Particle swarm optimization system and method for microgrids

• US 8,606,424 B2
• Filed: 04/05/2011
• Issued: 12/10/2013
• Est. Priority Date: 04/05/2011
• Status: Expired due to Fees

• Current Assignee: King Fahd University Of Petroleum And Minerals
• Original Assignee: King Fahd University Of Petroleum And Minerals
• Inventors: Abido, Mohammad Ali Yousef, Hassan, Mohammad Ali Ali
• Primary Examiner(s): Padmanabhan, Kavita
• Assistant Examiner(s): STEVENS, THOMAS H
• Application Number:
  US13/080,423
• Publication Number:
  US 20120259477A1
• Time in Patent Office: 980 Days
• Field of Search: 700/297
• US Class Current: 700/297
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/38 Arrangements for parallely feeding a single network by two or more generators, converters or transformers
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

The particle swarm optimization method for microgrids formulates a control problem as an optimization problem and PSO is used to search the solution space for optimal parameter settings in each mode. The procedure models optimal design of an LC filter, controller parameters and damping resistance in grid-connected mode. Moreover, the procedure optimizes controller parameters and power sharing coefficients in autonomous mode. The method uses particular nonlinear time-domain-based and eigenvalue-based objective functions to minimize the error in the measured power, and also to enhance the damping characteristics, respectively.

Claims

1. A particle swarm optimization method for a microgrid providing stability enhancement to the microgrid in a grid-connected mode when the microgrid is connected to a grid power supply and in an autonomous mode when the microgrid is disconnected from the grid power supply, the method comprising the steps of:

(a) calculating an LC filter and coupling inductor reference current representing output reactive and active reference power components, the filter and coupling inductor being connected between the microgrid and the grid power supply when in the grid-connected mode, the filter and coupling inductor being connected between the microgrid and a load when in the autonomous mode;

(b) minimizing error in the filter and coupling inductor current with respect to the reference current of the filter and coupling inductor;

(c) providing a reference angle for a rotating current control reference frame;

(d) controlling current of the microgrid using the rotating current control reference frame, the current controlling step being based on the current control reference frame reference angle;

(e) controlling power of the microgrid utilizing three phase (abc) and direct quadrature (dq) calculations of the reference currents, the microgrid having a power controller outputting a modified direct current available at the LC filter and coupling inductor and a modified quadrature current available at the LC filter and coupling inductor;

(f) providing a small signal model characterizing the LC filter and coupling inductor;

(g) optimizing inductive and capacitive values of the LC filter and coupling inductor connected between the microgrid and the grid power supply when the microgrid is in the grid-connected mode, the filter optimization being performed by a particle swarm optimization procedure utilizing the small signal model characterizing the LC filter and coupling inductor;

(h) optimizing inductive and capacitive values of the LC filter and coupling inductor connected between the microgrid and a load when the microgrid is in the autonomous mode, the filter optimization being performed by a particle swarm optimization procedure utilizing the small signal model characterizing the LC filter and coupling inductor; and

(i) outputting a modified system angle control signal, system frequency control signal, and a system current control signal to phase control, frequency control, and system current control circuits of a microgrid power source,

2. The particle swarm optimization method for a microgrid according to claim 1, wherein:

step (g) further comprises optimizing k_p^d, k_i^d, k_p^q, k_i^q, L_f, C_f, and R_d parameters subject to constraints k_p^{d min}≦

k_p^d≦

k_p^{d max}, k_i^{d min}≦

k_i^d≦

k_i^{d max}, k_p^{q min}≦

k_p^q≦

k_p^{q max}, k_i^{q min}≦

k_i^q≦

k_i^{q max}, L_f^min≦

L_f≦

L_f^max, C_f^min≦

C_f≦

C_f^max, R_d^min≦

R_d≦

R_d^max when the microgrid is operated in grid-connected mode, where k_p^d is a direct inductive current error proportional gain constant, k_i^d is a direct inductive current error integral gain constant, k_p^q is a quadrature inductive current error proportional gain constant, k_i^q is a quadrature inductive current error integral gain constant, L_f is a total filter inductance, C_f is total filter capacitance, and R_d is a ground return resistance; and

said step (g) uses an objective function for performing the optimizing, the objective function being minimized in order to optimize the optimizing parameters.

3. The particle swarm optimization method for a microgrid according to claim 2, wherein said objective function comprises a linear model objective function, the linear model objective function being J_linear—

_model=max{Real(λ

_i)}.

4. The particle swarm optimization method for a microgrid according to claim 2, wherein said objective function comprises a nonlinear model objective function, the nonlinear model objective function being J_nonlinear—

_model=min{P_measured−

P_reference}².

5. The particle swarm optimization method for a microgrid according to claim 1, wherein:

step (h) further comprises optimizing said k_p^d, k_i^d , k_p^q, k_i^q, L_f, C_f, and R_d parameters subject to constraints k_pv^min≦

k_pv≦

k_pv^max, k_iv^min≦

k_iv≦

k_iv^max, k_pc^min≦

k_pc≦

k_pc^max, m_p^min≦

m_p≦

m_p^max, n_q^min≦

n_q≦

n_q^max when the microgrid is operated in autonomous mode, where k_pv is a voltage controller proportional gain constant, k_iv is a voltage controller integral gain constant, k_pc is a current controller proportional gain constant, m_p is an active power droop characteristic, and n_q is a reactive power droop characteristic; and

said step (h) uses an objective function for performing the optimizing, the objective function being minimized in order to optimize the optimizing parameters.

6. The particle swarm optimization method for a microgrid according to claim 5, wherein said objective function comprises a linear model objective function, the linear model objective function being J_linear—

_model=max{Real(λ

_i)}.

7. The particle swarm optimization method for a microgrid according to claim 5, wherein said objective function comprises a nonlinear model objective function, the nonlinear model objective function being J_nonlinear—

_model=min{P<sub>measured−

Preference}</sub>².

8. A particle swarm optimization system for a microgrid, comprising:

means for calculating an LC filter and coupling inductor reference current representing output reactive and active reference power components, the filter and coupling inductor being connected between the microgrid and a power grid when operated in grid-connected mode, the LC filter and coupling inductor being connected between the microgrid and a load when operated in autonomous mode;

means for minimizing error in the current of the filter and coupling inductor with respect to the reference current of the filter and coupling inductor;

means for providing a reference angle for a rotating current control reference frame;

means for controlling current of the microgrid using the rotating current control reference frame, the current controlling being based on the current control reference frame reference angle;

means for controlling power of the microgrid utilizing three-phase (abc) and direct quadrature (dq) calculations of the reference currents, the means for controlling power outputting a modified direct current available at the LC filter and coupling inductor and a modified quadrature current available at the LC filter and coupling inductor;

means for providing a small signal model characterizing the LC filter and coupling inductor;

means for optimizing inductive and capacitive values of the LC filter and coupling inductor connected between the microgrid and the conventional grid power supply when the microgrid is in grid-connected mode, the filter optimization being performed by a particle swarm optimization procedure utilizing the small signal model characterizing the LC filter and coupling inductor;

means for optimizing inductive and capacitive values of the LC filter and coupling inductor connected between the microgrid and a load when the microgrid is in autonomous mode, the filter optimization being performed by a particle swarm optimization procedure utilizing the small signal model characterizing the LC filter and coupling inductor; and

means for outputting a modified system angle control signal, a system frequency control signal, and a system current control signal to phase control, frequency control, and system current control circuits of a microgrid power source.

9. The particle swarm optimization system for a microgrid according to claim 8, further comprising:

means for optimizing k_p^d,k_i^d, k_p^q, k_i^q, L_f, C_f, and R_d parameters subject to constraints k_p^{d min}≦

k_p^d≦

k_p^{d max}, k_i^{d min}≦

k_i^d≦

k_i^{d max}, k_p^{q min}≦

k_p^q≦

k_p^{q max}, k_i^{q min}≦

k_i^q≦

k_i^{q max}, L_f^min≦

L^f≦

L_f^max, C_f^min≦

C_f≦

C_f^max, R_d^min≦

R_d≦

R_d^max when the microgrid is in grid-connected mode, where k_p^d is a direct inductive current error proportional gain constant, k_i^d is a direct inductive current error integral gain constant, k_p^q is a quadrature inductive current error proportional gain constant, k_i^q is a quadrature inductive current error integral gain constant, L_f is a total filter inductance, C_f is total filter capacitance, and R_d is a ground return resistance;

means for optimizing k_p^d, k_i^d, k_p^q, k_i^q, L_f, C_f , and R_d parameters subject to constraints k_pv^min≦

k_pv≦

k_pv^max, k_iv^min≦

k_iv≦

k_iv^max, k_pc^min≦

k_pc≦

k_pc^max, m_p^min≦

m_p≦

m_p^max, n_q^min≦

n_q≦

n_q^max when the microgrid is in autonomous mode, where k_pv is a voltage controller proportional gain constant, k_iv is a voltage controller integral gain constant, k_pc is a current controller proportional gain constant, m_p is an active power droop characteristic, and n_q is a reactive power droop characteristic;

means for selecting an objective function from linear and nonlinear models, the linear model objective function being J<sub>linear—

model</sub>=max{Real(λ

_i)}, the nonlinear model objective function being J<sub>nonlinear—

model</sub>=min{P_measured−

P_reference}²; and

means for minimizing the objective function to optimize the optimizing parameters.

10. A computer software product, comprising a non-transitory storage medium readable by a processor, the non-transitory storage medium having stored thereon a set of instructions for performing a particle swarm optimization method for a microgrid providing stability enhancement to the microgrid in a grid-connected mode when the microgrid is connected to a grid power supply and in an autonomous mode when the microgrid is disconnected from the grid power supply, the set of instructions including:

(a) a first sequence of instructions which, when executed by the processor, causes said processor to calculate an LC filter and coupling inductor reference current representing output reactive and active reference power components, the filter and coupling inductor being connected between the microgrid and the power grid when operated in grid-connected mode, said LC filter and coupling inductor being connected between said micro grid and a load when operated in autonomous mode;

(b) a second sequence of instructions which, when executed by the processor, causes said processor to minimize error in the current of the filter and coupling inductor with respect to the reference current of the filter and coupling inductor;

(c) a third sequence of instructions which, when executed by the processor, causes said processor to provide a reference angle for a rotating current control reference frame;

(d) a fourth sequence of instructions which, when executed by the processor, causes said processor to control current of the microgrid using the rotating current control reference frame, the current control being based on the current control reference frame reference angle;

(e) a fifth sequence of instructions which, when executed by the processor, causes said processor to control power of the microgrid utilizing three-phase (abc) and direct quadrature (dq) calculations of the reference currents, the micro grid having a power controller outputting a modified direct current available at the LC filter and coupling inductor and a modified quadrature current available at the LC filter and coupling inductor;

(f) a sixth sequence of instructions which, when executed by the processor, causes said processor to provide a small signal model characterizing the LC filter and coupling inductor;

(g) a seventh sequence of instructions which, when executed by the processor, causes said processor to optimize inductive and capacitive values of the LC filter and coupling inductor connected between the micro grid and the grid power supply when the microgrid is operated in grid-connected mode, the filter optimization being performed by a particle swarm optimization procedure utilizing the small signal model characterizing the LC filter and coupling inductor;

(h) a eighth sequence of instructions which, when executed by the processor, causes said processor to optimize inductive and capacitive values of the LC filter and coupling inductor connected between the microgrid and a load when the microgrid is operated in autonomous mode, the filter optimization being performed by a particle swarm optimization procedure utilizing the small signal model characterizing the LC filter and coupling inductor; and

(i) a ninth sequence of instructions which, when executed by the processor, causes said processor to output a modified system angle control signal, a system frequency control signal, and a system current control signal to phase control, frequency control, and system current control circuits of a microgrid power source.

11. The computer software product for a microgrid according to claim 10, wherein said particle swarm optimization sequence of instructions further comprises:

(j) a tenth sequence of instructions which, when executed by the processor, causes said processor to optimize k_p^d, k_i^d, k_p^q, k_i^q, L_f, C_f, and R_d parameters subject to constraints k_p^{d min}≦

k_p^d≦

k_p^{d max}, k_i^{d min}≦

k_i^d≦

k_i^{d max}, k_p^{q min}≦

k_p^q≦

k_p^{q max}, k_i^{q min}≦

k_i^q≦

k_i^{q max}, L_f^min≦

L_f≦

L_f^max, C_f^min≦

C_f≦

C_f^max, R_d^min≦

R_d≦

R_d^max when the micro grid is operated in grid-connected mode, where k_p^d is a direct inductive current error proportional gain constant, k_i^d is a direct inductive current error integral gain constant, k_p^q is a quadrature inductive current error proportional gain constant, k_i^q is a quadrature inductive current error integral gain constant, L_f is a total filter inductance, C_f is total filter capacitance, and R_d is a ground return resistance;

(k) a eleventh sequence of instructions which, when executed by the processor, causes said processor to optimize k_p^d, k_i^d, k_p^q, k_i^q, L_f, C_f, and R_d parameters subject to constraints k_pv^min≦

k_pv≦

k_pv^max, k_iv^min≦

k_iv≦

k_iv^max, k_pc^min≦

k_pc≦

k_pc^max, m_p^min≦

m_p≦

m_p^max, n_q^min≦

n_q≦

n_q^max when the microgrid is operated in autonomous mode, where k_pv is a voltage controller proportional gain constant, k_iv is a voltage controller integral gain constant, k_pc is a current controller proportional gain constant, m_p is an active power droop characteristic, and n_q is a reactive power droop characteristic;

(l) a twelfth sequence of instructions which, when executed by the processor, causes said processor to use an objective function selected from linear and nonlinear models, the linear model objective function being J<sub>linear—

model</sub>=max{Real(λ

_i)}, the nonlinear model objective function being J<sub>nonlinear—

model</sub>=min{P_measured−

P_reference}²;

(m) a thirteenth sequence of instructions which, when executed by the processor, causes said processor to minimize the objective function to optimize the optimizing parameters.