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Security-Constrained Unit Commitment with Wind Energy Resource Using Universal Generating Function

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Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

Two evolutionary algorithms, namely, genetic algorithm (GA) and particle swarm optimisation (PSO), were used to solve constrained unit commitment problems in a power system. Renewable resource that is wind power was incorporated in the system. Given the fluctuating and intermittent nature of wind, different wind scenarios were considered. Wind power and its associated probabilities were derived using universal generating function (UGF). Constraints such as generation limit, power balance, minimum uptime and minimum downtime were considered. Transmission losses were taken into account, and in certain cases, security constraint was applied. GA and PSO were tested on IEEE 6-bus system and IEEE 30-bus system to confirm their effectiveness. The results were compared based on the total costs, and numerical results suggest that PSO was slightly better than the GA.

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Abbreviations

a :

Particle number

C 1 :

Cognitive parameter

C 2 :

Social parameter

COSTji:

Cost of i-th generator at j-th time period

D :

Dimension

D ij :

Shutdown cost for i-th generator at j-th time period

M:

Number of wind scenarios

MDTi:

Minimum downtime of i-th generator

MUTi:

Minimum uptime of i-th generator

N :

Number of thermal generators available for dispatching (i = 1, … N)

NW:

Numbers of wind turbines

P Dj :

Load demand at j-th time period

P ij :

Output of i-th generator during j-th time period

P i max :

Maximum generation capacity of i-th generator

P i min :

Minimum generation capacity of i-th generator

P jsce :

Probability of wind at j-th time period for scenario sce

P Lj :

System losses at j-th time period

P Rj :

Spinning reserve required at j-th time period

P wc :

Total power curtailed

P Wj :

Power produced by wind turbine

S.D.C:

Shutdown cost of the system

S.U.C:

Total startup cost of the system

Sce:

Scenarios of wind modelling

S ij :

Startup cost for i-th generator at j-th time period

t :

Current iteration

T :

Number of time periods making the schedule

T ij OFF :

Off time of unit i at j-th time period

T ij ON :

On time of unit i at j-th time period

U ij :

‘On/off’ status of i-th generator during j-th time period. ON = 1,OFF = 0

w :

Weight

W.C.C:

Total wind curtailment cost

W c :

Cost of curtailed wind power per MW for a unit NW

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Mauritius, Reduit, Mauritius

    Robert T. F. Ah King & Doorgesh Balgobin

Authors
  1. Robert T. F. Ah King
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  2. Doorgesh Balgobin
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Correspondence to Robert T. F. Ah King .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Punjabi University Patiala, Patiala, Punjab, India

    Mukhdeep Singh Manshahia

  2. Federal Scientific Agroengineering Center VIM, Moscow, Russia

    Valeriy Kharchenko

  3. Institute of Applied Mathematics, Middle East Technical University, Ankara, Türkiye

    Gerhard-Wilhelm Weber

  4. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Pandian Vasant

Appendix

Appendix

Table 8 Annual wind speed data
Full size table
Table 9 Load demand and wind profile
Full size table

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Ah King, R.T.F., Balgobin, D. (2023). Security-Constrained Unit Commitment with Wind Energy Resource Using Universal Generating Function. In: Manshahia, M.S., Kharchenko, V., Weber, GW., Vasant, P. (eds) Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-26496-2_13

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  • DOI: https://doi.org/10.1007/978-3-031-26496-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26495-5

  • Online ISBN: 978-3-031-26496-2

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