Integration of wind generation uncertainties into frequency dynamic constrained unit commitment considering reserve and plug in electric vehicles

Mousavi-Taghiabadi, S. Mohammadreza; Sedighizadeh, Mostafa; Zangiabadi, Mansoureh; Fini, Alireza Sheikhi

doi:10.1016/j.jclepro.2020.124272

Cited by 27 publications

(13 citation statements)

References 41 publications

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“…T A B L E 4 Expected profit obtained along with respective probability considering all types of power sources for all 10 scenarios in a 10-unit test system, a 24 It is visible from this Figure that the SOC levels of batteries are within the limits that is, 30% lower and 95% upper limit.…”

Section: Test Casementioning

confidence: 99%

“…In these research publications, information related to RESs included in UC problems is provided. In references, [20][21][22][23][24] the penalty cost of RESs and discharging cost of BESSs & EVs have not been taken into consideration. In references, [25][26][27] it was established that the impact of the charging/discharging behaviour of PHEVs and BESSs is to reduce the system peak demand in the distribution system.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic profit‐based unit commitment problem considering renewable energy sources with battery storage systems and plug‐in hybrid electric vehicles

Kumar

Naresh

Sharma

2022

Intl J of Energy Research

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The paper presents a novel methodology based on a BARON solver in general algebraic modeling system (GAMS) computational environment to solve the stochastic profit-based unit commitment (SPBUC) problem in the deregulated energy market. The profit is considered as the difference between the total revenue collected by selling energy in the competitive energy market and the production cost of energy. The concepts of the deregulated energy market like market clearing price and market reserve price are employed for the determination of the expected revenue. Energy storage systems and electric vehicles have the benefit to act as load as well as generating sources during the uncertain and intermittent nature of renewable energy sources (RES). Thus, the RES in combination with battery energy storage systems and plug-in hybrid electric vehicles (PHEVs) are modelled and included in the present study. The Monte Carlo Simulation technique is employed to deal with the uncertain nature of wind speed, solar irradiance and PHEVs. First, the proposed solution methodology is tested on a 10-unit thermal system with and without valve point loading wherein it has provided an enhanced profit of 1.28% when compared with existing results reported in published literature. Secondly, the comparative results obtained by solving the SPBUC problem integrated with RES with different solvers available in GAMS computational environment demonstrate that the BARON solver outperformed the other solvers in terms of computation time and expected profit attained.

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Section: Test Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic profit‐based unit commitment problem considering renewable energy sources with battery storage systems and plug‐in hybrid electric vehicles

Kumar

Naresh

Sharma

2022

Intl J of Energy Research

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“…Ref. [21] formulates an EV charging/ discharging schedule for regulating the primary frequency and dynamic grid support. Ref.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic peer to peer energy trading among charging station of electric vehicles based on blockchain mechanism

2022

Self Cite

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Fossil-fuelled vehicles are being replaced by electric vehicles (EVs) around the world due to environmental pollution and high fossil fuel price. On the one hand, the electrical grid is faced with some challenges when too many EVs are improperly integrated. On the other hand, using massive unexploited capacity of the battery storage in too many EVs makes these challenges to opportunities. This unused capacity can be employed for the grid ancillary services and trading peer-to-peer (P2P) energy. However, the preference of EV users is one of the most important factors, which has to be considered within the scheduling process of EVs. Therefore, this paper proposes a stochastic model for EVs bidirectional smart charging taking into account the preferences of EV users, P2P energy trading, and providing ancillary services of the grid based on blockchain mechanism. Considering the preferences of EV users makes the proposed scheduling model adaptive against changing operating conditions. The presented model is formulated as an optimisation problem aiming at optimal management of EV battery state of charge and energy placement of several services considering the provision of ancillary services and contributing to P2P transactions. To evaluate the proposed model, real-world data collected from Tehran city are used as input data of simulation. Numerical results demonstrate the efficacy of the presented model. Simulation results show that considering the preferences of EV users in the proposed model can enhance the total income provided by the EV energy-planning model such that it could balance the charging cost. Moreover, this advanced user-based smart charging model increases P2P energy transactions amongst EVs and raises the ancillary services facility to the grid.

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“…This study has assumed that frequency deviation is a linear function of the RoCoF, and has added it as a constraint to the RUC problem. In [13], a reformulation linearization technique is employed to linearize the frequency nadir limit equation. To address the uncertainties of wind generation, an improved interval unit commitment is used.…”

Section: Introductionmentioning

confidence: 99%

Robust frequency constrained uc using data driven logistic regression for island power systems

Rajabdorri

Lobato

Sigrist

2022

IET Generation Trans & Dist

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In the current practice of short-term power scheduling, online power reserves are used to address generation mismatches and contingencies. Neither online inertia nor the speed of the committed units is considered in the scheduling process. With the increasing injection of uncertain renewable energy sources, this practice is starting to fall short, especially in island power systems, where the primary frequency response is already scarce, and any contingency leads to potentially poor frequency response. This paper introduces a data-driven linear constraint to improve the post-fault frequency quality in island power systems. A coherent initial data-set is obtained by simulating the system frequency response of single outages. Then logistic regression is employed as a predictive analytic procedure to differentiate the acceptable and unacceptable incidents. To compare the conventional methods with the proposed approach and also to handle the uncertain nature of renewable energy generation, an adaptive robust unit commitment formulation is utilized. Results for the island power system of La Palma show that depending on the chosen cut-point on the logistic regression estimation the proposed method can improve the frequency response quality of the system while reducing the operation costs.

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Integration of wind generation uncertainties into frequency dynamic constrained unit commitment considering reserve and plug in electric vehicles

Cited by 27 publications

References 41 publications

Stochastic profit‐based unit commitment problem considering renewable energy sources with battery storage systems and plug‐in hybrid electric vehicles

Stochastic profit‐based unit commitment problem considering renewable energy sources with battery storage systems and plug‐in hybrid electric vehicles

Stochastic peer to peer energy trading among charging station of electric vehicles based on blockchain mechanism

Robust frequency constrained uc using data driven logistic regression for island power systems

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