Enogh for every meal Home-made apple pie Qantity 2006 Daniel Kirschen 6

34 How mch do I vale apples? Price One apple for my break Take some back for lnch Enogh for every meal Home-made apple pie Home-made cider? Qantity Consmers spend ntil the price is eqal to their marginal tility 2006 Daniel Kirschen 7

35 Demand crve Price Aggregation of the individal demand of all consmers Demand fnction: q = D(π) Qantity Inverse demand fnction: π = D 1 (q) 2006 Daniel Kirschen 8

36 Elasticity of the demand Price High elasticity good Slope is an indication of the elasticity of the demand High elasticity Non-essential good Easy sbstittion Price Qantity Low elasticity Essential good No sbstittes Low elasticity good Electrical energy has a very low elasticity in the short term Qantity 2006 Daniel Kirschen 9

37 Elasticity of the demand Mathematical definition: ε = dq q dπ π = π q dq dπ Dimensionless qantity 2006 Daniel Kirschen 10

38 Spply side How many widgets shall I prodce? Goal: make a profit on each widget sold Prodce one more widget if and only if the cost of prodcing it is less than the market price Need to know the cost of prodcing the next widget Considers only the variable costs Ignores the fixed costs Investments in prodction plants and machines 2006 Daniel Kirschen 11

39 How mch does the next one costs? Cost of prodcing a widget Total Qantity Normal prodction procedre 2006 Daniel Kirschen 12

40 How mch does the next one costs? Cost of prodcing a widget Total Qantity Use older machines 2006 Daniel Kirschen 13

41 How mch does the next one costs? Cost of prodcing a widget Total Qantity Second shift prodction 2006 Daniel Kirschen 14

42 How mch does the next one costs? Cost of prodcing a widget Total Qantity Third shift prodction 2006 Daniel Kirschen 15

43 How mch does the next one costs? Cost of prodcing a widget Total Qantity Extra maintenance costs 2006 Daniel Kirschen 16

44 Spply crve Price or marginal cost Aggregation of marginal cost crves of all sppliers Considers only variable operating costs Does not take cost of investments into accont Spply fnction: Qantity π = S 1 (q) Inverse spply fnction: q = S(π) 2006 Daniel Kirschen 17

45 Market eqilibrim Price Spply crve Willingness to sell market clearing price market eqilibrim Demand crve Willingness to by volme transacted Qantity 2006 Daniel Kirschen 18

46 Spply and Demand Price spply eqilibrim point demand Qantity 2006 Daniel Kirschen 19

47 Market eqilibrim Price spply q * = D(π * ) = S(π * ) π * = D 1 (q * ) = S 1 (q * ) market clearing price volme transacted demand Qantity Sellers have no incentive to sell for less Byers have no incentive to by for more 2006 Daniel Kirschen 20

48 Centralised action Prodcers enter their bids: qantity and price Bids are stacked p to constrct the spply crve Consmers enter their offers: qantity and price Offers are stacked p to constrct the demand crve Intersection determines the market eqilibrim: Market clearing price Transacted qantity Price Qantity 2006 Daniel Kirschen 21

49 Centralised action Everything is sold at the market clearing price Price is set by the last nit sold Marginal prodcer: Sells this last nit Gets exactly its bid Infra-marginal prodcers: Get paid more than their bid Collect economic profit Extra-marginal prodcers: Sell nothing Price Inframarginal spply Extra-marginal demand Qantity Marginal prodcer 2006 Daniel Kirschen 22

50 Bilateral transactions Prodcers and consmers trade directly and independently Consmers shop arond for the best deal Prodcers check the competition s prices An efficient market discovers the eqilibrim price 2006 Daniel Kirschen 23

51 Efficient market All byers and sellers have access to sfficient information abot prices, spply and demand Factors favoring an efficient market nmber of participants Standard definition of commodities Good information exchange mechanisms 2006 Daniel Kirschen 24

52 Examples Efficient markets: Open air food market Chicago mercantile exchange Inefficient markets: Used cars 2006 Daniel Kirschen 25

53 Consmer s Srpls By 5 apples at 10p Total cost = 50p At that price I am getting apples for which I wold have been ready to pay more Srpls: 12.5p 15p 10p Price Total cost Consmer s srpls 5 Qantity 2006 Daniel Kirschen 26

54 Economic Profit of Sppliers Price spply Price spply π Profit demand demand Cost Revene Qantity Qantity Cost incldes only the variable cost of prodction Economic profit covers fixed costs and shareholders retrns 2006 Daniel Kirschen 27

55 Social or Global Welfare Consmers srpls + Sppliers profit Price spply demand = Social welfare Qantity 2006 Daniel Kirschen 28

56 Market eqilibrim and social welfare π spply π Operating point spply demand Q Welfare loss demand Q Market eqilibrim Artificially high price: larger spplier profit smaller consmer srpls smaller social welfare 2006 Daniel Kirschen 29

57 Market eqilibrim and social welfare π spply π Welfare loss spply demand Q demand Operating point Q Market eqilibrim Artificially low price: smaller spplier profit higher consmer srpls smaller social welfare 2006 Daniel Kirschen 30

58 Market Eqilibrim: Smmary Price = marginal revene of spplier = marginal cost of spplier = marginal cost of consmer = marginal tility to consmer Market price varies with offer and demand If demand increases Price increases beyond tility for some consmers Demand decreases Market settles at a new eqilibrim If demand decreases Price decreases Some prodcers leave the market Market settles at a new eqilibrim Never a shortage 2006 Daniel Kirschen 31

59 Advantages over a Tariff Tariff: fixed price for a commodity Assme tariff = average of market price Period of high demand Tariff < marginal tility and marginal cost Consmers contine bying the commodity rather than switch to another commodity Period of low demand Tariff > than marginal tility and marginal cost Consmers do not switch from other commodities 2006 Daniel Kirschen 32

60 Concepts from the Theory of the Firm Daniel Kirschen University of Manchester 2006 Daniel Kirschen 1

61 Prodction fnction y = f ( x,x ) 1 2 y: otpt x 1, x 2 : factors of prodction y x 2 fixed y x 1 fixed x 1 x 2 Law of diminishing marginal prodcts 2006 Daniel Kirschen 2

62 Long rn and short rn Some factors of prodction can be adjsted faster than others Example: fertilizer vs. planting more trees Long rn: all factors can be changed Short rn: some factors cannot be changed No general rle separates long and short rn 2006 Daniel Kirschen 3

63 Inpt-otpt fnction y = f ( x 1, x 2 ) x 2 fixed The inverse of prodction fnction is the inpt-otpt fnction x 1 = g ( y ) for x 2 = x 2 Example: amont of fel reqired to prodce a certain amont of power with a given plant 2006 Daniel Kirschen 4

64 Short rn cost fnction c SR ( y ) = w 1 x 1 + w 2 x 2 = w 1 g( y ) + w 2 x 2 w 1, w 2 : nit cost of factors of prodction x 1, x 2 c SR ( y ) 2006 Daniel Kirschen 5 y

65 Short rn marginal cost fnction c SR ( y ) Convex de to law of marginal retrns y dc SR ( y ) dy Non-decreasing fnction 2006 Daniel Kirschen 6 y

66 Optimal prodction Prodction that maximizes profit: max y { π y c SR ( y ) } d{ π y c SR ( y ) } dy = 0 π = dc SR ( y ) dy Only if the price π does not depend on y perfect competition 2006 Daniel Kirschen 7

67 Costs: Accontant s perspective In the short rn, some costs are variable and others are fixed Variable costs: labor materials fel transportation Fixed costs (amortised): eqipments land Overheads Qasi-fixed costs Startp cost of power plant Snk costs vs. recoverable costs Prodction cost [ ] Qantity 2006 Daniel Kirschen 8

68 Average cost c( y ) = c v ( y) + c f AC( y) = c( y ) y = c v ( y ) y + c f y = AVC ( y ) + AFC ( y ) Prodction cost [ ] Average cost [ /nit] Qantity Qantity 2006 Daniel Kirschen 9

69 Marginal vs. average cost /nit MC AC Prodction 2006 Daniel Kirschen 10

70 When shold I stop prodcing? Marginal cost = cost of prodcing one more nit If MC > ϖ next nit costs more than it retrns If MC < ϖ next nit retrns more than it costs Profitable only if Q 4 > Q 2 becase of fixed costs π Average cost [ /nit] Marginal cost [ /nit] Q 1 Q 2 Q 3 Q Daniel Kirschen 11

71 Costs: Economist s perspective Opportnity cost: What wold be the best se of the money spent to make the prodct? Not taking the opportnity to sell at a higher price represents a cost Examples: Growing apples or growing kiwis? Use the money to grow apples or pt it in the bank where it earns interests? Incldes a normal profit Selling at cost does not mean no profit 2006 Daniel Kirschen 12

72 Perfect competition Perfect competition The volme handled by each market participant is small compared to the overall market volme No market participant can inflence the market price by its actions All market participants act like price takers Price Inframarginal spply Extra-marginal demand Qantity Marginal prodcer 2006 Daniel Kirschen 13

73 Imperfect competition One or more competitors can inflence the market price throgh their actions Strategic players Participants with a large market share Can inflence the market price Competitive fringe Participants with a small market share Take the market price Cornot and Bertrand models of competition 2006 Daniel Kirschen 14

74 Cornot model in a dopoly Competition on qantity Problem for firm 1: y 1 = f 1 ( y 2 e ) max y 1 π ( y 1 + y 2 e ) y 1 c ( y 1 ) Similar problem for firm 2 y 2 = f 2 ( y 1 e ) y * 1 = f 1 ( y * 2 ) Cornot eqilibrim: y 2* = f 2 ( y 1* ) Neither firm has any incentive to deviate from the eqilibrim 2006 Daniel Kirschen 15

75 Cornot model in an oligopoly Total indstry otpt: Firm i: Y = max { y i π (Y ) c ( y i ) } y i d y dy i π (Y ) c( y i ) i y 1 +L + y n { } = 0 dπ (Y ) π (Y ) + y i = dc ( y i ) dy i dy i π (Y ) 1 + π (Y ) 1 y i Y Y dπ (Y ) dy i π (Y ) s i ε (Y ) = dc ( y i ) dy i = dc ( y ) i dy i Difference with perfect competition 2006 Daniel Kirschen 16

76 Cornot model in an oligopoly π (Y ) 1 s i ε (Y ) < 1 = dc ( y i ) dy i Strategic player operates at a marginal cost less than the market price Ability to maniplate prices is a fnction of: s i = y i Y Market share Elasticity of demand ε 2006 Daniel Kirschen 17

77 Bertrand model in a dopoly Competition on price Firm that sets the lowest price captres the entire market No firm will bid below its marginal cost of prodction becase it wold sell at a loss At eqilibrim, both firms sell at the same price, which is the marginal cost of prodction Eqivalent to competitive eqilibrim! Not a realistic model! 2006 Daniel Kirschen 18

78 Risks, Markets and Contracts Daniel Kirschen The University of Manchester

79 Concept of Risk Ftre is ncertain Uncertainty translates into risk In this case, risk of loss of income Risk = probability x conseqences Doing bsiness means accepting some risks Willingness to accept risk varies: ventre capitalist vs. old-age pensioner Ability to control risk varies: Professional traders vs. novice investors 2006 Daniel Kirschen 2

80 Sorces of Risk Technical risk Fail to prodce or deliver becase of technical problem Power plant otage, congestion in the transmission system External risk Fail to prodce or deliver becase of cataclysmic event Price risk Weather, earthqake, war Having to by at a price mch higher than expected Having to sell at a price mch lower than expected 2006 Daniel Kirschen 3

81 Managing Risks Excessive risk hampers economic activity Not everybody can srvive short term losses Society benefits if more people can take part Bsiness shold not be limited to large companies with deep pockets How can risk be managed: Redce the risk Share the risk Relocate the risk 2006 Daniel Kirschen 4

82 Redcing the Risks Redce freqency or conseqences of technical problems Those who can shold have an incentive to do it! Owners of power plants when otages are rare Owners and operators of transmission system when congestion is small Redce conseqences of natral catastrophes Design systems to be able to withstand rare events Enogh crews to repair the power system after a hrricane Avoid nnecessarily large price swings Develop market rles that do not create artificial spikes in the price of electrical energy Shold only be done to a reasonable extent 2006 Daniel Kirschen 5

83 Sharing the Risks Insrance: All the members of a large grop each pay a small amont to compensate a few that have sffered a big loss The conseqences of a catastrophic event are shared by a large grop rather than a few Secrity margin in power system operation Limits the conseqences of rare bt npredictable and catastrophic events Increases the daily cost of electrical energy Grid operator does not have to pay compensation in the event of a blackot 2006 Daniel Kirschen 6

84 Relocating Risk Possible if one party is more willing or able to accept it Loss is not catastrophic for this party This party can offset this loss against gains in other activities Applies mostly to price risk How does this apply to markets? 2006 Daniel Kirschen 7

85 Spot Market Sellers Spot Market Byers Immediate market, On the Spot Agreement on price Agreement on qantity Agreement on location Unconditional delivery Immediate delivery 2006 Daniel Kirschen 8

86 Examples of Spot Markets Examples Food market Basic shopping Rotterdam spot market for oil Commodities markets: corn, wheat, cocoa, coffee Formal or informal 2006 Daniel Kirschen 9

87 Advantages and Disadvantages Advantages: Simple Flexible Immediate Disadvantages Prices can flctate widely based on circmstances Example: Effect of frost in Brazil on price of coffee beans Effect of troble in the Middle East on the price of oil 2006 Daniel Kirschen 10

88 Spot Market Risks Problems with wide price flctations Small prodcer may have to sell at a low price Small prchaser may have to by at a high price Price risk Market may not have mch depth Not enogh sellers: market is short Not enogh byers: market is long Bying or selling large qantities when the market is short or long can affect the price Relying on the spot market for bying or selling large qantities is a bad idea 2006 Daniel Kirschen 11

89 Example: bying and selling wheat Farmer prodces wheat Miller bys wheat to make flor Farmer carries the risk of bad weather Miller carries the risk of breakdown of flor mill Neither farmer nor miller control price of wheat 2006 Daniel Kirschen 12

90 Harvest time If price of wheat is low: Possibly devastating for the farmer Good deal for the miller If the price is high: Good deal for the farmer Possibly devastating for the miller 2006 Daniel Kirschen 13

91 What shold they do? Option 1: Accept the spot price of wheat Eqivalent to gambling Option 2: Agree ahead of time on a price that is acceptable to both parties Forward contract 2006 Daniel Kirschen 14

92 Forward Contract Agreement: Qantity and qality Price Date of delivery (not immediate) Paid at time of delivery Unconditional delivery 2006 Daniel Kirschen 15

93 Forward Contract Contract (1Jne) 1 ton of wheat at 100 on 1 September 2006 Daniel Kirschen Matrity (1 September) Seller delivers 1 ton of wheat Byer pays 100 Spot Price = 90 Profit to seller = 10 16

94 How is the forward price set? Spot Price? Time Both parties look at their alternative: spot price Both forecast what the spot price is likely to be 2006 Daniel Kirschen 17

95 Case 1: Farmer estimates that the spot price will be 100 Miller also forecasts that the spot price will be 100 They can agree on a forward price of Daniel Kirschen 18

96 Case 2: Farmer estimates that the spot price will be 90 Miller also forecasts that the spot price will be 110 They can easily agree on a forward price of somewhere between 90 and 110 Exact price will depend on negotiation ability 2006 Daniel Kirschen 19

97 Case 3: Farmer estimates that the spot price will be 110 Miller also forecasts that the spot price will be 90 Agreeing on a forward price is likely to be difficlt 2006 Daniel Kirschen 20

98 Sharing risk In a forward contract, the byer and seller share the risk that the price differs from their expectation Difference between contract price and spot price at time of delivery represents a profit for one party and a loss for the other However, in the meantime they have been able to get on with their bsiness By new farm machinery Sell the flor to bakeries 2006 Daniel Kirschen 21

99 Attitdes towards risk Sppose that both parties forecast the same vale spot price at time of delivery Eqal attitde towards risk Forward price is eqal to expected spot price If byer is less risk adverse than seller Byer can negotiate a forward price lower than the expected spot price Seller agrees to this lower price becase it redces its risk Difference between expected spot price and forward price is called a premim Premim = price that seller is willing to pay to redce risk 2006 Daniel Kirschen 22

100 Attitdes towards risk If byer is more risk adverse than seller Seller can negotiate a forward price higher than the expected spot price Byer agrees to this lower price becase it redces its risk Byer is willing to pay the premim to redce risk 2006 Daniel Kirschen 23

101 Forward Markets Since there are many millers and farmers, a market can be organised for forward contracts Forward price represents the aggregated expectation of the spot price, pls or mins a risk premim 2006 Daniel Kirschen 24

102 What if... Spot Price Forward Price Sppose that millers are less risk adverse Premim below the expected spot price 2006 Daniel Kirschen Time Spot price trns ot to be mch lower than forward price becase of a bmper harvest 25

103 What if... Spot Price Forward Price Farmers breathe a sigh of relief Millers take a big loss The following year the millers asks for a mch bigger premim Is agreement between the millers and the farmers going to be possible? Time 2006 Daniel Kirschen 26

104 Undiversified risk Farmers and millers deal only in wheat Their risk is ndiversified Can only offset good years against bad years Risk remains high Redcing the risk frther wold help bsiness 2006 Daniel Kirschen 27

105 Diversification Diversification: deal with more than one commodity Average risk over different commodities 2006 Daniel Kirschen 28

106 Physical participants vs. traders Physical participants Prodce, consme or can store the commodity Face ndiversified risk becase they deal in only one commodity Traders (a.k.a. speclators) Cannot take physical delivery of the commodity Diversify their risk by dealing in many commodities Specialize in risk management 2006 Daniel Kirschen 29

107 Trading by speclators Cannot take physical delivery of the commodity Mst balance their position on date of delivery Qantity boght mst eqal qantity sold By or sell from spot market if necessary May involve many transactions Forward contracts limited to parties who can take physical delivery Need a standardised contract to redce the cost of trading: ftre contract Ftre contracts (ftres) allow others to participate in the market and share the risk 2006 Daniel Kirschen 30

108 Ftres Contract 2 tons at tons at 90 1 ton at 95 1 ton at 115 All contracts for wheat on 1 September 2006 Daniel Kirschen 31

109 Ftres Contract Shortly before 1 September Spot Price 100 boght 2 tons at 110 boght 1 ton at 95 sold 1 ton at 115 sold 2 tons at 110 sold 2 tons at 90 boght 2 tons at 90 sold 1 ton at 95 Delivers 4 tons Sells 2 tons at 100 Sells 1 ton at 100 boght 1 ton at Daniel Kirschen 32

110 Ftres Contract sold 2 tons at 110 sold 2 tons at 90 boght 2 tons at 110 boght 1 ton at 95 sold 1 ton at 115 sold 2 tons at 100 net profit: 0 boght 2 tons at 90 sold 1 ton at 95 sold 1 ton at 100 net profit: 15 Spot Price = Daniel Kirschen boght 1 ton at 115 boght 3 tons at

111 Importance of information Speclators own some of the commodity before it is delivered They carry the risk of a price change dring that period Need deep pockets Withot additional information, this is gambling Information helps speclators make money Example: Global perspective on the harvest for wheat Long term weather forecast and its effect on the demand for gas and electricity 2006 Daniel Kirschen 34

112 Options Spot, forward and ftre contracts: nconditional delivery Options: conditional delivery Call Option: right to by at a certain price at a certain time Pt Option: right to sell at a certain price at a certain time Two elements of the price: Exercise or strike price = price paid when option is exercised Premim or option fee = price paid for the option itself 2006 Daniel Kirschen 35

113 Example of Call Option Call Option with an exercise price of 100 Abot to expire If the spot market price is 90 the option is worth nothing If the spot market price is 110 the option is worth 10 Holder makes money if vale > option fee 2006 Daniel Kirschen 36

114 Example of Pt Option Pt Option with an exercise price of 100 Abot to expire If the spot market price is 90 the option is worth 10 If the spot market price is 110 the option is worth nothing Holder makes money if vale > option fee 2006 Daniel Kirschen 37

115 Financial Contracts Contracts withot any physical delivery A B C D Financial contract Physical Market (Spot) X Y W Z 2006 Daniel Kirschen 38

116 One-way contract for difference Example: byer has call option for 50 nits at 100 per nit spot price goes p to 110 per nit holder calls the option to by 50 nits at 100 byer owes seller 5000 (50 x 100) seller owes the byer 5500 (vale of 50 nits) seller transfers 500 to the byer to settle the contract 2006 Daniel Kirschen 39

117 Two-Way Contract for Difference Combination of a call and a pt option for the same price --> will always be sed Example 1: CFD for 50 nits at 100 spot price = 110 byer pays 5500 on spot market seller gets 5500 on spot market seller pays byer 500 byer effectively pays 5000 seller effectively gets Daniel Kirschen 40

118 Two-Way Contract for Difference Example 2: CFD for 50 nits at 100 spot price = 90 byer pays 4500 on spot market seller gets 4500 on spot market byer pays seller 500 byer effectively pays 5000 seller effectively gets 5000 Byer and seller inslated from spot market 2006 Daniel Kirschen 41

119 Exchanges Location where the market takes place Can be electronic Trading Spot Forwards Ftres Options Participants mst provide credit garantee Needs rles, policing mechanisms 2006 Daniel Kirschen 42

120 Organisation of Electricity Markets Daniel Kirschen

121 Differences between electricity and other commodities Electricity is inextricably linked with a physical delivery system Physical delivery system operates mch faster than any market Generation and load mst be balanced at all times Failre to balance leads to collapse of system Economic conseqences of collapse are enormos Balance mst be maintained at almost any cost Physical balance cannot be left to a market Daniel Kirschen

122 Differences between electricity and other commodities Electricity prodced by different generators is pooled Generator cannot direct its prodction to some consmers Consmer cannot choose which generator prodces its load Electrical energy prodced by all generators is indistingishable Pooling is economically desirable A breakdown of the system affects everybody Daniel Kirschen

123 Differences between electricity and other commodities Demand for electricity exhibits predictable daily, weekly and seasonal variations Similar to other commodities (e.g. coffee) Electricity cannot be stored in large qantities Mst be consmed when it is prodced Jst in Time Manfactring Prodction facilities mst be able to meet peak demand Very low price elasticity of the demand Demand crve is almost vertical Daniel Kirschen

124 Balancing spply and demand Demand side: Flctations in the needs Errors in forecast Spply side: Disrption in the prodction Spot market: Provides an easy way of bridging the gap between spply and demand Daniel Kirschen

125 Spot market for other commodities Characteristics of a spot market: Unconditional delivery Immediate delivery Price determined throgh interactions of byers and sellers Price tends to be volatile becase market is short term To redce the price risk, byers and sellers tend to trade mostly throgh longer term contracts Spot market is sed for adjstments Spot market is the market of last resort Daniel Kirschen

126 Spot market for electrical energy Demand side: Errors in load forecast Spply side: Unpredicted generator otages Gaps between load and generation mst be filled qickly Market mechanisms Too slow Too expensive Need fast commnication Need to reach lots of participants Daniel Kirschen

127 Managed spot market Balance load and generation Rn by the system operator Maintains the secrity of the system Mst operate on a sond economic basis Use competitive bids for generation adjstments Shold ideally accept demand-side bids Determine a cost-reflective spot price Not a tre market becase price is not set throgh interactions of byers and sellers Indispensable for treating electricity as a commodity Daniel Kirschen

128 Managed spot market Generation srpls Generation deficit Load srpls Load deficit System operator Managed Spot Market Control actions Spot price Bids to increase prodction Bids to decrease prodction Bids to decrease load Bids to increase load Daniel Kirschen

129 Managed spot market Also know as: Reserve market Balancing mechanism In North America, the day-ahead horly market is often called the spot market Daniel Kirschen

130 Other markets Well-fnctioning spot market is essential Ensres that imbalances will be settled properly Makes the development of other markets possible Spot price is volatile Most participants want more certainty Redce risk by trading ahead of the spot market Forward markets and derivative markets help redce risks Forward markets mst close before the managed spot market Daniel Kirschen

131 Why is the spot price for electricity so volatile? Load Peak load Minimm load 00:00 06:00 12:00 18:00 24:00 Time Daniel Kirschen

132 Demand crves for electricity /MWh Minimm load Peak load Daily flctations MWh Daniel Kirschen

133 Spply crve for electricity /MWh Peaking generation Base generation Intermediate generation MWh Daniel Kirschen

134 Spply and demand for electricity /MWh Minimm load Peak load π max π min Price of electricity flctates dring the day MWh Daniel Kirschen

135 Spply and demand for electricity π ext /MWh Extreme peak Normal peak π nor Small increases in peak demand case large changes in peak prices MWh Daniel Kirschen

136 Spply and demand for electricity π ext /MWh Normal spply Redced spply π nor Normal peak Small redctions in spply case large changes in peak prices MWh Daniel Kirschen

137 Price dration crve Percentage of Hors PJM system (USA) for 1999 Actal peak price reached $1000/MWh for a few hors (Sorce: Daniel Kirschen

138 Forward markets Two approaches: Centralised trading (also known as Pool Trading ) Bilateral trading Daniel Kirschen

139 Pool trading All prodcers sbmit bids All consmers sbmit offers Market operator determines sccessfl bids and offers and the market price In many electricity pools, the demand side is passive. A forecast of demand is sed instead. Daniel Kirschen

140 Example of pool trading Bids and offers in the Electricity Pool of Syldavia for the period from 9:00 till 10:00 on 11 Jne: Bids Offers Company Qantity [MWh] Price [$/MWh] Red Red Red Green Green Ble Ble Yellow Yellow Prple Prple Orange Orange Daniel Kirschen

141 Example of pool trading Orange Yellow Prple 20 Red Price [$/MWh] Red Ble Red Green Yellow Green Prple Ble Orange Qantity [MWh] Daniel Kirschen

142 Example of pool trading Orange Yellow Prple Accepted offers 20 Red Price [$/MWh] Market price Red Accepted bids Ble Red Green Yellow Green Prple Ble Orange 5 Qantity traded Qantity [MWh] Daniel Kirschen

143 Example of pool trading Market price: $/MWh Volme traded: 450 MWh Company Prodction [MWh] Consmption [MWh] Revene [$] Expense [$] Red 250 4,000. Ble 100 1,600 Green 100 1,600 Orange 200 3,200 Yellow 100 1,600 Prple 150 2,400 Total ,200 7,200 Daniel Kirschen

144 Unit commitment-based pool trading Reasons for not treating each market period separately: Operating constraints on generating nits Minimm p and down times, ramp rates Savings achieved throgh schedling Start-p and no-load costs Redce risk for generators Uncertainty on generation schedle leads to higher prices Daniel Kirschen

145 Unit commitment-based pool trading Load Forecast Generators Bids Unit Commitment Program Minimm Cost Schedle Market Prices Daniel Kirschen

146 Generator Bids All nits are bid separately Components: piecewise linear marginal price crve start-p price parameters (min MW, max MW, min p, min down,...) Bids do not have to reflect costs Bidding very low to get in the schedle is allowed Daniel Kirschen

147 MW Load Forecast Load is sally treated as a passive market participant Assme that there is no demand response to prices Time Daniel Kirschen

148 Generation Schedle MW Time Daniel Kirschen

149 Marginal Units MW Most expensive nit needed to meet the load at each period Restrictions may apply Time Daniel Kirschen

150 Market price Bid from marginal nit sets the market clearing price at each period System Marginal Price (SMP) All energy traded throgh the pool dring that period is boght and sold at that price MW Time Daniel Kirschen

151 Why trade all energy at the SMP? Why not pay the generators what they bid? Cheaper generators wold not want to leave money on the table Wold try to gess the SMP and bid close to it Occasional mistakes Ë get left ot of the schedle Increased ncertainty Ë increase in price Daniel Kirschen

152 Bilateral trading Pool trading is an nsal form of market Bilateral trading is the classical form of trading Involves only two parties: Seller Byer Trading is a private arrangement between these parties Price and qantity negotiated directly between these parties Nobody else is involved in the decision Daniel Kirschen

153 Bilateral trading Unlike pool trading, there is no official price Occasionally facilitated by brokers or electronic market operators Takes different forms depending on the time scale Daniel Kirschen

154 Types of bilateral trading Cstomised long-term contracts Flexible terms Negotiated between the parties Dration of several months to several years Advantage: Garantees a fixed price over a long period Disadvantages: Cost of negotiations is high Worthwhile only for large amonts of energy Daniel Kirschen

155 Types of bilateral trading Over the Conter trading Smaller amonts of energy Delivery according to standardised profiles Advantage: Mch lower transaction cost Used to refine position as delivery time approaches Daniel Kirschen

156 Types of bilateral trading Electronic trading Byers and sellers enter bids directly into compterised marketplace All participants can observe the prices and qantities offered Atomatic matching of bids and offers Participants remain anonymos Market organiser handles the settlement Advantages: Very fast Very cheap Good sorce of information abot the market Daniel Kirschen

157 Example of bilateral trading Generating nits owned by Bordria Power: Unit P min [MW] P max [MW] MC [$/MWh] A B C Daniel Kirschen

158 Example of bilateral trading Trades of Bordria Power for 11 Jne from 2:00 pm till 3:00 pm Type Contract Date Identifier Byer Seller Amont [MWh] Price [$/MWh] Long term 10 Janary LT1 Cheapo Energy Bordria Power Long term 7 Febrary LT2 Bordria Steel Bordria Power Ftre 3 March FT1 Qality Electrons Bordria Power Ftre 7 April FT2 Bordria Power Perfect Power Ftre 10 May FT3 Cheapo Energy Bordria Power Net position: Prodction capacity: Sold 570 MW 750 MW Daniel Kirschen

159 Example of bilateral trading Pending offers and bids on Bordria Power Exchange at mid-morning on 11 Jne for the period from 2:00 till 3:00 pm: 11 Jne 14:00-15:00 Identifier Amont [MW] Price [$/MWh] Bids to sell energy B B B B B Offers to by energy O O O O O Daniel Kirschen

160 Example of bilateral trading Electronic trades made by Bordria Power: 11 Jne 14:00-15:00 Identifier Amont [MW] Price [$/MWh] Bids to sell energy B B B B B Offers to by energy O O O O O Net position: Sold 630 MW Self schedle: Unit A: 500 MW Unit B: 130 MW Unit C: 0 MW Daniel Kirschen

161 Example of bilateral trading Unexpected problem: nit B can only generate 80 MW Options: - Do nothing and pay the spot price for the missing energy - Make p the deficit with nit C - Trade on the power exchange 11 Jne 14:00-15:00 Identifier Amont [MW] Price [$/MWh] Bids to sell energy B B B B B Offers to by energy O O O Bying is cheaper than prodcing with C New net position: Sold 580 MW New schedle: A: 500 MW, B: 80 MW, C: 0 MW Daniel Kirschen

162 Pool vs. bilateral trading Pool Unsal becase administered centrally Price not transparent Facilitates secrity fnction Makes possible central optimisation Historical origins in electricity indstry Bilateral Economically prer Price set by the parties Hard bargaining possible Generator assme schedling risk Mst be coordinated with secrity fnction More opportnities to innovate Both forms of trading can coexist to a certain extent Daniel Kirschen

163 Bidding in managed spot market Bordria Power s position: Unit P sched P min P max MC [MW] [MW] [MW] [$/MWh] A B C Bordria Power s spot market bids: Type Unit Price Amont [$/MWh] [MW] Bid (increase) C Offer (decrease) B Offer (decrease) A Spot market assmed imperfectly competitive Bids/offers can be higher/lower than marginal cost Daniel Kirschen

164 Settlement process Pool trading: Market operator collects from consmers Market operator pays prodcers All energy traded at the pool price Bilateral trading: Bilateral trades settled directly by the parties as if they had been performed exactly Managed spot market: Prodced more or consmed less Ë receive spot price Prodced less or consmed more Ë pay spot price Daniel Kirschen

165 Example of settlement 11 Jne between 2:00 pm and 3:00 pm Spot price: $/MWh Unit B of Bordria Power cold prodce only 10 MWh instead of 80 MWh Bordria Power ths had a deficit of 70 MWh for this hor 40 MW of Bordria Power s spot market bid of 50 MW at $/MWh was called by the operator Daniel Kirschen

166 Bordria Power s Settlement Market Type Amont [MWh] Ftres and Forwards Power Exchange Price [$/MWh] Income [$] Sale , Sale , Sale , Expense [$] Prchase Sale Sale Sale Sale Prchase Prchase Prchase Spot Sale Market Imbalance , Total 550 9, , Daniel Kirschen

167 Example of an electricity market: NETA NETA = New Electricity Trading Arrangements Market operating in England and Wales since April 2001 Relies on bilateral trading as mch as possible Replaced the Electricity Pool of England and Wales, which was a centralised market Extended to Scotland on 1 April 2005 (BETTA) Daniel Kirschen

168 NETA Timeline Forward Markets Electronic Power Exchange Gate Closre Balancing Mechanism Real Time Settlement Process T-several months T-1day T-1hr T T+1/2 hr Bilateral Centralized Daniel Kirschen

169 Price volatility in the balancing mechanism Daniel Kirschen

170 Participating in Electricity Markets: The Generator s Perspective D. Kirschen

171 Market Strctre Monopoly Oligopoly Perfect Competition Monopoly: Monopolist sets the price at will Mst be reglated Perfect competition: No participant is large enogh to affect the price All participants act as price takers Oligopoly: Some participants are large enogh to affect the price Strategic bidders have market power Others are price takers D. Kirschen

172 Perfect competition All prodcers have a small share of the market All consmers have a small share of the market Individal actions have no effect on the market price All participants are price takers D. Kirschen

173 Short rn profit maximisation for a price taker y : Otpt of one of the generators max y { π.y c(y) } d { π.y c(y) } dy π = dc(y) dy = 0 Prodction cost Revene Independent of qantity prodced becase price taker Adjst prodction y ntil the marginal cost of prodction is eqal to the price π D. Kirschen

174 Market strctre No difference between centralised action and bilateral market Everything is sold at the market clearing price Price is set by the last nit sold Marginal prodcer: Sells this last nit Gets exactly its bid Infra-marginal prodcers: Get paid more than their bid Collect economic profit Extra-marginal prodcers: Sell nothing Price Infra-marginal spply Extra-marginal demand Qantity Marginal prodcer D. Kirschen

175 Bidding nder perfect competition No incentive to bid anything else than marginal cost of prodction Lots of small prodcers Change in bid cases a change in stacking p order If bid is higher than marginal cost Cold become extra marginal and miss an opportnity to sell at a profit Price spply demand Qantity D. Kirschen

176 Bidding nder perfect competition If bid is lower than marginal cost Cold have to prodce at a loss If bid is eqal to marginal cost Get paid market price if marginal or infra-marginal prodcer Price spply demand Qantity D. Kirschen

177 Oligopoly and market power A firm exercises market power when It redces its otpt (physical withholding) or It raises its offer price (economic withholding) in order to change the market price D. Kirschen

178 Example A firm sells 10 nits and the market price is $15 Option 1: offer to sell only 9 nits and hope that the price rises enogh to compensate for the loss of volme Option 2: offer to sell the 10th nit for a price higher than $15 and hope that this will increase the price Profit increases if price rises sfficiently to compensate for possible decrease in volme D. Kirschen

179 Short rn profit maximisation with market power { } y i : Prodction of generator i max y i y i π (Y ) c ( y i ) d y dy i π (Y ) c( y i ) i { } = 0 π (Y ) + y i dπ (Y ) dy i = dc ( y i ) dy i Y = y 1 +L + y n is the total indstry otpt Not zero becase of market power π (Y ) 1 + y i Y Y dπ (Y ) dy i π (Y ) = dc ( y ) i dy i D. Kirschen

180 Short rn profit maximisation with market power π (Y ) 1 + ε = dy y dπ π y i Y Y dπ (Y ) dy i π (Y ) = π y dy dπ = dc ( y ) i dy i is the price elasticity of demand s i = y i Y is the market share of generator i π (Y ) 1 s i ε (Y ) = dc ( y i ) dy i < 1 optimal price for generator i is higher than its marginal cost D. Kirschen

181 When is market power more likely? Imperfect correlation with market share Demand does not have a high price elasticity Spply does not have a high price elasticity: Highly variable demand All capacity sometimes sed Otpt cannot be stored ËElectricity markets are more vlnerable than others to the exercise of market power D. Kirschen

182 Elasticity of the demand for electricity Price High elasticity good Slope is an indication of the elasticity of the demand High elasticity Non-essential good Easy sbstittion Price Qantity Low elasticity Essential good No sbstittes Low elasticity good Electrical energy has a very low elasticity in the short term Qantity D. Kirschen

183 How Inelastic is the demand for electricity? Price of electrical energy in England and Wales [ /MWh] Min Max Average Janary Febrary March Vale of Lost Load (VoLL) in England and Wales: 2,768 /MWh D. Kirschen

184 Price spikes becase of increased demand π ext $/MWh Extreme peak Normal peak π nor Small increases in peak demand case large changes in peak prices MWh D. Kirschen

185 Price spikes becase of redced spply π ext $/MWh Normal spply Redced spply π nor Normal peak Small redctions in spply case large changes in peak prices MWh D. Kirschen

186 Increasing the elasticity redces price spikes and the generators ability to exercise market power $/MWh π max π min MWh D. Kirschen

187 Increasing the elasticity of the demand Obstacles Tariffs Need for commnication Need for storage (heat, intermediate prodcts, dirty clothes) Not everybody needs to respond to price signals to get sbstantial benefits Increased elasticity redces the average price Not in the best interests of generating companies Impets will need to come from somewhere else D. Kirschen

188 Frther comments on market power ALL firms benefit from the exercise of market power by one participant Unilaterally redcing otpt or increasing offer price to increase profits is legal Collsion between firms to achieve the same goal is not legal Market power interferes with the efficient dispatch of generating resorces Cheaper generation is replaced by more expensive generation D. Kirschen

189 Modelling imperfect competition Bertrand model Competition on prices Cornot model Competition on qantities D. Kirschen

190 Game theory and Nash eqilibrim Each firm mst consider the possible actions of others when selecting a strategy Classical optimisation theory is insfficient Two-person non-co-operative game: One firm against another One firm against all the others Nash eqilibrim: given the action of its rival, no firm can increase its profit by changing its own action: Ω i (a i *,a j * ) Ω i (a i,a j * ) i,a i D. Kirschen

191 Bertrand Competition Example 1 C A = 35. P A /h C B = 45. P B /h Bid by A? Bid by B? Market price? Qantity traded? P A P B A B π = 100 D [ / MWh] C A (P A ) C B (P B ) Inverse demand crve D. Kirschen

192 Bertrand Competition Example 1 C A = 35. P A /h C B = 45. P B /h P A P B A B π = 100 D [ / MWh] Marginal cost of A: 35 /MWh Marginal cost of B: 45 /MWh A will bid jst below 45 /MWh C A (P A ) C B (P B ) B cannot bid below 45 /MWh becase it wold loose money on every MWh Market price: jst below 45 /MWh Demand: 55 MW P A = 55MW P B = 0 D. Kirschen

193 Bertrand Competition Example 2 C A = 35. P A /h C B = 35. P B /h P A P B A B π = 100 D [ / MWh] C A (P A ) C B (P B ) Bid by A? Bid by B? Market price? Qantity traded? D. Kirschen

194 Bertrand Competition Example 2 C A = 35. P A /h C B = 35. P B /h P A P B A B C A (P A ) C B (P B ) π = 100 D [ / MWh] A cannot bid below 35 /MWh becase it wold loose money on every MWh A cannot bid above 35 /MWh becase B wold bid lower and grab the entire market Market price: 35 /MWh Identical generators: bid at marginal cost Non-identical generators: cheapest gets the whole market Not a realistic model of imperfect competition D. Kirschen

195 Cornot competition: Example 1 C A = 35. P A /h C B = 45. P B /h P A P B A B π = 100 D [ / MWh] C A (P A ) C B (P B ) Sppose P A = 15 MW and P B = 10 MW Then D = P A + P B = 25 MW π = D = 75 /MW R A = = 1125 ; C A = = 525 R B = = 750 ; C B = = 450 Profit of A = R A - C A = 600 Profit of B = R B - C B = 300 D. Kirschen

196 Cornot competition: Example 1 Smmary: For P A =15MW and P B = 10MW, we have: Demand Profit of A Profit of B Price D. Kirschen

197 Cornot competition: Example 1 P A =15 P A =20 P A =25 P A =30 P B =10 P B =15 P B =20 P B = Demand Profit B Profit A Price D. Kirschen

198 Cornot competition: Example 1 P A =15 P A =20 P A =25 P A =30 P B =10 P B =15 P B =20 P B = Demand Profit B Profit A Price Price decreases as spply increases Profits of each affected by other Complex relation between prodction and profits D. Kirschen

199 Let s play the Cornot game! P A =15 P A =20 P A =25 P A =30 P B =10 P B =15 P B =20 P B = Demand Profit B Profit A Price Eqilibrim soltion! A cannot do better withot B doing worse B cannot do better withot A doing worse Nash eqilibrim D. Kirschen

200 Cornot competition: Example 1 Demand P B =15 Profit of B P A = Profit of A Price C A = 35. P A /h C B = 45. P B /h Generators achieve price larger than their marginal costs The cheapest generator does not grab the whole market Generators balance price and qantity to maximise profits D. Kirschen

201 Cornot competition: Example 2 C A = 35. P A /h C B = 45. P B /h C N = 45. P N /h P A P B P N... A B N C A (P A ) C B (P B ) C N (P N ) π = 100 D [ / MWh] D. Kirschen

202 Cornot competition: Example Total prodction of other firms Prodction of firm A Prodction of another firm Nmber of Firms D. Kirschen

203 Cornot competition: Example Price Demand Nmber of Firms D. Kirschen

204 Cornot competition: Example Profit of firm A Total profit of the other firms Profit of another firm Nmber of Units D. Kirschen

205 Other competition models Spply fnctions eqilibria Bid price depends on qantity Agent-based simlation Represent more complex interactions Maximising short-term profit is not the only possible objective Maximising market share Avoiding reglatory intervention D. Kirschen

206 Conclsions on imperfect competition Electricity markets do not deliver perfect competition Some factors facilitate the exercise of market power: Low price elasticity of the demand Large market shares Cyclical demand Operation close to maximm capacity Stdy of imperfect competition in electricity markets is a hot research topic Generator s perspective Market designer s perspective D. Kirschen

207 Participating in Electricity Markets: The consmer s perspective D. Kirschen

208 Options for the consmers By at the spot price Lowest cost, highest risk Mst be managed careflly Reqires sophisticated control of the load By from a retailer at a tariff linked to the spot price Retailers acts as intermediary between consmer and market Risk can be limited by placing cap (and collar) on the price Interrptible contract Reasonable option only if cost of interrption is not too high Savings can be sbstantial D. Kirschen

209 Options for the consmers By from a retailer on a time-of-se tariff Shifts some of the risk to the consmer Need to control the load to save money By from a retailer at a fixed tariff Lowest risk, highest cost Two components to the price: average cost of energy and risk premim D. Kirschen

210 Choosing a contract Best type of contract depends on the characteristics of the consmer: Cost of electricity as a proportion of total cost Risk aversion Flexibility in the se of electricity Potential savings big enogh to jstify transactions cost D. Kirschen

211 Bying at the spot price Mst forecast prices Mch harder than load forecasting becase price depends on demand and spply Spply factors are particlarly difficlt to predict (otages, maintenance, gaming, locational effects) Good accracy for average price and volatility Predicting spikes is mch harder Mst optimize prodction taking cost of electricity into accont Complex problem becase of: Prodction constraints Cost of storage (losses, loss of efficiency in other steps, ) Price profiles D. Kirschen

212 Participating in Electricity Markets: The retailer s perspective D. Kirschen

213 The retailer s perspective Sell energy to consmers, mostly at a flat rate By energy in blk Spot market Contracts Want to redce risks associated with spot market Increase proportion of energy boght nder contracts Mst forecast the load of its cstomers Regional monopoly: traditional top-down forecasting Retail competition: bottom-p forecasting Difficlt problem: cstomer base changes Mch less accrate than traditional load forecasting D. Kirschen

214 Participating in Electricity Markets: The hybrid participant s perspective D. Kirschen

215 Example: pmped storage hydro plant D. Kirschen