A Data-Driven Model of Virtual Power Plants in Day-Ahead Unit Commitment

Babaei, Sadra; Zhao, Chaoyue; Fan, Lei

doi:10.1109/tpwrs.2018.2890714

Cited by 96 publications

(38 citation statements)

References 37 publications

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“…To eliminate the max operator on the right‐hand side, the maximisation problem can be transformed into a minimisation problem based on dual theory, and the minimisation problem is equivalent to the existence of a feasible solution where the min operator can be neglected. Take constraint (37a) as an example, and we can get the dual problem of the right maximisation based on conic duality [33, 38] as follows:

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ min_{bold-italicψ} {\bar{w}}^{normal⊤} \overset{false¯}{bold-italicδ} - {\underline{w}}^{normal⊤} \tilde{δ} - {bold-italicμ}^{normal⊤} \overset{false¯}{bold-italicθ} - \frac{1}{2} {bold1}^{normal⊤} \tilde{θ} + \frac{1}{2} {bold1}^{normal⊤} \hat{θ} \\ - \sum_{t} \sum_{k \in [1 : t]} \sum_{l = k}^{t} {bold1}^{normal⊤} {bold-italicμ}_{l} {\bar{ρ}}_{k t} - \frac{1}{2} {bold1}^{normal⊤} \tilde{ρ} + \frac{1}{2} {bold1}^{normal⊤} \hat{ρ} \end{matrix}

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ {\overset{false~}{δ}}_{n t} - {\bar{δ}}_{n t} + {\bar{θ}}_{n t} + \sum_{k = 1}^{t} \sum_{l = t}^{T} {\bar{ρ}}_{k l} = {bold-italice}_{n t}^{normal⊤} (η - ({bold-italicb}^{normal⊤} {bold-italicX}^{bold-italicw})^{'}), \end{matrix}

…”

Section: Solution Methodologymentioning

confidence: 99%

“…However, there is an unrealistic assumption that non‐anticipativity is not considered in the second stage. In other words, it is assumed that the second‐stage dispatch decisions are optimised simultaneously with the disclosure of all uncertainty realisations in the beginning [33]. However, the uncertain wind power is revealed sequentially in practice and the dispatch decisions can only be made according to the uncertainty realisations up to current period, i.e.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The main parameters of MTs are given in Table 1, which are collected from relevant references [3, 33]. In addition, the start‐up (shut‐down) costs of three MTs are 3, 3, and 1.5, respectively, and the no‐load operation costs are 3, 6, and 1, respectively.…”

Section: Case Studiesmentioning

confidence: 99%

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Distributionally robust multi‐period energy management for CCHP‐based microgrids

Shi

Liang

Dinavahi

2020

IET Generation, Transmission & Distribution

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\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ min_{bold-italicψ} {\bar{w}}^{normal⊤} \overset{false¯}{bold-italicδ} - {\underline{w}}^{normal⊤} \tilde{δ} - {bold-italicμ}^{normal⊤} \overset{false¯}{bold-italicθ} - \frac{1}{2} {bold1}^{normal⊤} \tilde{θ} + \frac{1}{2} {bold1}^{normal⊤} \hat{θ} \\ - \sum_{t} \sum_{k \in [1 : t]} \sum_{l = k}^{t} {bold1}^{normal⊤} {bold-italicμ}_{l} {\bar{ρ}}_{k t} - \frac{1}{2} {bold1}^{normal⊤} \tilde{ρ} + \frac{1}{2} {bold1}^{normal⊤} \hat{ρ} \end{matrix}

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ {\overset{false~}{δ}}_{n t} - {\bar{δ}}_{n t} + {\bar{θ}}_{n t} + \sum_{k = 1}^{t} \sum_{l = t}^{T} {\bar{ρ}}_{k l} = {bold-italice}_{n t}^{normal⊤} (η - ({bold-italicb}^{normal⊤} {bold-italicX}^{bold-italicw})^{'}), \end{matrix}

…”

Section: Solution Methodologymentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

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Distributionally robust multi‐period energy management for CCHP‐based microgrids

Shi

Liang

Dinavahi

2020

IET Generation, Transmission & Distribution

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“…In [6], the "Big Bang Big Crunch" optimization method has been used to minimize the annual purchase of electricity in unbalanced distribution networks. Other works have applied stochastic optimization ( [7], [8]) and non-linear optimization programming ( [9], [10]). However, the most frequently applied optimization technique has been mixed-integer linear programming since it suits the characteristics of dispatch problems ( [11]- [17]).…”

Section: Introductionmentioning

confidence: 99%

“…The study in [7] has provided a combination of adaptive robust and stochastic optimization for VPP models that participate in dayahead and real-time electricity markets. In [9], the distributionally robust optimization approach has been proposed to determine the optimal values of parameters for the bidding strategy, such as capacity or cost curve. The authors of [10] have presented a fuzzy optimization technique to address the bidding problem and have achieved lower computation times with this method than with other deterministic and probabilistic methods.…”

Section: Introductionmentioning

confidence: 99%

A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation

Naval

Sánchez²,

Yusta

2020

Renewable Energy

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Volatility and sharp increases in the price of electricity are serious economic problems in the primary sector because they affect modernization investments for irrigation systems in Spain. This paper presents a new virtual power plant (VPP) model that integrates all available full-scale distributed renewable generation technologies. The proposed VPP operates as a single plant in the wholesale electricity market and aims to maximize profit from its operation to meet demand. Two levels of renewable energy integration in the VPP were considered: first, a wind farm and six hydroelectric power plants that inject the generated electricity directly to the distribution network, and second, on-site photovoltaic plants associated with each of the electricity supply points in the system that are designed to prioritize self-consumption. The proposed technicaleconomic dispatch model was developed as a mixed-integer optimization problem that determines the hourly operation of distributed large-scale renewable generation plants and on-site generation plants. The model was applied to real data from an irrigation system comprising a number of water pumping stations in Aragon (Spain). The results of the VPP model demonstrate the importance of the technical and economic management of all production facilities to significantly reduce grid dependence and final electricity costs.

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Comprehensive review of energy management strategies: Considering battery energy storage system and renewable energy sources

Onsomu,

Terciyanlı,

Yeşilata

2024

Engineering Reports

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The transformation of power system networks is slowly taking shape, the advent of interruptive technological platforms dealing with digitalization and real‐time trading of power has gained attention based on incorporation of more renewables into the grid. The stochastic nature of renewables pauses security of supply challenges and other related stability concerns, and for this reason efficient methods are investigated in this review to build an understanding of microgrid energy management system (MG‐EMS) and distribution‐based energy management strategies aimed at transforming the conventional grid network into smart grid network. In essence, propagating a technological shift to microgrids which have proven to be ideal distribution networks for residential and commercial loads, have become indispensable in handling distributed energy resources (DER), such as solar, PV, wind, battery energy storage systems (BESS) and small‐scale microgrids, for example in case of excess supply, energy storage system (ESS) has been formulated as a solution to curb excess supply and can offer ancillary services to the grid network. Within the perspective of electricity generation and distribution, microgrid control methodologies, distribution network (DN) management approaches and incumbent optimization strategies used to coordinate and manage grid‐level uncertainties are investigated. In addition, this study proposes distributionally robust optimization (DRO), to manage and mitigate risks associated to shortage or oversupply of power from RESs.

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A Data-Driven Model of Virtual Power Plants in Day-Ahead Unit Commitment

Cited by 96 publications

References 37 publications

Distributionally robust multi‐period energy management for CCHP‐based microgrids

Distributionally robust multi‐period energy management for CCHP‐based microgrids

A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation

Comprehensive review of energy management strategies: Considering battery energy storage system and renewable energy sources

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