Unit commitment in achieving low carbon smart grid environment with virtual power plant

Abstract

Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-This paper proposes a novel unit commitment (UC) model under smart grid (SG) environment, which intends to strike a balance pursuing minimum carbon emissions for policy maker, minimum costs for generators and minimum payment bills for consumers. This leads to a multiobjective optimization problem (MOP) which can be solved through the multiobjective immune algorithm (MOIA). Therefore, the energy market scheduling problem considering low carbon smart grid environment can be analysed. The case studies are conducted to demonstrate the proposed model and present the allocation of power generations as well as the daily energy market scheduling results. It has been proved that the penetration of SG contributes to the mitigation of carbon emissions during the peak demand time by around 500 ton/h. It is also suggested that if the policy maker can provide appropriate monetary compensation for the deployment of SG technologies, generators will be encouraged to participate in the SG deployment.Index Terms-Smart grid (SG), unit commitment (UC), multiobjective optimization (MOP), virtual power plant (VPP), electric vehicle (EV), demand response (DR).

A new operation strategy of PEV parking and hydro storage in smart grid environment

“…Hua et al propose a novel scheduling model under a smart grid environment (Hua et al , 2017), which intends to strike a balance pursuing minimum carbon pollutions for the policymaker, minimum costs for generators and minimum payment bills for consumers. The case studies are conducted to demonstrate the proposed model and present the allocation of power generations, as well as the daily energy market scheduling results.…”

“…As a summary, according to literature (Yang et al , 2017; Tumuluru and Tsang, 2018; Howlader et al , 2018; Azari et al , 2018; Howlader et al , 2018; Goya et al , 2012; Kyoho et al , 2013; Goya et al , 2010; Imani et al , 2018; Shahinzadeh et al , 2017; Sadeghian and Wang, 2017; Saber and Venayagamoorthy, 2010; Saber and Venayagamoorthy, 2012; Zhang et al , 2015; Madzharov et al , 2014; Goleijani et al , 2013; Liu et al , 2012; Bahreyni et al , 2012; Yu et al , 2013; Khodayar et al , 2012; Abdollahi et al , 2012; Zhang et al , 2016; Kumar et al , 2017; Howlader et al , 2016; Hua et al , 2017; Peng et al , 2017; Deepak et al , 2019; Jain and Huddar, 2019), the impacts of PEV parking and hydro storage on smart grid scheduling are not studied (Table 1).…”

Stochastic environmental and economic dispatch of power systems with virtual power plant in energy and reserve markets

“…However, due to the system uncertainties caused by distributed energy sources and load predictions, there are opportunities in applying the stochastic approach to cope with system uncertainties. H. Wu et al [10] proposed stochastic programming methods for security constrained unit commitment to deal with uncertainties in renewable energy. The uncertainties of system intermittency and incidents were investigated by using frequency-constrained stochastic optimization model in [11].…”