CN113239607A - Economic dispatching optimization method, system, equipment and storage medium for comprehensive energy system - Google Patents

Economic dispatching optimization method, system, equipment and storage medium for comprehensive energy system Download PDF

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CN113239607A
CN113239607A CN202110667615.XA CN202110667615A CN113239607A CN 113239607 A CN113239607 A CN 113239607A CN 202110667615 A CN202110667615 A CN 202110667615A CN 113239607 A CN113239607 A CN 113239607A
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energy system
day
comprehensive energy
economic dispatching
power
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唐剑
王珂
吴华华
朱克东
张洁
王礼文
楼贤嗣
石飞
苏熀兴
刘俊
吴利锋
王刚
蒙志全
徐鹏
黄启航
钱甜甜
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Priority to CN202110667615.XA priority Critical patent/CN113239607A/en
Publication of CN113239607A publication Critical patent/CN113239607A/en
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The invention provides an economic dispatching optimization method, a system, equipment and a storage medium of a comprehensive energy system, wherein the method comprises the following steps: acquiring relevant original data of a day-ahead economic dispatching scene of the comprehensive energy system; according to the original data, establishing a comprehensive energy system day-ahead economic dispatching optimization objective function aiming at minimizing the comprehensive energy system economic operation containing energy purchase cost and charge-discharge depreciation cost of electric energy storage, and establishing a comprehensive energy system day-ahead economic dispatching constraint condition covering the power balance, external energy supply and equipment operation of the comprehensive energy system; and solving the optimal scheme of the day-ahead economic dispatching of the integrated energy system by adopting a multi-population particle swarm algorithm based on the day-ahead economic dispatching optimization objective function of the integrated energy system and the day-ahead economic dispatching constraint condition of the integrated energy system. The method optimizes energy supply, realizes transverse cooperative complementation and longitudinal cascade utilization among multiple energy sources, promotes the consumption of distributed renewable energy sources, and improves the utilization rate of the energy sources.

Description

Economic dispatching optimization method, system, equipment and storage medium for comprehensive energy system
Technical Field
The invention relates to the field of power dispatching, in particular to an economic dispatching optimization method, system, equipment and storage medium of a comprehensive energy system.
Background
In order to solve the problems of low comprehensive energy efficiency of renewable clean energy access and traditional energy, the safe, clean and efficient modern energy technology is developed, an energy interconnection shared network with multi-source efficient utilization and multi-element main body cooperation is constructed, green and low-carbon development is realized, and wide attention and positive response of the whole society are caused.
The comprehensive energy system is used as a physical expression form of an energy internet, and plays an increasingly important role in improving comprehensive energy utilization efficiency of energy and consumption of renewable energy. However, in the prior art, energy systems such as electric power, heat, natural gas and the like cannot cooperate with each other to supply energy, so that the utilization rate of the existing clean energy is not high.
Disclosure of Invention
In order to solve the problem of low utilization rate of clean energy in the prior art, the invention provides an economic dispatching optimization method, a system, equipment and a storage medium of an integrated energy system.
In order to achieve the purpose, the invention adopts the following technical scheme:
an economic dispatching optimization method of an integrated energy system comprises the following steps:
acquiring relevant original data of a day-ahead economic dispatching scene of the comprehensive energy system;
according to the original data, establishing a comprehensive energy system day-ahead economic dispatching optimization objective function aiming at minimizing the comprehensive energy system economic operation containing energy purchase cost and charge-discharge depreciation cost of electric energy storage, and establishing a comprehensive energy system day-ahead economic dispatching constraint condition covering the power balance, external energy supply and equipment operation of the comprehensive energy system;
and solving the optimal scheme of the day-ahead economic dispatching of the integrated energy system based on the day-ahead economic dispatching optimization objective function of the integrated energy system and the day-ahead economic dispatching constraint condition of the integrated energy system.
As a further improvement of the invention, the comprehensive energy system comprises a combined heat and power unit, a photovoltaic unit, an electric energy storage unit, a gas boiler and a user electricity-heat load unit.
As a further improvement of the invention, the day-ahead economic dispatch optimization objective function of the integrated energy system comprises the energy purchase cost CECharging and discharging depreciation cost C of power storageBThe objective function is calculated by the following method:
F＝min(CE+CB) (1)
wherein, the energy purchase cost CEComprises the following steps:
Figure BDA0003117514360000021
in the formula: p is a radical ofg(t) exchanging the comprehensive energy system with the power grid at the moment tPower of electric power exchanged, ce(t) the electricity price at time t; c. Cgas(t) is the natural gas unit heating value price at time t; p is a radical ofchp(t) is the output electric power of the cogeneration unit at the moment t; h isgfb(t) is the output thermal power of the gas boiler at time t; etachpThe electric efficiency of the cogeneration unit; etagfbEfficiency of a gas boiler; t is the total time interval scheduled on the same day;
wherein the depreciation cost of charging and discharging of the electric energy storage CBComprises the following steps:
Figure BDA0003117514360000022
in the formula: p is a radical ofbes(t) is the charge/discharge power of the electrical energy storage at time t; mu.sBIs the depreciation cost coefficient of the electric energy storage.
As a further improvement of the present invention, the integrated energy system day-ahead economic dispatch constraints include power balance constraints, external energy supply constraints, and equipment operation constraints;
the power balance constraint is an electric power balance constraint and a thermal power balance constraint of the comprehensive energy system at a time t, and specifically comprises the following steps:
pg(t)+pde(t)+pbes(t)+pchp(t)＝pload(t) (4)
hchp(t)+hgfb(t)＝hload(t) (5)
in the formula: h ischp(t) is the thermal power output by the cogeneration unit at the moment t; p is a radical ofde(t) is the output power of the distributed power supply; p is a radical ofload(t) is the electrical load at time t; h isload(t) is the thermal load at time t;
the external energy supply constraint is the constraint requirement that the power interaction of the power grid to the comprehensive energy system has upper and lower limits, and specifically comprises the following steps:
pg,min≤pg(t)≤pg,max (6)
in the formula: p is a radical ofg,maxAnd pg,minRepresenting the upper and lower limits of the interaction power of the comprehensive energy system and the power grid;
the device operation constraint is that a combined heat and power unit, an electric energy storage device and a gas boiler in the comprehensive energy system all have operation upper and lower limit constraints, and specifically comprises the following steps:
pchp,min≤pchp(t)≤pchp,max (7)
pbes,min≤pbes(t)≤pbes,max (8)
hgfb,min≤hgfb(t)≤hgfb,max (9)
in the formula: p is a radical ofchp,minAnd pchp,maxRespectively is the lower limit and the upper limit of the output electric power of the cogeneration unit; p is a radical ofbes,minAnd pbes,maxLower and upper limits for electrical energy storage charge/discharge power, respectively; h isgfb,minAnd hgfb,maxRespectively the lower limit and the upper limit of the thermal power output by the gas boiler.
As a further improvement of the present invention, the electrical energy storage device also satisfies electrical state constraints, the electrical state constraints specifically being:
socmin≤soc(t)≤socmax (10)
in the formula: socminAnd socmaxRespectively setting the lower limit and the upper limit of the electric energy storage charge state;
wherein the state of charge soc (t) of the electrical energy storage device is expressed as follows
Figure BDA0003117514360000031
soc(0)＝socini (12)
Figure BDA0003117514360000032
In the formula: qbesIs the power capacity of the electrical energy storage device; SOCiniFor initial charging of the electrical energy storage deviceState; etabesCoefficient of charge/discharge for electrical energy storage, ηchAnd ηdiRepresenting the charging efficiency and the discharging efficiency of the electrical energy storage.
As a further improvement of the invention, the best scheme for solving the day-ahead economic dispatch of the comprehensive energy system is solved by adopting a multi-population particle swarm algorithm, and the solving process specifically comprises the following steps:
constructing a target function, particle position limit and particle speed limit of a multi-population particle swarm algorithm according to a day-ahead economic dispatching optimization target function of the comprehensive energy system and a day-ahead economic dispatching constraint condition of the comprehensive energy system;
randomly generating a particle population; sorting the particle groups according to the fitness of the optimization objective function of the day-ahead economic dispatching of the comprehensive energy system, and dividing the particle groups into a plurality of groups;
sequentially solving and iterating the particle positions in each group according to the day-ahead economic dispatching constraint conditions of the comprehensive energy system; simultaneously updating the individual optimal position of each particle and the global optimal position of the particle group;
and if the iteration times are larger than the set value, stopping calculation to obtain the optimal scheme of the day-ahead economic dispatching of the comprehensive energy system.
As a further improvement of the present invention, the dividing into several groups is specifically to divide the order into an excellent group, a medieval group and a poor group;
the excellent population is solved by the following method:
xj(k+1)＝xj+N(0,1)×xj (14)
in the formula, k represents the number of iterations; x is the number ofjRepresents the position of the jth particle in the solution space; n (0,1) represents a Gaussian distribution function;
the poor population is solved by adopting the following method:
xj(k+1)＝xj+C(0,1)×xj (15)
wherein C (0,1) represents a Cauchy distribution function;
the mediocre population is solved using the following method:
vj(k+1)＝ωvj(k)+φ1r1(pbestj-xj(k))+φ2r2(gbest-xj(k)) (16)
xj(k+1)＝xj(k)+vj(k+1) (17)
in the formula, vjRepresents the moving speed of the jth particle; ω represents an inertia factor; phi is a1And phi2Represents an acceleration factor; r is1And r2Represents a random variable of 0 to 1; gbestRepresents the current global best position of all particles; pbestjRepresenting the individual optimal position of the jth particle.
An integrated energy system economic dispatch optimization decision system comprising:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring relevant original data of a day-ahead economic dispatching scene of the comprehensive energy system;
the model establishing unit is used for establishing a comprehensive energy system day-ahead economic dispatching optimization objective function aiming at minimizing the comprehensive energy system economic operation containing the energy purchase cost and the charge-discharge depreciation cost of the electricity storage according to the original data, and establishing a comprehensive energy system day-ahead economic dispatching constraint condition covering the power balance, the external energy supply and the equipment operation of the comprehensive energy system;
and the algorithm solving unit is used for solving the optimal scheme of the day-ahead economic dispatching of the comprehensive energy system based on the day-ahead economic dispatching optimization objective function of the comprehensive energy system and the day-ahead economic dispatching constraint condition of the comprehensive energy system.
An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method for optimizing economic dispatch of an integrated energy system when executing the computer program.
A computer readable storage medium storing a computer program which, when executed by a processor, performs the steps of the integrated energy system economic dispatch optimization method.
The invention has the beneficial effects that:
the method provides a day-ahead economic dispatching optimization model of the comprehensive energy system, and starting from the overall situation, the economic operation minimization of the comprehensive energy system comprising the energy purchase cost and the charge-discharge depreciation cost of the electric energy storage is taken as an objective function, the power balance, the external energy supply and the equipment operation constraint of common units of the comprehensive energy system, such as a combined heat and power unit, a distributed power supply, the electric energy storage, a gas boiler, a user electricity-heat load and the like, are comprehensively considered, and the economic optimization under the safe operation of the comprehensive energy system is realized. Due to the fact that the operation characteristics of a comprehensive energy system and the advantages of the multi-population particle swarm optimization are considered, energy systems such as electric power, heating power and natural gas are coupled through energy conversion equipment, energy supply is optimized, transverse cooperative complementation and longitudinal cascade utilization among multiple energy sources are achieved, distributed renewable energy consumption is promoted, and the energy utilization rate is improved.
Drawings
FIG. 1 is a schematic diagram of an integrated energy system architecture to which the present invention relates;
FIG. 2 is a schematic flow chart of an economic dispatch optimization method for an integrated energy system according to a preferred embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an economic dispatch optimization decision system of an integrated energy system according to a preferred embodiment of the present invention;
FIG. 4 is a schematic diagram of an electronic device according to a preferred embodiment of the present invention;
FIG. 5 integrates the results of the daily scheduling of electrical power for each unit in the energy system.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
The invention aims to design an economic dispatching optimization method of an integrated energy system based on an improved multi-population particle swarm algorithm by considering the operation characteristics of the integrated energy system and the advantages of the multi-population particle swarm algorithm.
The noun explains:
particle Swarm Optimization (PSO) is in turn translated into a Particle Swarm algorithm, or a Particle Swarm optimization algorithm. The method is a random search algorithm based on group cooperation and developed by simulating foraging behavior of a bird group. It is generally considered to be one of the cluster intelligence (SI). It can be incorporated into a multi-agent Optimization System (MAOS). The particle swarm optimization algorithm obviously cannot solve the operation characteristics of the comprehensive energy system, and the particle swarm optimization algorithm is based on the improved multi-population particle swarm optimization algorithm.
The primary objective of the operation optimization of the comprehensive energy system is to improve the economic benefit of the system, namely, the output of each device in each time interval is effectively arranged by taking the optimal economic operation as the target on the premise of meeting the load requirements of users. The architecture of the comprehensive energy system considering the optimal economic dispatch is shown in fig. 1, and mainly comprises common units of the comprehensive energy system, such as a cogeneration unit, a distributed power supply, an electric energy storage unit, a gas boiler, a user electricity-heat load and the like.
With reference to fig. 2, a first object of the present invention is to provide a day-ahead economic dispatching method for an integrated energy system, comprising the following steps:
acquiring relevant original data of a day-ahead economic dispatching scene of the comprehensive energy system;
according to the original data, establishing a comprehensive energy system day-ahead economic dispatching optimization objective function aiming at minimizing the comprehensive energy system economic operation containing energy purchase cost and charge-discharge depreciation cost of electric energy storage, and establishing a comprehensive energy system day-ahead economic dispatching constraint condition covering the power balance, external energy supply and equipment operation of the comprehensive energy system;
and solving the optimal scheme of the day-ahead economic dispatching of the integrated energy system by adopting a multi-population particle swarm algorithm based on the day-ahead economic dispatching optimization objective function of the integrated energy system and the day-ahead economic dispatching constraint condition of the integrated energy system.
The comprehensive energy system comprises a combined heat and power unit, a photovoltaic unit, an electric energy storage unit, a gas boiler and a user electricity-heat load unit.
As a preferred embodiment of the present invention, the above scheme for solving the optimal scheme of the day-ahead economic dispatch of the integrated energy system by using the multi-population particle swarm algorithm specifically includes:
constructing a target function, particle position limit and particle speed limit of a multi-population particle swarm algorithm according to a day-ahead economic dispatching optimization target function of the comprehensive energy system and a day-ahead economic dispatching constraint condition of the comprehensive energy system;
randomly generating a particle population; sorting the particle groups according to the fitness of the optimization objective function of the day-ahead economic dispatching of the comprehensive energy system, and dividing the particle groups into a plurality of groups; the division into several groups is specifically to divide the order into an excellent group, a mediocre group and a poor group.
Sequentially solving and iterating the particle positions in each group according to the day-ahead economic dispatching constraint conditions of the comprehensive energy system; simultaneously updating the individual optimal position of each particle and the global optimal position of the particle group;
and if the iteration times are larger than the set value, stopping calculation to obtain the optimal scheme of the day-ahead economic dispatching of the comprehensive energy system.
As a preferred embodiment, the present invention divides the front 1/4 population into excellent populations, the rear 1/4 population into poor populations, and the remaining populations into mediocre populations. Of course, not limited to the above classification. The method specifically comprises the following steps:
step (1) design of day-ahead economic dispatching optimization objective function of comprehensive energy system
The economic dispatching target of the integrated energy system is the minimization of the operation cost of the system, mainly comprising the purchase cost C of energyECharging and discharging depreciation cost C of power storageB. Operation of comprehensive energy systemThe mathematics are expressed as
F＝min(CE+CB) (1)
Wherein, the energy purchase cost CEComprises the following steps:
Figure BDA0003117514360000081
in the formula: p is a radical ofg(t) the electric power exchanged by the integrated energy system and the grid at time t, pg(t)>0 represents the power purchase from the comprehensive energy system to the power grid, pg(t)<0 represents that the comprehensive energy system supplies power to the power grid; c. Ce(t) the electricity price at time t; c. Cgas(t) is the natural gas unit heating value price at time t; p is a radical ofchp(t) is the output electric power of the cogeneration unit at the moment t; h isgfb(t) is the output thermal power of the gas boiler at time t; etachpThe electric efficiency of the cogeneration unit; etagfbEfficiency of a gas boiler; t is the total time period scheduled on the current day.
Cost of charge and discharge depreciation of electrical energy storage CBComprises the following steps:
Figure BDA0003117514360000082
in the formula: p is a radical ofbes(t) the charging/discharging power of the electrical energy store at time t, pbes(t)>0 represents that the electrical energy storage is in a discharging state, and is in a charging state otherwise; mu.sBIs the depreciation cost coefficient of the electric energy storage.
Step (2) design of day-ahead economic dispatching constraint conditions of comprehensive energy system
The constraint conditions of the day-ahead economic dispatching problem of the integrated energy system are mainly divided into power balance, external energy supply and equipment operation.
1) Power balance constraint
The electric power balance constraint and the thermal power balance constraint of the integrated energy system at the time t are as follows:
pg(t)+pde(t)+pbes(t)+pchp(t)＝pload(t) (4)
hchp(t)+hgfb(t)＝hload(t) (5)
in the formula: h ischp(t) is the thermal power output by the cogeneration unit at the moment t; p is a radical ofde(t) is the output power of the distributed power supply; p is a radical ofload(t) is the electrical load at time t; h isload(t) is the thermal load at time t.
2) External energy supply restraint
Considering the requirements of operation safety and stability of the power grid side, the power grid has upper and lower limit constraint requirements on the power interaction of the comprehensive energy system, namely:
pg,min≤pg(t)≤pg,max (6)
in the formula: p is a radical ofg,maxAnd pg,minRepresenting the upper and lower limits of the interaction power of the comprehensive energy system and the power grid.
3) Plant operating constraints
In the day-ahead optimization scheduling process, a combined heat and power unit, electric energy storage equipment and a gas boiler in the comprehensive energy system are all restricted by upper and lower limits of operation, and the mathematical formula is as follows:
pchp,min≤pchp(t)≤pchp,max (7)
pbes,min≤pbes(t)≤pbes,max (8)
hgfb,min≤hgfb(t)≤hgfb,max (9)
in the formula: p is a radical ofchp,minAnd pchp,maxRespectively is the lower limit and the upper limit of the output electric power of the cogeneration unit; p is a radical ofbes,minAnd pbes,maxLower and upper limits for electrical energy storage charge/discharge power, respectively; h isgfb,minAnd hgfb,maxRespectively the lower limit and the upper limit of the thermal power output by the gas boiler.
In order to avoid damage to the electrical energy storage device due to deep charging and discharging, the state of charge soc (t) of the electrical energy storage device needs to be restricted within a certain range, that is:
socmin≤soc(t)≤socmax (10)
in the formula: socminAnd socmaxRespectively the lower limit and the upper limit of the electric energy storage state of charge.
Wherein the state of charge soc (t) of the electrical energy storage device is expressed as follows
Figure BDA0003117514360000091
soc(0)＝socini (12)
Figure BDA0003117514360000092
In the formula: qbesIs the power capacity of the electrical energy storage device; SOCiniThe initial charge state of the electric energy storage equipment; etabesCoefficient of charge/discharge for electrical energy storage, ηchAnd ηdiRepresenting the charging efficiency and the discharging efficiency of the electrical energy storage.
Step (3) improving the multi-population particle swarm algorithm to solve the day-ahead economic dispatching optimal scheme of the comprehensive energy system
The day-ahead economic dispatching of the integrated energy system is an ultra-large-scale complex nonlinear solving problem, and a plurality of local optimal solutions exist. For this reason, the particle populations are classified into excellent populations, intermediate populations, and poor populations according to the particle fitness size ranking at each iteration.
Excellent group: because the optimal solution is close, the particles in the group only need to pay attention to how to move the optimal solution, and the individual characteristics can be ignored, so that the Gaussian variation is adopted to help the related particles to strengthen local search and accelerate the convergence speed, namely the formula (14);
xj(k+1)＝xj+N(0,1)×xj (14)
in the formula, k represents the number of iterations; x is the number ofjRepresents the position of the jth particle in the solution space; n (0,1) represents a Gaussian distribution function.
Poor population: since the particles in the population are far away from the optimal solution, the Cauchy mutation is adopted to enhance the jumping performance of the related particles, other local optimal solutions are searched, and more chances are provided for moving to a middle-quality population or an excellent population, namely formula (15);
xj(k+1)＝xj+C(0,1)×xj (15)
wherein C (0,1) represents a Cauchy distribution function.
The mediocre population: the conventional particle swarm algorithm is adopted to search in a solution space, namely equations (16) - (17).
vj(k+1)＝ωvj(k)+φ1r1(pbestj-xj(k))+φ2r2(gbest-xj(k)) (16)
xj(k+1)＝xj(k)+vj(k+1) (17)
In the formula, vjRepresents the moving speed of the jth particle; ω represents an inertia factor; phi is a1And phi2Represents an acceleration factor; r is1And r2Represents a random variable of 0 to 1; gbestRepresents the current global best position of all particles; pbestjRepresenting the individual optimal position of the jth particle.
Therefore, the following solution process for improving the multi-population particle swarm optimization algorithm is given as follows:
step 1) initializing particle swarm parameters;
step 2) constructing a target function, particle position limit and particle speed limit of a group particle swarm algorithm according to the day-ahead scheduling optimization model formulas (1) - (13) of the comprehensive energy system;
step 3) randomly generating particle populations
Step 4) sorting the particle populations according to the fitness of the target function, dividing the front 1/4 population into an excellent population, dividing the rear 1/4 population into an inferior population, and dividing the rest populations into a middle-grade population;
step 5) updating the positions of the particles in the high-quality population according to a formula (14); updating the positions of the particles in the poor population according to equation (15); updating particle positions in the mediocre population according to equations (16) - (17);
step 6) updating the optimal position of each particle individual and the global optimal position of the particle group;
and 7) if the iteration times are larger than a set value, stopping the optimization calculation and outputting a day-ahead economic dispatching result.
The invention is suitable for the day-ahead economic dispatching scene of the comprehensive energy system comprising the common units of the comprehensive energy system such as a combined heat and power unit, photovoltaic, electric energy storage, a gas boiler, user electricity-heat load and the like, and can realize the safe operation and economic optimization of the comprehensive energy system.
Based on the above description of the method of the present invention, it can be seen that the present invention has the following advantages:
(1) and (4) establishing an economic dispatching optimization model facing the comprehensive energy system by considering the characteristics of the day-ahead optimized dispatching scene of the comprehensive energy system.
(2) The improved multi-population particle swarm algorithm provides an optimal scheme for the day-ahead economic dispatching problem of the comprehensive energy system.
Therefore, the invention provides a day-ahead economic dispatching optimization model of the comprehensive energy system, and provides an improved multi-population particle swarm algorithm for solving the day-ahead economic dispatching optimization model of the comprehensive energy system from the overall view by using the minimization of the economic operation of the comprehensive energy system including the energy purchase cost and the charge-discharge depreciation cost of the electricity energy storage as an objective function. The method comprehensively considers the power balance, external energy supply and equipment operation constraint of common units of the comprehensive energy system, such as a combined heat and power unit, a distributed power supply, electric energy storage, a gas boiler, user electricity-heat load and the like, and realizes the economic optimization of the comprehensive energy system under the safe operation.
As shown in fig. 3, another objective of the present invention is to provide an economic dispatch optimization decision system for an integrated energy system, comprising:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring relevant original data of a day-ahead economic dispatching scene of the comprehensive energy system;
the model establishing unit is used for establishing a comprehensive energy system day-ahead economic dispatching optimization objective function aiming at minimizing the comprehensive energy system economic operation containing the energy purchase cost and the charge-discharge depreciation cost of the electricity storage according to the original data, and establishing a comprehensive energy system day-ahead economic dispatching constraint condition covering the power balance, the external energy supply and the equipment operation of the comprehensive energy system; the model refers to a day-ahead economic dispatching optimization objective function of the comprehensive energy system and a day-ahead economic dispatching constraint condition of the comprehensive energy system.
And the algorithm solving unit is used for solving the optimal scheme of the day-ahead economic dispatching of the comprehensive energy system by adopting a multi-population particle swarm algorithm based on the day-ahead economic dispatching optimization objective function of the comprehensive energy system and the day-ahead economic dispatching constraint condition of the comprehensive energy system.
Wherein, the solving adopts a multi-population particle swarm algorithm.
A third object of the present invention is to provide an electronic device, as shown in fig. 4, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for optimizing the economic dispatch of the integrated energy system when executing the computer program.
A fourth object of the present invention is to provide a computer readable storage medium, which stores a computer program, which when executed by a processor, implements the steps of the method for optimizing economic dispatch of an integrated energy system.
Examples of embodiment
In the embodiment, the comprehensive energy system shown in fig. 1 is adopted to carry out simulation research, the length of the scheduling time period of the system is 24h, and the interval of every two adjacent time periods is 15 min. The range of the power exchange between the comprehensive energy system and the main power grid is [ -5, 5] MW, and the capacity of electric energy storage is 2000 kW.h. The electricity price of the embodiment adopts time-of-use electricity price, wherein the peak time period is 12: 00-19: 00) flat time interval is 07:00-12:00, 19:00-23:00, and valley time interval is 23:00-07: 00). The natural gas price is 0.45 yuan/(kW.h) of fixed price.
G1-G5 in fig. 5 represent daily load curves of the distributed power supply, the first electric load, the second electric load, the electric energy storage and the power delivered by the power grid, respectively.
As can be seen from the figure, the main component of the operation of the integrated energy system is the power transmission of the power grid, and the daily load curves of the distributed power supply and the first electrical load are relatively stable. In the example, the optimal operation cost of the comprehensive energy system is about 18000 yuan.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (10)

1. An economic dispatching optimization method of an integrated energy system is characterized by comprising the following steps:
acquiring relevant original data of a day-ahead economic dispatching scene of the comprehensive energy system;
according to the original data, establishing a comprehensive energy system day-ahead economic dispatching optimization objective function aiming at minimizing the comprehensive energy system economic operation containing energy purchase cost and charge-discharge depreciation cost of electric energy storage, and establishing a comprehensive energy system day-ahead economic dispatching constraint condition covering the power balance, external energy supply and equipment operation of the comprehensive energy system;
and solving the optimal scheme of the day-ahead economic dispatching of the integrated energy system based on the day-ahead economic dispatching optimization objective function of the integrated energy system and the day-ahead economic dispatching constraint condition of the integrated energy system.
2. The method of claim 1, wherein:
the comprehensive energy system comprises a combined heat and power unit, a photovoltaic unit, an electric energy storage unit, a gas boiler and a user electricity-heat load unit.
3. The method of claim 1, wherein:
said combined energyThe source system day-ahead economic dispatching optimization objective function comprises energy purchase cost CECharging and discharging depreciation cost C of power storageBThe objective function is calculated by the following method:
F＝min(CE+CB) (1)
wherein, the energy purchase cost CEComprises the following steps:
Figure FDA0003117514350000011
in the formula: p is a radical ofg(t) the electric power exchanged by the integrated energy system and the grid at time t, ce(t) the electricity price at time t; c. Cgas(t) is the natural gas unit heating value price at time t; p is a radical ofchp(t) is the output electric power of the cogeneration unit at the moment t; h isgfb(t) is the output thermal power of the gas boiler at time t; etachpThe electric efficiency of the cogeneration unit; etagfbEfficiency of a gas boiler; t is the total time interval scheduled on the same day;
wherein the depreciation cost of charging and discharging of the electric energy storage CBComprises the following steps:
Figure FDA0003117514350000012
in the formula: p is a radical ofbes(t) is the charge/discharge power of the electrical energy storage at time t; mu.sBIs the depreciation cost coefficient of the electric energy storage.
4. The method of claim 1, wherein:
the day-ahead economic dispatching constraint conditions of the integrated energy system comprise power balance constraint, external energy supply constraint and equipment operation constraint;
the power balance constraint is an electric power balance constraint and a thermal power balance constraint of the comprehensive energy system at a time t, and specifically comprises the following steps:
pg(t)+pde(t)+pbes(t)+pchp(t)＝pload(t) (4)
hchp(t)+hgfb(t)＝hload(t) (5)
in the formula: h ischp(t) is the thermal power output by the cogeneration unit at the moment t; p is a radical ofde(t) is the output power of the distributed power supply; p is a radical ofload(t) is the electrical load at time t; h isload(t) is the thermal load at time t;
the external energy supply constraint is the constraint requirement that the power interaction of the power grid to the comprehensive energy system has upper and lower limits, and specifically comprises the following steps:
pg,min≤pg(t)≤pg,max (6)
in the formula: p is a radical ofg,maxAnd pg,minRepresenting the upper and lower limits of the interaction power of the comprehensive energy system and the power grid;
the device operation constraint is that a combined heat and power unit, an electric energy storage device and a gas boiler in the comprehensive energy system all have operation upper and lower limit constraints, and specifically comprises the following steps:
pchp,min≤pchp(t)≤pchp,max (7)
pbes,min≤pbes(t)≤pbes,max (8)
hgfb,min≤hgfb(t)≤hgfb,max (9)
in the formula: p is a radical ofchp,minAnd pchp,maxRespectively is the lower limit and the upper limit of the output electric power of the cogeneration unit; p is a radical ofbes,minAnd pbes,maxLower and upper limits for electrical energy storage charge/discharge power, respectively; h isgfb,minAnd hgfb,maxRespectively the lower limit and the upper limit of the thermal power output by the gas boiler.
5. The method of claim 4, wherein:
the electric energy storage equipment charge also meets the electric state constraint, and the electric state constraint specifically comprises the following steps:
socmin≤soc(t)≤socmax (10)
in the formula: socminAnd socmaxRespectively setting the lower limit and the upper limit of the electric energy storage charge state;
wherein the state of charge soc (t) of the electrical energy storage device is expressed as follows
Figure FDA0003117514350000031
soc(0)＝socini (12)
Figure FDA0003117514350000032
In the formula: qbesIs the power capacity of the electrical energy storage device; SOCiniThe initial charge state of the electric energy storage equipment; etabesCoefficient of charge/discharge for electrical energy storage, ηchAnd ηdiRepresenting the charging efficiency and the discharging efficiency of the electrical energy storage.
6. The method of claim 1, wherein:
solving the optimal scheme of the day-ahead economic dispatch of the comprehensive energy system by adopting a multi-population particle swarm algorithm, wherein the solving process specifically comprises the following steps:
constructing a target function, particle position limit and particle speed limit of a multi-population particle swarm algorithm according to a day-ahead economic dispatching optimization target function of the comprehensive energy system and a day-ahead economic dispatching constraint condition of the comprehensive energy system;
randomly generating a particle population; sorting the particle groups according to the fitness of the optimization objective function of the day-ahead economic dispatching of the comprehensive energy system, and dividing the particle groups into a plurality of groups;
sequentially solving and iterating the particle positions in each group according to the day-ahead economic dispatching constraint conditions of the comprehensive energy system; simultaneously updating the individual optimal position of each particle and the global optimal position of the particle group;
and if the iteration times are larger than the set value, stopping calculation to obtain the optimal scheme of the day-ahead economic dispatching of the comprehensive energy system.
7. The method of claim 6, wherein:
the division into a plurality of groups specifically divides the sequence into an excellent group, a mediocre group and a poor group;
the excellent population is solved by the following method:
xj(k+1)＝xj+N(0,1)×xj (14)
in the formula, k represents the number of iterations; x is the number ofjRepresents the position of the jth particle in the solution space; n (0,1) represents a Gaussian distribution function;
the poor population is solved by adopting the following method:
xj(k+1)＝xj+C(0,1)×xj (15)
wherein C (0,1) represents a Cauchy distribution function;
the mediocre population is solved using the following method:
vj(k+1)＝ωvj(k)+φ1r1(pbestj-xj(k))+φ2r2(gbest-xj(k)) (16)
xj(k+1)＝xj(k)+vj(k+1) (17)
in the formula, vjRepresents the moving speed of the jth particle; ω represents an inertia factor; phi is a1And phi2Represents an acceleration factor; r is1And r2Represents a random variable of 0 to 1; gbestRepresents the current global best position of all particles; pbestjRepresenting the individual optimal position of the jth particle.
8. An economic dispatch optimization decision system of an integrated energy system, comprising:
the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring relevant original data of a day-ahead economic dispatching scene of the comprehensive energy system;
the model establishing unit is used for establishing a comprehensive energy system day-ahead economic dispatching optimization objective function aiming at minimizing the comprehensive energy system economic operation containing the energy purchase cost and the charge-discharge depreciation cost of the electricity storage according to the original data, and establishing a comprehensive energy system day-ahead economic dispatching constraint condition covering the power balance, the external energy supply and the equipment operation of the comprehensive energy system;
and the algorithm solving unit is used for solving the optimal scheme of the day-ahead economic dispatching of the comprehensive energy system based on the day-ahead economic dispatching optimization objective function of the comprehensive energy system and the day-ahead economic dispatching constraint condition of the comprehensive energy system.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method for economic dispatch optimization of an integrated energy system according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium storing a computer program which when executed by a processor implements the steps of the method for economic dispatch optimization of an integrated energy system according to any of claims 1 to 7.
CN202110667615.XA 2021-06-16 2021-06-16 Economic dispatching optimization method, system, equipment and storage medium for comprehensive energy system Pending CN113239607A (en)

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