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Hydro-thermal scheduling under RE uncertainties using an improved cheetah optimization

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Abstract

To minimize total operating costs, emissions, and power losses, the optimum power generation and scheduling of renewable integrated hydro-thermal systems are one of the most significant objectives in short-term scheduling. The solution to these problems becomes more difficult with constraints and renewable uncertainties. The paper presents an improved cheetah optimizer (ICO) for solving the optimum wind-solar-hydro-thermal scheduling problem considering valve loading effects, ramp-rate limits, power loss, and prohibited operational zone constraints. The main objective is to optimize the total fuel cost and emissions for thermal power generators, where electric power can be fully harnessed from renewable generators. Different test systems are employed to evaluate the proposed ICO solution method’s performance. The proposed ICO solution method is compared with other algorithms like grey wolf optimizer, and particle swarm optimizer, in terms of optimal fuel costs, emissions, convergence success rate, and computation time. The test systems are incorporated with wind farms, solar farm, hydropower generators, and thermal power generators scheduled for 24-h, 1-h subintervals. The simulation solutions of the renewable integrated system have been acquired by ICO, CO, GWO, and PSO. The total generation cost obtained by ICO is 0.0698%, and 0.1514% lower than the cost obtained by GWO, and PSO respectively. The total power loss was minimized by 1.8554% and 7.4002%. The total emissions can be reduced to 25% with increasing penetration of renewable energy sources. It is realized from the comparison that the proposed ICO method has the potential to provide better-quality solutions.

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Data availability statement

Data will be made available by the corresponding author upon reasonable request.

Abbreviations

ABC:

Artificial bee colony algorithm

BWO:

Beluga whale optimization

CDF:

Cumulative distribution function

CO2 :

Carbon dioxide

CSA:

Cuckoo search algorithm

CSA:

Clonal selection algorithm

DE:

Differential evolution

ELD:

Economic load dispatch

ESCSDO:

Eagle-strategy supply–demand-based optimization algorithm

GA:

Genetic algorithm

GOA:

Grasshopper optimization algorithm

GWO:

Grey wolf optimization

ICO:

Improved cheetah optimization

LR:

Lagrangian relaxation

MILP:

Mixed integer linear programming

MSSA:

Multi-objective Salp swarm algorithm

MW:

Mega watt

NLP:

Nonlinear programming

OPF:

Optimal power flow

OWSHTS:

Optimum wind-solar-hydro-thermal scheduling

PDF:

Probability density function

PPSO:

Parallel particle swarm optimization

PSO:

Particle swarm optimization

POZs:

Prohibited operational zones

RRLs:

Ramp-rate limits

SCA:

Sine cosine algorithm

SDO:

Supply–demand-based optimization

SPV:

Solar photovoltaic

STHTSP:

Short-term hydro-thermal scheduling problem

TLBO:

Teaching learning-based optimization

UCF:

Underestimation cost function

UCP:

Unit commitment problem

VLEs :

Valve loading effects

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Authors and Affiliations

  1. Electrical Engineering Department, MNIT Jaipur, Jaipur, India

    Prahlad Mundotiya & H. P. Tiwari

  2. Electrical Engineering Department, Engineering College Bikaner, Bikaner, India

    Mahendra Bhadu

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Contributions

PM involved in software, data curation, simulation, writing—original draft, conceptualization, methodology, writing—original draft, writing—review and editing, and validation. MB involved in visualization, investigation, formal analysis, and writing—review and editing. HPT involved in visualization, supervision, investigation, formal analysis, and writing—review and editing.

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Correspondence to Prahlad Mundotiya.

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Appendix A

Appendix A

See Tables 

Table 11 Thermal cost coefficient and generation limits
Full size table

11,

Table 12 Thermal emission coefficient, RRLs, and POZs of generators
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12,

Table 13 Hydro-units coefficient
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13,

Table 14 Hydro-units coefficient, generating limits, and reservoir volume
Full size table

14,

Table 15 Wind cost coefficient and PDF parameters
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15 and

Table 16 Solar cost coefficient and PDF parameters
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16.

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Mundotiya, P., Bhadu, M. & Tiwari, H.P. Hydro-thermal scheduling under RE uncertainties using an improved cheetah optimization. Electr Eng (2024). https://doi.org/10.1007/s00202-023-02218-2

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