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Distributed Neural Network and Particle Swarm Optimization for Micro-grid Adaptive Power Allocation

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Abstract

The hybrid algorithm strategy proposed in this paper aims to combine the optimal power flow with voltage-var optimization to meet the load demand, reduce the transmission line losses and maintain the voltage within a practicable range. A distributed neural network algorithm is used to seek an optimal solution of active power flow which minimizes the cost of active power. In order to ensure that the optimal power flow will not cause a serious impact to the stability of the power grid, voltage-var optimization engines which employ a multi-algorithm coordination are presented to optimize the losses of power grid and the bus voltage. The simulation of IEEE 30-bus shows that the proposed hybrid algorithm strategy can not only minimize the cost of active power generation, but also satisfy the load demand under the precondition that all the bus voltage is within the reference range. The percentages of power losses comparisons verify that the proposed hybrid algorithm strategy can decrease the transmission line losses of the power grid effectively, which will not bring a serious influence to the stability of the power grid.

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Abbreviations

\(\alpha ,\beta \) :

Fuzzy coefficients of object function

\(\bigtriangledown \) :

Gradient calculation

\(\Delta q_{ci,t}\) :

Unit compensation of capacitor bank i

\(\Delta V_{tri,t}\) :

The maximum voltage changes of transformer i

\(\mathrm {S}_{LOSS}\) :

Whole day transmission losses of apparent power

\(\mathrm {S}_{LOSS}^{max}\) :

Expected maximum whole day transmission losses of apparent power

\(\mathrm {S}_{LOSS}^{min}\) :

Expected minimum whole day transmission losses of apparent power

\(\rho _{tri,t}\) :

Transformer ratio of bus i at time t

\(\xi _{ci,t}\) :

Switched capacitors number of capacitor bank i

\(\xi _{ci,t}\) :

Switched numbers of capacitances

\(a_i,b_i,c_i\) :

Active power cost coefficients of generator i

\(C_{loss}\) :

The cost coefficient of transmission line losses

\(C_{v}\) :

The cost coefficient of voltage deviation

L :

Laplacian matrix

N, D, B :

Total number of buses, loads, capacitor banks

n, M, T :

Total number of generators, transmission lines, time slots

\(P_{G_{i,t}}^{max}\) :

Maximum output limits of generator i

\(P_{G_{i,t}}^{min}\) :

Minimum output limits of generator i

\(P_{Gi,t}\) :

Active power generation by generator i at time t

\(P_{Gi,t}^{down}\) :

Lower ramp-rate limits of generator i

\(P_{Gi,t}^{up}\) :

Upper ramp-rate limits of generator i

\(P_{i,t}\) :

Active power of bus i

\(P_{loadi,t}\) :

Active power demand of bus i

\(P_{lossl,t}\) :

Active power loss of transmission line l

\(Q_{Ci,t}\) :

Reactive power compensation of bus i

\(Q_{Gi,t}\) :

Reactive power output of generator i

\(Q_{Gi,t}^{max}\) :

Maximum reactive power output limits of generator i

\(Q_{Gi,t}^{min}\) :

Minimum reactive power output limits of generator i

\(Q_{i,t}\) :

Reactive power of bus i

\(Q_{loadi,t}\) :

Reactive power demand of bus i

\(S_{lossl,t}\) :

Apparent power loss in line l

\(tap_{tri,t}\) :

Tap setting of transformer i

\(tap_{tri,t}^{max}\) :

Maximum tap setting of transformer i

\(tap_{tri,t}^{min}\) :

Minimum tap setting of transformer i

\(V_{base}\) :

Nominal voltage

\(V_{Gi,t}\) :

Voltage of generator bus i

\(V_{i,t}\) :

Voltage of bus i

\(V_{loadi,t}\) :

Voltage of load node i

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Acknowledgements

This work is supported by Natural Science Foundation of China (Grant nos: 61773320), Fundamental Research Funds for the Central Universities (Grant No. XDJK2020TY003), and also supported by the Natural Science Foundation Project of Chongqing CSTC (Grant No. cstc2018jcyjAX0583).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Zao Fu, Xing He & Ping Liu

  2. British Columbia Institute of Technology, Burnaby, BC V5A 1S6, Canada

    Ali Palizban

  3. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Wengjing Liao

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  1. Zao Fu
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Correspondence to Xing He.

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Fu, Z., He, X., Liu, P. et al. Distributed Neural Network and Particle Swarm Optimization for Micro-grid Adaptive Power Allocation. Neural Process Lett 54, 3215–3233 (2022). https://doi.org/10.1007/s11063-022-10760-6

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