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A slack-bus-independent loss sensitivity approach for optimal day-ahead generation scheduling

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Abstract

In a power system, the incurred transmission loss and the associated sensitivity factors are dependent on the selection of the slack bus and the dispatch. In this paper, a fast two-stage, hydro-thermal generation scheduling process, which is also inclusive of the system power loss, is proposed. A novel approach towards estimating power loss sensitivity factors is presented which is independent of the choice/location of the slack bus in the network. In addition to this, the approach identifies the loss incurred by the operation of different market players including the generation and distribution companies for penalizing. Mixed integer linear programming is used to model the said optimal day-ahead scheduling problem. The two-stage process employed is such that the first stage is applied only in the planning stage, while second is a fast responsive algorithm suitable for very short-term applications. A real network, Vietnam Power Grid, is used for testing this proposed approach, and the results obtained demonstrated that there is significant reduction in the electricity cost and total transmission loss.

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Abbreviations

Symbol:

Explanation

T :

Generation scheduling period

K :

Number of power plant

\(C_T\) :

Total amount paid by electricity buyers

\(Pr_k\) :

Contractual electricity price of power plant k

\(Q_k (t)\) :

Contractual energy generated at hour t by power plant k

Pm(t):

Electric price at hour t

\(Qm_k (t)\) :

Actual energy generated at hour t by power plant k

\(Csd_k (t)\) :

Shutdown cost of power plant k

\(Cst_k (t)\) :

Start-up cost of power plant k

GD(t):

Gross demand of system at hour t

ND(t):

Net demand at hour t

Loss(t):

Transmission loss at hour k

\(Qmax_k\) :

Maximum power of power plant k

\(Qmin_k\) :

Minimum power of power plant k

\(u_k (t)\) :

Status of power plant k at hour t, 1 if power plant is synchronized, 0 if vice versa

UR(t):

Power requirement for ramping up at hour t

DR(t):

Power requirement for ramping down at hour k

\(Emax_k\) :

Maximum energy available for hydro-power plant k at scheduling period because of long-term optimization

\(Ethmax_k\) :

Upper energy limit of thermal power plant k

\(Ethmin_k\) :

Lower energy limit of thermal power plant k

\(RU_k\) :

Ramping up per hour limit of thermal power plant k

\(RD_k\) :

Ramping down per hour limit of thermal power plant k

\(on_k (t)\) :

Variable presents state of power plant k at hour t, equal to 1 if start-up, 0 if not

\(off_k (t)\) :

Variable presents state of power plant k at hour t, equal to 1 if shutdown, 0 if not

\(Ton_k \) :

Minimum time thermal power plant k must synchronized before changing state

\(Toff_k\) :

Minimum time thermal power plant k must unsynchronized before changing state

\(\varDelta t\) :

Time for water flow from hydro-power plant k to h

\(WQ_k (t)\) :

Minimum energy requirement of hydro-power plant k at hour t, represented water discharge required by local government

\(Emax_{Ng} (t)\) :

Maximum total power at hour t of group of thermal power plant which is supplied by common gas system

\(T'_k\) :

Period power plant k must be synchronized due to grid security constraint

\(lmin_{ij}\) :

Minimum transfer capability from area i to area j

\(lmax_{ij}\) :

Maximum transfer capability from area i to area j

\(GD_i (t)\) :

Gross demand of area i at hour t

\(Q_{(k,i)} (t)\) :

Output of generation k in area i at hour t

\( \varDelta Loss\) :

Transmission loss change due to generation change

\( \varDelta Q_i\) :

Generation change of power plant i

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Acknowledgements

Authors would like to acknowledge the financial and technical support provided by NLDC Vietnam and Asian Institute of Technology, Thailand.

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Authors and Affiliations

  1. Department of Energy, Environment and Climate Change, School of Environment, Resources and Development, Asian Institute of Technology, Pathumthani, Thailand

    Nguyen Huy Phuoc & Weerakorn Ongsakul

  2. Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham University, Coimbatore, Tamil Nadu, India

    Nimal Madhu Manjiparambil

  3. Department of Electrical Engineering, National Institute of Technology Calicut, Kozhikode, Kerala, India

    Nikhil Sasidharan

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  1. Nguyen Huy Phuoc
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  2. Weerakorn Ongsakul
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  3. Nimal Madhu Manjiparambil
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  4. Nikhil Sasidharan
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Correspondence to Weerakorn Ongsakul.

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Phuoc, N.H., Ongsakul, W., Manjiparambil, N.M. et al. A slack-bus-independent loss sensitivity approach for optimal day-ahead generation scheduling. Electr Eng 104, 421–434 (2022). https://doi.org/10.1007/s00202-021-01297-3

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