Pricing Chance Constraints in Electricity Markets

Kuang, Xiaolong; Dvorkin, Yury; Lamadrid, Alberto J.; Ortega‐Vazquez, Miguel A.; Zuluaga, Luis F.

doi:10.1109/tec.2018.2821922

Cited by 24 publications

(44 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(7)), the controllable DER at this node is implicitly discouraged from providing balancing regulation and, therefore, ν v i drives the optimal choice of α i from the system perspective. However, since ν v i is not part of (22) and thus uncontrolled by DERs, it will not affect (23). This result shows that internalizing the effect of stochasticity on voltage limits, which are enforced by the DSO and by producers, will prevent the existence of a competitive equilibrium enforced by Theorem 1 and, in this case, balancing participation price γ must be adjusted to reflect this difference between the decision-making process of the DSO and controllabe DERs.…”

Section: B Dlmps With Chance-constrained Voltage Limitsmentioning

confidence: 90%

“…The need to consider stochasticity of renewable generation resources in the price formation process is recognized for transmission (wholesale) electricity pricing, e.g. [13]- [16], [21], [22], but there is no framework for stochasticity-cognizant pricing in emerging distribution markets.…”

Section: Setsmentioning

confidence: 99%

“…The models in [17], [23]- [25] improve compliance with distribution system limits at a moderate increase in operating costs. However, with the exception of our prior work in [22], their application for electricity pricing has not been considered. This paper fills this gap and derives stochasticity-cognizant DLMPs using the chance constrained framework.…”

Section: Setsmentioning

confidence: 99%

“…These functions depend on whether constraints in (22) are binding or not. Since (22) has two inequality constraints, we consider the following four cases: 1) δ +, * i = δ −, * i = 0: When (22) has no binding constraints, it follows from (23a) and (23b) that:…”

Section: Dlmps With Chance-constrained Limits a Dlmps With Chanmentioning

confidence: 99%

See 3 more Smart Citations

Distribution Electricity Pricing Under Uncertainty

Mieth

Dvorkin

2020

IEEE Trans. Power Syst.

Self Cite

View full text Add to dashboard Cite

Distribution locational marginal prices (DLMPs) facilitate the efficient operation of low-voltage electric power distribution systems. We propose an approach to internalize the stochasticity of renewable distributed energy resources (DERs) and risk tolerance of the distribution system operator in DLMP computations. This is achieved by means of applying conic duality to a chance-constrained AC optimal power flow. We show that the resulting DLMPs consist of the terms that allow to itemize the prices for the active and reactive power production, balancing regulation, and voltage support provided. Finally, we prove the proposed DLMPs constitute a competitive equilibrium, which can be leveraged for designing a distribution electricity market, and show that imposing chance constraints on voltage limits distorts the equilibrium.

show abstract

Section: B Dlmps With Chance-constrained Voltage Limitsmentioning

confidence: 90%

Section: Setsmentioning

confidence: 99%

Section: Setsmentioning

confidence: 99%

Section: Dlmps With Chance-constrained Limits a Dlmps With Chanmentioning

confidence: 99%

See 2 more Smart Citations

Distribution Electricity Pricing Under Uncertainty

Mieth

Dvorkin

2020

IEEE Trans. Power Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Proof. See our previous work in [24]. In other words, Theorem 1 establishes that dual variables λ, χ, and γ represent prices for energy, reserve, and commitment allocations that attain the least-cost solution and support a market equilibrium, i.e., no generator has any incentive to deviate from the solution of (8).…”

Section: A Pricing With Chance Constraints Via Lp Dualitymentioning

confidence: 94%

A Chance-Constrained Stochastic Electricity Market

Dvorkin

2020

IEEE Trans. Power Syst.

Self Cite

View full text Add to dashboard Cite

Efficiently accommodating uncertain renewable resources in wholesale electricity markets is among the foremost priorities of market regulators in the US, UK and EU nations. However, existing deterministic market designs fail to internalize the uncertainty and their scenario-based stochastic extensions are limited in their ability to simultaneously maximize social welfare and guarantee non-confiscatory market outcomes in expectation and per each scenario. This paper proposes a chance-constrained stochastic market design, which is capable of producing a robust competitive equilibrium and internalizing uncertainty of the renewable resources in the price formation process. The equilibrium and resulting prices are obtained for different uncertainty assumptions, which requires using either linear (restrictive assumptions) or second-order conic (more general assumptions) duality in the price formation process. The usefulness of the proposed stochastic market design is demonstrated via the case study carried out on the 8-zone ISO New England testbed.1 Current deterministic US markets are also not revenue-adequate, but use out-of-market corrections and uplift payments to retain market participants. arXiv:1906.06963v2 [eess.SY]

show abstract

A locational marginal price for frequency balancing operations in regulation markets

Brooks

Lesieutre

2022

Applied Energy

View full text Add to dashboard Cite

Pricing Chance Constraints in Electricity Markets

Cited by 24 publications

References 4 publications

Distribution Electricity Pricing Under Uncertainty

Distribution Electricity Pricing Under Uncertainty

A Chance-Constrained Stochastic Electricity Market

A locational marginal price for frequency balancing operations in regulation markets

Contact Info

Product

Resources

About