ENERGY RESOURCE-GRID-LOAD AUTOMATIC CONTROL SYSTEM OF SMART MICROGRID AND CONTROL METHODS THEREOF

• US 20140297051A1
• Filed: 03/25/2014
• Published: 10/02/2014
• Est. Priority Date: 03/26/2013
• Status: Active Grant

• Current Assignee: Northeastern University
• Original Assignee: Northeastern University
• Inventors: Yang, Jun, Teng, Fei, Guo, Jing, Zhang, HuaGuang, Sun, QiuYe, Liu, XinRui, Wang, ZhiLiang, Wang, ZhanShan, Liu, ZhenWei, Wang, YingChun
• Granted Patent: US 9,618,914 B2
• Time in Patent Office: Days
• US Class Current: 700/287
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 2300/10 The dispersed energy generation being of fossil origin, e.g. diesel generators
• CPC Class Codes: H02J 2300/20 The dispersed energy generation being of renewable origin
• CPC Class Codes: H02J 2300/24 of photovoltaic origin
• CPC Class Codes: H02J 2300/28 The renewable source being wind energy
• CPC Class Codes: H02J 2300/40 wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
• CPC Class Codes: H02J 2310/10 The network having a local or delimited stationary reach
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: H02J 3/007 Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing uninterruptable power supply H02J9/00
• CPC Class Codes: H02J 3/32 using batteries with converting means
• CPC Class Codes: H02J 3/381 Dispersed generators
• CPC Class Codes: H02J 7/34 Parallel operation in networks using both storage and other dc sources, e.g. providing buffering H02J7/14 takes precedence
• CPC Class Codes: H02J 9/066 characterised by the use of dynamo-electric machines H02J9/08 takes precedence
• CPC Class Codes: Y02B 10/70 Hybrid systems, e.g. uninterruptible or back-up power supplies integrating renewable energies
• CPC Class Codes: Y02E 10/56 Power conversion systems, e.g. maximum power point trackers
• CPC Class Codes: Y02E 70/30 Systems combining energy storage with energy generation of non-fossil origin
• CPC Class Codes: Y02P 80/14 District level solutions, i.e. local energy networks

Abstract

The present invention relates to an energy resource-grid-load automatic control system of smart microgrid and control methods thereof, which belong to the field of microgrid control and electrical technology. The energy resource-grid-load automatic control system comprises a distributed renewable energy power generation module, a distributed renewable energy inverter module, a conventional power generation module, a user load module, a bidirectional grid-connected control module, a distributed renewable energy intelligent optimizing power generation control module, an energy storage module, an intelligent energy storage unit adjuster and a storage battery pack. Compared with the conventional microgrid comprising a distributed renewable energy power generation system, the present invention guarantees the stability and the high energy utilization of the power generation system and effectively solves the problem of non-uniform frequency of use of storage batteries to unify the service life of the storage battery pack.

Claims

1. An energy resource-grid-load automatic control system of a smart microgrid, comprising:

a distributed renewable energy power generation module, a distributed renewable energy inverter module, a conventional power generation module, a user load module, a bidirectional grid-connected control module, a distributed renewable energy intelligent optimizing power generation control module and an energy storage module;

wherein the distributed renewable energy power generation module is used for converting solar energy, wind energy and biomass energy into electric energy and supplying power to a plurality of user loads or a plurality of power grids;

the distributed renewable energy inverter module is used for converting direct current discharged from a plurality of photovoltaic panels, a plurality of wind turbines and a biomass energy power generation equipment into alternating current;

the conventional power generation module is used for controlling a plurality of micro gas turbines to supply power to the user loads or the power grids;

the user load module is a local load in the microgrid; and

the bidirectional grid-connected control module is used for controlling connection/disconnection and energy transfer between the microgrid and the power grids;

wherein the distributed renewable energy intelligent optimizing power generation control module is used for;

     1) acquiring nodal voltage and current of the photovoltaic panels, the wind turbines and the biomass energy power generation equipment, estimating the power generation capacity of the photovoltaic panels, the wind turbines and the biomass energy power generation equipment within 4 to 5 sampling periods by uncertainty estimation, plotting the forecast fluctuation curves of the power generation capacity, respectively calculating the robustness of the photovoltaic panels, the wind turbines and the biomass energy power generation equipment, and selecting the one with the best robustness to supply power to the user loads or the power grids;

     2) sending a control signal to an intelligent energy storage unit adjuster when there is a difference between the actual power generation capacity of the photovoltaic panels, the wind turbines and the biomass energy power generation equipment and the total electric energy supplied to the user loads and the power grids; and

     3) sending control signals to a plurality of micro gas turbine power generation controllers when the power generation capacity of the photovoltaic panels, the wind turbines and the biomass energy power generation equipment and the electric energy released from the energy storage module differ from the total electric energy required by the user loads and the power grids;

wherein the energy storage module is used for controlling the charging and discharging of a storage battery pack to ensure the average charging and discharging times of each storage battery;

wherein the energy storage module comprises an intelligent energy storage unit adjuster and the storage battery pack;

wherein if the remaining electric energy of the photovoltaic panels, the wind turbines and the biomass energy power generation equipment is received, the intelligent energy storage unit adjuster calculates the total charging and discharging times of each storage battery, then compares the result with the average of the total charging and discharging times of all storage batteries, and selects the storage battery with the largest difference for charging;

if the power generation capacity of the photovoltaic panels, the wind turbines and the biomass energy power generation equipment cannot meet the needs of the user loads or the power grids, the intelligent energy storage unit adjuster receives a power generation control signal sent by a distributed renewable energy intelligent optimizing power generation controller to control the storage battery pack to discharge, calculates the total charging and discharging times of each storage battery, compares the result with the average of the total charging and discharging times of all storage batteries, and selects the storage battery with the largest difference for discharging;

wherein the storage battery pack is used for storing or releasing the remaining electric energy of the photovoltaic panels, the wind turbines and the biomass energy power generation equipment;

wherein the conventional power generation module comprises a micro gas turbine intelligent optimizing power generation control unit and a micro gas turbine power generation unit;

wherein the micro gas turbine intelligent optimizing power generation control unit is used for replenishing the user loads or the power grids with the electric energy generated by the micro gas turbines when the power generation capacity of the photovoltaic panels, the wind turbines and the biomass energy power generation equipment and the electric energy released from the energy storage module cannot meet the needs of the user loads or the power grids; and

the micro gas turbine power generation unit is used for supplying power to the user loads or the power grids;

wherein the user load module comprises an intelligent optimizing power consumption controller and a plurality of state collectors;

wherein the intelligent optimizing power consumption controller is used for establishing a load matrix of voltage and current signals acquired at load ends by the state collectors, constructing laws of power consumption of the user loads by neural network modeling, and automatically controlling the power consumption of the user loads by the laws; and

the state collectors are used for acquiring nodal voltage and current at the user load ends in real time.

2. The energy resource-grid-load automatic control system of the smart microgrid according to claim 1, wherein the distributed renewable energy power generation module is connected with the distributed renewable energy inverter module by DC buses;

the DC buses consist of three segments and are arranged separately;

output ends of a photovoltaic power generation unit are respectively connected with input ends of a photovoltaic power generation inverter unit by the first segment of DC bus (Bus 1), the second segment of DC bus (Bus 2) and the third segment of DC bus (Bus 3);

output ends of a wind power generation unit are respectively connected with input ends of a wind power generation inverter unit by the first segment of DC bus (Bus 1), the second segment of DC bus (Bus 2) and the third segment of DC bus (Bus 3);

output ends of a biomass energy power generation unit are respectively connected with input ends of a biomass energy power generation inverter unit by the first segment of DC bus (Bus 1), the second segment of DC bus (Bus 2) and the third segment of DC bus (Bus 3).

3. The energy resource-grid-load automatic control system of the smart microgrid according to claim 2, wherein an input end of the energy storage module is simultaneously connected with the first segment of DC bus (Bus 1), the second segment of DC bus (Bus 2) and the third segment of DC bus (Bus 3).

4. An intelligent optimizing control method for distributed renewable energy power generation, using the energy resource-grid-load automatic control system of a smart microgrid of claim 1, the method comprising the following steps:

step 1;

acquiring nodal voltage and current of the photovoltaic panels, the wind turbines and the biomass energy power generation equipment by Hall element measurement;

step 2;

estimating the power generation capacity of the photovoltaic panels, the wind turbines and the biomass energy power generation equipment by uncertainty estimation, including resistance, reactance and PV nodal voltage;

wherein the specific formulas are shown as follows;

for estimation of resistance and reactance;

R_m+4=R_m+3=R_m+2=R_m+1=0.01R<sub>m−

7</sub>+0.03R<sub>m−

6</sub>+0.06R<sub>m−

5</sub>+0.1R<sub>m−

4</sub>+0.15R<sub>m−

3</sub>+0.16R<sub>m−

2</sub>+0.22R<sub>m−

1</sub>+0.30R_m 

 

(1)

where;

R_m represents the resistance in the mth sampling period;

R_m+n represents the estimated resistance in the (m+n)th sampling period, wherein n=−

7, −

6, −

5, −

4, −

3, −

2, −

1, 1, 3, 4;

X_m+4=X_m+3=X_m+2=X_m+1=0.15X<sub>m−

2</sub>+0.25X<sub>m−

1</sub>+0.6X_m 

 

(2)

where;

X_m represents the reactance in the mth sampling period;

X_m+p represents the estimated reactance in the (m+p)th sampling period, wherein p=−

2, −

1, 1, 2, 3, 4;

for estimation of PV nodal voltage;

U₊₄=U₊₃=U₊₂=U₊₁=0.01U<sub>m−

7</sub>+0.03U<sub>m−

6</sub>+0.06U<sub>m−

5</sub>+0.01U<sub>m−

4</sub>+0.15U<sub>m−

3</sub>+0.16U<sub>m−

2</sub>+0.22U<sub>m−

1</sub>+0.30U_m 

 

(3)

where;

U_m represents the PV nodal voltage in the mth sampling period;

U_m+n represents the estimated PV nodal voltage in the (m+n)th sampling period, wherein n=−

7, −

6, −

5, −

4, −

3, −

2, −

1, 1, 2, 3, 4;

step 3;

plotting the forecast fluctuation curves of the power generation capacity according to the PV nodal voltage obtained in Step 2, with the time as the abscissa and the PV nodal voltage as the ordinate;

step 4;

calculating the robustness of the power generation capacity of the photovoltaic panels, the wind turbines and the biomass energy power generation equipment, and selecting the one with the best robustness to supply power to the user loads or the power grids;

the formula for calculation of the robustness is shown as follows;

5. A control method for charging and discharging of the energy storage unit, using the energy resource-grid-load automatic control system of smart microgrid of claim 1, the method comprising the following steps:

step 1;

if the remaining electric energy released by the photovoltaic panels, the wind turbines and the biomass energy power generation equipment is received, carrying out step 2 for storage battery charging;

if a control signal sent by the distributed renewable energy intelligent optimizing power generation controller is received, carrying out step 3 for storage battery discharging;

step 2;

counting the charging and discharging times c of each storage battery;

calculating the total charging and discharging times of all storage batteries and obtaining an average C;

then calculating the difference between C and c, and taking the storage battery with the largest absolute value of the difference as a charging object; and

step 3;

counting the charging and discharging times c of each storage battery;

calculating the total charging and discharging times of all storage batteries and obtaining an average C;

then calculating the difference between C and c and taking the storage battery with the largest absolute value of the difference as a discharging object.

6. A coordination method for laws of power consumption of the user loads, using the energy resource-grid-load automatic control system of smart microgrid of claim 1, the method comprising the following steps:

step 1;

establishing a neural network model according to the historical data of nodal voltage and current acquired at the user load ends to predict laws of power consumption of the user loads;

step 2;

acquiring nodal voltage and current at the user load ends in real time by the state collectors, and continually correcting the neural network model in Step 1; and

step 3;

releasing the total electric energy actually required by the user loads from the intelligent optimizing power consumption controller to the distributed renewable energy intelligent optimizing power generation controller according to the electric energy corrected in Step 2, and starting the corresponding ones of the photovoltaic panels, the wind turbines, the biomass energy power generation equipment, the energy storage module and the conventional power generation module to supply power.