Distributed renewable energy grid controller

1. A controller for a grid-connected renewable energy source (RES) power plant, comprising:

a plurality of sensors configured to sense power generation characteristics from a grid-connected RES power plant and load characteristics of consumers of the grid-connected RES power plant; and

a non-transitory computer readable medium in operable communication with the plurality of sensors and comprising stored instructions that when executed cause at least one processor to:

receive, by a receiving device, the power generation characteristics from the grid-connected RES power plant and the load characteristics of the consumers of the grid-connected RES power plant,

perform an alternating current optimal power flow (ACOPF) analysis on the grid-connected RES power plant by performing the following:

characterize a need for a high-speed convergence of the ACOPF analysis in terms of a general primal and dual linear programming function, the general primal and dual linear programming function being as follows:

Primal: min m T x; Ax=n,x≥ 0

Dual: max n T λ; A T λ+n=m, η≥ 0,

where m and n are variables in real space and λ is a Lagrangian multiplier;

apply a logarithmic barrier function using natural logarithm to the general primal and dual linear programming function, the logarithmic barrier function (L B ) being as follows:

L

B

⁡

(

x

,

μ

)

=

m

T

⁢

x

-

μ

⁢

∑

i

=

1

k

⁢

ln

⁢

⁢

x

i

;

μ

>

0

,

where i and k are binary variable indices;

select a point comprising a point in real space, the Lagrangian multiplier, and a slack variable, such that the point in real space is feasible for a positive primal solution and both the Lagrangian multiplier and slack variable are feasible for a dual solution, the Lagrangian multiplier (L) being defined as follows:

L

⁡

(

x

,

λ

,

μ

)

=

m

T

⁢

x

-

μ

⁢

∑

i

=

1

k

⁢

ln

⁢

⁢

x

i

-

λ

T

⁡

(

Ax

-

n

)

,

and the slack variable η being defined as μX −1 e, where X is a diagonal matrix [x 1 , . . . , x k ] and e is matrix [1, 1, . . . , 1] T ;

solve for the logarithmic barrier function by iteratively applying a Newton method of optimization to reduce each current logarithmic barrier function and decreasing the logarithmic barrier function with each iteration, the solution being characterized by the following conditions:

A T λ+η=m,

Ax=n,

x≥ 0,

η≥0,

x i η i =0,1≥ i≥K,

where K is a number of transmission lines; and

calculate complimentary positive primal and dual solutions;

update system state variables based upon the complimentary positive primal and dual solutions; and

direct power output from a plurality of power sources of the grid-connected RES power plant to a power grid based upon the updated state variables.

2. The controller of claim 1 , the plurality of power sources comprising a renewable energy source, a battery bank, and a supercapacitor; and the battery bank and the supercapacitor forming a hybrid energy storage system (HESS).

3. The controller of claim 2 , the non-transitory computer readable medium comprising stored instructions that when executed further cause at least one processor to:

predict an amount of power generated by the grid-connected RES power plant for a specified time period using a Long Short-Term Memory (LTSM) neural network and based on historical data of local weather patterns;

predict an amount of consumer demand for power generated by the grid-connected RES power plant for the specified time period using historical data of energy consumption patterns;

solve an optimization problem to determine an optimal combination of power output of each power source of the plurality of power sources,

a solution of the optimization problem accounting for reduction of power costs for the consumers demanding power, outputting power to meet the power demands of the consumers, an amount of ramp-ups and ramp-downs in a power generation profile for the specific time period, available power from battery bank and supercapacitor, and any grid conditions determined by solving for the ACOPF analysis; and

transmit, by a transmitting device, an optimum solution of allocation of power output by each power source of the plurality of power sources.

4. The controller of claim 1 , the non-transitory computer readable medium comprising stored instructions that when executed further cause at least one processor to:

execute a dispatch for a bulk energy transfer using a hybrid energy storage system coupled to an energy management system,

the energy management system employing a adaptive cell balancing technique and a decoupled active and reactive (P-Q) technique;

engage in peak load shaving;

support functionalities for energy pricing and economics of the grid-connected RES power plant;

provide demand-side functionalities to utility companies through; and

reduce interruptions in power supply to the consumers through intelligent control.

5. The controller according to claim 2 , further comprising:

a plurality of power controllers, each configured to be connected to a respective power source of the plurality of power sources;

an inverter for interfacing at least one power source of the plurality of power sources with a grid of the grid-connected RES power plant and for transmitting to and receiving from a signal with at least one power source controller of the plurality of power source controllers;

a command and control center for transmitting a signal to the inverter;

a plurality of intelligent electronic devices in electronic communication with the controller; and

an energy management system configured to be in electronic communication with the controller and to receive state information of the grid-connected RES power plant from the plurality of sensors and the plurality of intelligent electronic devices.

6. The controller according to claim 1 , the plurality of sensors being connected to a first bus of the grid-connected RES power plant.

7. The controller according to claim 6 , the plurality of sensors being configured to sense at least one of a maximum voltage at the first bus of the grid-connected RES power plant, a minimum voltage at the first bus of the grid-connected RES power plant, an injected real power into the first bus of the grid-connected RES power plant, an absorbed real power from the first bus of the grid-connected RES power plant, an absorbed reactive power from the first bus of the grid-connected RES power plant, and a voltage at the first bus of the grid-connected RES power plant.

8. The controller according to claim 5 , the plurality of sensors being connected to a second bus of the grid-connected RES power plant.

9. The controller according to claim 8 , the plurality of sensors being further configured to sense at least one of a maximum voltage angle difference between the first bus and the second bus of the grid-connected RES power plant, a minimum voltage angle difference between the first bus and the second bus of the grid-connected RES power plant, a maximum apparent power between the first bus and the second bus of the grid-connected RES power plant, a conductance across the first bus and the second bus of the grid-connected RES power plant, a susceptance across the first bus and the second bus of the grid-connected RES power plant, a real power flow from the first bus and the second bus of the grid-connected RES power plant, and a reactive power flow from the first bus and the second bus of the grid-connected RES power plant.

10. The controller according to claim 1 , the controller being configured to be electrically connected to and located physically remotely from a plurality of photovoltaic power sources.

11. The controller according to claim 2 , the renewable energy source comprising a photovoltaic power source.

12. A grid-connected RES power plant, comprising:

a controller comprising a non-transitory computer readable medium;

a plurality of sensors configured to sense power generation characteristics of a grid-connected RES power plant and load characteristics of consumers of the grid-connected RES power plant;

a photovoltaic cell, a battery, and a super capacitor, each configured to be connected to a respective power source controller of a plurality of power source controllers;

an inverter for interfacing at least one power source of a plurality of power sources with a grid of the grid-connected RES power plant and for transmitting to and receiving from a signal with at least one power source controller of the plurality of power source controllers; and

an energy management system configured to be in electronic communication with the controller and to receive state information of the grid-connected RES power plant from the plurality of sensors and a plurality of intelligent electronic devices,

the non-transitory computer readable medium comprising stored instructions that when executed cause at least one processor to:

receive, by a receiving device, the power generation characteristics of the grid-connected RES power plant and the load characteristics of the consumers of the grid-connected RES power plant,

perform an alternating current optimal power flow (ACOPF) analysis on the grid-connected RES power plant by performing the following:

characterize the need for high-speed convergence of the ACOPF analysis in terms of a general primal and dual linear programming function, the general primal and dual linear programming function being as follows:

Primal: min m T x; Ax=n,x≥ 0

Dual: max n T λ; A T λ+n=m, η≥ 0,

where m and n are variables in real space and λ is a Lagrangian multiplier;

apply a logarithmic barrier function using natural logarithm to the general primal and dual linear programming function, the logarithmic barrier function (L B ) being as follows:

L

B

⁡

(

x

,

μ

)

=

m

T

⁢

x

-

μ

⁢

∑

i

=

1

k

⁢

ln

⁢

⁢

x

i

;

μ

>

0

,

where i and k are binary variable indices;

select a point comprising a point in real space, the Lagrangian multiplier, and a slack variable such that the point in real space is feasible for a positive primal solution and both the Lagrangian multiplier and slack variable are feasible for a dual solution, the Lagrangian multiplier (L) being defined as follows:

L

⁡

(

x

,

λ

,

μ

)

=

m

T

⁢

x

-

μ

⁢

∑

i

=

1

k

⁢

ln

⁢

⁢

x

i

-

λ

T

⁡

(

Ax

-

n

)

,

and the slack variable η being defined as μX −1 e, where X is a diagonal matrix [x 1 , . . . , x k ] and e is matrix [1, 1, . . . , 1] T ;

solve for the logarithmic barrier function by iteratively applying a Newton method of optimization to reduce each current logarithmic barrier function and decreasing the logarithmic barrier function with each iteration, the solution being characterized by the following conditions:

A T λ+η=m,

Ax=n,

x≥ 0,

η≥0,

x i η i =0,1≥ i≥K,

where K is a number of transmission lines; and

calculate complimentary positive primal and dual solutions; and

update system state variables based on the complimentary positive primal and dual solutions.

13. A method for operation of a grid-connected RES power plant, comprising:

sensing, by a plurality of sensors, power generation characteristics of a grid-connected RES power plant and load characteristics of consumers of the grid-connected RES power plant;

providing a non-transitory computer readable medium comprising stored instructions that when executed cause at least one processor to:

receive, by a receiving device, the power generation characteristics of the grid-connected RES power plant and the load characteristics of the consumers of the grid-connected RES power plant,

perform an alternating current optimal power flow (ACOPF) analysis on the grid-connected RES power plant by performing the following:

characterize the need for high-speed convergence of the ACOPF analysis in terms of a general primal dual and linear programming function, the general primal and dual linear programming function being as follows:

Primal: min m T x; Ax=n,x≥ 0

Dual: max n T λ; A T λ+n=m, η≥ 0,

where m and n are variables in real space and λ is a Lagrangian multiplier;

apply a logarithmic barrier function using natural logarithm to the general primal and dual linear programming function, the logarithmic barrier function (L B ) being as follows:

L

B

⁡

(

x

,

μ

)

=

m

T

⁢

x

-

μ

⁢

∑

i

=

1

k

⁢

ln

⁢

⁢

x

i

;

μ

>

0

,

where i and k are binary variable indices;

select a point comprising a point in real space, the Lagrangian multiplier, and a slack variable such that the point in real space is feasible for a positive primal solution and both the Lagrangian multiplier and slack variable are feasible for a dual solution, the Lagrangian multiplier (L) being defined as follows:

L

⁡

(

x

,

λ

,

μ

)

=

m

T

⁢

x

-

μ

⁢

∑

i

=

1

k

⁢

ln

⁢

⁢

x

i

-

λ

T

⁡

(

Ax

-

n

)

,

and the slack variable η being defined as μX −1 e, where X is a diagonal matrix [x 1 , . . . , x k ] and e is matrix [1, 1, . . . , 1] T ;

solve for the logarithmic barrier function by iteratively applying a Newton method of optimization to reduce each current logarithmic barrier function and decreasing the logarithmic barrier function with each iteration, the solution being characterized by the following conditions:

A T λ+η=m,

Ax=n,

x≥ 0,

η≥0,

x i η i =0,1≥ i≥K,

where K is a number of transmission lines; and

calculate complimentary positive primal and dual solutions;

update system state variables based on the complimentary positive primal and dual solutions, and

direct power output from a plurality of power sources of the grid-connected RES power plant to a power grid based upon the updated state variables.

14. The method of claim 13 , further comprising adjusting a power output of each power source of a plurality of power sources connected to the grid-connected RES power plant based on the complimentary positive primal and dual solutions.

15. The method of claim 13 , further comprising connecting the plurality of sensors to a first bus of the grid-connected RES power plant.

16. The method of claim 15 , further comprising configuring the plurality of sensors to sense at least one of a maximum voltage at the first bus of the grid-connected RES power plant, a minimum voltage at the first bus of the grid-connected RES power plant, an injected real power into the first bus of the grid-connected RES power plant, an absorbed real power from the first bus of the grid-connected RES power plant, an absorbed reactive power from the first bus of the grid-connected RES power plant, and a voltage at the first bus of the grid-connected RES power plant.

17. The method of claim 16 , further comprising connecting the plurality of sensors to a second bus of the grid-connected RES power plant.

18. The method of claim 17 , further comprising configuring the plurality of sensors to sense at least one of a maximum voltage angle difference between the first bus and the second bus of the grid-connected RES power plant, a minimum voltage angle difference between the first bus and the second bus of the grid-connected RES power plant, a maximum apparent power between the first bus and the second bus of the grid-connected RES power plant, a conductance across the first bus and the second bus of the grid-connected RES power plant, susceptance across the first bus and the second bus of the grid-connected RES power plant, real power flow from the first bus and the second bus of the grid-connected RES power plant, and a reactive power flow from the first bus and the second bus of the grid-connected RES power plant.

19. The method of claim 13 , further comprising:

executing a dispatch for a bulk energy transfer;

engaging in peak load shaving;

supporting functionalities for energy pricing and economics of the grid-connected RES power plant;

providing demand-side functionalities to utility companies; and

reducing interruptions in power supply to the consumers through intelligent control.

20. The method of claim 13 , the plurality of sensors being configured to sense respective power generation and load characteristics of a plurality of grid-connected RES power plants, the method further comprising:

receiving respective power generation characteristics of each grid-connected RES power plant of the plurality of grid-connected RES power plants and respective load characteristics of consumers of each grid-connected RES power plant of the plurality of grid-connected RES power plants; and

performing an alternating current optimal power flow (ACOPF) analysis on each grid-connected RES power plant of the plurality of grid-connected RES power plants.