Reliability constrained unit commitment in smart grid environment

Goleijani, Sassan; Ghanbarzadeh, Taraneh; Nikoo, F. Sadeghi; Moghaddam, Mohsen Parsa

doi:10.1016/j.epsr.2012.12.011

Cited by 31 publications

(17 citation statements)

References 16 publications

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“…2) Constrains: UC model usually contains power balance constraint, spinning reserve constraint, generation limits constraint, minimum on/off time constraint and network security constraint as follows [19].…”

Section: Uc Modelmentioning

confidence: 99%

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Unit Commitment Model in Smart Grid Environment Considering Carbon Emissions Trading

Zhang

Dai

et al. 2016

IEEE Trans. Smart Grid

150

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With the development of smart grid, demand-side resources (DSR) will play an increasingly important role in the power balance of supply and demand. In addition, the requirement of a low-carbon smart grid means some policy backgrounds, such as carbon emissions trading (CET), should not be ignored. Under these circumstances, it is a good idea to construct a novel unit commitment (UC) model. This paper proposes a model that not only takes advantage of various resources on the demand side, such as electric vehicles, demand response, and distributed generation, but also reflects the effects of CET on generation schedule. Then, an improved particle swarm optimization (IPSO) algorithm is applied to solve the problem. In numerical studies, we analyze the impacts of DSR and CET on the results of UC, respectively. In addition, two meaningful experiments are conducted to study the approaches to allocate emission quotas and the effects of price transmission mechanism. Index Terms-Carbon emission quotas, carbon emissions trading (CET), demand response (DR), distributed generation (DG), electric vehicle (EV), improved particle swarm optimization (IPSO), smart grid, unit commitment (UC). NOMENCLATURE Index bIndex of bus. i Index of generating unit. j Index of electric vehicle (EV). t Index of hour. Variables and Functions DGa t Distributed generation (DG) used by its owners at time t. DGb t Output of grid-connected DG at time t. DGC Total cost (TC) of DG. DRC TC of demand response (DR). Emission of unit i at time t. E V2G,t Emission of vehicle-to-grid (V2G) at time t. FC i Fuel cost of unit i. I i,t On/off status of unit i at time t. Iter Current number of iteration. pc Probability of crossover. P i,t Output of unit i at time t. pm Probability of mutation. SC i,t Start-up cost of unit i at time t. SoC t,j State of charge of EV j at time t. TC TC of unit commitment (UC). V2GC TC of V2G. V2G t Output of V2G at time t. V2G t,j Output of V2G of EV j at time t. X on i,t , X off i,t Duration of continuously on/off of unit i at time t. η t Penetration rate of DG at time t.

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Section: Uc Modelmentioning

confidence: 99%

“…Typically, particle swarm optimization (PSO) has been widely used in recent ten years owing to its good performance in convergence rate and solution precision [1], [19], [20]. However, the main disadvantage of PSO is that it may work out a local optimal solution instead of the global optimal solution.…”

Section: Index Bmentioning

confidence: 99%

Unit Commitment Model in Smart Grid Environment Considering Carbon Emissions Trading

Zhang

Dai

et al. 2016

IEEE Trans. Smart Grid

150

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“…PSO has been widely applied to solve the UC optimization problem [6,25,26]. But its principle drawback is that it tends to lost itself in locally optimal solution.…”

Section: Description Of the Modified Psomentioning

confidence: 99%

“…With the development of smart grid, elements in demand side, such as demand response (DR) and electric vehicle (EV), are able to play an increasingly important role in power system. Some researchers have considered them together with traditional generators in supply side [4][5][6]. DR and EV are flexible and low-carbon elements.…”

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confidence: 99%

A fuzzy chance-constrained program for unit commitment problem considering demand response, electric vehicle and wind power

Zhang

Han

et al. 2015

International Journal of Electrical Power & Energy Systems

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“…In addition to the aforementioned constraints in (2), (5), and (7), there are three common constraints in the UC model including power balance constraint, limitations of power output, and ramp rate constraint [30].…”

Section: Constraintsmentioning

confidence: 99%

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

Hua

Sun

et al. 2017

2017 International Smart Cities Conference (ISC2)

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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).

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Reliability constrained unit commitment in smart grid environment

Cited by 31 publications

References 16 publications

Unit Commitment Model in Smart Grid Environment Considering Carbon Emissions Trading

Unit Commitment Model in Smart Grid Environment Considering Carbon Emissions Trading

A fuzzy chance-constrained program for unit commitment problem considering demand response, electric vehicle and wind power

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

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