Electricity transmission pricing: getting the prices “good enough”? Richard Green Institute for Energy Research and Policy

Transmission pricing Geographical differentiation in the wholesale market Prices for connecting to and using the transmission network

Six objectives 1.Promote the efficient day ‑ to ‑ day operation of the bulk power market 2.Signal locational advantages for investment in generation and demand 3.Signal the need for investment in the transmission system

Six objectives 4Compensate the owners of existing transmission assets 5Be simple and transparent 6Be politically implementable Green (Utilities Policy, 1997)

Three approaches Ignore transmission issues Ignore transmission issues, then bribe market participants to sort things out Integrate transmission issues into your market design(s)

Major power flows Source: UCTE

Major power flows and congestion Source: UCTE Congested 26-75% 76-99% 100%

If costs differ between areas GW P P PLPL PHPH Price trade Xpts Mpts

If costs differ between areas GW P P Price trade Xpts Mpts

Price trade If costs differ between areas GW P P and the lines are too thin… Xpts Mpts

If costs differ between areas T { GW P P and the lines are too thin… Xpts Mpts

Price trade If costs differ between areas GW P P and the lines are too thin… you could still ignore the problem but someone will want money to sort it out! Xpts Mpts

Zones in the NEM NEM runs nodal model and dispatches according to nodal conditions (prices) Generators / loads grouped into regions All generators in a region receive the regional reference price –Marginal cost at a reference node No compensation for constrained running

From a line to a network… Electricity will flow along every path between two nodes It “cannot” be steered If one line fails, the flows instantly change Overloading any line can be catastrophic

(for example…)

AB C The impact of loop flows

AB C

Nodal prices Set price of power equal to marginal cost at each point (node) on the network –Marginal cost of generation (if variable) –MC of bringing in power from elsewhere Centralised market uses the nodal prices Bilateral trades which move power pay the difference in nodal prices

Nodal trading Price at A = 20, Price at B = 30 I sell at A, I receive 20 I sell at B, I receive 30 I generate at A and sell at B, I receive the agreed bilateral price and pay (30 – 20) I generate at B and sell at A, I receive the agreed bilateral price and pay (20 – 30) A B

AB C 6 MW at C needs 3 MW from A and 3 MW from B The impact of loop flows and constraints

Prices – constraint AB Price at C = (Pa + Pb)/2 1 MW extra capacity allows 1.5 MW from A to replace 1.5 MW from B Shadow cost of constraint = 1.5 (Pb – Pa) If Pa = 10, Pb = 30 Pc = 20, shadow cost = 30 Pc = Pa + 1/3 shadow cost = Pb – 1/3 shadow cost

AB C 3 MW at C needs –3 MW from A The impact of loop flows and constraints and 6 MW from B

Prices – constraint AC Price at C = 2Pb – Pa 1 MW extra capacity allows 3 MW from A to replace 3 MW from B Shadow cost of constraint = 3 (Pb – Pa) If Pa = 10, Pb = 30 Pc = 50, shadow cost = 60 Pc = Pa + 2/3 Shadow cost = Pb + 1/3 Shadow cost

AB C and constraints 3 MW at C needs 6 MW from A The impact of loop flows and –3 MW from B

Prices – constraint CB Price at C = 2 Pa – Pb 1 MW extra capacity allows 3 MW from A to replace 3 MW from B Shadow cost of constraint = 3 (Pb – Pa) If Pa = 10, Pb = 30 Pc = –10, shadow cost = 60 Pc = Pa – 1/3 shadow cost = Pb – 2/3 shadow cost

Summary Constraint is on line: NoneABACBC Price at A10 Price at B1030 Price at C

Implications Nodal prices can vary significantly –Over time –Over space The first creates a need for hedging The second makes it harder The prices may be counter-intuitive

How to hedge Transmission Congestion Contract Spatial contract for differences –Pays the holder the difference in nodal prices between two specified points (from A to B) –Price at B – Price at A –Perfect hedge if you generate that amount of power at A and sell it at B Remember the real-time charge is (P B – P A )

Who’d sell that hedge? The spot market charges raise a surplus –Who gets it? If the Transmission Congestion Contracts allocation is feasible, Hogan (1992) shows spot market surplus ≥ TCC payments Organisation receiving the spot surplus can issue TCCs and find itself hedged!

Inferior ways of hedging Financial Transmission Rights (options) –Only pay out when value is positive –Payments may exceed spot revenues Physical Transmission Rights –Limited by system capacity –If line limit on AB is 100, can only issue 100 –With TCCs, 100 BA “allows” an extra 100 AB “Smeared” share of congestion revenues

What if you get it wrong? Operational difficulties –PJM’s first market Economic operating mistakes Investment mistakes –At present, we don’t know much about these

How much does it cost to get it wrong? Compare demand and operating patterns with different pricing rules Model applied to England and Wales, 1996 data Numbers are country- and time-specific Approach is general

The model NGC system in 1996/97 Thirteen zones (two pairs of NGC’s zones are combined, one zone split into two) Iso-elastic demand in every zone Generation in most £/MWh GW Gas, Coal, Nuclear Oil

North South-West A DC load flow model with losses (proportional to the square of flows) and constraints on the total flows across NGC’s system boundaries Transmission system model

Three pricing rules One price for generation and for demand in each zone (optimal) One price at each node for generation, but a common national price for demand One national price for generation and one national price for demand (actual system) –Constraints are managed via payments for constrained-on and constrained-off running

What is welfare? NGC’s operating surplus –Kept the same under each of the rules Generators’ operating surpluses –Energy revenues less variable fuel costs –Gas contracts assumed not to be variable Consumer surplus –Area under their demand curve and above the price they actually pay

Prices – winter peak

Prices – summer trough

Basic results Pricing SystemOptimalNodal (for Generators) Uniform Av. Revenue (£/MWh) Changes (% of optimal, competitive, revenue): Consumer surplus-0.2%-3.4% Generators’ profits-0.9%2.1% Welfare-1.2%-1.3%

Intuition for the results Adjustments to generation for constraints have to happen, whatever the pricing rule –Here, these are in the same direction as the economic response to marginal losses Cost differences at stations partially offset marginal transmission losses

Market power Sometimes a problem in this market –General incentive to raise prices –Particular incentive to raise prices in import- constrained area –Uniform pricing gives incentive to reduce prices in export-constrained area Model two strategic generators plus fringe –Both firms change slope of bids (by region)

Generators’ capacities North South-West

Prices – winter peak

Prices – summer trough

Prices – zone 12

Prices – zone 1

Market power Pricing SystemOptimalNodal (for Generators) Uniform Av. Revenue (£/MWh) Changes (% of optimal, competitive, revenue): Consumer surplus-6.6%-1.0% Generator profit4.3%-2.1% WelfareRel. to optimal-2.3%-3.1% Rel. to comp-5.4%-6.5%-7.2%

Conclusions of this study Optimal pricing would create winners (northern consumers, southern generators) and losers (northern generators, southern consumers) It would be less vulnerable to market power Welfare gains of 1% of turnover are quite large as Harberger triangles go!

Other transmission charges Connection assets – local costs Capacity-based use of system –Affect investment decision, not operating choices Output-based use of system –Affect operating choices and might be used to offset consistent errors in the market rules Contracts for constrained running

Interactions between charges Investing generators should consider both spot market and transmission charges –With the right spot signals, transmission charges should be uniform –Differentiated transmission charges needed if spot prices send inadequate signals –Using both would over-signal, reducing transmission costs, but raising generators’

Conclusion For major changes, transmission charging creates well-informed winners and losers –Gains typically small relative to transfers With good operators, the system is resilient to poor rules Better rules will create gains worth having