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An Effective Stochastic Approach for Optimal Energy Resource Management in Hybrid AC–DC Microgrids

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Iranian Journal of Science and Technology, Transactions of Electrical Engineering Aims and scope Submit manuscript

Abstract

This paper develops a new management framework for optimal operation of the hybrid AC–DC microgrids incorporating renewable energy sources and storages. Hybrid microgrid consists of two parts of AC and DC to supply the AC and DC loads, respectively. The power exchange capability of hybrid microgrids between the AC and DC parts makes it possible to reduce the total microgrid costs, effectively. To make it a realistic analysis, a stochastic method based on cloud theory is proposed to model the uncertainty effects of wind turbine power, photovoltaic power, load demand and market price sufficiently. The proposed framework makes use of a new optimization algorithm based on flower pollination mechanism to minimize the total network costs through the optimal dispatch of the units. Also, a three-stage modification method is proposed to improve the population diversity and avoid the premature convergence. The performance of the proposed method is examined on the IEEE test system through two different operation scenarios.

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Abbreviations

He :

Hyper entropy

Iter :

Iteration number in algorithm

Ex :

Standard deviation

En :

Entropy

\(C_{{{\text{G}}i}}^{t}\) and \(C_{{{\text{s}}j}}^{t}\) :

Cost of the ith RES and jth storage device at hour t

\(C_{\text{Grid}}^{t}\) :

Price of utility at hour t

d :

Length of the control vector

f :

Cost objective function

N T :

Number of scheduling time intervals in the study (in this paper 24)

N s :

Number of storage devices

N g :

Number of power units

N Load :

Number of load levels

n :

Total number of energy generation sources in MG

\(p_{\text{Grid}}^{t}\) :

Active power bought/sold from/to the utility at time t

\(p_{{{\text{G}}i}}^{t}\) and \(p_{{{\text{s}}j}}^{t}\) :

Active power output of the ith generator and jth storage device at time t

\(P_{{{\text{Load}},k}}^{t}\) :

Load value in kth level of tth hour

\(P_{m}^{{{\text{Inj}},t}}\)/\(Q_{m}^{{{\text{Inj}},t}}\) :

Active and reactive power injection in bus m at time t

\(p_{{{\text{G}}i,\hbox{min} }}^{t}\) and \(p_{{{\text{G}}i,\hbox{max} }}^{t}\) :

Minimum and maximum active power production of ith RES at hour t

\(p_{{{\text{s}}j,{ \hbox{min} }}}^{t} \,{\text{and}}\,p_{{{\text{s}}j , {\text{max}}}}^{t}\) :

Minimum and maximum active power production of jth storage device at hour t

\(p_{{{\text{grid}},{ \hbox{min} }}}^{t} \,{\text{and}}\,p_{{{\text{grid}},{ \hbox{max} }}}^{t}\) :

Minimum and maximum active power production of the utility at hour t

Pcharge (Pdischarge):

Permitted rate of charge (discharge) through a definite period of time Δt

Pcharge,max (Pdischarge,max):

Maximum rate of charge (discharge) during definite period of time Δt

\(P_{i}^{{{\text{Line}},t}}\) :

Amount of active power flow in line i at time t

\(P_{i,\hbox{max} }^{\text{Line}}\) :

Allowed maximum power flow at line i

pro :

Switching probability parameter

Rest :

Scheduled spinning reserve at time t

r i :

Random number with uniform distribution between 0 and 1

\(S_{{{\text{G}}i}}^{\text{on}}\) and \(S_{{{\text{G}}i}}^{\text{off}}\) :

Start-up/shutdown costs for the ith RES unit

\(S_{{{\text{s}}j}}^{\text{on}}\) and \(S_{{{\text{s}}j}}^{\text{off}}\) :

Start-up/shutdown costs for the jth storage device

z t i :

Status of the ith unit at hour t

Z g :

ON/OFF status of RESs

V min /V max :

Minimum and maximum values of voltage

V/δ :

Voltage magnitude/phase

Wess,min (Wess,max):

Lower (upper) bounds on the battery energy storage

W tess :

Battery energy storage at time t

u :

Membership cloud of L

X :

Control variable (or solution vector in CSA)

X best :

Best solution in the algorithm

x i,j :

jth element of the ith control vector Xi

Y/θ :

Magnitude/phase of line admittance

Ψ :

Levy movement

M pop :

Mean value of the population

r k :

Random number in the range of [0,1]

ηcharge (ηdischarge):

Charge (discharge) efficiency of the battery

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Authors and Affiliations

  1. Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran

    Mohamadreza Askari & Taher Niknam

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  1. Mohamadreza Askari
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  2. Taher Niknam
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Correspondence to Taher Niknam.

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Askari, M., Niknam, T. An Effective Stochastic Approach for Optimal Energy Resource Management in Hybrid AC–DC Microgrids. Iran J Sci Technol Trans Electr Eng 44, 835–848 (2020). https://doi.org/10.1007/s40998-019-00266-8

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  • DOI: https://doi.org/10.1007/s40998-019-00266-8

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