CN113657017A - Energy management algorithm applied to 10kV charging station - Google Patents

Energy management algorithm applied to 10kV charging station Download PDF

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Publication number
CN113657017A
CN113657017A CN202110071517.XA CN202110071517A CN113657017A CN 113657017 A CN113657017 A CN 113657017A CN 202110071517 A CN202110071517 A CN 202110071517A CN 113657017 A CN113657017 A CN 113657017A
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time
ess
scheduling
real
charging
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范建华
曹乾磊
尹怀强
张乐群
王伟强
徐鹏飞
杨森
李保安
马鲁宁
邱慧冬
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Qingdao Topscomm Communication Co Ltd
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Qingdao Topscomm Communication Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/06—Power analysis or power optimisation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations

Abstract

The invention discloses an energy management algorithm applied to a 10kV charging station, which comprises the following steps: predicting photovoltaic output, other loads of a charging station system and EV load data of 1 complete dispatching cycle in the future based on a random forest algorithm and historical data; running an ESS day-ahead scheduling algorithm to obtain an optimal SOC time-sharing distribution strategy of the ESS in the next 1 complete scheduling period; running an ESS real-time scheduling algorithm at an ESS real-time scheduling running time point, and configuring an optimal charging and discharging strategy of the remaining scheduling time slots for the ESS; performing information interaction through the charge pile and EV handshake operation, and recording vehicle SOC and inbound time information; optimizing all scheduled timeslot CP charging power for the EV from initial to full charge state time period. The invention makes a reasonable energy storage power station dispatching strategy and an EV ordered charging strategy, and meets the energy charging requirement of large-scale electric vehicles by using limited charging stations. Meanwhile, the effects of restraining the load fluctuation rate and reducing the load peak-valley difference are achieved for the charging station system, and the negative influence on the power system is reduced.

Description

Energy management algorithm applied to 10kV charging station
Technical Field
The invention relates to the technical field of energy management of electric vehicle charging stations, in particular to an energy management algorithm applied to a 10kV charging station.
Background
Energy Storage system ess (energy Storage system): an electric energy storage device capable of performing charging and discharging operations;
charging pile cp (charging pile): an apparatus for supplying electric power to an electric vehicle;
state of charge soc (state of charge): the ratio of the residual capacity of the battery to the rated capacity of the battery under the same condition under a certain discharge rate;
10kV charging station: a novel intelligent electric vehicle charging station for realizing voltage conversion and energy transmission in a power system based on a power electronic conversion technology and an electromagnetic induction principle comprises equipment such as an energy router, a photovoltaic system, a direct current CP (direct current) and an ESS (extended service system).
With the gradual depletion of fossil energy and the increasing severity of environmental pollution, the automobile field is in the key period of energy transformation, and the electric vehicle EV is widely concerned and agreed to all countries in the world due to the characteristics of environmental protection, low carbon, high efficiency and the like. With the guidance and popularization of national policies, the national quantity of electric vehicles is rapidly increased, and the quantity of electric vehicles in China is estimated to be more than 2 hundred million by 2040 years. The pressure of a power distribution network is greatly increased by the large-scale popularization of electric automobiles, and according to the statistical data of the national energy source bureau, the 5 th month in 2020 shows that the charging capacity of the public charging infrastructure in China is about 3.6 hundred million kilowatts, along with the further increase of the national reserves of the electric automobiles, the existing power distribution network is difficult to bear the high charging load of the electric automobiles, and the investment cost and the time cost of the transformation of the power distribution network are high, so that how to utilize the limited charging stations to meet the energy supplement requirements of the large-scale electric automobiles and reduce the negative influence on a power system as much as possible is a great challenge in the technical field of energy management of the current charging stations of the electric automobiles.
Disclosure of Invention
Aiming at the defects and shortcomings of the prior art, the invention provides an energy management algorithm applied to a 10kV charging station, a reasonable energy storage power station scheduling strategy and an electric vehicle ordered charging strategy are formulated, the energy supplement requirements of large-scale electric vehicles are met by using limited charging stations, and the effects of inhibiting load fluctuation rate and reducing load peak-valley difference are achieved for a charging station system.
The purpose of the invention can be realized by the following technical scheme:
an energy management algorithm applied to a 10kV charging station comprises the following steps:
step 1: predicting photovoltaic output, other loads of a charging station system and electric vehicle load data of 1 complete scheduling cycle in the future based on a random forest algorithm and historical data information;
step 2: based on the prediction data, operating an ESS day-ahead scheduling algorithm to obtain an optimal SOC time-sharing distribution strategy of the ESS in the next 1 complete scheduling period;
and step 3: at an ESS real-time scheduling operation time point, based on prediction data and SOC information of the ESS, the energy master station control system operates an ESS real-time scheduling algorithm, takes 5 minutes as a scheduling time slot, and configures an optimal charging and discharging strategy of the remaining scheduling time slot between the time and the next integer time for the ESS;
and 4, step 4: at the entry moment of the EV, performing information interaction through the charging pile and EV handshake operation to obtain the current SOC information of the vehicle, recording the entry time, and reporting to an energy management master station;
and 5: and running a CP real-time scheduling algorithm based on the prediction data, the ESS running state and the SOC data of the EV, optimizing the CP charging power of all scheduling time slots in the time period from the initial state to the full-charge state of the EV by taking 5 minutes as one scheduling time slot, and obtaining an optimal CP scheduling strategy.
Further, the ESS day-ahead scheduling process in step 2 is: the ESS transmits the ESS initial SOC data acquired by the information acquisition module to the master station control system in a power line narrow-band carrier communication mode; the master station control system predicts CP load, other loads in the station and new energy output conditions according to historical load information, and calculates equivalent load P of the 10kV charging stationeqMeanwhile, acquiring real-time electricity price information from the power system; based on equivalent load PeqOptimizing an ESS day-ahead scheduling function with real-time electricity price information to obtain an optimized energy storage SOC time-sharing distribution strategy, further estimating the ESS power in time-sharing, recalculating the equivalent load, and performing cyclic iteration until the absolute value of the difference of the objective function values of two adjacent iterations is smaller than a threshold Tth1And ending iteration to obtain an SOC distribution strategy of the optimal day-ahead ESS with a scheduling interval of 1 hour, and ending the day-ahead scheduling of the ESS.
Further, in step 3, starting from 0:00, every 5 minutes is an ESS real-time scheduling operation point, i.e. 0:05, 0:10, 0:15, … …, 24: 00.
Further, the ESS real-time scheduling process in step 3 is: calculating real-time equivalent load based on an SOC distribution strategy of the ESS before the day, real-time SOC data of the ESS, time-of-use electricity price information, actual CP load and photovoltaic unit output information; optimizing a real-time scheduling function of the ESS based on implementing equivalent load and real-time electricity price information, thereby obtaining real-time P with 5 minutes as a scheduling intervalESSThe equivalent load is recalculated, and iteration is performed in a loop until the absolute value of the difference between the objective function values of two adjacent iterations is less than the threshold value Tth2The iteration is terminated to obtain the optimal PESSAnd (5) scheduling strategies.
Further, the CP real-time scheduling process in step 5 is: SOC score based on optimal day-ahead ESS obtained in step 2Strategy distribution, optimal P obtained in step 3ESSScheduling strategy, real-time load data, photovoltaic output data and EV SOC data, wherein the master station control system calculates a schedulable time period when each EV enters, and then estimates EV charging characteristic data information according to the EV model, the current SOC data and other information to obtain charging duration information; and optimizing a CP real-time scheduling function based on the charging characteristic data information, the charging duration information, the real-time SOC data of the ESS, the time-of-use electricity price and the photovoltaic unit output information of the EV to obtain the optimal time-of-use charging strategy of the CP.
The invention has the beneficial technical effects that: by designing an energy management algorithm of a 10kV charging station, ESS day-ahead scheduling, ESS real-time scheduling and CP real-time scheduling are combined, reasonable energy storage power station day-ahead and real-time scheduling strategies and electric vehicle ordered charging strategies are formulated, the energy supplement requirements of large-scale electric vehicles are met by using limited charging stations, and the economical efficiency of a 10kV charging station system is improved. Meanwhile, the effects of restraining the load fluctuation rate and reducing the load peak-valley difference are achieved for the charging station system, and the negative influence on the power system is reduced.
Drawings
FIG. 1 is a general flow diagram of the present invention.
Fig. 2 is a model diagram of a 10kV charging station system in the embodiment of the present invention.
FIG. 3 is a diagram illustrating the effect of ESS day-ahead scheduling in an embodiment of the present invention.
Fig. 4 is a diagram illustrating the effect of ESS and CP real-time scheduling in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
Examples are given.
And carrying out simulation verification on the proposed algorithm based on system data and real-time electricity price information of a certain 10kV charging station in Shandong province. The 10kV charging station system is provided with 29 groups of photovoltaic power generation devices, and each group of devices is provided with 3 photovoltaic panels; configuring an energy storage power station, wherein the capacity is 368 kW.h, and the rated power is 350 kW; one energy router is configured, the rated power is 2MW, and the power factor can be adjusted randomly in four quadrants; this charging station contains 29 seats of direct current charging stake, rated power 120 kW.
As shown in fig. 1, an energy management algorithm applied to a 10kV charging station includes the following steps:
step 1: predicting photovoltaic output, other loads of a charging station system and electric vehicle load data of 1 complete scheduling cycle in the future based on a random forest algorithm and historical data information;
step 2: based on the prediction data, operating an ESS day-ahead scheduling algorithm to obtain an optimal SOC time-sharing distribution strategy of the ESS in the next 1 complete scheduling period; the ESS day-ahead scheduling process is as follows: the ESS transmits the ESS initial SOC data acquired by the information acquisition module to the master station control system in a power line narrow-band carrier communication mode; the master station control system predicts CP load, other loads in the station and new energy output conditions according to historical load information, and calculates equivalent load P of the 10kV charging stationeqMeanwhile, acquiring real-time electricity price information from the power system; based on equivalent load PeqOptimizing an ESS day-ahead scheduling function with real-time electricity price information to obtain an optimized energy storage SOC time-sharing distribution strategy, further estimating the ESS power in time-sharing, recalculating the equivalent load, and performing cyclic iteration until the absolute value of the difference of the objective function values of two adjacent iterations is smaller than a threshold Tth1And ending iteration to obtain an SOC distribution strategy of the optimal day-ahead ESS with a scheduling interval of 1 hour, and ending the day-ahead scheduling of the ESS.
And step 3: at an ESS real-time scheduling operation time point, based on prediction data and SOC information of the ESS, the energy master station control system operates an ESS real-time scheduling algorithm, takes 5 minutes as a scheduling time slot, and configures an optimal charging and discharging strategy of the remaining scheduling time slot between the time and the next integer time for the ESS; starting from 0:00, every 5 minutes is an ESS real-time scheduling operating point, namely 0:05, 0:10, 0:15, … … and 24: 00; the ESS real-time scheduling process comprises the following steps: SOC distribution strategy based on day-ahead ESS, real-time SOC data of ESS and time-sharing electricityCalculating real-time equivalent load according to the price information, the actual CP load and the photovoltaic unit output information; optimizing a real-time scheduling function of the ESS based on implementing equivalent load and real-time electricity price information, thereby obtaining real-time P with 5 minutes as a scheduling intervalESSThe equivalent load is recalculated, and iteration is performed in a loop until the absolute value of the difference between the objective function values of two adjacent iterations is less than the threshold value Tth2The iteration is terminated to obtain the optimal PESSAnd (5) scheduling strategies.
And 4, step 4: at the entry moment of the EV, performing information interaction through the charging pile and EV handshake operation to obtain the current SOC information of the vehicle, recording the entry time, and reporting to an energy management master station;
and 5: and running a CP real-time scheduling algorithm based on the prediction data, the ESS running state and the SOC data of the EV, optimizing the CP charging power of all scheduling time slots in the time period from the initial state to the full-charge state of the EV by taking 5 minutes as one scheduling time slot, and obtaining an optimal CP scheduling strategy. The CP real-time scheduling process comprises the following steps: based on the SOC distribution strategy of the optimal day-ahead ESS obtained in step 2 and the optimal P obtained in step 3ESSScheduling strategy, real-time load data, photovoltaic output data and EV SOC data, wherein the master station control system calculates a schedulable time period when each EV enters, and then estimates EV charging characteristic data information according to the EV model, the current SOC data and other information to obtain charging duration information; and optimizing a CP real-time scheduling function based on the charging characteristic data information, the charging duration information, the real-time SOC data of the ESS, the time-of-use electricity price and the photovoltaic unit output information of the EV to obtain the optimal time-of-use charging strategy of the CP.
As shown in fig. 2, the 10kV charging station system model includes, from top to bottom: the main station energy management system performs data preparation work of ESS scheduling, runs an ESS day-ahead scheduling algorithm, and obtains an SOC distribution strategy of the optimal day-ahead ESS with a scheduling interval of 1 hour; and the main station energy management system runs an ESS real-time scheduling algorithm to obtain a real-time energy storage charging and discharging optimization strategy taking 5 minutes as a scheduling interval. And the CP scheduling layer is used for carrying out data preparation work of CP scheduling, and the main station energy management system runs a CP real-time scheduling algorithm to obtain the optimal time-sharing charging strategy of the CP.
As shown in fig. 3, after the ESS day-ahead scheduling algorithm is used, the ESS stores low-price electric energy during the low-price periods of electricity, such as 1:00-5:00 and 7:00-8:00, and releases electric energy during the peak periods of electricity, such as 11:00-13:00 and 17:00-20:00, so as to supply energy to a 10kV charging station system, improve the economy of the system, and simultaneously, the load fluctuation rate and the peak-valley difference are reduced, so that the negative influence on the power system is reduced.
After the ESS and the CP are used for real-time scheduling, in order to match the change condition of photovoltaic output along with time, the ESS and the CP load power are changed, the CP load is transferred from a time period with small photovoltaic output to a time period with large photovoltaic output, and the ESS load is scheduled once every 5 minutes. As shown in fig. 4, the comprehensive load change is large before and after real-time optimization, the load fluctuation rate and the peak-to-valley difference are greatly reduced, and the negative influence on the power system is reduced.
The above-mentioned embodiments are illustrative of the specific embodiments of the present invention, and are not restrictive, and those skilled in the relevant art can make various changes and modifications to obtain corresponding equivalent technical solutions without departing from the spirit and scope of the present invention, so that all equivalent technical solutions should be included in the scope of the present invention.

Claims (5)

1. An energy management algorithm applied to a 10kV charging station is characterized by comprising the following steps:
step 1: predicting photovoltaic output, other loads of a charging station system and electric vehicle load data of 1 complete scheduling cycle in the future based on a random forest algorithm and historical data information;
step 2: based on the prediction data, operating an ESS day-ahead scheduling algorithm to obtain an optimal SOC time-sharing distribution strategy of the ESS in the next 1 complete scheduling period;
and step 3: at an ESS real-time scheduling operation time point, based on prediction data and SOC information of the ESS, the energy master station control system operates an ESS real-time scheduling algorithm, takes 5 minutes as a scheduling time slot, and configures an optimal charging and discharging strategy of the remaining scheduling time slot between the time and the next integer time for the ESS;
and 4, step 4: at the entry moment of the EV, performing information interaction through the charging pile and EV handshake operation to obtain the current SOC information of the vehicle, recording the entry time, and reporting to an energy management master station;
and 5: and running a CP real-time scheduling algorithm based on the prediction data, the ESS running state and the SOC data of the EV, optimizing the CP charging power of all scheduling time slots in the time period from the initial state to the full-charge state of the EV by taking 5 minutes as one scheduling time slot, and obtaining an optimal CP scheduling strategy.
2. The algorithm of claim 1, wherein the ESS schedule procedure before day in step 2 is as follows: the ESS transmits the ESS initial SOC data acquired by the information acquisition module to the master station control system in a power line narrow-band carrier communication mode; the master station control system predicts CP load, other loads in the station and new energy output conditions according to historical load information, and calculates equivalent load P of the 10kV charging stationeqMeanwhile, acquiring real-time electricity price information from the power system; based on equivalent load PeqOptimizing an ESS day-ahead scheduling function with real-time electricity price information to obtain an optimized energy storage SOC time-sharing distribution strategy, further estimating the ESS power in time-sharing, recalculating the equivalent load, and performing cyclic iteration until the absolute value of the difference of the objective function values of two adjacent iterations is smaller than a threshold Tth1And ending iteration to obtain an SOC distribution strategy of the optimal day-ahead ESS with a scheduling interval of 1 hour, and ending the day-ahead scheduling of the ESS.
3. The algorithm of claim 1, wherein the step 3 starts from 0:00 and schedules an ESS real-time operation point, i.e. 0:05, 0:10, 0:15, … …, 24:00 every 5 minutes.
4. The algorithm as claimed in claim 1, wherein the ESS real-time scheduling process in step 3 is ESS real-time scheduling process: calculating real-time equivalent load based on an SOC distribution strategy of the ESS before the day, real-time SOC data of the ESS, time-of-use electricity price information, actual CP load and photovoltaic unit output information; optimizing a real-time scheduling function of the ESS based on implementing equivalent load and real-time electricity price information, thereby obtaining real-time P with 5 minutes as a scheduling intervalESSThe equivalent load is recalculated, and iteration is performed in a loop until the absolute value of the difference between the objective function values of two adjacent iterations is less than the threshold value Tth2The iteration is terminated to obtain the optimal PESSAnd (5) scheduling strategies.
5. The algorithm of claim 1, wherein the CP real-time scheduling process in step 5 is as follows: based on the SOC distribution strategy of the optimal day-ahead ESS obtained in step 2 and the optimal P obtained in step 3ESSScheduling strategy, real-time load data, photovoltaic output data and EV SOC data, wherein the master station control system calculates a schedulable time period when each EV enters, and then estimates EV charging characteristic data information according to the EV model, the current SOC data and other information to obtain charging duration information; and optimizing a CP real-time scheduling function based on the charging characteristic data information, the charging duration information, the real-time SOC data of the ESS, the time-of-use electricity price and the photovoltaic unit output information of the EV to obtain the optimal time-of-use charging strategy of the CP.
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