Exploiting DERs’ Flexibility Provision in Distribution and Transmission Systems Interface

Pourghaderi, Niloofar; Fotuhi‐Firuzabad, Mahmud; Moeini‐Aghtaie, Moein; Kabirifar, Milad; Lehtonen, Matti

doi:10.1109/tpwrs.2022.3209132

Cited by 13 publications

(10 citation statements)

References 33 publications

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“…Refs. [20] and [21] exploit the DERs' flexibility capabilities while considering agents' preferences and hierarchical transactions in flexibility market. In [15][16][17] and [2], network operational constraints are not addressed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In [15][16][17] and [2], network operational constraints are not addressed. Moreover [15][16][17], and [19] do not consider DERs uncertainties in their models [21], considers the load and renewable generation uncertainties using a statistical approach and [2,4,18] and [20] address the DERs' uncertainties using stochastic approach.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, ref. [21] considers the uncertainties using a statistical approach and [2,4,12,13,18] and [20] address the DERs' uncertainties using stochastic approach. As presented in Section 1.1, using the stochastic approach has some challenges such as the large number of scenarios for accurate modelling of the uncertainty sources and computational complexity, as well as difficulty to identify the uncertain parameters' PDFs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this regard, Equations ( 27) and ( 28) respectively represent the upward and downward flexibility power balance at the network buses by adjusting the TVPP's and the DERs' active powers. Since the power balance in the energy scheduling is satisfied in (2), considering the flexibility balance in ( 27) and (28), which satisfy the balance in power adjustment by providing flexibility products, guarantees that the power balance in flexibility market is preserved [21]. The TVPP's offered flexibility to market is considered in the equations for the point of common coupling.…”

Section: Second Stagementioning

confidence: 99%

“…The flexibility products are considered as upward and downward flexibility capabilities [21]; upward flexibility (UF) is the capability of increasing/decreasing the DERs’ production/consumption power from their initial energy dispatches, respectively. In contrast, downward flexibility (DF) is the capability of decreasing/increasing the DERs’ production/consumption power, respectively.…”

Section: System Model and Problem Descriptionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal energy and flexibility self‐scheduling of a technical virtual power plant under uncertainty: A two‐stage adaptive robust approach

Pourghaderi

Fotuhi‐Firuzabad

Moeini‐Aghtaie

et al. 2023

IET Generation Trans & Dist

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This paper presents a two‐stage adaptive robust optimization framework for day‐ahead energy and intra‐day flexibility self‐scheduling of a technical virtual power plant (TVPP). The TVPP exploits diverse distributed energy resources’ (DERs) flexibility capabilities in order to offer flexibility services to wholesale flexibility market as well as preserving the distribution network's operational constraints in the presence of DER uncertainties. The TVPP aims at maximizing its profit in energy and flexibility markets considering the worst‐case uncertainty realization. In the proposed framework, the first stage models the TVPP's participation strategy in day‐ahead energy market and determines the DERs’ optimal energy dispatch. The second stage addresses the TVPP's strategy in intra‐day flexibility market to determine the DERs’ optimal flexibility capability provision by adjusting their energy dispatch for the worst‐case realization of uncertainties. The uncertainty characteristics associated with photovoltaic units, electric vehicles, heating, ventilation and air conditioning systems, and other responsive loads as well as the transmission network's flexibility capability requests are considered using an adaptive robust approach. Adopting the duality theory, the model is formulated as a mixed‐integer linear programming problem and is solved using a column‐and‐constraint generation algorithm. This model is implemented on a standard test system and the model effectiveness is demonstrated.

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