Distribution Electricity Pricing Under Uncertainty

Mieth, Robert; Dvorkin, Yury

doi:10.1109/tpwrs.2019.2954971

Cited by 59 publications

(39 citation statements)

References 40 publications

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“…There are several works that model AC OPF in a stochastic setting, e.g., through chanceconstraints in centralized control [29] and in data-driven approaches [30] that emphasize on the operational aspect. A chance-constrained AC OPF formulation is also employed in [31], which accounts for small-scale generators as controllable DERs, while treating all behind-the-meter DER as uncontrollable, and includes pricing considerations with chanceconstrained generation and voltage limits. On the other hand, [6] is the first work that refers to reserves in the context of distribution network marginal pricing.…”

Section: Discussionmentioning

confidence: 99%

Optimal Distributed Energy Resource Coordination: A Decomposition Method Based on Distribution Locational Marginal Costs

Andrianesis¹,

Caramanis²,

Li³

2021

Preprint

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In this paper, we consider the day-ahead operational planning problem of a radial distribution network hosting Distributed Energy Resources (DERs) including rooftop solar and storage-like loads, such as electric vehicles. We present a novel decomposition method that is based on a centralized AC Optimal Power Flow (AC OPF) problem interacting iteratively with self-dispatching DER problems adapting to real and reactive power Distribution Locational Marginal Costs. We illustrate the applicability and tractability of the proposed method on an actual distribution feeder, while modeling the full complexity of spatiotemporal DER capabilities and preferences, and accounting for instances of non-exact AC OPF convex relaxations. We show that the proposed method achieves optimal Grid-DER coordination, by successively improving feasible AC OPF solutions, and discovers spatiotemporally varying marginal costs in distribution networks that are key to optimal DER scheduling by modeling losses, ampacity and voltage congestion, and, most importantly, dynamic asset degradation.

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Section: Discussionmentioning

confidence: 99%

Optimal Distributed Energy Resource Coordination: A Decomposition Method Based on Distribution Locational Marginal Costs

Andrianesis¹,

Caramanis²,

Li³

2021

Preprint

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“…As an attempt to increase spatiotemporal granularity of tariffs, Caramanis et al [22] proposed to introduce DLMPs that would internalize these network peculiarities and dynamically changing demand conditions in the price formation process (similarly to wholesale locational marginal prices), thus improving pricing fidelity. The DLMPs have been shown to accurately reflect the physics of AC power flows in distribution systems, [21], and can be extended to accommodate uncertain nodal injections, [23]. However, in practice, DLMPs have not been implemented yet, in part due to lacking advanced metering infrastructure and socio-economic implications that granular electricity prices may cause, [24].…”

Section: Distribution System With the P2p Platformmentioning

confidence: 99%

A P2P-Dominant Distribution System Architecture

Kim

Dvorkin

2020

IEEE Trans. Power Syst.

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171

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Peer-to-peer interactions between small-scale energy resources exploit distribution network infrastructure as an electricity carrier, but remain financially unaccountable to electric power utilities. This status-quo raises multiple challenges. First, peer-to-peer energy trading reduces the portion of electricity supplied to end-customers by utilities and their revenue streams. Second, utilities must ensure that peer-to-peer transactions comply with distribution network limits. This paper proposes a peer-to-peer energy trading architecture, in two configurations, that couples peer-to-peer interactions and distribution network operations. The first configuration assumes that these interactions are settled by the utility in a centralized manner, while the second one is peer-centric and does not involve the utility. Both configurations use distribution locational marginal prices to compute network usage charges that peers must pay to the utility for using the distribution network.

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“…Typically, ω u is assumed to be zero-mean and normally distributed, [3], [4], [9], [11]- [13], [17]. However, in practice, empirical measurements of solar and wind power forecast errors are often asymmetric and can not be captured well by a normal distribution, [14], [15].…”

Section: Asymmetric Chance-constrained Opfmentioning

confidence: 99%

“…See in [18,Section 6.6.]. As a result, assuming normally distributed (symmetric) forecast errors in combination with a symmetric balancing reserve policy as in [3], [4], [9], [11], [12], [17], [19]- [24] can lead to ineffective and inefficient operating decisions and electricity prices. Therefore, to adequately capture possible forecast error asymmetries and improve the efficiency of balancing reserve quantification and allocation, this paper explicitly models negative and positive forecast errors (i.e., real-time energy deficit and surplus, respectively) and proposes an asymmetric, node-to-node balancing reserve policy.…”

Section: Asymmetric Chance-constrained Opfmentioning

confidence: 99%

Electricity and Reserve Pricing in Chance-Constrained Electricity Markets with Asymmetric Balancing Reserve Policies

Gonzalez-Castellanos,

Hinneck,

Mieth

et al. 2021

Preprint

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Recently, chance-constrained stochastic electricity market designs have been proposed to address shortcomings of scenario-based stochastic market designs. In particular, the use of chance-constrained market-clearing avoids trading off inexpectation and per-scenario characteristics and yields unique energy and reserves prices. However, current formulations rely on symmetric control policies based on the aggregated system imbalance, which restricts balancing reserve providers in their energy and reserve commitments. This paper extends existing chanceconstrained market-clearing formulations by leveraging node-tonode and asymmetric balancing reserve policies and deriving the resulting energy and reserve prices. The proposed node-tonode policy allows for relating the remuneration of balancing reserve providers and payment of uncertain resources using a marginal cost-based approach. Further, we introduce asymmetric balancing reserve policies into the chance-constrained electricity market design and show how this additional degree of freedom affects market outcomes.

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Distribution Electricity Pricing Under Uncertainty

Cited by 59 publications

References 40 publications

Optimal Distributed Energy Resource Coordination: A Decomposition Method Based on Distribution Locational Marginal Costs

Optimal Distributed Energy Resource Coordination: A Decomposition Method Based on Distribution Locational Marginal Costs

A P2P-Dominant Distribution System Architecture

Electricity and Reserve Pricing in Chance-Constrained Electricity Markets with Asymmetric Balancing Reserve Policies

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