CN113394777A - Power distribution network operation and user side energy system demand response based collaborative optimization method - Google Patents

Power distribution network operation and user side energy system demand response based collaborative optimization method Download PDF

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Publication number
CN113394777A
CN113394777A CN202110767170.2A CN202110767170A CN113394777A CN 113394777 A CN113394777 A CN 113394777A CN 202110767170 A CN202110767170 A CN 202110767170A CN 113394777 A CN113394777 A CN 113394777A
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energy system
power
distribution network
power distribution
layer
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迟福建
张梁
张雪菲
滑雪娇
张章
李娟�
刘洪�
杨帆
王哲
夏冬
赵长伟
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
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    • 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
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Abstract

The invention provides a power distribution network operation and user side energy system demand response based collaborative optimization method, which relates to the technical field of power supply and distribution and comprises the following steps: constructing a power distribution network layer and a comprehensive energy system layer, and constructing an objective function and a constraint condition of the comprehensive energy system layer and an objective function and a constraint condition of the power distribution network layer; the power distribution network layer obtains a demand response subsidy coefficient based on the objective function and the constraint condition of the power distribution network layer and transmits the demand response subsidy coefficient to the comprehensive energy system layer, and the objective function and the constraint condition of the comprehensive energy system layer determine the electric quantity demand of the comprehensive energy system and feed back the electric quantity demand to the power distribution network layer. By the method, a power distribution network layered cooperative regulation and control framework can be constructed, an optimized scheduling strategy of a power distribution network complete scheduling period is formed, and in the operation of the power distribution network comprising the comprehensive energy system, the operation of the power distribution network is optimized by considering the effect of the comprehensive energy system on large-scale photovoltaic consumption and the demand response of the comprehensive energy system, so that the benefit of the power distribution network is maximized.

Description

Power distribution network operation and user side energy system demand response based collaborative optimization method
Technical Field
The invention relates to the technical field of power supply and distribution of a power grid, in particular to a collaborative optimization method based on power distribution network operation and user-side energy system demand response.
Background
At present, a plurality of researches are carried out at home and abroad in the aspect of operation and scheduling of a power distribution network containing an integrated energy system, and certain achievements are obtained. The existing strategy considers the adjusting function of the micro-grid aiming at the operation scheduling problem of the power distribution network, effectively improves the economical efficiency of power distribution network scheduling, but fails to consider the heating and cooling demands of personnel. The comprehensive energy system can simultaneously supply various loads such as electricity, cold and heat, and the energy utilization efficiency of the power distribution network is improved through the complementation of various energy sources. In addition, in the research of a combined dispatching model of the comprehensive energy system and the power distribution network, the existing research considers the environmental protection cost and the electricity purchasing cost of a user of the power distribution network, establishes the economic optimization target of the power distribution network, but fails to consider the role of the comprehensive energy system in large-scale photovoltaic consumption.
In the aspect of introducing demand response into optimal scheduling of a power distribution network, the prior art carries out global consideration from the whole power distribution network, constructs a hierarchical coordinated regulation and control framework of the power distribution network, and forms an optimal scheduling strategy of a complete scheduling period of the power distribution network. The existing research considers the influence of demand response in the optimal comprehensive benefits of the power distribution network and the power distribution network comprising the micro-grid, but the research of introducing the demand response into the power distribution network comprising the comprehensive energy system does not exist.
In summary, how to optimize the operation of the power distribution network by considering the effect of the integrated energy system on large-scale photovoltaic consumption and the demand response thereof in the operation of the power distribution network containing the integrated energy system, so that the benefit of the power distribution network is maximized, needs to be further researched.
Disclosure of Invention
In view of the above, the invention aims to provide a power distribution network operation and user-side energy system demand response collaborative optimization method, so as to construct a power distribution network layered collaborative regulation and control framework and form an optimized scheduling strategy of a power distribution network complete scheduling period, and in the operation of a power distribution network containing an integrated energy system, the operation of the power distribution network is optimized by considering the effect of the integrated energy system on large-scale photovoltaic consumption and the demand response thereof, so that the benefit of the power distribution network is maximized.
The invention provides a collaborative optimization method based on power distribution network operation and user side energy system demand response, which comprises the following steps:
constructing a power distribution network layer and a comprehensive energy system layer, and constructing an objective function and a constraint condition of the comprehensive energy system layer and an objective function and a constraint condition of the power distribution network layer;
the power distribution network layer obtains a demand response subsidy coefficient based on the objective function and the constraint condition of the power distribution network layer and transmits the demand response subsidy coefficient to the comprehensive energy system layer, and the objective function and the constraint condition of the comprehensive energy system layer determine the electric quantity demand of the comprehensive energy system and feed back the electric quantity demand to the power distribution network layer.
Preferably, the power distribution network layer obtains a demand response subsidy coefficient based on an objective function and a constraint condition of the power distribution network layer and transmits the demand response subsidy coefficient to the integrated energy system layer, and the step of determining the electric quantity demand of the integrated energy system by the objective function and the constraint condition of the integrated energy system layer and feeding the electric quantity demand back to the power distribution network layer includes:
the objective function of the integrated energy system layer is as follows:
minF1＝Cup+Cdevice-Cbonus；
Figure BDA0003152226240000021
Figure BDA0003152226240000022
F1-operating costs of the integrated energy system layer;
Figure BDA0003152226240000031
the output of the ith equipment at the moment t;
Figure BDA0003152226240000032
-cost of output of the ith device;
Idev-total number of devices in the integrated energy system;
Cup-the electricity purchase cost of the distribution network;
Cdevice-operating costs of equipment within the integrated energy system;
Cbonus-demand response subsidy costs;
the constraint conditions of the comprehensive energy system layer are as follows:
1) air source heat pump
Har,out＝ηarPar,in；
Har,out＝0.00002To 4+0.0007To 3-0.0008To 2
+0.00014To+8.465；
ηar＝5×10-6To 4+0.0001To 3-0.001To 2
+0.0596To+3.1984；
Har.out-heating power of the air source heat pump;
ηar-coefficient of refrigeration (thermal) performance;
Par,in-the electric power consumed by the air source heat pump;
2) electric heating stove:
Heh,out＝ηehPeh,in
Heh,out-heating power of an electric heating stove;
ηeh-conversion efficiency of electric heating stove;
Peh,inthe power consumption of the electric heating furnace;
3)Ht＝Ht-1+(Qst,t-Qex,t-KlossHt-1)Δt；
in the formula, St-the remaining heat storage capacity of the heat storage device at time t;
Qst,t、Qex,t-thermal storage of thermal energy for a time period t and release of power;
Klossthe heat dissipation loss rate;
4) absorption refrigerator:
Dac,out＝ηacHac,in；
Dac,out-the refrigeration power output by the absorption chiller;
Hac,in-the input electrical power of the absorption chiller;
ηac-the conversion efficiency of the absorption chiller;
5) energy storage battery restraint
Figure BDA0003152226240000041
Figure BDA0003152226240000042
Figure BDA0003152226240000043
Figure BDA0003152226240000044
-the energy stored by the energy storage battery at node i at time t, Δ t being the time interval;
Figure BDA0003152226240000045
and
Figure BDA0003152226240000046
respectively the minimum and maximum stored energy of the energy storage battery;
Figure BDA0003152226240000047
and
Figure BDA0003152226240000048
respectively the upper limit power of energy storage charging and discharging;
Figure BDA0003152226240000049
and
Figure BDA00031522262400000410
respectively are flag bits for energy storage charging and discharging;
6) electric power balance constraint of comprehensive energy system
Figure BDA00031522262400000411
Figure BDA00031522262400000412
-discharge power of the stored energy in the integrated energy system during the time period t;
Pup,tthe comprehensive energy system is supplied with power by a superior power grid in a period t;
Figure BDA00031522262400000413
storing the charging power of the energy in the comprehensive energy system in a time period t;
Pl,tsynthesizing the electric load power in the energy system for a period t;
Figure BDA00031522262400000414
the consumed power of the air source heat pump in the comprehensive energy system is t;
Figure BDA0003152226240000051
the consumed power of the electric heating furnace in the energy system is synthesized for the time period t.
Preferably, the objective function of the power distribution network layer is the maximization of target income;
maxF2＝Csale-Cbonus-Closs
Csale＝Cup
Figure BDA0003152226240000052
PL,t-the electrical load demand of the integrated energy system at the moment;
ce,the time t-t is the price of electricity sold by the power distribution network;
closs-cost per energy loss:
rlis the resistance value of branch I;
Ilis the current amplitude of branch l;
Ψba branch set of the power distribution network;
t is the total number of calculation periods
The power distribution network layer satisfies the following constraint functions:
1) power flow constraint of power distribution network
Figure BDA0003152226240000053
Figure BDA0003152226240000054
In the formula, Pi,tAnd Qi,tRespectively the active power and the reactive power of the node i in the t time period;
PDGi,tand QDGi,tThe active power output and the reactive power output of the distributed power supply of the node i in the time period t are respectively;
Gijand BijRespectively the conductance and susceptance between the nodes i and j;
ei,tand fi,tRespectively representing the real part and the imaginary part of the voltage of the node i in a period t;
2) branch current constraint
The constraint condition which should be met in the whole time period T is the branch current constraint;
Il≤Ilmax
in the formula IlmaxThe maximum value of the current amplitude of the branch I.
3) Distribution system node voltage constraints
Uimin≤Ui≤Uimax
In the formula of UiIs the node voltage amplitude, U, of node iimaxAnd UiminRespectively the upper and lower limits of the node voltage amplitude of the node i.
4) Renewable energy power generation constraints
The renewable energy power generation constraint needs to consider the constraint of the capacity of an inverter on one hand and the constraint of a power factor on the other hand;
Figure BDA0003152226240000061
Figure BDA0003152226240000062
in the formula, PDGi,tAnd QDGi,tRepresenting the active and reactive power, S, of the renewable energy generation at node i at time tDGiRepresenting the capacity of a renewable energy power generation inverter accessed by the node i; cos phi represents the lowest value of the power factor of the power generation output of the renewable energy source.
Preferably, the power distribution network layer obtains a demand response subsidy coefficient based on an objective function and a constraint condition of the power distribution network layer and transmits the demand response subsidy coefficient to the integrated energy system layer, and the step of determining the electric quantity demand of the integrated energy system by the objective function and the constraint condition of the integrated energy system layer and feeding the electric quantity demand back to the power distribution network layer includes:
the power distribution network adopts a chaotic particle swarm algorithm based on particle dimension entropy and a greedy variation strategy:
Figure BDA0003152226240000063
xidposition coordinates of an individual i in a d-dimension;
p(xid) Is xidProcessing a probability function;
xid,minas a position coordinate xidMinimum value of (d);
the quantity of the dimension entropies of the particles in the population is the same as the space dimension, and the maximum value of the dimension entropies is set as EmaxAnd carrying out chaotic variation on part of particle coordinates higher than the maximum value:
Figure BDA0003152226240000071
Figure BDA0003152226240000072
wherein k is the number of iterations, z(k+1)And taking values of the Logistic chaotic equation of the (k + 1) th generation.
The embodiment of the invention has the following beneficial effects: the invention provides a collaborative optimization method based on power distribution network operation and user side energy system demand response, which comprises the following steps: constructing a power distribution network layer and a comprehensive energy system layer, and constructing an objective function and a constraint condition of the comprehensive energy system layer and an objective function and a constraint condition of the power distribution network layer; the power distribution network layer obtains a demand response subsidy coefficient based on the objective function and the constraint condition of the power distribution network layer and transmits the demand response subsidy coefficient to the comprehensive energy system layer, and the objective function and the constraint condition of the comprehensive energy system layer determine the electric quantity demand of the comprehensive energy system and feed back the electric quantity demand to the power distribution network layer. By the method, a power distribution network layered cooperative regulation and control framework can be constructed, an optimized scheduling strategy of a power distribution network complete scheduling period is formed, and in the operation of the power distribution network comprising the comprehensive energy system, the operation of the power distribution network is optimized by considering the effect of the comprehensive energy system on large-scale photovoltaic consumption and the demand response of the comprehensive energy system, so that the benefit of the power distribution network is maximized.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a power distribution network double-layer optimization scheduling framework based on a power distribution network operation and user-side energy system demand response collaborative optimization method according to an embodiment of the present invention;
fig. 2 is a flowchart of a power distribution network operation and user-side energy system demand response collaborative optimization method according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, a plurality of researches are carried out at home and abroad in the aspect of operation and scheduling of a power distribution network containing an integrated energy system, and certain achievements are obtained. The existing strategy considers the adjusting function of the micro-grid aiming at the operation scheduling problem of the power distribution network, effectively improves the economical efficiency of power distribution network scheduling, but fails to consider the heating and cooling demands of personnel. The comprehensive energy system can simultaneously supply various loads such as electricity, cold and heat, and the energy utilization efficiency of the power distribution network is improved through the complementation of various energy sources. In addition, in the research of a combined scheduling model of the integrated energy system and the power distribution network, the existing research considers the environmental protection cost and the user electricity purchasing cost of the power distribution network, establishes an economic optimization target of the power distribution network, but fails to consider the effect of the integrated energy system in large-scale photovoltaic consumption.
For the convenience of understanding the embodiment, first, a method for collaborative optimization based on power distribution network operation and user-side energy system demand response disclosed by the embodiment of the present invention is described in detail,
the invention provides a collaborative optimization method based on power distribution network operation and user side energy system demand response, which comprises the following steps:
constructing a power distribution network layer and a comprehensive energy system layer, and constructing an objective function and a constraint condition of the comprehensive energy system layer and an objective function and a constraint condition of the power distribution network layer;
the power distribution network layer obtains a demand response subsidy coefficient based on the objective function and the constraint condition of the power distribution network layer and transmits the demand response subsidy coefficient to the comprehensive energy system layer, and the objective function and the constraint condition of the comprehensive energy system layer determine the electric quantity demand of the comprehensive energy system and feed back the electric quantity demand to the power distribution network layer.
Preferably, the power distribution network layer obtains a demand response subsidy coefficient based on an objective function and a constraint condition of the power distribution network layer and transmits the demand response subsidy coefficient to the integrated energy system layer, and the step of determining the electric quantity demand of the integrated energy system by the objective function and the constraint condition of the integrated energy system layer and feeding the electric quantity demand back to the power distribution network layer includes:
the objective function of the integrated energy system layer is as follows:
minF1＝Cup+Cdevice-Cbonus；
Figure BDA0003152226240000091
Figure BDA0003152226240000092
F1-operating costs of the integrated energy system layer;
Figure BDA0003152226240000093
the output of the ith equipment at the moment t;
Figure BDA0003152226240000094
-cost of output of the ith device;
Idev-total number of devices in the integrated energy system;
Cup-the electricity purchase cost of the distribution network;
Cdevice-operating costs of equipment within the integrated energy system;
Cbonus-demand response subsidy costs;
the constraint conditions of the comprehensive energy system layer are as follows:
1) air source heat pump
Har,out＝ηarPar,in；
Har,out＝0.00002To 4+0.0007To 3-0.0008To 2
+0.00014To+8.465；
ηar＝5×10-6To 4+0.0001To 3-0.001To 2
+0.0596To+3.1984；
Har.out-heating power of the air source heat pump;
ηar-coefficient of refrigeration (thermal) performance;
Par,in-the electric power consumed by the air source heat pump;
2) electric heating stove:
Heh,out＝ηehPeh,in
Heh,out-heating power of an electric heating stove;
ηeh-conversion efficiency of electric heating stove;
Peh,inthe power consumption of the electric heating furnace;
3)Ht＝Ht-1+(Qst,t-Qex,t-KlossHt-1)Δt；
in the formula, St-the remaining heat storage capacity of the heat storage device at time t;
Qst,t、Qex,t-thermal storage of thermal energy for a time period t and release of power;
Klossfor the heat dissipation loss rate, 1%/h is generally taken;
4) absorption refrigerator:
Dac,out＝ηacHac,in；
Dac,out-the refrigeration power output by the absorption chiller;
Hac,in-the input electrical power of the absorption chiller;
ηacthe conversion efficiency of absorption refrigerators, usually taken to be 0.7;
5) energy storage battery restraint
Figure BDA0003152226240000111
Figure BDA0003152226240000112
Figure BDA0003152226240000113
Figure BDA0003152226240000114
-the energy stored by the energy storage battery at node i at time t, Δ t being the time interval;
Figure BDA0003152226240000115
and
Figure BDA0003152226240000116
respectively the minimum and maximum stored energy of the energy storage battery;
Figure BDA0003152226240000117
and
Figure BDA0003152226240000118
respectively the upper limit power of energy storage charging and discharging;
Figure BDA0003152226240000119
and
Figure BDA00031522262400001110
respectively are flag bits for energy storage charging and discharging;
6) electric power balance constraint of comprehensive energy system
Figure BDA00031522262400001111
Figure BDA00031522262400001112
-discharge power of the stored energy in the integrated energy system during the time period t;
Pup,tthe comprehensive energy system is supplied with power by a superior power grid in a period t;
Figure BDA00031522262400001113
storing the charging power of the energy in the comprehensive energy system in a time period t;
Pl,tsynthesizing the electric load power in the energy system for a period t;
Figure BDA00031522262400001114
the consumed power of the air source heat pump in the comprehensive energy system is t;
Figure BDA00031522262400001115
the consumed power of the electric heating furnace in the energy system is synthesized for the time period t.
Preferably, the objective function of the power distribution network layer is the maximization of target income;
maxF2＝Csale-Cbonus-Closs
Csale＝Cup
Figure BDA0003152226240000121
PL,t-the electrical load demand of the integrated energy system at the moment;
ce,tthe power selling price of the power distribution network is obtained at the moment t;
closs-cost per energy loss:
rlis the resistance value of branch I;
Ilis the current amplitude of branch l;
Ψba branch set of the power distribution network;
t is the total number of calculation periods
The power distribution network layer satisfies the following constraint functions:
1) power flow constraint of power distribution network
Figure BDA0003152226240000122
Figure BDA0003152226240000123
In the formula, Pi,tAnd Qi,tRespectively the active power and the reactive power of the node i in the t time period;
PDGi,tand QDGi,tThe active power output and the reactive power output of the distributed power supply of the node i in the time period t are respectively;
Gijand BijRespectively the conductance and susceptance between the nodes i and j;
ei,tand fi,tRespectively representing the real part and the imaginary part of the voltage of the node i in a period t;
2) branch current constraint
The constraint condition which should be met in the whole time period T is the branch current constraint;
Il≤Ilmax
in the formula IlmaxThe maximum value of the current amplitude of the branch I.
3) Distribution system node voltage constraints
Uimin≤Ui≤Uimax
In the formula of UiIs the node voltage amplitude, U, of node iimaxAnd UiminRespectively the upper and lower limits of the node voltage amplitude of the node i.
4) Renewable energy power generation constraints
The renewable energy power generation constraint needs to consider the constraint of the capacity of an inverter on one hand and the constraint of a power factor on the other hand;
Figure BDA0003152226240000131
Figure BDA0003152226240000132
in the formula, PDGi,tAnd QDGi,tRepresenting the active and reactive power, S, of the renewable energy generation at node i at time tDGiRepresenting the capacity of a renewable energy power generation inverter accessed by the node i; cos phi represents the lowest value of the power factor of the power generation output of the renewable energy source.
Preferably, the power distribution network layer obtains a demand response subsidy coefficient based on an objective function and a constraint condition of the power distribution network layer and transmits the demand response subsidy coefficient to the integrated energy system layer, and the step of determining the electric quantity demand of the integrated energy system by the objective function and the constraint condition of the integrated energy system layer and feeding the electric quantity demand back to the power distribution network layer includes:
the power distribution network adopts a chaotic particle swarm algorithm based on particle dimension entropy and a greedy variation strategy:
Figure BDA0003152226240000133
xidposition coordinates of an individual i in a d-dimension;
p(xid) Is xidProcessing a probability function;
xid,minas a position coordinate xidMinimum value of (d);
the quantity of the dimension entropies of the particles in the population is the same as the space dimension, and the maximum value of the dimension entropies is set as EmaxAnd carrying out chaotic variation on part of particle coordinates higher than the maximum value:
Figure BDA0003152226240000134
Figure BDA0003152226240000141
wherein k is the number of iterations, z(k+1)Value of Logistic chaotic equation for k +1 generation
In addition, fig. 2 is a schematic view of only one scene of the present disclosure, the number of the image capturing devices 2, the detailed structures of the walker 1 and the image capturing devices 2, and the positions and relative relationships between the two devices are not limited in the drawing, and those skilled in the art can freely arrange the positions and relative relationships of the parts according to design or field requirements.
Unless specifically stated otherwise, the relative steps, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of the present invention.
In all examples shown and described herein, any particular value should be construed as merely exemplary, and not as a limitation, and thus other examples of example embodiments may have different values.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (4)

1. A collaborative optimization method based on power distribution network operation and user side energy system demand response is characterized by comprising the following steps:
constructing a power distribution network layer and a comprehensive energy system layer, and constructing an objective function and a constraint condition of the comprehensive energy system layer and an objective function and a constraint condition of the power distribution network layer;
the power distribution network layer obtains a demand response subsidy coefficient based on the objective function and the constraint condition of the power distribution network layer and transmits the demand response subsidy coefficient to the comprehensive energy system layer, and the objective function and the constraint condition of the comprehensive energy system layer determine the electric quantity demand of the comprehensive energy system and feed back the electric quantity demand to the power distribution network layer.
2. The method of claim 1, wherein the power distribution network layer obtains a demand response subsidy factor based on an objective function of the power distribution network layer and a constraint condition and transmits the demand response subsidy factor to the integrated energy system layer, and the step of determining the electric demand of the integrated energy system and feeding back the electric demand of the integrated energy system to the power distribution network layer based on the objective function and the constraint condition of the integrated energy system layer comprises:
the objective function of the integrated energy system layer is as follows:
minF1＝Cup+Cdevice-Cbonus；
Figure FDA0003152226230000011
Figure FDA0003152226230000012
F1-operating costs of the integrated energy system layer;
Figure FDA0003152226230000013
the output of the ith equipment at the moment t;
Figure FDA0003152226230000014
-cost of output of the ith device;
Idev-total number of devices in the integrated energy system;
Cup-the electricity purchase cost of the distribution network;
Cdevice-operating costs of equipment within the integrated energy system;
Cbonus-demand response subsidy costs;
the constraint conditions of the comprehensive energy system layer are as follows:
1) air source heat pump
Har,out＝ηarPar,in；
Har,out＝0.00002To 4+0.0007To 3-0.0008To 2+0.00014To+8.465；
ηar＝5×10-6To 4+0.0001To 3-0.001To 2+0.0596To+3.1984；
Har.out-heating power of the air source heat pump;
ηar-coefficient of refrigeration (thermal) performance;
Par,in-the electric power consumed by the air source heat pump;
2) electric heating stove:
Heh,out＝ηehPeh,in
Heh,out-heating power of an electric heating stove;
ηeh-conversion efficiency of electric heating stove;
Peh,inthe power consumption of the electric heating furnace;
3)Ht＝Ht-1+(Qst,t*Qex,t*KlossHt*1)Δt；
in the formula, St-the remaining heat storage capacity of the heat storage device at time t;
Qst,t、Qex,t-thermal storage of thermal energy for a time period t and release of power;
Klossthe heat dissipation loss rate;
4) absorption refrigerator:
Dac,out＝ηacHac,in；
Dac,out-the refrigeration power output by the absorption chiller;
Hac,ininput electric power for absorption refrigeratorRate;
ηac-the conversion efficiency of the absorption chiller;
5) energy storage battery restraint
Figure FDA0003152226230000031
Figure FDA0003152226230000032
Figure FDA0003152226230000033
Figure FDA0003152226230000034
-the energy stored by the energy storage battery at node i at time t, Δ t being the time interval;
Figure FDA0003152226230000035
and
Figure FDA0003152226230000036
respectively the minimum and maximum stored energy of the energy storage battery;
Figure FDA0003152226230000037
and
Figure FDA0003152226230000038
respectively the upper limit power of energy storage charging and discharging;
Figure FDA0003152226230000039
and
Figure FDA00031522262300000310
respectively are flag bits for energy storage charging and discharging;
6) electric power balance constraint of comprehensive energy system
Figure FDA00031522262300000311
Figure FDA00031522262300000312
-discharge power of the stored energy in the integrated energy system during the time period t;
Pup,tthe comprehensive energy system is supplied with power by a superior power grid in a period t;
Figure FDA00031522262300000313
storing the charging power of the energy in the comprehensive energy system in a time period t;
Pl,tsynthesizing the electric load power in the energy system for a period t;
Figure FDA00031522262300000314
the consumed power of the air source heat pump in the comprehensive energy system is t;
Figure FDA00031522262300000315
the consumed power of the electric heating furnace in the energy system is synthesized for the time period t.
3. The method of claim 1, wherein the objective function of the power distribution grid layer is to maximize a target revenue;
maxF2＝Csale-Cbonus-Closs
Csale＝Cup
Figure FDA0003152226230000041
PL,t-the electrical load demand of the integrated energy system at the moment;
ce,tthe power selling price of the power distribution network is obtained at the moment t;
closs-cost per energy loss:
rlis the resistance value of branch I;
Ilis the current amplitude of branch l;
Ψba branch set of the power distribution network;
t is the total number of calculation periods
The power distribution network layer satisfies the following constraint functions:
1) power flow constraint of power distribution network
Figure FDA0003152226230000042
Figure FDA0003152226230000043
In the formula, Pi,tAnd Qi,tRespectively the active power and the reactive power of the node i in the t time period;
PDGi,tand QDGi,tThe active power output and the reactive power output of the distributed power supply of the node i in the time period t are respectively;
Gijand BijRespectively the conductance and susceptance between the nodes i and j;
ei,tand fi,tRespectively representing the real part and the imaginary part of the voltage of the node i in a period t;
2) branch current constraint
The constraint condition which should be met in the whole time period T is the branch current constraint;
Il≤Ilmax
in the formula IlmaxThe maximum value of the current amplitude of the branch I.
3) Distribution system node voltage constraints
Uimin≤Ui≤Uimax
In the formula of UiIs the node voltage amplitude, U, of node iimaxAnd UiminRespectively the upper and lower limits of the node voltage amplitude of the node i.
4) Renewable energy power generation constraints
The renewable energy power generation constraint needs to consider the constraint of the capacity of an inverter on one hand and the constraint of a power factor on the other hand;
Figure FDA0003152226230000051
Figure FDA0003152226230000052
in the formula, PDGi,tAnd QDGi,tRepresenting the active and reactive power, S, of the renewable energy generation at node i at time tDGiRepresenting the capacity of a renewable energy power generation inverter accessed by the node i;
Figure FDA0003152226230000054
and the lowest value of the power factor representing the power generation output of the renewable energy source.
4. The method of claim 1, wherein the power distribution network layer obtains a demand response subsidy factor based on an objective function of the power distribution network layer and a constraint condition and transmits the demand response subsidy factor to the integrated energy system layer, and the step of determining the electric demand of the integrated energy system and feeding back the electric demand of the integrated energy system to the power distribution network layer based on the objective function and the constraint condition of the integrated energy system layer comprises:
the power distribution network adopts a chaotic particle swarm algorithm based on particle dimension entropy and a greedy variation strategy:
Figure FDA0003152226230000053
xidposition coordinates of an individual i in a d-dimension;
p(xid) Is xidProcessing a probability function;
xid,minas a position coordinate xidMinimum value of (d);
the quantity of the dimension entropies of the particles in the population is the same as the space dimension, and the maximum value of the dimension entropies is set as EmaxAnd carrying out chaotic variation on part of particle coordinates higher than the maximum value:
Figure FDA0003152226230000061
Figure FDA0003152226230000062
wherein k is the number of iterations, z(k+1)And taking values of the Logistic chaotic equation of the (k + 1) th generation.
CN202110767170.2A 2021-07-07 2021-07-07 Power distribution network operation and user side energy system demand response based collaborative optimization method Pending CN113394777A (en)

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