CN115795881B - Comprehensive energy system heat storage device planning method and system - Google Patents
Comprehensive energy system heat storage device planning method and system Download PDFInfo
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Abstract
A comprehensive energy system heat storage device planning method and system relate to the comprehensive energy system planning field; the method comprises the following steps: generating renewable energy output and electric heating load scene data; establishing a first-stage heat storage tank scale planning model; establishing a second-stage optimal scheduling model; converting the two-stage planning into a single-stage planning problem by adopting a scene method; and converting the double-objective optimization problem into a single-objective optimization problem by adopting a weighting coefficient method and solving the single-objective optimization problem. The invention considers the value of the heat storage tank in the comprehensive energy system for reducing the system operation cost and carbon emission, and the proposed method can realize the cooperation between the system operation cost and the carbon emission while planning the heat storage tank.
Description
Technical Field
The invention belongs to the field of economic low-carbon planning of comprehensive energy systems, and particularly relates to a comprehensive energy system heat storage device planning method and system for economic-low-carbon collaborative optimization.
Background
In the context of low carbon development, power systems face a comprehensive revolution. Comprehensive energy systems which utilize multiple energy sources to couple and complement and aim at improving energy utilization efficiency are widely focused. The comprehensive energy system breaks the existing mode of independent planning and independent operation of the traditional single energy system, performs unified planning on air supply, heat supply, power supply, cold supply and the like, and can realize coordinated optimization of multiple links of system source, network, load and storage. Not only can the energy utilization efficiency be improved, but also the low-carbon transformation of the energy system can be promoted. The comprehensive energy system can realize the cooperative complementation of multiple energy sources such as electric heat and the like in the aspects of source network charge storage and the like. The energy storage equipment can improve the optimizable space of the energy flow of the system, improve the flexibility of the system and improve the carbon emission reduction space. Therefore, the integrated energy system plays an important role in promoting the development of low-carbon transformation of the power grid.
However, achieving synergy at low carbon economy still faces many challenges. The value of the heat storage tank for reducing the economic cost of the system and carbon emission is lack of research. Under the low-carbon background, a certain contradiction exists between the economic operation of the system and the reduction of carbon emission, and how to ensure the economy and the carbon emission reduction characteristic of the system while planning the heat storage tank, and the realization of the maximum carbon emission reduction by the cooperation of the whole system is a difficult problem to be solved in engineering application.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a planning method and a planning system for a heat storage device of a comprehensive energy system, and an economic low-carbon collaborative optimal scheduling scheme of the comprehensive energy system is obtained by planning a heat storage tank.
The invention adopts the following technical scheme.
A planning method of a heat storage device of a comprehensive energy system comprises the following steps:
step 1, collecting renewable energy output and electric heating load scene data;
step 2, establishing a first-stage heat storage tank scale planning model according to the unit capacity investment cost and annual investment return of the heat storage tank;
step 3, simultaneously considering the running cost and carbon emission problem of the comprehensive energy system, establishing a second-stage optimal scheduling model, and constructing a comprehensive energy system running cost objective function and a comprehensive energy system carbon emission objective function which consider the thermodynamic characteristics;
step 4, converting the two-stage planning into a single-stage planning model by adopting a scene method based on the selected random variable and the scene probability thereof;
and step 5, giving double-target weight by adopting a weighting coefficient method, and converting the double-target optimization problem into a single-target optimization problem.
Preferably, in step 2, the first-stage heat storage tank scale planning model objective function is:
wherein C is inv Representing annual investment costs of the heat storage tank; r represents capital interest rate; m represents annual return on investment;representing the investment cost per unit capacity of the heat storage tank, +.>Representing the energy of the heat storage tank.
In the step 2, constraint conditions of the scale planning model of the heat storage tank in the first stage are as follows:
preferably, in step 3, the second-stage optimal scheduling model is:
min{f 1 ,f 2 }
s.t. grid constraints
Constraint condition of heat supply network
Wherein f 1 For the running cost objective function of the comprehensive energy system, f 2 The carbon emission objective function of the comprehensive energy system is as follows;
f 1 ＝C grid +C m +C run
f 2 ＝CO 2e +CO 2h +CO 2grid
wherein C is grid For electricity purchasing cost, C m For equipment maintenance cost, C run For the running cost of the equipment, CO 2e Carbon emissions, CO, for power system generation 2h For carbon emissions generated by heating systems, CO 2grid Carbon emission generated for purchasing electricity for the upper power grid.
The electricity purchasing cost is as follows:
wherein lambda is t pbuy ，λ t psell The electricity purchasing price and the electricity selling price of the comprehensive energy system at the time t; p (P) t buy ，P t sell The electricity purchasing quantity and the electricity selling quantity of the comprehensive energy system are obtained at the moment t; Δt is the time interval length;
the equipment maintenance cost is as follows:
wherein m is i Maintenance costs for the ith unit or energy coupling device; m is m j Maintenance costs for the jth energy storage device; p (P) t,i Processing t moment of the ith unit or energy coupling equipment;and->Respectively charging energy and releasing energy of the j-th energy storage device at the moment t;
the running cost of the equipment is as follows:
wherein a, b, d, e and f are fuel cost coefficients of the CHP unit; g is the fuel cost coefficient of the gas boiler; c. h is the start-stop cost coefficient of the operation of the CHP unit and the gas boiler respectively; p (P) t CHP 、H t CHP The electric output and the thermal output of the CHP unit at the moment t are respectively;the hot output of the gas boiler at the moment t respectively; /> The starting and stopping states of the CHP unit and the gas boiler at the moment t are respectively>
The electricity system generates carbon emissions as follows:
wherein P is t CHP Generating power of the CHP unit at the moment t;carbon dioxide emission coefficient for CHP unit power generation; the fan belongs to clean energy power generation, and the carbon dioxide emission is 0;
the heating system generates carbon emissions as follows:
wherein,the heat generation amount of the CHP unit and the gas boiler at the time t is respectively; />Carbon dioxide emission coefficients of heat generated by the CHP unit and the gas boiler are respectively;
the carbon emission generated by electricity purchase and selling of the upper power grid is as follows:
wherein, gamma grid And the equivalent carbon dioxide emission coefficient is used for purchasing electricity to the upper power grid.
Preferably, in step 4, the single-stage planning model is:
s.t.Ax≤b
Ex+Fy i -Gu i ≤h i＝1,2,...,M
wherein, it is assumed that it is randomA finite number of realizations of variable u, denoted scene u 1 ,u 2 ,...,u M The method comprises the steps of carrying out a first treatment on the surface of the The probability of these scenes is defined as ω 1 ,ω 2 ,...,ω M The method comprises the steps of carrying out a first treatment on the surface of the x is a first stage decision variable; y is i Is a decision variable corresponding to the second stage scene i; c, b, h are constant vectors; a, E, F, G are constant matrices; m is the number of scenes.
Preferably, in step 5, the cost objective function f is calculated for the integrated energy system 1 And a carbon emission objective function f of a comprehensive energy system 2 Respectively carrying out single-objective optimization solution to obtain an optimal solution f under a single objective 1* And f 2* ；
The optimization objective is standardized to be used in the optimization process,is a normalized target value;
giving a cost objective function and a weight coefficient k before a carbon emission objective function 1 And k 2 Converting double targets into single target functionsAnd (5) carrying out optimization solution.
A comprehensive energy system heat storage device planning system comprises an objective function construction module and a model solving module.
The objective function construction module is used for constructing a first-stage heat storage tank scale planning model and a second-stage optimal scheduling model based on renewable energy output and electric heating load scene data;
the model solving module converts the two-stage planning into a single-stage planning model by adopting a scene method, and converts the double-objective optimization problem into a single-objective optimization problem by adopting a weighting coefficient method for optimization solving.
Compared with the prior art, the method has the beneficial effects that the economic low-carbon collaborative optimal scheduling scheme of the comprehensive energy system is obtained through planning the heat storage tank, the collaborative effect of the economical efficiency and the carbon emission reduction characteristic of the comprehensive energy system can be improved through the method, the conflict and the contradiction between the economical efficiency and the environmental protection of the comprehensive energy system are effectively solved, the efficient low-carbon scheduling of the comprehensive energy system is realized, and the carbon emission reduction space of the comprehensive energy system is improved.
Drawings
FIG. 1 is a block diagram of an integrated energy system;
FIG. 2 is a flow chart of a method for planning a heat storage device of an integrated energy system;
FIG. 3 is a diagram of a power distribution network of the integrated energy system in an embodiment of the present invention;
FIG. 4 is a diagram of a heating network of the integrated energy system in an embodiment of the present invention;
FIG. 5 is a graph showing the scene data of renewable energy output and electric heating load according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are merely some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present invention.
The present invention operates in an integrated energy system as shown in fig. 1. The comprehensive energy system comprises three energy networks of electric heating gas, and meets the requirements of electric heating two energy sources. The energy supply unit comprises a fan, a cogeneration unit and a gas boiler. The energy storage system consists of a heat storage tank and a storage battery.
The planning method of the heat storage device of the comprehensive energy system is shown in fig. 2, and comprises the following steps:
and step 1, collecting renewable energy output and electric heating load scene data.
And 2, establishing a first-stage heat storage tank scale planning model according to the unit capacity investment cost and annual investment return of the heat storage tank.
Establishing a first-stage model objective function and a first-stage model constraint condition:
wherein C is inv Representing annual investment costs of the heat storage tank; r represents capital interest rate; m represents annual return on investment;representing the unit capacity investment cost of the heat storage tank; />Representing the energy of the heat storage tank.
And 3, simultaneously considering the running cost and carbon emission problem of the comprehensive energy system, establishing a second-stage optimal scheduling model, and constructing a comprehensive energy system running cost objective function and a comprehensive energy system carbon emission objective function which consider the thermodynamic characteristics.
min{f 1 ,f 2 }
s.t. grid constraints
Constraint condition of heat supply network
Wherein f 1 For the running cost objective function of the comprehensive energy system, f 2 Is a carbon emission objective function of the comprehensive energy system.
f 1 ＝C grid +C m +C run
f 2 ＝CO 2e +CO 2h +CO 2grid
C grid For electricity purchasing cost, C m For equipment maintenance cost, C run For the running cost of the equipment, CO 2e Carbon emissions, CO, for power system generation 2h For carbon emissions generated by heating systems, CO 2grid Carbon emission generated for purchasing electricity for the upper power grid.
The purchase cost objective function is:
wherein lambda is t pbuy ，λ t psell The electricity purchasing price and the electricity selling price of the comprehensive energy system at the time t; p (P) t buy ，P t sell And (5) purchasing electricity quantity and selling electricity quantity for the comprehensive energy system at the time t.
The equipment maintenance cost objective function is:
wherein m is i Maintenance costs for the ith unit or energy coupling device; m is m j Maintenance costs for the jth energy storage device; p (P) t,i Processing t moment of the ith unit or energy coupling equipment;and->And respectively charging energy and releasing energy at the moment t of the j-th energy storage device.
The equipment operation cost objective function is:
wherein a, b, d, e and f are fuel cost coefficients of the CHP unit; g is the fuel cost coefficient of the gas boiler; c. h is the start-stop cost coefficient of the operation of the CHP unit and the gas boiler respectively; p (P) t CHP 、H t CHP The electric output and the thermal output of the CHP unit at the moment t are respectively;the hot output of the gas boiler at the moment t respectively; /> The starting and stopping states of the CHP unit and the gas boiler at the moment t are respectively>
The electric power system generates carbon emission objective functions as follows:
wherein P is t CHP Generating power of the CHP unit at the moment t;carbon dioxide emission coefficient of CHP unit power generation. The fan belongs to clean energy power generation, and the carbon dioxide emission is 0.
The heat supply system generates carbon emission objective functions as follows:
wherein,the heat generation amount of the CHP unit and the gas boiler at the time t is respectively; />Carbon dioxide emission coefficients of heat generated by the CHP unit and the gas boiler are respectively;
the carbon emission objective function generated by electricity purchase and selling of the upper power grid is as follows:
wherein, gamma grid And the equivalent carbon dioxide emission coefficient is used for purchasing electricity to the upper power grid.
The constraint conditions of the objective function include:
the grid tie capacity constraint is:
wherein,representing the active power flow on branch b at time t; />Representing the upper limit of the active power flow of the branch b;
V i an upper limit and a lower limit indicating the voltage of the node i;
the heat supply network pipeline constraint comprises a heat source power balance constraint, a power and temperature equation of a heat source and a heat load node, a pipeline transmission delay and heat loss constraint, a heat supply network node power balance constraint, a heat supply network node water temperature mixed constraint and a heat supply network water supply and return temperature upper and lower limit constraint;
the heat source power balance constraint is:
wherein,injecting a thermal power variable into the thermal network at the t period; e (E) chp 、E gb 、E tst An index set for a cogeneration unit, a gas boiler and a heat storage device; />The heat output power variable of the cogeneration unit i is t time period; />The heat output power variable of the gas boiler i in the t period is as follows; />The heat accumulation amount and the heat release amount of the ith heat storage device at the moment t are respectively;
the power and temperature equations for the heat source and heat load nodes are:
wherein,pipe index sets for egress/ingress node k, respectively; phi sn 、Φ ln Index sets of a source node and a load node in the heat supply network respectively; c w Is the specific heat capacity of water; m is m j The mass flow of the heating medium is the pipeline j; />Supplying water at the moment t and returning heat medium temperature variables at a node k in a network; />The thermal load power variable at node k for the period t;
the pipeline transmission delay and heat loss constraint are as follows:
wherein phi is p Index set for the heat network pipe;a coefficient related to the transmission delay for the pipeline j; parameter beta j The heat preservation coefficient of the pipeline j; />The environmental temperature of the pipeline at the moment t; />The temperature variation of the heating medium at the inlet and outlet of the water supply pipeline j at the moment t; />The temperature variables of the heating medium at the inlet and the outlet of the water return pipeline j at the moment t;
the power balance constraint of the heat supply network node is as follows:
wherein phi is in Is a collection of junction nodes in a heat supply network;supplying water for a period t and returning the temperature of the heating medium at a node k in the network;
the mixing constraint of the water temperature of the heat supply network node is as follows:
wherein phi is in Is a collection of junction nodes in a heat supply network;supplying water for a period t and returning the temperature of the heating medium at a node k in the network;
the upper and lower limits of the heat supply network water supply and return temperature are constrained as follows:
wherein,τ s supplying water to the heat supply network with an upper limit and a lower limit; />τ r The upper limit and the lower limit of the return water temperature of the heat supply network are adopted.
The energy storage device stores/discharges energy constraint as follows:
wherein,charging and discharging the variable of the energy storage device 0-1 at the j th moment; />Charging energy or discharging energy for the j-th energy storage device at the moment t; />The maximum value of energy charging and discharging of the j-th energy storage equipment at the t moment; e (E) t,j ,E t-1,j The total energy stored at the moment t and the moment t-1 of the j-th energy storage device; e (E) min,j /E max,j Minimum or maximum total energy stored for the jth energy storage device; η (eta) ch,j /η dis,j And charging or discharging energy efficiency of the j-th energy storage device.
The coupling device operating constraints are:
wherein P is t,i The capacity of the ith coupling equipment at the moment t; p (P) max,i An upper capacity limit for the ith coupling device;
and PtCHP are the thermal and electrical outputs of the CHP unit respectively; p (P) max,i An upper capacity limit for the ith coupling device; deltaR max,i A hill climbing power limit for the i-th coupling device; />Is CHP unitIs a heat-to-electricity ratio of (c).
And 4, converting the two-stage planning into a single-stage planning model by adopting a scene method.
s.t.Ax≤b
Ex+Fy i -Gu i ≤h i＝1,2,...,M
Wherein a limited number of realizations of the random variable u is assumed, denoted as scene u 1 ,u 2 ,...,u M The method comprises the steps of carrying out a first treatment on the surface of the The probability of these scenes is defined as ω 1 ,ω 2 ,...,ω M The method comprises the steps of carrying out a first treatment on the surface of the x is a first stage decision variable; y is i Is a decision variable corresponding to the second stage scene i; c, b, h are constant vectors; a, E, F, G are constant matrices; m is the number of scenes.
And step 5, converting the double-objective optimization problem into a single-objective optimization problem by adopting a weighting coefficient method.
Cost objective function f for comprehensive energy system 1 And a carbon emission objective function f of a comprehensive energy system 2 Respectively carrying out single-objective optimization solution to obtain an optimal solution f under a single objective 1* And f 2* ；
The optimization objective is standardized to be used in the optimization process,is a normalized target value;
giving a cost objective function and a weight coefficient k before a carbon emission objective function 1 And k 2 Converting double targets into single target functionsAnd (5) carrying out optimization solution.
Example 1:
the multi-energy flow system of the embodiment consists of a 33-node power distribution system and a 51-node heat supply system, as shown in fig. 3 and 4, the system comprises 1 10MW cogeneration unit, 1 10MW gas-fired boiler, 4 fans with rated power of 1MW, the optimization period is 24 hours, the scheduling time interval is 1 hour, and the weight settings before the cost objective function and the carbon emission objective function in the weight method are 0.5 and 0.5 respectively.
According to the method, the comprehensive energy system heat storage device planning oriented to the economic low-carbon cooperation is carried out.
The heat storage tank plays an important role in improving reliability, economy and operational existence. The method can fully excavate the value of the heat storage tank for reducing the system operation cost and carbon emission, and realizes the synergy between the economic cost of the comprehensive energy system and the carbon emission reduction.
A comprehensive energy system heat storage device planning system comprises an objective function construction module and a model solving module.
The objective function construction module is used for constructing a first-stage heat storage tank scale planning model and a second-stage optimal scheduling model based on renewable energy output and electric heating load scene data;
the model solving module converts the two-stage planning into a single-stage planning model by adopting a scene method, and converts the double-objective optimization problem into a single-objective optimization problem by adopting a weighting coefficient method for optimization solving.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (7)
1. The planning method of the heat storage device of the comprehensive energy system is characterized by comprising the following steps of:
step 1, collecting renewable energy output and electric heating load scene data;
step 2, establishing a first-stage heat storage tank scale planning model according to the unit capacity investment cost and annual investment return of the heat storage tank;
the first-stage heat storage tank scale planning model objective function is as follows:
wherein C is inv Representing annual investment costs of the heat storage tank; r represents capital interest rate; m represents annual return on investment;representing unit capacity investment of heat storage tankCost (S)/(S)>Representing the energy of the heat storage tank;
step 3, simultaneously considering the running cost and carbon emission problem of the comprehensive energy system, and establishing a second-stage optimal scheduling model;
the second-stage optimal scheduling model is as follows:
min{f 1 ,f 2 }
s.t. grid constraints
Constraint condition of heat supply network
Wherein f 1 For the running cost objective function of the comprehensive energy system, f 2 The carbon emission objective function of the comprehensive energy system is as follows;
f 1 ＝C grid +C m +C run
f 2 ＝CO 2e +CO 2h +CO 2grid
wherein C is grid For electricity purchasing cost, C m For equipment maintenance cost, C run For the running cost of the equipment, CO 2e Carbon emissions, CO, for power system generation 2h For carbon emissions generated by heating systems, CO 2grid Carbon emission generated by purchasing electricity for the upper power grid;
constructing an integrated energy system operation cost objective function and an integrated energy system carbon emission objective function which consider thermodynamic characteristics;
step 4, converting the two-stage planning into a single-stage planning model by adopting a scene method based on the selected random variable and the scene probability thereof;
the single-stage planning model comprises the following steps:
s.t.Ax≤b
Ex+Fy i -Gu i ≤h i＝1,2,...,M
wherein it is assumed that the random variable u hasA limited number of implementations, denoted scene u 1 ,u 2 ,...,u M The method comprises the steps of carrying out a first treatment on the surface of the The probability of these scenes is defined as ω 1 ,ω 2 ,...,ω M The method comprises the steps of carrying out a first treatment on the surface of the x is a first stage decision variable; y is i Is a decision variable corresponding to the second stage scene i; c, b, h are constant vectors; a, E, F, G are constant matrices; m is the number of scenes;
and step 5, giving double-target weight by adopting a weighting coefficient method, and converting the double-target optimization problem into a single-target optimization problem.
2. The method for planning the heat storage device of the comprehensive energy system according to claim 1, wherein the method comprises the following steps:
in the step 2, constraint conditions of the scale planning model of the heat storage tank in the first stage are as follows:
3. the method for planning the heat storage device of the comprehensive energy system according to claim 1, wherein the method comprises the following steps:
the electricity purchasing cost is as follows:
wherein,the electricity purchasing price and the electricity selling price of the comprehensive energy system at the time t; p (P) t buy ，P t sell The electricity purchasing quantity and the electricity selling quantity of the comprehensive energy system are obtained at the moment t; t is the total time; Δt is the time interval length;
the equipment maintenance cost is as follows:
wherein the method comprises the steps of，m i Maintenance costs for the ith unit or energy coupling device; m is m j Maintenance costs for the jth energy storage device; p (P) t,i Processing t moment of the ith unit or energy coupling equipment;and->Respectively charging energy and releasing energy of the j-th energy storage device at the moment t;
the running cost of the equipment is as follows:
wherein a, b, d, e and f are fuel cost coefficients of the CHP unit; g is the fuel cost coefficient of the gas boiler; c. h is the start-stop cost coefficient of the operation of the CHP unit and the gas boiler respectively; p (P) t CHP 、The electric output and the thermal output of the CHP unit at the moment t are respectively; />The hot output of the gas boiler at the moment t respectively; /> The starting and stopping states of the CHP unit and the gas boiler at the moment t are respectively>
The electricity system generates carbon emissions as follows:
wherein P is t CHP Generating power of the CHP unit at the moment t;carbon dioxide emission coefficient for CHP unit power generation; the fan belongs to clean energy power generation, and the carbon dioxide emission is 0;
the heating system generates carbon emissions as follows:
wherein,the heat generation amount of the CHP unit and the gas boiler at the time t is respectively; />Carbon dioxide emission coefficients of heat generated by the CHP unit and the gas boiler are respectively;
the carbon emission generated by electricity purchase and selling of the upper power grid is as follows:
wherein, gamma grid And the equivalent carbon dioxide emission coefficient is used for purchasing electricity to the upper power grid.
4. The method for planning the heat storage device of the comprehensive energy system according to claim 1, wherein the method comprises the following steps:
in step 5, the cost objective function f of the comprehensive energy system 1 And a carbon emission objective function f of a comprehensive energy system 2 Respectively carrying out single-objective optimization solution to obtainOptimal solution f under single target 1* And f 2* ；
The optimization objective is standardized to be used in the optimization process,is a normalized target value;
giving a cost objective function and a weight coefficient k before a carbon emission objective function 1 And k 2 Converting double targets into single target functionsAnd (5) carrying out optimization solution.
5. A comprehensive energy system heat storage device planning system for implementing a comprehensive energy system heat storage device planning method according to any one of claims 1-4, comprising an objective function construction module and a model solving module, and characterized in that:
the objective function construction module is used for constructing a first-stage heat storage tank scale planning model and a second-stage optimal scheduling model based on renewable energy output and electric heating load scene data;
the model solving module converts the two-stage planning into a single-stage planning model by adopting a scene method, and converts the double-objective optimization problem into a single-objective optimization problem by adopting a weighting coefficient method for optimization solving.
6. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-4.
7. Computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of claims 1-4.
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