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Fuzzy Markov-EPO: an energy management scheme for the integration of hybrid RES with DC microgrid

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Abstract

Due to the intermittent character of renewable energy resources, there is a need for the storage unit to supply energy to the direct current microgrid. It is also important that the storage unit schedule the energy demanded by the load with the reduction in the power loss. Scheduling is the major task faced by the storage unit since it requires a special optimization for the charging and discharging of energy. This paper proposes a fuzzy Markov-emperor penguin optimization-based multi-objective optimal solution for direct current microgrid energy management problems with hybrid energy sources and energy storage systems. Battery packs and superconducting magnetic energy storage are used as the storage system, reducing the delay between the transition of charging and discharging modes. The proposed work is implemented on the MATLAB/Simulink platform, and when comparing the simulation results, 15.45% of operating cost is reduced from the hybrid optimization approaches.

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Abbreviations

\(P_{pv}\) :

Power generated by the PV panel

\(C_{pv}\) :

Rated capacity of the PV panel

\(d_{pv}\) :

PV de-rating factor (%)

\(R\) :

Solar radiation

\(R_{STC}\) :

Solar radiation defined by STC

\(R_{NOCT}\) :

Solar radiation defined by NOCT

\(\alpha_{pv}\) :

Temperature coefficient of PV cells

\(\eta_{pv}\) :

Efficiency of PV panel

\(T\) :

Ambient temperature

\(T_{NOCT}\) :

Ambient temperature defined by the NOCT

\(TC\) :

PV cell temperature

\(TC_{STC}\) :

PV cell temperature defined by the STC

\(TC_{NOCT}\) :

PV cell temperature defined by the NOCT

\(P_{R}\) :

Rated power at the wind turbine

\(P_{Rwt}\) :

Rated output power generated by the wind turbine

\(P_{wt}\) :

Output of the wind Turbine

\(\eta_{wt}\) :

Efficiency of the wind turbine

\(\nu \,\) :

Velocity or speed of the wind

\(\nu_{i} \,\) :

Velocity or speed of wind during turbine cut-in

\(\nu_{0}\) :

Velocity or speed of wind during turbine cut-out

\(\nu_{r\,}\) :

Rated velocity of the wind

\(V_{oc}\) :

Open circuit voltage

\(V_{t}\) :

Terminal voltage

\(I_{l}\) :

Total current

\(R_{i}\) :

Resistance in the circuit

\(V_{d}\) :

Diffusion voltage

\(SOC\left( n \right)\) :

Charge state of battery at time n

\(SOC\left( {n - 1} \right)\) :

Previously available charge state of battery at time n

\(E_{c} (n)\) :

Energy charged during n time interval at the battery

\(\varepsilon\) :

Self-discharging rate

\(E_{s}\) :

Energy stored in the SMES

L:

Inductance in SMES

I:

Current flows into the coil

\(GC_{i} ,\,EC_{i} ,\,\,OC_{i}\) :

Generation cost, emission cost, and operation cost of the ith energy sources

\(E_{i}\) :

Overall power produced by the ith energy sources

\(\alpha_{i} ,\,\beta_{i} ,\,\gamma_{i} ,\,\delta_{i} ,\,\lambda_{i}\) :

Coefficients of the emission

i:

Number of energy sources

\(A_{m}\) :

Coefficient of operation cost

\(E_{g}^{p} E_{h}^{w}\) :

Power generated by the PV cell and WT of ith energy sources

\(A_{gh} ,\,A_{g} ,\,A_{0}\) :

Coefficient of power loss

\(P_{g}\) :

Power imported from the grid

\(E_{H}\) :

Energy stored at HESS unit

k:

Number of states

\(z_{j}\) :

Fuzzy Markov relation

\(\alpha ,\,\beta\) :

Threshold values of generated energy that meet ST2

\(E_{ch} (n)\) :

Energy charged in the HESS

\(E_{dch} (n)\) :

Energy discharged in the HESS

\(\eta_{r}\) :

Efficiency of rectifier

\(\eta_{ch}\) :

Efficiency during the charging process by the HESS

\(\eta_{dch}\) :

Efficiency during the discharge process by the HESS

\(\Delta n\) :

Change in time

ES(s):

Energy at the storage unit after the performing charging or discharging

s:

Time interval during charged or discharged

\(SOC(s + 1)\) :

State of charge at HESS in s + 1 time interval

\(SOC(s)\) :

State of charge at HESS in s time interval

\(\eta\) :

Efficiency of a storage unit

C:

Capacity of a storage unit

L:

Load demand

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Authors and Affiliations

  1. School of Renewable Energy and Efficiency (SREE), National Institute of Technology Kurukshetra, Haryana, 136119, India

    Arpit Varshnry

  2. Department of Electrical Engineering, National Institute of Technology Kurukshetra, Haryana, 136119, India

    Jagdeep Singh Lather & Anil Kumar Dahiya

  3. IILM University, Greater Noida, Uttar Pradesh, 201306, India

    Arpit Varshnry

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  1. Arpit Varshnry
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Varshnry, A., Lather, J.S. & Dahiya, A.K. Fuzzy Markov-EPO: an energy management scheme for the integration of hybrid RES with DC microgrid. Energy Syst (2023). https://doi.org/10.1007/s12667-023-00581-4

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