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Cogent Engineering Volume 11, 2024 - Issue 1
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Electrical & Electronic Engineering

Metaphor-less Rao-3 and artificial neural network with parallel computing-based wheeling pricing in competitive power market

Abhishek Saxenaa Department of Electrical Engineering, Madhav Institute of Technology and Science, Gwalior, IndiaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-9803-4772View further author information
ORCID Icon,
Seema N. Pandeyb Department of Electrical Engineering, Dr. Bhim Rao Ambedkar Polytechnic College, Gwalior, IndiaView further author information
&
Shishir Dixita Department of Electrical Engineering, Madhav Institute of Technology and Science, Gwalior, IndiaView further author information
Article: 2340321 | Received 12 Aug 2023, Accepted 03 Apr 2024, Published online: 03 May 2024

Figures & data

Figure 1. RBFNN configuration.

Figure 1. RBFNN configuration.

Figure 2. Block diagram of research methodology.

Figure 2. Block diagram of research methodology.

Table 1. Clustering details for generators in IEEE-30 bus system.

Figure 3. Testing performance of ANN in IEEE-30 bus system (cluster 1).

Figure 3. Testing performance of ANN in IEEE-30 bus system (cluster 1).

Table 2. Wheeling Price prediction for Generators in IEEE-30 bus system (cluster 1).

Figure 4. Testing performance of ANN in IEEE-30 bus system (cluster 2).

Figure 4. Testing performance of ANN in IEEE-30 bus system (cluster 2).

Table 3. Wheeling price prediction for generators in IEEE-30 bus system (cluster 2).

Figure 5. Testing performance of ANN in IEEE-30 bus system (cluster 3).

Figure 5. Testing performance of ANN in IEEE-30 bus system (cluster 3).

Table 4. Wheeling price prediction for generators in IEEE-30 bus system (cluster 3).

Table 4. Summary of wheeling price prediction for generators.

Table 5. Clustering details for wheeling pricing of loads in IEEE-30 bus system.

Figure 6. Testing performance of ANN in IEEE-30 bus system (cluster 4).

Figure 6. Testing performance of ANN in IEEE-30 bus system (cluster 4).

Table 6. Wheeling price prediction for loads in IEEE-30 bus system (cluster 4).

Figure 7. Testing performance of ANN in IEEE-30 bus system (cluster 5).

Figure 7. Testing performance of ANN in IEEE-30 bus system (cluster 5).

Table 7. Wheeling price prediction for loads in IEEE-30 bus system (cluster 5).

Figure 8. Testing performance of ANN in IEEE-30 bus system (cluster 6).

Figure 8. Testing performance of ANN in IEEE-30 bus system (cluster 6).

Table 8. Wheeling price prediction for loads in IEEE-30 bus system (cluster 6).

Figure 9. Testing performance of ANN in IEEE-30 bus system (cluster 7).

Figure 9. Testing performance of ANN in IEEE-30 bus system (cluster 7).

Table 9. Wheeling price prediction for loads in IEEE-30 bus system (cluster 7).

Figure 10. Testing performance of ANN in IEEE-30 bus system (cluster 8).

Figure 10. Testing performance of ANN in IEEE-30 bus system (cluster 8).

Table 10. Wheeling price prediction for loads in IEEE-30 bus system (cluster 8).

Figure 11. Testing performance of ANN in IEEE-30 bus system (cluster 9).

Figure 11. Testing performance of ANN in IEEE-30 bus system (cluster 9).

Table 11. Wheeling price prediction for loads in IEEE-30 bus system (cluster 9).

Figure 12. Testing performance of ANN in IEEE-30 bus system (cluster 10).

Figure 12. Testing performance of ANN in IEEE-30 bus system (cluster 10).

Table 12. Wheeling price prediction for loads in IEEE-30 bus system (cluster 10).

Table 13. Parallel computing details for wheeling pricing in IEEE 30-bus system.

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