CN112116131A - Multi-level optimization method for comprehensive energy system considering carbon emission - Google Patents
Multi-level optimization method for comprehensive energy system considering carbon emission Download PDFInfo
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Abstract
The invention discloses a comprehensive energy system multi-level optimization method considering carbon emission, which comprises the following steps: dividing the comprehensive energy system into three levels, namely a regional layer, a block layer and a local layer according to the node type, wherein the local layer is positioned at the bottom layer, the block layer is positioned at the middle layer, and the regional layer is positioned at the top layer; establishing an optimal configuration model objective function of each level of the comprehensive energy system; establishing optimized configuration model constraints of each level of the comprehensive energy system, wherein the optimized configuration model constraints comprise energy network flow constraints, energy coupling constraints and carbon emission constraints; and optimizing the comprehensive energy system based on the genetic algorithm according to the optimized configuration model objective function and the optimized configuration model constraint of each level to obtain the optimized configuration result of each level of the system. The invention can realize the collaborative planning of centralized energy supply and distributed energy supply in the comprehensive energy system and meet the flexible design requirement of the comprehensive energy system.
Description
Technical Field
The invention relates to the technical field of energy internet planning, in particular to a multi-level optimization method for a comprehensive energy system considering carbon emission.
Background
The existing energy system planning is usually based on the supply and demand relationship, and the special planning of each energy source is respectively carried out. In the actual process, the special plans are influenced interactively, so that the problems of repeated load calculation, occupied land conflict, unreasonable energy structure and the like can be caused. In energy internet planning, planning of all energy systems is integrated, and how to realize optimal matching of energy supply and demand under interaction influence and constraint relation of all energy is a key problem of comprehensive energy system planning and design. The problem can be described as: in a limited geographical area and a planning period, under the condition of meeting various energy requirements of users such as cold/heat/electricity/gas and the like, the optimal configuration of each energy resource, the combination of each energy conversion and storage technology type and capacity and the layout of each energy supply network are determined, so that the system is optimal under multiple targets in the aspects of technology, economy, energy efficiency, environmental benefit and the like. Compared with the planning of a single energy system, the planning of the comprehensive energy system is not only a multi-objective optimization problem, but also faces more complexity and uncertainty, including: more optimization objects, such as a Combined Cooling Heating and Power (CCHP) system, wherein the thermoelectric proportion, the steam pressure, the temperature of the cooling and heating water, the diameter and the length of a pipe network and the like are variables to be determined; complex constraint relations are brought by coupling and conversion of various energy sources; multiple uncertainties and correlations exist in the energy input and output, such as the uncertainty of wind energy, solar energy and other energy sources in the energy input and the uncertainty of various energy using requirements in the energy service, and meanwhile, certain correlations exist in time or space between the input and the output, for example, meteorological changes can simultaneously affect wind power, photovoltaic power generation and the cold/heat requirements of users; the differences in service radius and dynamics of the various energy sources make the optimization model difficult to grasp in the selection of the temporal and spatial scales. The above factors make planning work of an integrated energy system very challenging. The integrated energy system relates to the collaborative planning of local energy supply, regional energy supply networks and regional centralized energy supply devices of all users. At present, the planning of the comprehensive energy system lacks of guiding principles and methods, and the realization of the energy-saving and emission-reducing targets of the comprehensive energy system at different levels is generally not considered in the current work.
Disclosure of Invention
In view of the above problems, the present invention provides a multi-level optimization method for a comprehensive energy system considering carbon emission, so as to realize cooperative optimization of local energy supply and regional centralized energy supply of each user on the energy saving and emission reduction target in consideration of the energy saving and emission reduction target in the optimization of the comprehensive energy system.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention relates to a multi-level optimization method of a comprehensive energy system considering carbon emission, which comprises the following steps:
dividing the comprehensive energy system into three levels, namely a regional layer, a block layer and a local layer according to the node type, wherein the local layer is positioned at the bottom layer, the block layer is positioned at the middle layer, and the regional layer is positioned at the top layer;
establishing an optimal configuration model objective function of each level of the comprehensive energy system;
establishing optimized configuration model constraints of each level of the comprehensive energy system, wherein the optimized configuration model constraints comprise energy network flow constraints, energy coupling constraints and carbon emission constraints;
and optimizing the comprehensive energy system based on the genetic algorithm according to the optimized configuration model objective function and the optimized configuration model constraint of each level to obtain the optimized configuration result of each level of the system.
Preferably, the regional layer energy network flow constraint F is represented by:
wherein the content of the first and second substances,
respectively carrying out natural gas network flow constraint, electric power network flow constraint and heating power network flow constraint on a regional layer;
respectively supplying natural gas flow rate, electric power and thermal power to the system at the tth hour for the regional nodes;
respectively supplying natural gas flow rate, electric power and thermal power to the region at the tth hour for the kth junction node, wherein k is 1,2, and n is the total number of the junction nodes;
the natural gas flow rate, the electric power and the thermal power supplied by the area to the ith block node at the tth hour are respectively, i is 1,2, and p is the total number of the block nodes;
the zone-level energy coupling constraint EH is represented by:
wherein the content of the first and second substances,
respectively supplying natural gas flow rate, electric power and thermal power to the region at the tth hour for the kth junction node, wherein k is 1,2, and n is the total number of the junction nodes;
the zonal carbon emission constraint is represented by the formula:
wherein the content of the first and second substances,
the carbon dioxide emission coefficients of natural gas, electric power and heat at the regional nodes are respectively constant;
respectively supplying natural gas flow rate, electric power and thermal power to the system at the tth hour for the regional nodes; CEaCarbon dioxide emissions are allowed for regional years.
Preferably, the block-level energy network flow constraint F is represented by the following formula:
wherein the content of the first and second substances,
respectively carrying out natural gas network flow constraint, electric power network flow constraint and heating power network flow constraint on a block layer;
the natural gas flow rate, the electric power and the thermal power supplied to the block by the block node at the t hour respectively;
the natural airflow rate, the electric power and the thermal power supplied by the block to the jth local node at the tth hour are respectively, j is 1,2, and q represents the total number of local nodes;
the bulk layer carbon emission constraint is represented by the following formula:
wherein the content of the first and second substances,
carbon dioxide emission coefficients for natural gas, electricity and heat at the block nodes;
the natural gas flow rate, the electric power and the thermal power supplied to the block by the block node at the t hour respectively; CEbCarbon dioxide emissions are allowed for the block year.
Preferably, the carbon dioxide emission coefficient at the block node is represented by the following formula:
wherein the content of the first and second substances,
carbon dioxide emission coefficients of natural gas, electricity and heat at the block nodes respectively;
carbon dioxide emission coefficients of natural gas, electricity and heat at the regional nodes respectively;
carbon dioxide emission coefficients of natural gas, electricity and heat produced by the kth hub node respectively;
respectively supplying natural gas flow rate, electric power and thermal power to the system at the tth hour for the regional nodes;
the natural gas flow rate, the electric power and the thermal power supplied to the region by the kth junction node at the tth hour are respectively, k is 1,2, and n is the total number of the junction nodes.
Preferably, the local layer energy coupling constraint EH is represented by:
wherein the content of the first and second substances,
respectively obtaining the natural gas flow rate, the electric power and the thermal power from the block at the t hour by the local node;
the local formation carbon emission constraint is represented by the following formula:
wherein the content of the first and second substances,
carbon dioxide emission coefficients for natural gas, electricity and heat at the local node, respectively;
respectively obtaining the natural gas flow rate, the electric power and the thermal power from the block at the t hour by the local node; CEcAllowing carbon dioxide emissions for the local layer users year.
Preferably, the carbon dioxide emission coefficient at the local node is represented by:
wherein the content of the first and second substances,
carbon dioxide emission coefficients for natural gas, electricity and heat at the local node, respectively;
respectively obtaining the natural gas flow rate, the electric power and the thermal power from the block at the t hour by the local node;
carbon dioxide emission coefficients of natural gas, electricity and heat at the block nodes respectively;
carbon dioxide emission coefficients of natural gas, electric power and heat produced by the jth local node distributed energy supply equipment respectively;
the natural gas flow rate, the electric power and the thermal power produced by the jth local node at the tth hour, j being 1,2, respectively..., q, q represents the total number of local nodes.
Preferably, the optimized configuration model objective function of each level is represented by the following formula:
wherein, Fα,iRepresents the total annual cost of the hierarchy, the superscript alpha represents the hierarchy of the model description, alpha is the [ a, b, c ]]A represents a zone layer, b represents a block layer, and c represents a local layer; i represents the sequence number of the level in the system; k denotes the serial number of the planning device in the hierarchy, k being 1,2.., n;
respectively representing the annual equipment investment cost and annual equipment operation and maintenance cost of the kth equipment in the ith alpha level;
representing the input energy cost for the ith alpha level.
Preferably, the input energy cost of the ith α level is calculated by:
wherein the content of the first and second substances,
represents the input energy cost of the ith alpha level; t is an hourly time sequence, t is 1,2, 8760,
the natural airflow speed, the electric power and the thermal power are respectively provided for the ith alpha level corresponding node at the t hour;
for natural gas, electric power and heat supply at corresponding nodes of ith alpha levelThe unit price.
Preferably, the optimizing the integrated energy system based on the genetic algorithm according to the optimized configuration model objective function and the optimized configuration model constraint of each level comprises:
initializing carbon dioxide emission coefficients of all levels;
according to the target function of the stratum optimization configuration model and the constraint of the optimization configuration model, optimizing and configuring the energy resources of the local layer by using a branch-and-bound method, and acquiring the annual energy utilization time sequence of the local node;
optimizing and configuring the energy resources of the block layer by using an interior point method according to the target function of the block layer optimized configuration model, the optimized configuration model constraint and the annual energy timing sequence of the local node, and acquiring the annual energy timing sequence of the block node;
performing optimized configuration on the regional layer energy resources by using a genetic algorithm according to the regional layer optimized configuration model objective function, the optimized configuration model constraint and the block node annual energy utilization time sequence;
calculating the carbon dioxide emission coefficient of the block node according to the regional layer energy production time sequence;
calculating the carbon dioxide emission coefficient of the local node according to the carbon dioxide emission coefficient of the block node and the energy production time sequence of the local node;
and judging whether the iteration reaches a stop condition, if the iteration does not reach the stop condition, returning to the optimization configuration solution of the local layer until the iteration reaches the stop condition, and if the iteration reaches the stop condition, outputting a system optimization configuration result.
Preferably, the regional layer corresponds to the whole integrated energy system under the regional node supply range; the block layer corresponds to the sub-blocks within the block node supply range; the local stratum corresponds to users under the local nodes and distributed energy supply systems thereof.
Compared with the prior art, the invention has the following advantages and beneficial effects:
according to the multi-level optimization method of the comprehensive energy system considering carbon emission, the comprehensive energy system is divided into three levels, so that the optimization method can be suitable for planning of the urban energy system. In addition, the invention establishes the relation of optimization design of each level through the calculation of the node energy utilization time sequence, simultaneously considers the realization of the carbon emission target in each level, adapts to the level characteristics of the comprehensive energy system, and compared with the planning method of limiting the current comprehensive energy system to a certain type of energy facilities, the invention realizes the collaborative planning of centralized energy supply and distributed energy supply in the comprehensive energy system and meets the flexible design requirement of the comprehensive energy system.
Drawings
FIG. 1 is a schematic flow chart of a multi-level optimization method of an integrated energy system with consideration of carbon emission according to the present invention;
FIG. 2 is a schematic diagram of an integrated energy system node and hierarchy in accordance with the present invention;
fig. 3 is a schematic flow chart of the optimization of the integrated energy system based on the genetic algorithm.
Detailed Description
The embodiments of the present invention will be described below with reference to the accompanying drawings. Those of ordinary skill in the art will recognize that the described embodiments can be modified in various different ways, or combinations thereof, without departing from the spirit and scope of the present invention. Accordingly, the drawings and description are illustrative in nature and not intended to limit the scope of the claims. Furthermore, in the present description, the drawings are not to scale and like reference numerals refer to like parts.
Fig. 1 is a schematic flow chart of a multi-level optimization method of an integrated energy system considering carbon emissions, and as shown in fig. 1, the multi-level optimization method of an integrated energy system considering carbon emissions includes the following steps:
step S1, dividing the comprehensive energy system into three levels, namely a regional level, a block level and a local level according to the node types, wherein the local level is positioned at the bottom layer, the block level is positioned at the middle layer, and the regional level is positioned at the top layer;
s2, establishing an optimal configuration model objective function of each level of the comprehensive energy system;
step S3, establishing optimized configuration model constraints of each level of the comprehensive energy system, including energy network power flow constraints, energy coupling constraints and carbon emission constraints;
and S4, optimizing the comprehensive energy system based on the genetic algorithm according to the optimized configuration model objective function and the optimized configuration model constraint of each level to obtain the optimized configuration result of each level of the system.
The comprehensive energy system is divided into three layers, firstly, the local energy supply system of each user is optimized based on various energy utilization requirements of each user in the region and by combining with the local resource conditions of the users, on the basis, the energy interaction requirements of each local system and the outside are determined, block optimization is established, then the regional centralized energy supply device is optimally configured, each layer needs to meet the carbon emission constraint, and the collaborative optimization design of the local energy supply and the regional centralized energy supply of each user on the energy conservation and emission reduction target is realized.
Fig. 2 is a schematic diagram of a node and a hierarchical structure of the integrated energy system according to the present invention, and as shown in fig. 2, the integrated energy system includes an electricity, gas, and heat energy supply network, a relatively centralized energy conversion and storage facility, and a distributed energy supply system on a user side, forming a spatial hierarchical distribution feature. The comprehensive energy system is divided according to four types of energy supply nodes, including a region node, a hub node, a block node and a local node. The comprehensive energy system obtains external energy input from the regional nodes; the hub node is an access node of a centralized energy supply and energy storage facility in a region, such as a regional combined heat and power system and a large-scale energy storage system; the block node is a node for connecting a certain sub-block in the area with an area energy system, and the sub-block in the area comprises a plurality of users and distributed energy supply systems thereof; the local node refers to an end user node, such as a residential building, a commercial building, a public facility and the like, and corresponds to a distributed energy supply system on a user side.
In the step S1, the integrated energy system is divided into three levels corresponding to four types of nodes: zone layers, block layers, and local layers. The regional layer corresponds to the whole comprehensive energy system under the regional node supply range; the block layer corresponds to the sub-blocks within the block node supply range; the local stratum corresponds to users under the local nodes and distributed energy supply systems thereof. When the comprehensive energy system is optimized, an object to be optimized and a corresponding level thereof are predetermined. Specifically, the optimization objects include the capacity of a regional centralized energy production facility, the capacity of a regional centralized energy storage facility, the capacity of a block energy supply network, and the capacity of each device in the distributed energy supply system. There may be multiple regional centralized energy production facilities, regional centralized energy storage facilities, sub-blocks, and distributed energy supply systems in an integrated energy system. The method comprises the steps of classifying regional concentrated energy production and energy storage equipment accessed through a hub node into a regional level, classifying an energy supply network accessed to a block node into a block level, and classifying a distributed energy supply system accessed to a local node into a local level.
In one embodiment, in the step S2, each hierarchy level has an optimization goal of minimizing its total annual cost, wherein the total annual cost includes three parts, i.e., equipment annual investment cost, equipment annual operation and maintenance cost, and input energy cost of the system. The system optimization design problem is decomposed into three layers, the bottom layer is the optimization configuration of a distributed energy system, the middle layer is the optimization configuration of a block power supply network, and the top layer is the optimization configuration of regional concentrated energy production and energy storage facilities.
Specifically, the optimized configuration model objective function of each level is represented by the following formula:
wherein, Fα,iRepresents the total annual cost of the hierarchy, the superscript alpha represents the hierarchy of the model description, alpha is the [ a, b, c ]]A represents a zone layer, b represents a block layer, and c represents a local layer; i represents the serial number of the hierarchy in the system, and for the regional layer, i is 1 and corresponds to the number of regional nodes; for a block layer, i is 1,2, and p is the number of sub-blocks in the region, corresponding to the number of block nodes; for theThe local stratum, i is 1,2, and q is the number of distributed energy supply systems in the region and corresponds to the number of local nodes; k denotes the serial number of the planning device in the hierarchy, k being 1,2.., n;
respectively representing the annual equipment investment cost and annual equipment operation and maintenance cost of the kth equipment in the ith alpha level;
representing the input energy cost for the ith alpha level.
Further, the input energy cost of the ith α level is calculated by:
wherein the content of the first and second substances,
represents the input energy cost of the ith alpha level; t is an hourly time sequence, t is 1,2, 8760,
the natural airflow speed, the electric power and the thermal power are respectively provided for the ith alpha level corresponding node at the t hour;
the unit prices of natural gas, electric power and heat supply at the corresponding nodes of the ith alpha level are respectively, and the unit energy price of each node is determined by a system energy operator.
In step S3, the optimized configuration models established according to the different levels of the integrated energy system have different constraints, and the carbon emission coefficient of each node is calculated according to the energy production conditions of each level.
Regional layer energy network trend constraint relates to regional node, hub node and block node. The regional layer energy network flow constraint F is represented by:
wherein the content of the first and second substances,
respectively carrying out natural gas network flow constraint, electric power network flow constraint and heating power network flow constraint on a regional layer; 
respectively supplying natural gas flow rate, electric power and thermal power to the system at the tth hour for the regional nodes;
respectively supplying natural gas flow rate, electric power and thermal power to the region at the tth hour for the kth junction node, wherein k is 1,2, and n is the total number of the junction nodes;
the natural gas flow rate, the electric power and the thermal power supplied by the area to the ith block node at the tth hour are respectively, i is 1,2, and p is the total number of the block nodes;
the regional layer energy coupling constraint is an energy coupling relation established by regional concentrated energy production and energy storage facilities at each pivot node, and the regional layer energy coupling constraint EH is represented by the following formula:
wherein the content of the first and second substances,
the natural gas flow rate, the electric power and the thermal power supplied to the zone at the kth hour by the kth hub node, respectively, k is 1,2..., n, n is the total number of hub nodes;
the zonal carbon emission constraint is represented by the formula:
wherein the content of the first and second substances,
the carbon dioxide emission coefficients of natural gas, electric power and heat at the regional nodes are respectively constant;
respectively supplying natural gas flow rate, electric power and thermal power to the system at the tth hour for the regional nodes; CEaCarbon dioxide emissions are allowed for regional years.
For a certain sub-block of the system, the block layer energy network flow constraint relates to the block nodes and the local nodes, and the block layer only relates to the energy supply network of the block nodes to each local node without energy coupling constraint.
The block-level energy network flow constraint F is represented by:
wherein the content of the first and second substances,
respectively carrying out natural gas network flow constraint, electric power network flow constraint and heating power network flow constraint on a block layer;
the natural gas flow rate, the electric power and the thermal power supplied to the block by the block node at the t hour respectively;
the natural airflow rate, the electric power and the thermal power supplied by the block to the jth local node at the tth hour are respectively, j is 1,2, and q represents the total number of local nodes;
the bulk layer carbon emission constraint is represented by the following formula:
wherein the content of the first and second substances,
carbon dioxide emission coefficients for natural gas, electricity and heat at the block nodes;
the natural gas flow rate, the electric power and the thermal power supplied to the block by the block node at the t hour respectively; CEbCarbon dioxide emissions are allowed for the block year.
The carbon dioxide emission coefficient at the block node is related to the regional layer energy production condition, and specifically, the carbon dioxide emission coefficient at the block node is represented by the following formula:
wherein the content of the first and second substances,
oxidation of natural gas, electricity and heat, respectively, at a node of a blockA carbon emission coefficient;
carbon dioxide emission coefficients of natural gas, electricity and heat at the regional nodes respectively;
carbon dioxide emission coefficients of natural gas, electricity and heat produced by the kth hub node respectively;
respectively supplying natural gas flow rate, electric power and thermal power to the system at the tth hour for the regional nodes;
the natural gas flow rate, the electric power and the thermal power supplied to the region by the kth junction node at the tth hour are respectively, k is 1,2, and n is the total number of the junction nodes.
The stratum does not relate to an energy network, so that no energy network flow constraint exists.
The local layer energy coupling constraint EH is represented by:
wherein the content of the first and second substances,
respectively obtaining the natural gas flow rate, the electric power and the thermal power from the block at the t hour by the local node;
the local formation carbon emission constraint is represented by the following formula:
wherein the content of the first and second substances,
carbon dioxide emission coefficients for natural gas, electricity and heat at the local node, respectively;
respectively obtaining the natural gas flow rate, the electric power and the thermal power from the block at the t hour by the local node; CEcAllowing carbon dioxide emissions for the local layer users year.
The carbon dioxide emission coefficient at a local node is related to the energy production situation of other local nodes, and specifically, the carbon dioxide emission coefficient at the local node is represented by the following formula:
wherein the content of the first and second substances,
carbon dioxide emission coefficients for natural gas, electricity and heat at the local node, respectively;
respectively obtaining the natural gas flow rate, the electric power and the thermal power from the block at the t hour by the local node;
carbon dioxide emission coefficients of natural gas, electricity and heat at the block nodes respectively;
respectively producing natural gas for the jth local node distributed energy supply equipment,Carbon dioxide emission coefficients for electricity and heat;
the natural gas flow rate, the electric power and the thermal power produced by the jth local node at the tth hour, j is 1,2.
In the step S4, a solution method for optimizing configuration of each level of the system is established based on a Genetic Algorithm (GA), and the comprehensive energy system is optimized, where the optimization configuration refers to an optimization configuration of energy resources to meet various energy requirements of users such as cold, heat, electricity, GAs, and the like. And establishing the relation between the optimized configurations of each hierarchy by using the energy time sequence through the nodes. Specifically, the bottom layer optimization configuration model transmits the local node energy utilization time sequence to the middle layer problem, the middle layer transmits the block node energy utilization time sequence to the upper layer problem, and the upper layer problem performs optimization configuration on the regional layer according to the GA. And calculating the carbon emission coefficient on the basis of the solution of the optimized configuration, and finishing the calculation until the three-layer problem iterative optimization reaches an iteration stopping condition, for example, the iteration times reach a given value.
Fig. 3 is a schematic flow chart of optimizing the integrated energy system based on the genetic algorithm, and as shown in fig. 3, the optimizing the integrated energy system based on the genetic algorithm according to the optimized configuration model objective function and the optimized configuration model constraint of each level includes:
step S41, initializing carbon dioxide emission coefficients of all levels;
step S42, solving a local layer optimization configuration problem, wherein the local layer optimization configuration problem is regarded as a mixed integer linear programming problem, specifically, according to a local layer optimization configuration model objective function and optimization configuration model constraint, the local layer energy resources are optimally configured by using a branch-and-bound method, and a local node annual energy utilization time sequence is obtained and used for substituting into a block layer optimization configuration solution;
step S43, solving a block layer optimal configuration problem, wherein the block layer optimal configuration problem is regarded as a nonlinear optimization problem, specifically, according to a block layer optimal configuration model objective function, an optimal configuration model constraint and a local node annual energy utilization time sequence, an interior point method is used for carrying out optimal configuration on block layer energy resources, and the block node annual energy utilization time sequence is obtained and used for substituting into the solution of the regional layer optimal configuration problem;
step S44, solving a regional layer optimization configuration problem, wherein the regional layer optimization configuration problem is regarded as a mixed integer nonlinear programming problem, and specifically, the regional layer energy resources are optimally configured by using a genetic algorithm according to a regional layer optimization configuration model objective function, optimization configuration model constraints and a block node annual energy utilization time sequence;
step S45, calculating the carbon dioxide emission coefficient of the block node according to the regional layer energy production sequence to determine the block layer carbon emission constraint;
step S46, calculating the carbon dioxide emission coefficient of the local node according to the carbon dioxide emission coefficient of the block node and the energy production time sequence of the local node to determine the carbon emission constraint of the local layer;
step S47, judging whether the iteration reaches a stop condition, wherein the iteration stop condition can be that the iteration frequency reaches a set value, if the iteration does not reach the stop condition, returning to the optimization configuration solution of the local layer until the iteration reaches the stop condition;
and step S48, if the iteration reaches the stop condition, outputting a system optimization configuration result.
Further, the optimal configuration of the regional layer energy resources by using the genetic algorithm comprises the following steps:
coding variables to be optimized;
determining a fitness function according to the optimization target;
performing genetic operation on the population chromosomes, wherein the genetic operation comprises selection operation, crossover operation and mutation operation, the selection operation is to select a certain number of better individuals in a certain mode to perform crossover operation, the crossover operation is to perform crossover operation on the selected better individuals in a certain mode, and the mutation operation is performed on part of chromosomes after the crossover operation is completed during the mutation operation, so that the diversity of the population is further expanded;
bringing the individuals in the group into an optimization objective function, and calculating the fitness value of each individual according to the fitness function;
and judging whether a stopping condition is met, wherein the stopping condition is that the optimization progress is met or the set maximum iteration number is reached, if the stopping condition is not met, continuing to iterate to perform genetic operation on the chromosome, and if the stopping condition is reached, outputting an optimal solution as an optimization configuration result of the regional layer.
It should be noted that, in the present invention, the encoding method of the genetic algorithm, the method for determining the fitness function, the method for calculating the fitness value, and the like may all be calculated by using corresponding methods in the existing genetic algorithm, and this part is not described in detail in the present invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A multi-level optimization method for an integrated energy system considering carbon emission is characterized by comprising the following steps:
dividing the comprehensive energy system into three levels, namely a regional layer, a block layer and a local layer according to the node type, wherein the local layer is positioned at the bottom layer, the block layer is positioned at the middle layer, and the regional layer is positioned at the top layer;
establishing an optimal configuration model objective function of each level of the comprehensive energy system;
establishing optimized configuration model constraints of each level of the comprehensive energy system, wherein the optimized configuration model constraints comprise energy network flow constraints, energy coupling constraints and carbon emission constraints;
and optimizing the comprehensive energy system based on the genetic algorithm according to the optimized configuration model objective function and the optimized configuration model constraint of each level to obtain the optimized configuration result of each level of the system.
2. The method for integrated energy system multi-level optimization taking into account carbon emissions according to claim 1, characterized in that the regional layer energy network flow constraint F is represented by the following formula:
wherein the content of the first and second substances,
respectively carrying out natural gas network flow constraint, electric power network flow constraint and heating power network flow constraint on a regional layer;
respectively supplying natural gas flow rate, electric power and thermal power to the system at the tth hour for the regional nodes;
respectively supplying natural gas flow rate, electric power and thermal power to the region at the tth hour for the kth junction node, wherein k is 1,2, and n is the total number of the junction nodes;
the natural gas flow rate, the electric power and the thermal power supplied by the area to the ith block node at the tth hour are respectively, i is 1,2, and p is the total number of the block nodes;
the zone-level energy coupling constraint EH is represented by:
wherein the content of the first and second substances,
respectively supplying natural gas flow rate, electric power and thermal power to the region at the tth hour for the kth junction node, wherein k is 1,2, and n is the total number of the junction nodes;
the zonal carbon emission constraint is represented by the formula:
wherein the content of the first and second substances,
the carbon dioxide emission coefficients of natural gas, electric power and heat at the regional nodes are respectively constant;
respectively supplying natural gas flow rate, electric power and thermal power to the system at the tth hour for the regional nodes; CEaCarbon dioxide emissions are allowed for regional years.
3. The method for integrated energy system multi-level optimization taking into account carbon emissions according to claim 1, characterized in that the block level energy network flow constraint F is represented by the following formula:
wherein the content of the first and second substances,
respectively carrying out natural gas network flow constraint, electric power network flow constraint and heating power network flow constraint on a block layer;
the natural gas flow rate, the electric power and the thermal power supplied to the block by the block node at the t hour respectively;
the natural airflow rate, the electric power and the thermal power supplied by the block to the jth local node at the tth hour are respectively, j is 1,2, and q represents the total number of local nodes;
the bulk layer carbon emission constraint is represented by the following formula:
wherein the content of the first and second substances,
carbon dioxide emission coefficients for natural gas, electricity and heat at the block nodes;
the natural gas flow rate, the electric power and the thermal power supplied to the block by the block node at the t hour respectively; CEbCarbon dioxide emissions are allowed for the block year.
4. The method of integrated energy system multi-level optimization taking into account carbon emissions of claim 3, wherein the carbon dioxide emission coefficients at the block nodes are represented by the following formula:
wherein the content of the first and second substances,
carbon dioxide emission coefficients of natural gas, electricity and heat at the block nodes respectively;
carbon dioxide emission coefficients of natural gas, electricity and heat at the regional nodes respectively;
carbon dioxide emission coefficients of natural gas, electricity and heat produced by the kth hub node respectively;
respectively supplying natural gas flow rate, electric power and thermal power to the system at the tth hour for the regional nodes;
the natural gas flow rate, the electric power and the thermal power supplied to the region by the kth junction node at the tth hour are respectively, k is 1,2, and n is the total number of the junction nodes.
5. The method of integrated energy system multi-tier optimization taking into account carbon emissions of claim 1, wherein the local tier energy coupling constraint EH is represented by the following equation:
wherein the content of the first and second substances,
respectively obtaining the natural gas flow rate, the electric power and the thermal power from the block at the t hour by the local node;
the local formation carbon emission constraint is represented by the following formula:
wherein the content of the first and second substances,
carbon dioxide emission coefficients for natural gas, electricity and heat at the local node, respectively;
respectively obtaining the natural gas flow rate, the electric power and the thermal power from the block at the t hour by the local node; CEcAllowing carbon dioxide emissions for the local layer users year.
6. The method of integrated energy system multi-level optimization taking into account carbon emissions according to claim 5, characterized in that the carbon dioxide emission coefficient at the local node is represented by the following formula:
wherein the content of the first and second substances,
carbon dioxide emission coefficients for natural gas, electricity and heat at the local node, respectively;
respectively obtaining the natural gas flow rate, the electric power and the thermal power from the block at the t hour by the local node;
carbon dioxide emission coefficients of natural gas, electricity and heat at the block nodes respectively;
carbon dioxide emission coefficients of natural gas, electric power and heat produced by the jth local node distributed energy supply equipment respectively;
the natural gas flow rate, the electric power and the thermal power produced by the jth local node at the tth hour, j is 1,2.
7. The method of claim 1, wherein the optimal configuration model objective function for each level is represented by the following formula:
wherein, Fα,iRepresents the total annual cost of the hierarchy, the superscript alpha represents the hierarchy of the model description, alpha is the [ a, b, c ]]A represents a zone layer, b represents a block layer, and c represents a local layer; i represents the sequence number of the level in the system; k denotes the serial number of the planning device in the hierarchy, k being 1,2.., n;
respectively representing the annual equipment investment cost and annual equipment operation and maintenance cost of the kth equipment in the ith alpha level;
representing the input energy cost for the ith alpha level.
8. The method of integrated energy system multi-tier optimization taking into account carbon emissions of claim 7, wherein the input energy cost of the ith alpha tier is calculated by:
wherein the content of the first and second substances,
represents the input energy cost of the ith alpha level; t is an hourly time sequence, t is 1,2, 8760,
the natural airflow speed, the electric power and the thermal power are respectively provided for the ith alpha level corresponding node at the t hour;
unit prices for natural gas, electricity and heat supply at corresponding nodes of the ith alpha level, respectively.
9. The method for multi-level optimization of an integrated energy system considering carbon emissions according to claim 1, wherein the optimization of the integrated energy system based on genetic algorithm according to the optimized configuration model objective function and the optimized configuration model constraint of each level comprises:
initializing carbon dioxide emission coefficients of all levels;
according to the target function of the stratum optimization configuration model and the constraint of the optimization configuration model, optimizing and configuring the energy resources of the local layer by using a branch-and-bound method, and acquiring the annual energy utilization time sequence of the local node;
optimizing and configuring the energy resources of the block layer by using an interior point method according to the target function of the block layer optimized configuration model, the optimized configuration model constraint and the annual energy timing sequence of the local node, and acquiring the annual energy timing sequence of the block node;
performing optimized configuration on the regional layer energy resources by using a genetic algorithm according to the regional layer optimized configuration model objective function, the optimized configuration model constraint and the block node annual energy utilization time sequence;
calculating the carbon dioxide emission coefficient of the block node according to the regional layer energy production time sequence;
calculating the carbon dioxide emission coefficient of the local node according to the carbon dioxide emission coefficient of the block node and the energy production time sequence of the local node;
and judging whether the iteration reaches a stop condition, if the iteration does not reach the stop condition, returning to the optimization configuration solution of the local layer until the iteration reaches the stop condition, and if the iteration reaches the stop condition, outputting a system optimization configuration result.
10. The method for multi-level optimization of an integrated energy system with consideration of carbon emissions according to claim 1, wherein the regional level corresponds to the entire integrated energy system under the regional node supply range; the block layer corresponds to the sub-blocks within the block node supply range; the local stratum corresponds to users under the local nodes and distributed energy supply systems thereof.
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