Progressive optimization dispatching method of smart distribution system

• US 9,891,645 B2
• Filed: 06/10/2014
• Issued: 02/13/2018
• Est. Priority Date: 10/30/2013
• Status: Active Grant

• Current Assignee: Hohai University, Nanjinghehai Technology Limited Company, China Electric Power Research Institute (State Grid Corporation of China), Jiangsu Electric Power Company (State Grid Corporation of China), Jiangsu Electric Power Company WUXI Power Supply Company (State Grid Corporation of China), State Grid Corporation of China
• Original Assignee: Hohai University, Nanjinghehai Technology Limited Company, China Electric Power Research Institute (State Grid Corporation of China), Jiangsu Electric Power Company (State Grid Corporation of China), Jiangsu Electric Power Company WUXI Power Supply Company (State Grid Corporation of China), State Grid Corporation of China
• Inventors: Yu, Kun, Shen, Peifeng, Chen, Xingying, Chen, Tingji, Chen, Kai, Zhu, Hong, Wei, Lei, Xiao, Jing, Wang, Chunning, Yao, Jianguo, Liao, Yingchen, Cai, Min
• Primary Examiner(s): Ali, Mohammad
• Assistant Examiner(s): Azad, Md
• Application Number:
  US14/647,910
• Publication Number:
  US 20160239032A1
• Time in Patent Office: 1,344 Days
• Field of Search: 700295
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05F 1/66 Regulating electric power
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: Y02E 40/70 Smart grids as climate change mitigation technology in the energy generation sector
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

smart power distribution system and a method to progressively dispatch the power is described. The method steps are all automatic and self-adaptive. The method can be executed in an unattended manner to automatically correlate real time data vs. historical data, planning data vs. operation data. Based on a long cycle periodical variation and short-term random variations in load, and taking into account the temporary load power supply and maintenance needs, a multi-stage progressive multiple time scales optimal dispatching method is developed, including the distributed power, micro-grids, energy storage devices, electric vehicles charge-discharge facility and other elements of the Intelligent power distribution systems, to achieve coordinated operation of the network, power, load resources to ensure a continuous safe and reliable smart power distribution system operated at high quality and efficiency.

Claims

1. A method for managing a smart power distribution system, the method comprising:

performing a four-step progressive dispatching long-term, mid-long-term, short-term and ultra-short-term optimal dispatching for the smart power distribution system, characterized in that;

1) using a long-term optimization dispatching coordination model to achieve network load source development by

connecting a plurality of distributed powers, interruptible loads and energy storage devices to a distribution network through switches, in which each switch opens and closes to allow access to the power distribution system;

2) using a mid-long term optimization dispatching model to coordinate under changeable normal operation conditions including load periodical variation, inspection, and maintenance triggered temporary power supply by

calculating loss rates of the smart power distribution system under various different operating modes using working day load curves and holiday load curves respectively,

selecting least power loss rate operation modes for each working day and holiday period, according to the sequence that working day and holiday alternate in time, comparing the difference between adjacent working day and holiday operation modes, and determining a mid to long term switch operating scheme;

3) using a coordination model for a short-term optimization dispatching plan, implementation of temporary repair and temporary holding for multi-period energy balance and operation mode by

dividing a next day load curve into different periods according to the load change trend with respect to time and maintenance information,

calculating loss rates of the smart power distribution system under different operating modes for each period,

selecting operation modes having the least power loss rate for each period,

comparing the difference between the second operation modes according to time sequence, and determining a short term switch operation scheme;

4) using ultra-short-term optimization dispatching model to achieve ultra-short-term energy balance and fault and defect management in network load source interaction by adjusting the power level of the distributed power and energy storage device in the event of emergencies, or transferring the load to other feeders,

using coordination model of short-term optimal dispatching plan help implementation of temporary repair and temporary electricity holding at multi period energy balance and operation mode, characterized in that

dividing a load curve into

a first period, a peak load period, having more than 70% of a maximum load,

a second period a low load period, having less than 50% of an average load, and other periods of there in between;

executing an adjustable normal operation mode, calculation a trend data of the smart power distribution system for each period when one or more switches are closed and off;

collecting a power loss rate for each period statistically,

selecting an operation mode having the least power loss rate;

planning a switch operation plan after comparing difference between each operation mode in accordance with their time sequence;

disconnecting switches or close switches when a power loss of the system is at 5.4%.

2. The method of claim 1, wherein the step 1 further comprises

reducing the difference between peak and valley in a load;

reducing as occurrences of peak load; and

optimizing distribution feeder focal points, distributed power and interruptible load including planning for electric vehicle charging and discharging facilities.

3. The method of claim 2, wherein the step 1 further comprises

ensuring important users have different power supplies;

dispatching the use of interruptible load as resource; and

changing the load curves based on electric vehicle charging and discharging protocol through networks.

4. The method of claim 1, wherein the step 2 further comprises maintaining a system'"'"'s energy consumption is at 5.2%.

5. The method of claim 1, wherein the step 4 further comprises disconnecting a switch connected to node 8 and setting the switch connecting to the load which lost power to close in the event of emergencies wherein node 9 is a critical load in need of a reliable power supply, and if a fault occurs at node 8, causing downstream nodes to have outage.

6. The method of claim 1, wherein the step 4 further comprises balancing a sudden change in energy by charging or discharging an energy storage device at a node through switch sequences:

first turning off the switch K13 between node 11 and node 12, then turning off the switch K7 between node 5 and node 11, and finally turning off the switch K15 between node 13 and node 16 when a sudden change at 30% in the load or in a distributed power output is received.

7. The method of claim 6, wherein the step 4 further comprises increasing power output of the distributed power installed at node 26 to balance the sudden energy change when load at node 23 double the load suddenly as the energy storage device loses its ability to self regulate.