Abstract
In the present paper, the optimum generation scheduling problem of deregulated power system is solved considering the non-smooth and non-convex generator fuel cost characteristics using the clustered adaptive teaching–learning-based optimization (CATLBO) technique. In CATLBO technique, the entire class is separated into many sections and assigned different teacher to each section depending on the performance of that particular section. This sectioning of the class makes proposed algorithm less prone to trapping in local optima and more robust. In this paper, three different objective functions are formulated, and they are total generation cost minimization considering the practical constraints, system loss minimization and L-index/ voltage stability enhancement index (VSEI). In this optimization problem, the generator active power outputs, generator bus voltage magnitudes, transformer tap ratios and bus shunt susceptances are selected as the control variables including the various equality and inequality constraints. The effectiveness and suitability of the proposed algorithm is examined on standard IEEE 30 bus, 57 bus, 118 bus and 300 bus systems, and the simulation results obtained using the proposed algorithm are also compared with many other optimization techniques reported in the literature.
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Abbreviations
- \(a_{i}\), \(b_{i}\), \(c_{i}\) :
-
Fuel cost coefficients of ith thermal generator
- \(d_i\), \(e_i\) :
-
Generator cost coefficients considering the valve point loading (VPL) effects
- \(G_{ij}\), \(B_{ij}\) :
-
Transfer conductance and susceptance between buses i and j
- n :
-
Total number of buses
- \(N_\mathrm{G}\) :
-
Total number of generators
- \(N_\mathrm{T}\) :
-
Number of transformers
- \(N_{\mathrm{sh}}\) :
-
Number of shunts
- \(P_{Gi}\) :
-
Scheduled power output from the ith generator (in MWs)
- \(P_{Di}\) :
-
Active power demand
- \(Q_{Di}\) :
-
Reactive power demand
- \(V_{Di}\) :
-
Load bus voltage magnitude
- \(V_{i}\), \(V_{j}\) :
-
Voltage magnitudes at bus i and bus j
- \(z_i\) :
-
Number of prohibited operating zones (POZs) of ith generating unit
- \(P_{Gi,k}^{l}\), \(P_{Gi,k}^{u}\) :
-
Lower and upper bounds of the kth prohibited operating zone (POZ) of ith generating unit
- \(Q_{Gi}\) :
-
Reactive power generation at bus i
- \(P_{\mathrm{loss}}\) :
-
Total power losses in the system
- \(Q_{Di}\) :
-
Reactive power load demand at bus i
- \(S_{ij}\) :
-
Line flow (in MVA) between the bus i and bus j
- \(S_{ij}^{\max }\) :
-
Thermal limit (in MVA) of the transmission line between bus i and bus j
- \(\delta _{i}\), \(\delta _{j}\) :
-
Voltage phase angles in radians at buses i and j
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Acknowledgements
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B0186-16-1001. Form factor-free Multi-input and output Power Module Technology for Wearable Devices).
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Appendix
Appendix
The generator fuel cost coefficients including the VPL and POZs effects for IEEE 30 bus system are reported in Table 10.
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Surender Reddy, S. Clustered adaptive teaching–learning-based optimization algorithm for solving the optimal generation scheduling problem. Electr Eng 100, 333–346 (2018). https://doi.org/10.1007/s00202-017-0508-4
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DOI: https://doi.org/10.1007/s00202-017-0508-4