WHITEPAPER

How to best price day-ahead markets?

Welfare optimality, algorithm scalability, and price signals

Since the early waves of electricity market liberalizations, pricing in day-ahead markets has raised challenging questions rooted in economics and optimization theory. In general, finding uniform paid-as-cleared market prices, is mathematically impossible in day-ahead (or more generally closed-gate) electricity markets, due to the presence of indivisibility constraints. 

We discuss here how that pricing challenge is addressed currently in the EU Single Day-Ahead Coupling (SDAC), in the US, and why Non-uniform Clearing prices may potentially be a key enabler leading to more welfare and better algorithm scalability. The question of obtaining meaningful price signals is also briefly discussed.

Perfect uniform prices, in general, do not exist

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Uniform pricing means that in the market outcome, every market participant of the same market segment (location and hour of the day) will pay or receive the same electricity price and no other transfers or payments are considered. Uniform prices correspond to the classic pay-as-clear design.

In day-ahead electricity markets, additional more advanced bids than simple demand and offer curves are used to take into account technical-economic constraints such as minimum power output levels or start-up costs for generation assets. In such a case, it is in general not possible to find a “single price fits all” supporting a competitive equilibrium and ensuring that every market player is perfectly fine with the market outcome. 

The current approach for Indian and European wholesale markets

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In the current design in the EU Single Day-ahead Coupling and in India, the welfare optimal allocation would most of the time be discarded, in case it is not possible to find uniform prices avoiding that some orders are paradoxically accepted. 

Such a design requires checking specific price conditions every time an order matching candidate solution is found, and to discard such a solution if paradoxical acceptance of some so-called block orders cannot be avoided. Technically speaking, this implies that the market-clearing algorithm needs to “iterate” between a volume problem (which a.o. determines the acceptance of bids), and a price problem (which verifies that prices are compatible with the accepted bids actually exist). 

Non-uniform pricing

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Non-uniform pricing essentially consists in dropping price conditions in the welfare optimization stage, and purely focusing on finding the welfare optimal order matching,optimizing the value of the accepted demand and of the supply costs.

Because the problem is less constrained, solutions with more welfare are allowed. It also eases calculations of the market results, since there is no need to iterate anymore between the search for optimal bid acceptance and the calculation of clearing prices when a candidate solution is found.

On another hand, to ensure that all market participants are cleared at least at the price of their bids, compensations should be paid to so-called “out-of-the-money” orders that are losing money. This results in side-payments that differ among the impacted market participants.

Meaningful price signals

Several questions arise with non-uniform pricing, and pricing rules in day-ahead electricity markets are still subject to active research in academia and in the industry. For the pricing stage aimed at computing market prices and side-payments, several requirements or objectives could be imposed. 

Another property not guaranteed with Convex Hull Pricing is fractionally accepted orders set the clearing prices. For those two reasons, Convex Hull Pricing may not be seen as a good candidate for implementation in EU or Indian markets.

On another hand, Convex Hull Pricing and variants generally focus on making market prices as significant as possible, by minimizing deviations from a competitive equilibrium and minimizing the discretionary payments complementing the settlements purely based on these market prices. 

Conclusion

The non-uniform pricing is by design leading to higher welfare and lowers computational complexity compared to pure uniform pricing. On another hand, non-uniform pricing implies side-payments, which in turn mean an additional complexity in terms of settlements. How market participants would behave under various pricing schemes is also an interesting question.

Non-uniform pricing is currently studied in the R&D program of Euphemia under the supervision of the Single Day-ahead Coupling Market & System Design body, as a promising avenue to improve market and algorithmic efficiency.

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