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A chaotic hybrid optimization technique for solution of dynamic generation scheduling problem considering effect of renewable energy sources

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Abstract

This research introduces a novel hybrid optimizer using two well-known metaheuristic algorithms, SMA and SCA. The suggested methodology was used to answer the problem of optimal dynamic generation scheduling for the thermal generation unit along with thermal unit integrated with renewable sources such as wind, solar, and electric vehicles. The problem is solved using a unique hybrid CSMA-SCA optimizer in three steps: first, the units are prioritized based on the average full load cost, and the unit scheduling solution is used without consideration of the many constraints that have an impact on the solutions. The second step is the establishment of a heuristic constraints repair mechanism, which forces previous solutions to comply with inescapable constraints. The third step is the implementation of an optimal power generation share allocation for all participating units. To model the stochastic behavior of wind speed and solar radiation, the Weibull probability distribution and Beta PDF functions are used. To avoid the algorithm from slipping into local minima and achieve a better balance between exploration and exploitation, a novel chaotic position updating method called Singer map-based position updating is proposed. The suggested method has proven effective in small-, medium-, and large-scale thermal power systems as well as thermal systems that integrate wind power. The extensive studies demonstrate that the CSMA-SCA methodology presented in this research outperforms most current methods in terms of producing high-quality solutions around global minima.

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Data availability

The authors certify that all the data generated or analyzed during this study (and its additional file) are available and may be found at the link given below. https://drive.google.com/file/d/1pMAJBW--BBxRHaZ0C0iq6W06cjjCWHOc/view?usp=sharing.

Code availability

Request for code should be made to corresponding author.

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Acknowledgments

No funding was available for this study. The authors state that they have no financial or non-financial interest in the topic or materials covered in this paper.

Author information

Authors and Affiliations

  1. Sant Longowal Institute of Engineering and Technology, Longowal, Sangrur, India

    Ashutosh Bhadoria & Sanjay Marwaha

Authors
  1. Ashutosh Bhadoria
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  2. Sanjay Marwaha
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Corresponding author

Correspondence to Ashutosh Bhadoria.

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Appendices

Appendix 1: Wind and solar power used in the article

Hour

Power (MW)

Hour

Power (MW)

 

Wind

Solar

 

Wind

Solar

1

148.4884

0

13

95.3485

104.9347

2

127.8895

0

14

77.6872

88.1518

3

120.6391

0

15

74.289

67.6923

4

114.2285

0

16

88.8189

42.9431

5

101.3937

0

17

109.2915

20.5279

6

101.6549

24.9491

18

41.9367

0

7

116.2773

48.8601

19

32.0173

0

8

117.4627

72.6721

20

8.531

0

9

126.8331

92.3087

21

12.8063

0

10

139.3862

106.0076

22

16.5485

0

11

160.8379

114.5426

23

22.0043

0

12

166.5372

111.5308

24

32.7779

0

Appendix 2: Wind power probability profile with respect to number of states

figure c

Appendix 3: Specification of wind turbines

Attribute

Value

Rated output power,\(P_{{{\text{rated}}}}\)

250 MW

Cut-in speed,\(v_{{{\text{cin}}}}\)

3 m/s

Nominal wind speed,\(v_{N}\)

12 m/s

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Bhadoria, A., Marwaha, S. A chaotic hybrid optimization technique for solution of dynamic generation scheduling problem considering effect of renewable energy sources. MRS Energy & Sustainability 10, 52–93 (2023). https://doi.org/10.1557/s43581-022-00050-y

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