CN112465200B - Energy optimization scheduling method for virtual power plant - Google Patents

Energy optimization scheduling method for virtual power plant Download PDF

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CN112465200B
CN112465200B CN202011293007.9A CN202011293007A CN112465200B CN 112465200 B CN112465200 B CN 112465200B CN 202011293007 A CN202011293007 A CN 202011293007A CN 112465200 B CN112465200 B CN 112465200B
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gas turbine
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power plant
virtual power
storage system
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马汝祥
周洪益
侍红兵
柏晶晶
胥峥
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Yancheng Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Yancheng Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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    • 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
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    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • 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
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
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    • 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
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/382—Payment protocols; Details thereof insuring higher security of transaction
    • G06Q20/3829—Payment protocols; Details thereof insuring higher security of transaction involving key management
    • 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
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    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
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    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/12—Billing, invoicing, buying or selling transactions or other related activities, e.g. cost or usage evaluation
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Abstract

The invention provides an energy optimization scheduling method for a virtual power plant, wherein a virtual power plant aggregator performs optimization scheduling according to energy information of distributed energy nodes by taking maximum accumulated profit as a target, and the optimization scheduling method specifically comprises the following steps: considering the situation that a virtual power plant participates in the day-ahead power market, and aiming at the uncertainty of photovoltaic output and market electricity price, processing by adopting a stochastic programming method; determining gas turbine operating constraints; determining an energy storage system operation constraint condition; determining a power market trading volume constraint condition; determining a power balance constraint condition; and the virtual power plant carries out optimized scheduling according to the operation constraint of the gas turbine, the operation constraint of the energy storage system, the trading volume constraint of the power market and the power balance constraint to obtain the day-ahead planned output of each distributed energy node and sends the day-ahead planned output to each distributed energy node. The invention can promote the local consumption of the distributed energy, and reduce the electric power transaction cost and the energy loss.

Description

Energy optimization scheduling method for virtual power plant
Technical Field
The invention belongs to the field of power supply transaction of power systems, and particularly relates to an energy optimization scheduling method for a virtual power plant.
Background
The promotion of clean energy consumption by virtual power plant technology is an important component of energy internet construction content. Meanwhile, modern information technologies and advanced communication technologies such as 'big cloud thing moving intelligent link' are applied as important carriers for achieving the construction goal of the energy internet, and the block chain technology is the important development of the modern information technology.
Energy concerns the country fundamentally, and in recent years, with the popularization of concepts such as comprehensive energy systems and energy internet, the energy industry is continuously developing towards an efficient, safe and sustainable energy utilization mode. The combined cooling heating and power system based on the distributed energy realizes the multifunctional aims of higher energy utilization rate, lower energy cost, better environmental protection performance and the like by the cascade utilization of energy, and becomes an important direction and form for the development of a regional comprehensive energy system. How to perform energy optimization scheduling according to the energy information of each distributed energy node by a virtual power plant aggregator is an urgent problem to be solved.
Disclosure of Invention
The invention provides an energy optimization scheduling method for a virtual power plant, which can promote local consumption of distributed energy and reduce electric power transaction cost and energy loss.
The invention specifically relates to an energy optimization scheduling method for a virtual power plant, wherein a virtual power plant aggregator performs optimization scheduling according to energy information of distributed energy nodes by taking maximum accumulated profit as a target, and the optimization scheduling method specifically comprises the following steps:
step (1), considering the situation that a virtual power plant participates in the day-ahead power market, and aiming at the uncertainty of photovoltaic output and market electricity price, processing by adopting a stochastic programming method;
step (2), determining the operation constraint conditions of the gas turbine;
step (3), determining an operation constraint condition of the energy storage system;
step (4), determining a power market trading volume constraint condition;
step (5), determining a power balance constraint condition;
and (6) performing optimized scheduling by the virtual power plant according to the operation constraint of the gas turbine, the operation constraint of the energy storage system, the trading volume constraint of the power market and the power balance constraint to obtain the day-ahead planned output of each distributed energy node, and sending the day-ahead planned output to each distributed energy node.
Further, the step (1) is specifically as follows: the objective function of the virtual power plant optimization scheduling is expressed as:
Figure BDA0002784375160000021
wherein T is oneTotal number of time segments in a day; n iss、npRespectively the number of photovoltaic scenes and the number of electricity price scenes; pi(s) and pi (p) are respectively the probabilities of the photovoltaic scene of the s-th group and the electrovalence scene of the p-th group; lambda [ alpha ]p,tThe price of electricity in the period t in the p-th group scene; gp,s,t、
Figure BDA0002784375160000022
The trading volume and the running cost of the virtual power plant in the power market at the t period under the conditions of the p-th group of power price scenes and the s-th group of photovoltaic scenes respectively;
Figure BDA0002784375160000023
is a start-stop variable of the gas turbine set; sGTThe start-stop cost of the gas turbine unit;
the operating cost of a gas turbine is expressed as a piecewise linear function:
Figure BDA0002784375160000024
wherein a is fixed production cost;
Figure BDA0002784375160000025
is an operating variable of the gas turbine; k is a radical ofjGenerating cost slope for the j section of the gas turbine;
Figure BDA0002784375160000026
is the j-th section output of the t period of the gas turbine.
Further, the gas turbine operation constraint conditions in the step (2) are as follows:
Figure BDA0002784375160000027
Figure BDA0002784375160000028
Figure BDA0002784375160000029
Figure BDA00027843751600000210
Figure BDA00027843751600000211
Figure BDA00027843751600000212
Figure BDA00027843751600000213
Figure BDA00027843751600000214
Figure BDA00027843751600000215
wherein, gGT,max、gGT,minMaximum and minimum output power of the gas turbine, respectively;
Figure BDA00027843751600000216
outputting force for the gas turbine in t time period; r isU、rDThe upward and downward ramp rates of the gas turbine;
Figure BDA00027843751600000217
the upper limit of the output of the j section of the gas turbine is;
Figure BDA00027843751600000218
the upper limits of the starting and shutdown of the gas turbine operation variables are respectively; are each tsu、tsdAre respectively burningMinimum on-off time of the gas turbine; t is tsu,0、tsd，0Respectively, the initial on-off time of the gas turbine.
Further, the energy storage system operation constraint conditions in the step (3) are as follows:
Figure BDA0002784375160000031
Figure BDA0002784375160000032
Figure BDA0002784375160000033
Figure BDA0002784375160000034
wherein the content of the first and second substances,
Figure BDA0002784375160000035
the storage capacity of the electric energy storage system; etac、ηdThe charging and discharging efficiencies of the electric energy storage system are respectively;
Figure BDA0002784375160000036
respectively the charge and discharge amount of the electric energy storage system; ses,min、Ses,maxRespectively the upper limit and the lower limit of the storage capacity of the electric energy storage system; gesc,max、gesd,maxRespectively the maximum charge-discharge power of the electric energy storage system.
Further, the constraint conditions of the electric power market trading volume in the step (4) are as follows:
Figure BDA0002784375160000037
Figure BDA0002784375160000038
wherein the content of the first and second substances,
Figure BDA0002784375160000039
the method comprises the steps of (1) purchasing and selling electric quantity of a virtual power plant in a day-ahead electric power market; pDA,max、SDA,maxThe maximum purchase and sale electric quantity of the virtual power plant in the electric power market in the day before is respectively.
Further, the power balance constraint conditions in step (5) are:
Figure BDA00027843751600000310
wherein the content of the first and second substances,
Figure BDA00027843751600000311
output for renewable energy;
Figure BDA00027843751600000312
load demand in the virtual power plant.
Detailed Description
The following describes a specific embodiment of the method for optimizing and scheduling energy of a virtual power plant in the invention in detail.
The invention discloses an energy optimization scheduling method for a virtual power plant, wherein a virtual power plant aggregator performs optimization scheduling according to energy information of distributed energy nodes by taking maximum accumulated profit as a target, and the optimization scheduling method specifically comprises the following steps: step (1), considering the situation that a virtual power plant participates in the day-ahead power market, and aiming at the uncertainty of photovoltaic output and market electricity price, processing by adopting a stochastic programming method; step (2), determining the operation constraint conditions of the gas turbine; step (3), determining an operation constraint condition of the energy storage system; step (4), determining a power market trading volume constraint condition; step (5), determining a power balance constraint condition; and (6) performing optimized scheduling by the virtual power plant according to the operation constraint of the gas turbine, the operation constraint of the energy storage system, the trading volume constraint of the power market and the power balance constraint to obtain the day-ahead planned output of each distributed energy node, and sending the day-ahead planned output to each distributed energy node.
The step (1) is specifically as follows: the objective function of the virtual power plant optimization scheduling is expressed as:
Figure BDA0002784375160000041
wherein T is the total time period number in one day; n iss、npRespectively the number of photovoltaic scenes and the number of electricity price scenes; pi(s) and pi (p) are respectively the probabilities of the photovoltaic scene of the s-th group and the electrovalence scene of the p-th group; lambda [ alpha ]p,tThe price of electricity in the period t in the p-th group scene; gp,s,t、
Figure BDA0002784375160000042
The trading volume and the running cost of the virtual power plant in the power market at the t period under the conditions of the p-th group of power price scenes and the s-th group of photovoltaic scenes respectively;
Figure BDA0002784375160000043
is a start-stop variable of the gas turbine set; sGTThe start-stop cost of the gas turbine unit;
the operating cost of a gas turbine is expressed as a piecewise linear function:
Figure BDA0002784375160000044
wherein a is fixed production cost;
Figure BDA0002784375160000045
is an operating variable of the gas turbine; k is a radical ofjGenerating cost slope for the j section of the gas turbine;
Figure BDA0002784375160000046
is the j-th section output of the t period of the gas turbine.
The gas turbine operation constraint conditions in the step (2) are as follows:
Figure BDA0002784375160000047
Figure BDA0002784375160000048
Figure BDA0002784375160000049
Figure BDA00027843751600000410
Figure BDA00027843751600000411
Figure BDA00027843751600000412
Figure BDA00027843751600000413
Figure BDA00027843751600000414
Figure BDA00027843751600000415
wherein, gGT,max、gGT,minMaximum and minimum output power of the gas turbine, respectively;
Figure BDA00027843751600000416
outputting force for the gas turbine in t time period; r isU、rDIs upward and downward of the gas turbineThe climbing rate;
Figure BDA0002784375160000051
the upper limit of the output of the j section of the gas turbine is;
Figure BDA0002784375160000052
the upper limits of the starting and shutdown of the gas turbine operation variables are respectively; are each tsu、tsdMinimum on-off time of the gas turbine; t is tsu,0、tsd，0Respectively, the initial on-off time of the gas turbine.
The operating constraint conditions of the energy storage system in the step (3) are as follows:
Figure BDA0002784375160000053
Figure BDA0002784375160000054
Figure BDA0002784375160000055
Figure BDA0002784375160000056
wherein the content of the first and second substances,
Figure BDA0002784375160000057
the storage capacity of the electric energy storage system; etac、ηdThe charging and discharging efficiencies of the electric energy storage system are respectively;
Figure BDA0002784375160000058
respectively the charge and discharge amount of the electric energy storage system; ses,min、Ses,maxRespectively the upper limit and the lower limit of the storage capacity of the electric energy storage system; gesc,max、gesd,maxRespectively the maximum charge-discharge power of the electric energy storage system.
The constraint conditions of the electric power market trading volume in the step (4) are as follows:
Figure BDA0002784375160000059
Figure BDA00027843751600000510
wherein the content of the first and second substances,
Figure BDA00027843751600000511
the method comprises the steps of (1) purchasing and selling electric quantity of a virtual power plant in a day-ahead electric power market; pDA,max、SDA,maxThe maximum purchase and sale electric quantity of the virtual power plant in the electric power market in the day before is respectively.
The power balance constraint conditions in the step (5) are as follows:
Figure BDA00027843751600000512
wherein the content of the first and second substances,
Figure BDA00027843751600000513
output for renewable energy;
Figure BDA00027843751600000514
load demand in the virtual power plant.
After the day-ahead optimization scheduling is finished, transaction matching between the distributed power supply and the load is further realized by adopting a block chain technology in the virtual power plant, and the method comprises the following steps:
step a: before the transactions are matched, the energy purchasing node/the energy selling node sends the demand information to a transaction processing center of the virtual power plant aggregator, wherein the demand information comprises information such as expected transaction electric quantity, expected transaction time and expected quotation.
Step b: the virtual power plant aggregator uses a continuous bi-directional auction mechanism to make trade matches between the energy purchasing nodes and the energy selling nodes. In the matching process, after the two transaction parties submit the quotes, the quotes of the buyers are arranged from high to low, and the optimal buying price is the highest quote of the buyers; and arranging the offers of the seller from low to high, wherein the optimal selling price is the lowest offer of the seller. And when the optimal buying price is greater than or equal to the optimal selling price, the buyer and the seller reach transaction matching, and the actual transaction price is the average value of the quoted prices of the buyer and the seller.
If the transaction matching cannot be completed in the current round of transaction period, the buyer and the seller need to update the quote according to the equations (19) and (20) and the optimal buying price/optimal selling price until the electricity is sold out or the transaction time is cut off.
Figure BDA0002784375160000061
Figure BDA0002784375160000062
Wherein p isb(t)、ps(t) a purchase offer and a sale offer, respectively; p is a radical ofcallIs an initial quote; p is a radical ofObid(t)、pOask(t) respectively the optimal purchase price and the optimal sale price in the iterative process of the tth transaction; etab、ηsThe quotation adjustment coefficients of the energy purchasing node and the energy selling node are respectively; tau isb(t)、τsAnd (t) respectively evaluating the energy purchasing node and the energy selling node in the process of the t-th transaction iteration.
After the transaction matching is completed, if the actual output of the distributed energy node is deviated from the planned output, punishment needs to be carried out on the actual output, and the punishment degree is determined by the output deviation so as to ensure the output reliability of the distributed energy node.
After the matching of the energy transaction is completed, the energy purchasing node transfers the energy currency to a wallet address provided by the energy selling node, and the energy selling node verifies the received energy payment information to realize the transaction settlement: after transaction matching between the distributed energy nodes is completed, the energy purchasing node transfers the energy currency from a digital wallet of the energy purchasing node to a wallet address provided by the energy selling node, and signature is carried out by adopting a private key; and the energy selling node downloads a public key corresponding to the energy purchasing node from the account storage center of the virtual power plant aggregator, and decrypts the received energy payment information so as to verify that the payment information comes from the corresponding energy purchasing node.
After the energy transaction settlement is completed, the virtual power plant aggregator collects all transaction records over a period of time and generates a data block: and collecting all transaction records of the virtual power plant aggregator within a period of time, completing a consensus process by adopting a PoS consensus algorithm, and acquiring block accounting rights by nodes with the highest rights and interests in a block chain. The equity, also called the day of the currency, is determined by the product of the number of tokens held by the node and the time of holding. And the distributed energy source node which obtains the accounting right broadcasts the block information to other nodes of the system, and the other nodes continue to broadcast the data block to other nodes after auditing and signing the data block. Each node compares the audit result with the results of other nodes and replies to the accounting node. If the other nodes agree on the block, the accounting node sends the currently audited data block to all other nodes for storage. After the above work is completed, the blocks are added to the energy block chain in time order.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the same. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (5)

1. The method for optimizing and scheduling the energy of the virtual power plant is characterized in that a virtual power plant aggregator performs optimized scheduling by taking maximum accumulated profit as a target according to the energy information of each distributed energy node, and the optimized scheduling method specifically comprises the following steps:
step (1), considering the situation that a virtual power plant participates in the day-ahead power market, and aiming at the uncertainty of photovoltaic output and market electricity price, processing by adopting a stochastic programming method;
step (2), determining the operation constraint conditions of the gas turbine;
step (3), determining an operation constraint condition of the energy storage system;
step (4), determining a power market trading volume constraint condition;
step (5), determining a power balance constraint condition;
performing optimized scheduling by the virtual power plant according to the operation constraint of the gas turbine, the operation constraint of the energy storage system, the trading volume constraint of the power market and the power balance constraint to obtain the day-ahead planned output of each distributed energy node, and sending the day-ahead planned output to each distributed energy node;
the step (1) is specifically as follows: the objective function of the virtual power plant optimization scheduling is expressed as:
Figure FDA0003310532270000011
wherein T is the total time period number in one day; n iss、npRespectively the number of photovoltaic scenes and the number of electricity price scenes; pi(s) and pi (p) are respectively the probabilities of the photovoltaic scene of the s-th group and the electrovalence scene of the p-th group; lambda [ alpha ]p,tThe price of electricity in the period t in the p-th group scene; gp,s,t、
Figure FDA0003310532270000012
The trading volume and the running cost of the virtual power plant in the power market at the t period under the conditions of the p-th group of power price scenes and the s-th group of photovoltaic scenes respectively;
Figure FDA0003310532270000013
is a start-stop variable of the gas turbine set; sGTThe start-stop cost of the gas turbine unit;
the operating cost of a gas turbine is expressed as a piecewise linear function:
Figure FDA0003310532270000014
wherein a is fixed production cost;
Figure FDA0003310532270000015
is an operating variable of the gas turbine; k is a radical ofjGenerating cost slope for the j section of the gas turbine;
Figure FDA0003310532270000016
is the j-th section output of the t period of the gas turbine.
2. The method for energy optimized scheduling of virtual power plant according to claim 1, wherein the gas turbine operation constraints in step (2) are:
Figure FDA0003310532270000017
Figure FDA0003310532270000018
Figure FDA0003310532270000021
Figure FDA0003310532270000022
Figure FDA0003310532270000023
Figure FDA0003310532270000024
Figure FDA0003310532270000025
Figure FDA0003310532270000026
Figure FDA0003310532270000027
wherein, gGT,max、gGT,minMaximum and minimum output power of the gas turbine, respectively;
Figure FDA0003310532270000028
outputting force for the gas turbine in t time period; r isU、rDThe upward and downward ramp rates of the gas turbine;
Figure FDA0003310532270000029
the upper limit of the output of the j section of the gas turbine is;
Figure FDA00033105322700000210
the upper limits of the starting and shutdown of the gas turbine operation variables are respectively; t is tsu、tsdMinimum on-off time of the gas turbine; t is tsu,0、tsd，0Respectively, the initial on-off time of the gas turbine.
3. The method for energy optimal scheduling of a virtual power plant according to claim 2, wherein the energy storage system operation constraint conditions in the step (3) are as follows:
Figure FDA00033105322700000211
Figure FDA00033105322700000212
Figure FDA00033105322700000213
Figure FDA00033105322700000214
wherein the content of the first and second substances,
Figure FDA00033105322700000215
the storage capacity of the electric energy storage system; etac、ηdThe charging and discharging efficiencies of the electric energy storage system are respectively;
Figure FDA00033105322700000216
respectively the charge and discharge amount of the electric energy storage system; ses,min、Ses,maxRespectively the upper limit and the lower limit of the storage capacity of the electric energy storage system; gesc,max、gesd,maxRespectively the maximum charge-discharge power of the electric energy storage system.
4. The method for energy optimal scheduling of a virtual power plant according to claim 2, wherein the constraint conditions of the electric power market trading volume in the step (4) are as follows:
Figure FDA00033105322700000217
Figure FDA0003310532270000031
wherein the content of the first and second substances,
Figure FDA0003310532270000032
day-ahead power city for virtual power plantThe purchase and sale electricity quantity of the farm; pDA,max、SDA,maxThe maximum purchase and sale electric quantity of the virtual power plant in the electric power market in the day before is respectively.
5. The method for energy-optimized scheduling of a virtual power plant according to claim 2, wherein the power balance constraint in step (5) is:
Figure FDA0003310532270000033
wherein the content of the first and second substances,
Figure FDA0003310532270000034
output for renewable energy;
Figure FDA0003310532270000035
load demand in the virtual power plant.
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