Abstract
The optimal power flow (OPF) problem is very important issue in operation, planning and energy management of power systems. OPF analysis aims to find the optimal solution of system nonlinear algebraic equations with satisfying operational constraints. Economic, environmental and technical objectives are considered for multi-dimensions efficient energy management. These objectives involve the reduction of the production costs, reduction of the environmental emissions, improving the voltage profile, reducing the power losses and enhancing the system stability. This paper presents a new high-efficiency technology that proposes a multi-objective version of the recently proposed moth swarm algorithm (MSA) i.e. enhanced MSA (EMSA). The modification is implemented based on quasi-opposition-based learning. In order to verify the efficacy of proposed EMSA, the simulations are done in the IEEE 30-bus and IEEE 57-bus test systems. The scalability of the proposed method is proved on the IEEE 118-bus test network. The outcomes are compared with that obtained by MSA and the reported methods in the literature. From the outcomes obtained, it is strongly confirmed that proposed EMSA performs considerably better than MSA to address different test objectives with significant improvements of the considered complex power system.
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Abbreviations
- \(a_{i}\), \(b_{i}\), \(c_{i}\) :
-
Cost coefficients of ith generator
- \(\alpha_{i}\), \(\beta_{i}\), \(\gamma_{i}\), \(\xi_{i}\) and \(\lambda_{i}\) :
-
Emission coefficients of ith unit
- \(P_{Gi}\) :
-
Real power bus generator
- \(P_{D}\), \(Q_{D}\) :
-
Active and reactive load demands
- \(G_{ij}\) :
-
Transfer conductance
- \(B_{ij}\) :
-
Susceptance between bus i and bus j
- \(V_{G}\) :
-
Voltage levels at generation buses
- \(T_{G}\) :
-
Transformers tap setting
- \(Q_{C}\) :
-
Shunt VAR compensation
- \(x_{j}^{\min }\), \(x_{j}^{\max }\) :
-
Upper and lower limit of candidate solutions
- \(n_{h}\) :
-
Small group of moths walks into random spiral path inside the neighborhood of light source
- \(n_{ol}\) :
-
Small moth group drifts towards the moonlight
- \(\sigma_{j}^{t}\) :
-
Normalized form of dispersal degree at iteration
- \(\mu^{t}\) :
-
Relative dispersion
- \(c_{p}\) :
-
Crossover points
- \(Best_{global}^{t}\) :
-
Best global solution
- \(n_{gw}\) :
-
Gaussian walk
- \(x_{i}^{t + 1}\) :
-
Onlooker moths
- \(X_{qo}\) :
-
Quasi-opposite point
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Bentouati, B., Khelifi, A., Shaheen, A.M. et al. An enhanced moth-swarm algorithm for efficient energy management based multi dimensions OPF problem. J Ambient Intell Human Comput 12, 9499–9519 (2021). https://doi.org/10.1007/s12652-020-02692-7
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DOI: https://doi.org/10.1007/s12652-020-02692-7