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Lightning attachment procedure optimization algorithm for nonlinear non-convex short-term hydrothermal generation scheduling

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Abstract

Short-term hydrothermal scheduling (STHS) is considered an important problem in the field of power system economics. The solution of this problem gives the hourly output of power generation schedule of the available hydro and thermal power units, which leads to minimization of the total fuel cost of thermal units for a given period of a time. The optimal generation of STHS is considered as a complicated and nonlinear optimization problem with a set of equality and inequality constraints such as the valve point loading effect of thermal units, the power transmission loss and the load balance. This paper proposes lightning attachment procedure Optimization (LAPO) algorithm for solving the nonlinear non-convex STHS optimization problem in order to minimize the operating fuel cost of thermal units with satisfying the operating constraints of the system. The performance of LAPO algorithm is validated using three different test systems considering the valve point loading effects of thermal units and the power transmission losses. The obtained results prove the effectiveness and superiority of LAPO algorithm for solving the STHS problem compared with other well-known optimization techniques.

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Abbreviations

F :

Total fuel cost from all thermal plants

N s :

Total number of thermal plants

T :

Total time of whole scheduling period

\( a_{i} ,b_{i} , c_{i} \) :

Power generation coefficients of thermal plant

\( P_{si}^{t} \) :

Output power generation from thermal plant

\( d_{i} ,e_{i} \) :

Coefficients of the valve point effects of the thermal plant

\( P_{si}^{\hbox{min} } \) :

Lower power generation limit of thermal plant

\( V_{hj}^{t} \) :

Reservoir storage volume of hydropower plant jth at a period of time t

\( I_{hj}^{t} \) :

External inflow to reservoir jth at a period of time t

\( Q_{hj}^{t} \) :

Water discharge amount of hydropower plant j at a period of time t

S :

Spillage discharge rate of reservoir jth at time interval t

\( R_{uj} \) :

Number of upstream hydropower plant

\( N_{h} \) :

Number of hydropower plant

\( P_{D}^{t} \) :

Power demand at a period of time t

\( P_{hj}^{t} \) :

Power generation of hydropower plant j at a period of time t

\( P_{L}^{t} \) :

Power transmission loss of the system at a period of time t

\( V_{hj}^{\hbox{min} } \) :

Minimum storage volume of hydro plant j

\( V_{hj}^{\hbox{max} } \) :

Maximum storage volume of hydro plant j

\( Q_{hj}^{\hbox{min} } \) :

Minimum water discharge of hydro plant j

\( Q_{hj}^{\text{Max}} \) :

Maximum water discharge of hydro plant j

\( P_{hj}^{\hbox{min} } ,P_{hj}^{\hbox{max} } \) :

Minimum and maximum power generation of hydro plant j

\( P_{si}^{\hbox{min} } ,P_{si}^{\hbox{max} } \) :

Minimum and maximum power generation of thermal plant i

\( V_{hj}^{\text{begin}} ,V_{hj}^{\text{end}} \) :

Initial and final reservoir storage volumes of hydropower plant j

\( V_{hj}^{T} \) :

Reservoir storage of hydro plant j at a period of time from (0 to 24)

STHS:

Short-term hydrothermal scheduling

LAPO:

Lightning attachment procedure optimization

VPL:

Valve point loading

LP:

Linear programming

NLP:

Nonlinear programming

DP:

Dynamic programming

GS:

Gradient search

GA:

Genetic algorithm

EP:

Evolutionary programming

DE:

Differential evolution

PSO:

Particle swarm optimization

IPSO:

Improved particle swarm optimization

MAPSO:

Modified adaptive PSO

SSPSO:

Small population-based particle swarm optimization

ABC:

Artificial bee colony

LR:

Lagrange relaxation

IDE:

Improved differential evolution

FAPSO:

Fuzzy adaptive particle swarm optimization

RCGA:

Real-coded genetic algorithm

HIS:

Improved harmony search

RCGA-IMM:

Real-coded genetic algorithm based on improved Mühlenbein mutation

CPSO:

Couple-based particle swarm optimization

TLBO:

Teaching learning-based optimization

ACABC:

Adaptive chaotic artificial bee colony

MDNLPSO:

Modified dynamic neighborhood learning-based particle swarm optimization

RCGA–AFSA:

Hybrid of real-coded genetic algorithm and artificial fish swarm algorithm

ORCCRO:

Oppositional real-coded chemical reaction based optimization

DRQEA:

Differential real-coded quantum-inspired evolutionary algorithm

MHDE:

Modified hybrid differential evolution

ACDE:

Adaptive chaotic differential evolution

ALO:

Ant lion optimization

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, South Valley University, Qena, Egypt

    Maha Mohamed & Abdel-Raheem Youssef

  2. Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswân, 81542, Egypt

    Salah Kamel

  3. Department of Electrical Engineering, Faculty of Engineering, Sohag University, Sohâg, Egypt

    Mohamed Ebeed

  4. State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing, China

    Salah Kamel

Authors
  1. Maha Mohamed
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  2. Abdel-Raheem Youssef
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  3. Salah Kamel
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  4. Mohamed Ebeed
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Correspondence to Salah Kamel.

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Mohamed, M., Youssef, AR., Kamel, S. et al. Lightning attachment procedure optimization algorithm for nonlinear non-convex short-term hydrothermal generation scheduling. Soft Comput 24, 16225–16248 (2020). https://doi.org/10.1007/s00500-020-04936-2

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