Optimal Decision-Making Strategy of an Electric Vehicle Aggregator in Short-Term Electricity Markets

Rashidizadeh‐Kermani, Homa; Anvari‐Moghaddam, Amjad; Guerrero, Josep M.

doi:10.3390/en11092413

Cited by 32 publications

(28 citation statements)

References 27 publications

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“…Here, the realizations of uncertainties are modeled using the scenario generation process based on Monte‐Carlo simulations (MCS) and roulette wheel mechanism (RWM). At first, the distribution function is separated into different intervals with different standard deviations . Then, each interval is related to a certain probability that is obtained by the probability density functions (PDF) .…”

Section: Stochastic‐based Decision‐making Problem Of Lsementioning

confidence: 99%

“…Here, the KKT conditions are applied to the lower‐level problem in to and are merged to the upper‐level. Also, by using duality theorem, the bilinear terms are substituted with their equivalent statements as follows:

Re v_{t, ω}^{D} = \frac{E_{t, ω}^{D_{D}}}{{\overset{⌢}{E}}_{t}^{D}} \sum_{ξ \in normalΞ} θ_{ξ}^{D} ([], - \sum_{\begin{array}{l} s \in S \\ s \neq 0 \end{array}} {\overset{truê}{E}}_{t}^{D} {Pr}_{s, t, ξ}^{D} L_{s, t, ξ}^{D} - \sum_{s \in S} \sum_{\begin{array}{l} s^{'} \in S \\ s^{'} \neq s \end{array}} {\overset{truê}{E}}_{t}^{D} R_{s, s^{'}}^{D} M_{s, s^{'}, t, ξ}^{D} + \sum_{s \in S} U_{s, t, ξ}^{int, D} e_{s, ξ}^{D} + w_{ξ}^{D}),

Re v_{t, ω}^{Ch} = \frac{E_{t, ω}^{D_{Ch}}}{{\overset{⌢}{E}}_{t}^{italicCh}} \sum_{ξ \in normalΞ} θ_{ξ}^{Ch} ([], - \sum_{\begin{array}{l} s \in S \\ s \neq 0 \end{array}} {\overset{truê}{E}}_{t}^{Ch} {Pr}_{s, t, ξ}^{Ch} L_{s, t, ξ}^{Ch} - \sum_{s \in S} \sum_{\begin{array}{l}  \end{array}})

…”

Section: The Proposed Decision‐making Formulationmentioning

confidence: 99%

“…19 Then, each interval is related to a certain probability that is obtained by the probability density functions (PDF). 19 Each scenario vector includes the information of electricity market, loads of customers, PEVs charging and discharging power, and the prices offered by the rivals. Then, a specified number of the probable scenarios are chosen precisely using K-means algorithm.…”

Section: Stochastic-based Decision-making Problem Of Lsementioning

confidence: 99%

“…Here, the KKT conditions are applied to the lower-level problem in (9) to (18) and are merged to the upper-level. Also, by using duality theorem, 19 the bilinear terms are substituted with their equivalent statements as follows:…”

Section: Combination Of Upper and Lower Levelsmentioning

confidence: 99%

“…Rashidizadeh‐Kermani et al have partly addressed the issue by proposing a stochastic bi‐level approach for the EV aggregator in order to participate in short‐term electricity market considering the preferences of EV owners. However, discharge process of EVs and DR participants has not been studied in decision‐making process.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A stochastic bi‐level decision‐making framework for a load‐serving entity in day‐ahead and balancing markets

Rashidizadeh‐Kermani

Vahedipour‐Dahraie

Anvari‐Moghaddam

2019

Int Trans Electr Energ Syst

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Summary This paper investigates a stochastic bi‐level scheduling model for decision‐making of a load‐serving entity (LSE) in competitive day‐ahead (DA) and regulating markets with uncertainties. In this model, LSE as the main interacting player of the market sells electricity to end‐use customers and plug‐in electric vehicles (PEVs) to maximize its expected profit. Therefore, a two‐level decision‐making process with different objectives is considered to solve the problem. In one level, the objective is to maximize the LSE's profit by optimally scheduling of responsive loads and PEVs charging/discharging process, while in the other level, the payments of the customers and PEV owners should be minimized in a competitive market. In the proposed decision‐making process, to model the uncertainties, market prices, required energy of customers and PEVs, and the rival LSEs' prices are considered as random variables. The bi‐level stochastic problem is then converted into a linear single‐level stochastic model with equilibrium constraints by using Karush‐Kuhn‐Tucker (KKT) optimality conditions as well as duality theory. A case study is implemented to indicate the applicability of the intended model. The applicability of the proposed model is tested on Nordic market and the results show that in a competitive market, the LSE can increase its revenue and attract more demand loads and PEV owners by offering more moderate prices.

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Section: Stochastic‐based Decision‐making Problem Of Lsementioning

confidence: 99%