Inefficiency in forward markets with supply friction

Cai, Desmond; Wierman, Adam

doi:10.1109/cdc.2013.6760771

“…Additionally, note that we have implicitly assumed that the randomness in Ci(di) is independent of the price p. These are standard assumptions in the electricity market literature, e.g., [4,8,40,50]. Given the model above, the total demand response the LSE achieves with price p is the random quantity i si(p).…”

Section: Model Formulationmentioning

confidence: 99%

“…Note that this may seem restrictive, but this form is standard within the electricity markets literature, e.g., [4,8,40,50]. Then, for each realization, we can explicitly compute the curtailments of the users.…”

Section: The Efficiency Of Prediction-based Pricingmentioning

confidence: 99%

See 1 more Smart Citation

Pricing data center demand response

LiuZhenhua

¹

,

LiuIris

²

,

LowSteven

³

et al. 2014

SIGMETRICS Perform. Eval. Rev.

Self Cite

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Demand response is crucial for the incorporation of renewable energy into the grid. In this paper, we focus on a particularly promising industry for demand response: data centers. We use simulations to show that, not only are data centers large loads, but they can provide as much (or possibly more) flexibility as large-scale storage if given the proper incentives. However, due to the market power most data centers maintain, it is difficult to design programs that are efficient for data center demand response. To that end, we propose that prediction-based pricing is an appealing market design, and show that it outperforms more traditional supply function bidding mechanisms in situations where market power is an issue. However, prediction-based pricing may be inefficient when predictions are inaccurate, and so we provide analytic, worst-case bounds on the impact of prediction error on the efficiency of prediction-based pricing. These bounds hold even when network constraints are considered, and highlight that prediction-based pricing is surprisingly robust to prediction error.

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“…Additionally, note that we have implicitly assumed that the randomness in Ci(di) is independent of the price p. These are standard assumptions in the electricity market literature, e.g., [4,8,41,51]. Given the model above, the total demand response the LSE achieves with price p is the random quantity i si(p).…”

Section: Model Formulationmentioning

confidence: 99%

Pricing data center demand response

Liu

¹

,

Liu

²

,

Low

³

et al. 2014

The 2014 ACM International Conference on Measurement and Modeling of Computer Systems

Self Cite

View full text Add to dashboard Cite

Demand response is crucial for the incorporation of renewable energy into the grid. In this paper, we focus on a particularly promising industry for demand response: data centers. We use simulations to show that, not only are data centers large loads, but they can provide as much (or possibly more) flexibility as large-scale storage if given the proper incentives. However, due to the market power most data centers maintain, it is difficult to design programs that are efficient for data center demand response. To that end, we propose that prediction-based pricing is an appealing market design, and show that it outperforms more traditional supply function bidding mechanisms in situations where market power is an issue. However, prediction-based pricing may be inefficient when predictions are inaccurate, and so we provide analytic, worst-case bounds on the impact of prediction error on the efficiency of prediction-based pricing. These bounds hold even when network constraints are considered, and highlight that prediction-based pricing is surprisingly robust to prediction error.

show abstract

“…Of course it would also be interesting to consider a more general model where the prices arrive endogenously from market behavior. However, incorporating multiple forward markets into multi-stage models is notoriously difficult and, as a result, the assumption of perfect foresight (no prediction error) is typically needed to obtain analytic results in these cases, e.g., [26,6].…”

Section: Evolution Of Pricesmentioning

confidence: 99%

Energy procurement strategies in the presence of intermittent sources

NairJayakrishnan

¹

,

AdlakhaSachin

²

,

WiermanAdam

³

2014

SIGMETRICS Perform. Eval. Rev.

Self Cite

View full text Add to dashboard Cite

The increasing penetration of intermittent, unpredictable renewable energy sources such as wind energy, poses significant challenges for utility companies trying to incorporate renewable energy in their portfolio. In this work, we study the problem of conventional energy procurement in the presence of intermittent renewable resources. We model the problem as a variant of the newsvendor problem, in which the presence of renewable resources induces supply side uncertainty, and in which conventional energy may be procured in three stages to balance supply and demand. We compute closed-form expressions for the optimal energy procurement strategy and study the impact of increasing renewable penetration, and of proposed changes to the structure of electricity markets. We explicitly characterize the impact of a growing renewable penetration on the procurement policy by considering a scaling regime that models the aggregation of unpredictable renewable sources. A key insight from our results is that there is a separation between the impact of the stochastic nature of this aggregation, and the impact of market structure and forecast accuracy. Additionally, we study the impact on procurement of two proposed changes to the market structure: the addition and the placement of an intermediate market. We show that addition of an intermediate market does not necessarily increase the efficiency of utilization of renewable sources. Further, we show that the optimal placement of the intermediate market is insensitive to the level of renewable penetration.

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Inefficiency in forward markets with supply friction

Cited by 10 publications

References 22 publications

Pricing data center demand response

Pricing data center demand response

Pricing data center demand response

Energy procurement strategies in the presence of intermittent sources

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