Use of smart grid based wind resources in isolated power systems

Khasanzoda, Nasrullo; Safaraliev, Murodbek; Zicmane, Inga; Beryozkina, Svetlana; Rahimov, Jamshed; Ahyoev, Javod

doi:10.1016/j.energy.2022.124188

Cited by 21 publications

(11 citation statements)

References 26 publications

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“…Considering the voluminous data acquired from all these units, controlling and managing these power sources becomes complicated and challenging [5]. The papers also discussed the hybridization of renewables [24][25][26][27] and flexible sources in the form of a VPP. Moreover, some of the references attempted to provide a modelling of uncertainties by adopting stochastic programming.…”

Section: Contributionsmentioning

confidence: 99%

“…The limitations applied to the ESS are presented in Equations ( 21)- (25), showing the charge and discharge limits ( 21) and ( 22) [55][56], energy storage in the ESS (23), the initial energy of the ESS (24) and the energy limit, (25) [57]. Additionally, the VPP power limit is shown by Equations ( 26) and (27), where constraint (26) expresses the maximum active power output of the VPP. Constraint (27) presents the maximum power consumed by the VPP.…”

Section: Vpp Constraintsmentioning

confidence: 99%

“…Additionally, the VPP power limit is shown by Equations ( 26) and (27), where constraint (26) expresses the maximum active power output of the VPP. Constraint (27) presents the maximum power consumed by the VPP. Finally, up/down reserves supplied by the VPP are expressed by Equations ( 28) and ( 29), which are always greater than zero as per Equation (30).…”

Section: Vpp Constraintsmentioning

confidence: 99%

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Network‐constrained unit commitment‐based virtual power plant model in the day‐ahead market according to energy management strategy

Pirouzi

2023

IET Generation Trans & Dist

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Energy management of a virtual power plant (VPP) that consists of wind farm (WF), energy storage systems and a demand response program is discussed in the present study. The introduced strategy is realized at the electrical power transmission level and takes into account the collaboration between VPPs in day‐ahead energy and reserve markets. One notable feature of the proposed strategy is attempting to make the revenue of VPPs close to the operating cost of generating units as much as possible. The objective function is subjected to the network‐constrained unit commitment model, up and down reserve requirements and the proposed VPP constraints. This method is taking into account the uncertainty in system and VPP loads, day‐ahead market energy and reserve price and WF power generation. This strategy is applied as hybrid stochastic‐robust scheduling to the VPP at the electrical power transmission level, where the scenario‐based stochastic programming models the uncertainty of day‐ahead market prices, and the bounded uncertainty‐based robust optimization has been adopted to model the uncertainties related to the load and WF power. Scheme has been tested on IEEE systems. According to the obtained results, the proposed coordination of VPPs in the mentioned markets demonstrates capability of suggested strategy.

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Section: Contributionsmentioning

confidence: 99%

Section: Vpp Constraintsmentioning

confidence: 99%

Section: Vpp Constraintsmentioning

confidence: 99%

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Network‐constrained unit commitment‐based virtual power plant model in the day‐ahead market according to energy management strategy

Pirouzi

2023

IET Generation Trans & Dist

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“…Uncertainties of the problem are the amount of demand, output power of RESs [32][33][34][35][36], and EVs parameters. The unscented transformation (UT)-based stochastic optimization has been adopted to provide a proper modelling of the mentioned uncertain parameters.…”

Section: Contributionsmentioning

confidence: 99%

“…Constraints and limitations of the problem are AC optimal power flow, voltage security, operation of DERs and IBVPP. Uncertainties of the problem are the amount of demand, output power of RESs [32–36], and EVs parameters. The unscented transformation (UT)‐based stochastic optimization has been adopted to provide a proper modelling of the mentioned uncertain parameters.…”

Section: Introductionmentioning

confidence: 99%

Energy management of distribution network with inverter‐based renewable virtual power plant considering voltage security index

Azarhooshang,

Rezazadeh

2023

IET Renewable Power Gen

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Virtual power plants (VPP) with resources and storages are able to control the active power of the network. They are also connected to the network through an inverter, which is capable of controlling reactive power. Therefore, it is expected that the optimal use of inverter‐based VPP can play an effective role in improving the economic and technical status of the distribution network. So, the operation of a smart distribution system is presented in this paper by considering inverter‐based VPPs constrained to the operator's measures. The weighted sum of expected energy loss (EEL) and voltage security index (VSI) is minimized while considering AC optimal power flow equations, restrictions of network's security, and operating model of the inverter‐based VPPs. Uncertainties with an origin of the amount of demand, renewable energy, and parameters of mobile energy storage are also discussed. The uncertainties are modelled using a stochastic optimization approach relying on the unscented transformation (UT). Evaluating inverter‐based VPP performance, providing models of flexible resources such as responsive loads and mobile storages, checking network voltage security status, and modelling uncertainties using the UT method are among the innovations of this study. According to the results, it is demonstrated that the technical situation of the distribution system is improved with the help of optimal management of the VPP. With energy management of the inverter‐based VPP, the suggested design has succeeded to enhance the operating status (voltage security) of the system by approximately 33–73% (12%) in comparison to power flow studies.

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Building energy efficiency: using machine learning algorithms to accurately predict heating load

Ahmadi

2024

Asian J Civ Eng

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Use of smart grid based wind resources in isolated power systems

Cited by 21 publications

References 26 publications

Network‐constrained unit commitment‐based virtual power plant model in the day‐ahead market according to energy management strategy

Network‐constrained unit commitment‐based virtual power plant model in the day‐ahead market according to energy management strategy

Energy management of distribution network with inverter‐based renewable virtual power plant considering voltage security index

Building energy efficiency: using machine learning algorithms to accurately predict heating load

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