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Multi-source Microgrid Frequency Stability Control Using Learning-Based Technology

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Advances in Control Techniques for Smart Grid Applications

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Abstract

Due to stochastic power generations and uncertainties, the high penetration rate of renewable energy makes frequency control very difficult. Therefore, for the photovoltaic (PV)-integrated multi-source microgrid, the load frequency control (LFC) problem is investigated. A learning-based frequency control strategy is developed including thermal controller, hydro controller, and auxiliary power controller when the power mismatches occur. The proportion-integral (PI) controller are used for thermal and hydro generation, and auxiliary power controller is designed based on Adaptive dynamic programming (ADP) to improve the adaptability. With the maximal PV power, the auxiliary power controller regulates the PV power to realize the frequency regulation or to be stored by charging electric vehicles (EVs). For the studied benchmark microgrid, several numerical cases are applied to verify the proposed control strategy, which demonstrates the superiority for stabilizing the frequency and fully using solar energy. Further, a model-based intelligent frequency control strategy is designed to adjust the power outputs of micro-turbine and energy storage system (ESS) in the expansion and prospect, which is no longer an auxiliary control strategy.

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Abbreviations

R s :

Series resistance (Ω)

R sh :

Parallel resistance (Ω)

I ph :

Current source (A)

R :

External resistance (Ω)

I ph :

Current of PV module (A)

I pv :

Output current (A)

U pv :

Output voltage (V)

I d :

Reverse saturation current of diode (A)

K :

Boltzmann constant

T :

Temperature (°C)

q :

Electronic charge of an electron

A :

Diode ideality factor

U oc :

Open-circuit voltage (V)

U m :

MPP output voltage (V)

I m :

MPP output current (A)

I sc :

Short-circuit current (A)

\(P_{{\text{pv}}}^*\) :

Maximum power of PV module (W)

δ1, δ2, δ3:

Distribution coefficients

u t :

Thermal control signal

u h :

Hydro control signal

u pv :

PV control signal

u e :

EV control signal

Δf :

Frequency deviation (Hz)

ΔP d :

Power mismatch from load change (p.u.)

ΔP pv :

Power mismatch from PV power generation (p.u.)

ΔP e :

Output power change of EV aggregator (p.u.)

ΔP h :

Output power change of hydro generation (p.u.)

ΔP t :

Output power change of thermal generation (p.u.)

ΔX hg :

Governor position change of hydro generation

ΔX tg :

Governor position change of thermal generation

K a :

Changing/discharging coefficient

K p :

Microgrid gain

K r :

Steam turbine reheat constant

N e :

Number of EVs in a EV aggregator

R h :

Speed regulation coefficient of hydro generation

R t :

Speed regulation coefficient of thermal generation

T t :

Steam turbine time constant

T e :

Time constant of EV

T lg :

Reset time of hydro turbine speed governor

T p :

Time constant of microgrid

T rh :

Time constant of hydro turbine speed governor transient droop

T r :

Steam turbine reheat time constant

T tg :

Time constant of speed governor

J :

Cost function

U :

Utility function

x :

State vector

γ :

Discount factor

µ :

Adaptive power control signal

x a :

Input vectors of action networks

x c :

Input vectors of critic networks

ψ :

Activation function

ι c / ι a :

Number of neurons in the input-layer of critic/action network

Ï„c/Ï„a:

Number of neurons in the hidden-layer of critic/action network

\(w_{ij}^{c1} /w_{ij}^{a1}\) :

Weights in input-to-hidden layer of critic/action network

\(w_j^{c2} /w_j^{a2}\) :

Weights in hidden-to-output layer of critic/action network

\(q_j^c /q_j^a\) :

Output of jth hidden-layer neuron of critic/action network

\(p_j^c /p_j^a\) :

Input of jth hidden-layer neuron of critic/action network

Ec/Ea:

Approximate error of critic/action network

r :

Reinforcement learning signal

Q, M:

Positive definite matrixes with proper dimensions

λc/λa:

Learning rate of critic/action network

u m :

Maximum value of PV reserve power

\({\mathcal{F}}\) :

Performance index

ΔP mt :

Output power change of micro-turbine (p.u.)

ΔP mg :

Governor position change of micro-turbine (p.u.)

ΔP ess :

Output power change of ESS (p.u.)

T mg :

Time constant of micro-turbine governor

T mt :

Time constant of micro-turbine

T ess :

Time constant of ESS

σ mg :

Uncertain parameter of micro-turbine governor

R mt :

Speed regulation coefficient of micro-turbine

u mg :

Control signal of micro-turbine

u ess :

Control signal of ESS

θ1, θ2:

Positive constants designed in the cost function

Ω c :

Admissible control set

Ess1:

Electrical changes of ESS due to frequency control (p.u.)

Ess2:

Electrical changes of ESS due to PV power dispatch (p.u.)

Soc0:

Initial electrical energy of ESS (p.u.h)

Socm/SocM:

Lower/upper bound of SOC (p.u.h)

\(\Delta P_{{\text{ess}}}^m /\Delta P_{{\text{ess}}}^M\) :

Lower/upper bound of ESS power output (p.u.)

β :

Charging/discharging coefficient of ESS

Bias:

Power deviation of load demands and PV

\(\Phi\) :

Regulation coefficient

A:

Ampere

AC:

Alternating current

ADP:

Adaptive dynamic programming

DC:

Direct current

ESS:

Energy storage system

EVs:

Electric vehicles

GRC:

Generation rate constraint

HJB:

Hamiltonian-Jacobi-Bellman

Hz:

Hertz

LFC:

Load frequency control

MPP:

Maximum power point

MPPT:

Maximum power point tracker

PI:

Proportion-integral

PID:

Proportion-integral-derivative

PV:

Photovoltaic

PWM:

Pulse width modulation

SMC:

Sliding mode control

SOC:

State of charge

V:

Voltage

V2G:

Vehicle-to-grid

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Authors and Affiliations

  1. Tianjin University, Tianjin, 300072, China

    Chaoxu Mu, Yong Zhang & Weiqiang Liu

  2. Huazhong University of Science and Technology, Wuhan, 430074, China

    Wei Xu

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Editors and Affiliations

  1. Department of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh

    Sajal Kumar Das

  2. Faculty of Engineering and Information Sciences, School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, Australia

    Md. Rabiul Islam

  3. School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Wei Xu

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Mu, C., Zhang, Y., Liu, W., Xu, W. (2022). Multi-source Microgrid Frequency Stability Control Using Learning-Based Technology. In: Das, S.K., Islam, M.R., Xu, W. (eds) Advances in Control Techniques for Smart Grid Applications. Springer, Singapore. https://doi.org/10.1007/978-981-16-9856-9_2

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