CN116227167A - Multi-target optimization method and system for multi-park comprehensive energy system - Google Patents
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Abstract
The invention provides a multi-target optimization method and a system for a multi-park comprehensive energy system, which are used for establishing a multi-park comprehensive energy system model; the method comprises the steps of taking the lowest running cost of a multi-park comprehensive energy system as an optimization target, establishing a system economic running objective function, taking the lowest carbon emission of the multi-park comprehensive energy system as the optimization target, establishing a system low-carbon running objective function, taking the highest primary energy utilization rate of the multi-park comprehensive energy system as the optimization target, and establishing a system efficient running objective function; and adopting a co-evolution constraint multi-objective optimization solving method to carry out multi-objective optimization solving, and obtaining an optimized scheduling strategy set after meeting convergence conditions. The invention realizes the low-carbon economical and efficient operation of the system.
Description
Technical Field
The invention belongs to the technical field of comprehensive energy systems, and relates to a multi-target optimization method and system for a multi-park comprehensive energy system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The multi-park comprehensive energy system (Multi Park Integrated Energy System, MPIES) has the characteristics of multi-energy synergy, complementary interaction and high energy utilization efficiency, and is effective power assisting for promoting pollution and carbon reduction synergy.
In the prior art, most of researches focus on modeling and optimizing scheduling of a single comprehensive energy system or a micro energy network, but energy interaction among a plurality of energy systems is not considered, and energy complementation and mutual compensation among the plurality of systems cannot be effectively realized. For different subjects in the system, the required indexes and application scenes are not consistent and single, and different benefit requirements exist in different application scenes. Particularly, the index of low-carbon operation of the system is paid attention to, and a more perfect method means is not available at present for realizing high-efficiency and economical operation of the system while reducing the carbon emission of the system.
In summary, the conventional method cannot perform modeling analysis and multi-scenario multi-objective optimization scheduling on systems of a plurality of interconnection parks, and cannot meet multi-scenario multi-objective operation requirements of the systems.
Disclosure of Invention
In order to solve the problems, the invention provides a multi-objective optimization method and a multi-objective optimization system for a multi-park comprehensive energy system, which are characterized in that the index of carbon emission is introduced on the basis of traditional economic operation, a multi-objective optimization model is constructed with the lowest overall operation cost and the least carbon emission of the system, the safe operation constraint of each device in the system is considered, and the problem solving is carried out by adopting a co-evolution constraint multi-objective optimization solving method, so that the low-carbon economic and efficient operation of the system is realized.
According to some embodiments, the present invention employs the following technical solutions:
a multi-target optimization method for a multi-park comprehensive energy system comprises the following steps:
establishing a multi-park comprehensive energy system model;
the method comprises the steps of taking the lowest running cost of a multi-park comprehensive energy system as an optimization target, establishing a system economic running objective function, taking the lowest carbon emission of the multi-park comprehensive energy system as the optimization target, establishing a system low-carbon running objective function, taking the highest primary energy utilization rate of the multi-park comprehensive energy system as the optimization target, and establishing a system efficient running objective function;
and adopting a co-evolution constraint multi-objective optimization solving method to carry out multi-objective optimization solving, and obtaining an optimized scheduling strategy set after meeting convergence conditions.
As an alternative implementation mode, the specific process of establishing the multi-park comprehensive energy system model comprises the steps of establishing each single-park comprehensive energy system model, and interconnecting energy subsystems of each single-park comprehensive energy system model to form the multi-park comprehensive energy system model.
Further, the energy supply equipment of the integrated energy system model of each single park comprises a plurality of distributed energy sources such as wind-electricity photovoltaic, a gas boiler, an electric refrigerating unit, a cogeneration unit and an absorption refrigerating unit, and the energy storage comprises an electric energy storage system and a thermal energy storage system.
Further, when the comprehensive energy system model of each single park is built, describing various energy conversion relations inside the energy station by adopting an energy hub model, and building an energy station model; and establishing an energy storage process model of the energy storage equipment, wherein the energy storage equipment and the energy storage equipment are provided with capacity constraint.
As an alternative embodiment, the multi-campus integrated energy system operation cost is the sum of various energy power and corresponding energy price products.
Alternatively, the total carbon emission is the sum of carbon emission of each park, and the carbon emission of each park is the sum of the product of the electricity purchase amount and the gas purchase amount at each moment of the corresponding park and the corresponding carbon emission coefficient.
As an alternative embodiment, the primary energy utilization rate is the highest and the total energy consumption of the power grid, the micro gas turbine and the gas boiler of each park is the smallest.
As an alternative embodiment, the specific process of adopting the co-evolution constraint multi-objective optimization solving method comprises the following steps: using two populations that undergo different evolutionary pathways, population 2 is used as an auxiliary population for unconstrained evolution to explore the target space, find viable solutions in the viable domain, and then store these viable solutions in population 1, continually guided through environmental selection of population 1, to obtain the optimal solution set.
A multi-target optimization system for a multi-campus integrated energy system, comprising:
a multi-park model building module configured to build a multi-park integrated energy system model;
the multi-objective function construction module is configured to establish a system economic operation objective function by taking the lowest operation cost of the multi-park comprehensive energy system as an optimization target, to establish a system low-carbon operation objective function by taking the lowest carbon emission of the multi-park comprehensive energy system as the optimization target, and to establish a system efficient operation objective function by taking the highest primary energy utilization rate of the multi-park comprehensive energy system as the optimization target;
and the collaborative solving module is configured to perform multi-objective optimization solving by adopting a collaborative evolution constraint multi-objective optimization solving method, and obtain an optimized scheduling strategy set after meeting convergence conditions.
A terminal device comprising a processor and a computer readable storage medium, the processor configured to implement instructions; the computer readable storage medium is for storing a plurality of instructions adapted to be loaded by a processor and to perform the steps in the method.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, different operation indexes of the multi-park comprehensive energy system under different operation scenes are comprehensively considered, and a co-evolution constraint multi-objective optimization solving method is adopted to obtain a low-carbon economic and efficient operation scheme, so that the economical efficiency, the low carbon property and the high efficiency of the system are considered.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a block diagram of a system in accordance with the present invention;
FIG. 2 is a schematic diagram of a single campus integrated energy system in the system of the present invention;
FIG. 3 is a Pareto front diagram of a solution algorithm;
FIG. 4 is a plot of power schedule for campus 3 in an example analysis of the present invention;
FIG. 5 is a thermal energy dispatch diagram for campus 3 in an example analysis of the present invention;
FIG. 6 is a graph of power interaction in an example analysis of the present invention;
FIG. 7 is a graph of thermal energy interactions in an example analysis of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The embodiment provides an economic low-carbon high-efficiency multi-objective optimization method for a multi-park comprehensive energy system.
Firstly, the present embodiment provides a system structure for implementing complementary energy mutual-aid by energy interaction in a plurality of parks in a multi-parks integrated energy system, as shown in fig. 1. As shown in fig. 1.
Firstly, modeling each part in the system according to a system structure diagram.
Single park comprehensive energy system model:
the energy supply equipment in the single park comprises wind-electricity-photovoltaic equal-distribution energy sources, a gas boiler, an electric refrigerating unit, a cogeneration unit, an absorption refrigerating unit and the like. The energy storage includes an electrical energy storage system and a thermal energy storage system, as shown in fig. 2.
In the embodiment, an energy hub model is adopted to describe various energy conversion relations inside an energy station, and an energy station model is built:
describing an energy balance relation in a system by adopting an energy hub model:
wherein: l (L) e 、L h And L c Respectively an electric load, a thermal load and a cold load; η (eta) T 、η gee 、η geh 、η gh The efficiency of the transformer, the power generation efficiency of the CHP unit, the heat generation efficiency of the CHP unit and the efficiency of the gas-fired boiler are respectively; p (P) e 、P ge 、P gh The electricity consumption of the CHP unit and the gas consumption of the gas boiler are respectively the electricity purchasing quantity; p (P) echar 、P edis 、P hchar 、P hdis The charging and discharging power of the electric energy storage system and the charging and discharging power of the thermal energy storage system are respectively; p (P) PVe 、P PVh Respectively generating power and heating power for distributed energy sources; p (P) ei For consuming electric power, P, for electric refrigerating units hi The heat power is consumed for the absorption refrigerating unit; p (P) eic 、P hic The cold power is respectively produced by an electric refrigerating unit and an absorption refrigerating unit; p (P) exe 、P exh For the electric power, thermal power flowing into the energy station.
For the energy storage process of the energy storage device:
meanwhile, the energy storage initial state is ensured to be consistent:
wherein: s is S *,t For the energy storage state of the energy storage device at the time t, delta * For static energy storage efficiency, P *char,t 、P *dis,t The power of charging and releasing energy at the time t respectively,
energy efficiency, t, of charging and discharging respectively 0 、t e The start time and the end time, respectively.
For energy and energy storage devices, the capacity thereof should be constrained:
P chp,min ≤P chp ≤P chp,max (6)
P qg,min ≤P qg ≤P qg,max (7)
P eic,min ≤P eic ≤P eic,max (8)
P hic,min ≤P hic ≤P hic,max (9)
wherein: p (P) chp 、P qg The power of the fuel gas is CHP unit and fuel gas boiler respectively; p (P) *,max 、P *,min The upper and lower limits of the power of each device are respectively.
Energy storage device capacity constraints:
0≤P echar ≤P echar,max (10)
0≤P edis ≤P edis,max (11)
0≤P hchar ≤P hchar,max (12)
0≤P hdis ≤P hdis,max (13)
S e,min ≤S e,t ≤S e,max (14)
S h,min ≤S h,t ≤S h,max (15)
wherein: s is S *,max 、S *,min The upper limit and the lower limit of the energy storage state of the energy storage device are respectively.
System multi-objective optimization scheduling model:
the method comprises the steps that a plurality of single-park energy subsystems in a multi-park comprehensive energy system are interconnected, and an economic operation objective function is established by taking the minimum total operation cost as an optimization target:
wherein: i epsilon {1, 2.,. N }, N is the number of parks, C *,t The price of various energy sources at the moment t.
Meanwhile, the lowest total carbon emission of the comprehensive energy system is considered, and a system carbon emission model is established as follows:
wherein: i e {1, 2.,. N }, N is the number of parks, a * 、b * 、c * Carbon emission coefficients, P e,i (t) is the electricity purchasing quantity at time t of i park, P ge,i And (t) is the air purchase amount at the time t of the i park.
The energy conservation of the i park is mainly reflected in the primary energy consumption of the system, namely, the smaller the total primary energy consumption of the system is, the better the energy conservation of the system is. The consumption of primary energy in the i park is mainly dependent on the operation of the grid, the micro gas turbines and the gas boilers, i.e. the total primary energy consumption PEC is expressed as:
wherein G is mt,i (t),G gb,i (t), and G grid,i And (t) respectively representing the consumption of fossil fuel energy such as coal and the like by the gas turbine, the gas boiler and the i park and the power grid at the moment t.
In order to evaluate the three aspects of the system, different objective functions are required to be established under three indexes, and the operation results are optimized respectively for analysis. And because different decision makers have different emphasis degrees on the three indexes, the three objective functions are comprehensively considered to carry out multi-objective optimization, and finally a compromise optimal solution is obtained.
Based on the three performance indexes, the minimum total running cost of the system, the minimum total consumption of primary energy and the minimum total discharge of pollutants are respectively taken as optimization objective functions, and can be expressed as:
aiming at the model, the embodiment also provides a co-evolution constraint multi-objective optimization solving method.
The central idea of the co-evolution constrained multi-objective optimization method is to use two populations that undergo different evolutionary pathways and then guide each other in some way to obtain the optimal solution set, specifically as follows:
the inputs are maximum iteration number T, population size N, mutation rate F, crossover rate CR.
Initializing, and randomly generating a dominant population into a population 1. The helper population is randomly generated as population 2.
When the iteration number T is smaller than T, the following steps are circularly executed:
Off 1 Σ generates N individuals by differential operation based on parent 1.
Off 2≡generates N individuals by differential operation based on parent 2.
t increases by 1 until a condition for ending the cycle is reached.
And outputting the population 1 when the condition is met.
In the above step, population 2 is used as an adjunct population to unconstrained evolution to explore the target space to find viable solutions in the viable domain, which are then stored in population 1 by environmental selection of population 1. At the same time, individuals in population 1 can instruct population 2 to some extent to find a viable solution.
According to the solving result, a low-carbon economic and efficient operation strategy of the multi-park comprehensive energy system is provided:
and obtaining an optimal operation strategy set of the system by comprehensively considering the influence of a plurality of indexes on the operation of the system. The multi-park comprehensive energy system in the analysis of the embodiment comprises three parks, a large power grid and a natural gas network are connected, energy interaction is carried out between each park through a connecting line, and the load of each park is the winter typical load of three different parks in the north.
And solving the proposed optimization model in the example analysis, and taking the economical efficiency, low carbon and high efficiency of the system into consideration, so as to obtain the optimal scheme of the comprehensive energy system of each single park.
FIG. 3 is a Pareto front diagram of a solution algorithm in an example analysis. In the multi-park comprehensive energy system, the set three target values are mutually coupled and mutually influenced, and the high-efficiency and low-carbon operation of the system inevitably brings higher operation cost.
Taking park 3 as an example, it can be seen from fig. 4 that in terms of electric load supply, electricity is mainly supplied through grid purchase, renewable energy sources, and CHP unit gas power generation. The renewable energy sources such as wind power, photovoltaic and the like are rich in the period from 10 a.m. to 6 a.m., and the carbon emission and the power generation cost are lower than those of the power grid electricity purchasing and the CHP unit power generation, so that the system preferentially uses part of the energy sources for supplying, and the power price peak area preferentially selects the CHP unit for supplying electric energy if the power supply shortage occurs, and the low-carbon economic operation of the system is realized by reasonably selecting the electric energy supply method. Meanwhile, an electric energy storage system is arranged in the system, electric energy is stored when the electricity price of the power grid is valley, and electric discharge is carried out in an electricity price peak area, so that the translation of the electric energy in time is realized, and the power supply pressure of the power grid and the running cost of the system are reduced. As can be seen from fig. 5, in terms of heat load supply, the heat is mainly supplied through solar heat collectors, gas filtration and CHP unit waste heat recovery, and when the illumination intensity is high in the daytime, the heat collectors with low carbon emission and low heating cost are preferably selected for heat energy supply. The CHP unit in the system works in an electric heating mode, and the CHP unit generates electricity and simultaneously supplies heat load through the waste heat recovery device.
In the example analysis, when the combination is operated, the complementary mutual energy of the energy of each park can be realized, and the optimization results are shown in fig. 6 and 7. The energy transfer between the various fields mutually works, so that the running cost and the carbon emission of the system are further reduced.
In the example analysis, the carbon emission and the running cost conditions of each park under the independent running and the combined running are compared and analyzed. The carbon emission of the CHP in the process of generating electric energy and heat energy by natural gas and the carbon emission of the wind turbine generator system in the process of generating electricity are less than the carbon emission of the electric energy purchased from the power grid, and the mode of realizing energy interaction by three parks is realized through interconnection, the CHP generator system and the wind turbine generator system have larger capacity and can interact to other parks through the natural gas to generate electric energy and heat energy, thereby reducing the carbon emission of the parks with smaller capacity and realizing the low-carbon operation of the multi-park comprehensive energy system. The carbon emissions and energy consumption for the three campuses operating individually and in conjunction were solved four times and the results are shown in table 1 and table 2. From the table, it can be seen that the total amount of carbon emissions per day can be reduced by about 7% and the energy consumption by about 3.8% by adopting the operation mode of interconnecting three parks.
TABLE 1 carbon emissions comparison
TABLE 2 comparison of energy consumption throughout the day
The invention also provides the following product examples:
a multi-target optimization system for a multi-campus integrated energy system, comprising:
a multi-park model building module configured to build a multi-park integrated energy system model;
the multi-objective function construction module is configured to establish a system economic operation objective function by taking the lowest operation cost of the multi-park comprehensive energy system as an optimization target, to establish a system low-carbon operation objective function by taking the lowest carbon emission of the multi-park comprehensive energy system as the optimization target, and to establish a system efficient operation objective function by taking the highest primary energy utilization rate of the multi-park comprehensive energy system as the optimization target;
and the collaborative solving module is configured to perform multi-objective optimization solving by adopting a collaborative evolution constraint multi-objective optimization solving method, and obtain an optimized scheduling strategy set after meeting convergence conditions.
A terminal device comprising a processor and a computer readable storage medium, the processor configured to implement instructions; the computer readable storage medium is for storing a plurality of instructions adapted to be loaded by a processor and to perform the steps in the method.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (10)
1. A multi-target optimization method for a multi-park comprehensive energy system is characterized by comprising the following steps:
establishing a multi-park comprehensive energy system model;
the method comprises the steps of taking the lowest running cost of a multi-park comprehensive energy system as an optimization target, establishing a system economic running objective function, taking the lowest carbon emission of the multi-park comprehensive energy system as the optimization target, establishing a system low-carbon running objective function, taking the highest primary energy utilization rate of the multi-park comprehensive energy system as the optimization target, and establishing a system efficient running objective function;
and adopting a co-evolution constraint multi-objective optimization solving method to carry out multi-objective optimization solving, and obtaining an optimized scheduling strategy set after meeting convergence conditions.
2. The multi-objective optimization method of multi-campus integrated energy system of claim 1, wherein the specific process of building the multi-campus integrated energy system model comprises building individual-campus integrated energy system models, and energy subsystems of the individual-campus integrated energy system models are interconnected to form the multi-campus integrated energy system model.
3. The multi-target optimization method for the multi-park comprehensive energy system according to claim 2, wherein the energy supply equipment of each single-park comprehensive energy system model comprises a plurality of distributed energy sources such as wind-electricity-photovoltaic power, a gas boiler, an electric refrigerating unit, a cogeneration unit and an absorption refrigerating unit, and the energy storage comprises an electric energy storage system and a thermal energy storage system.
4. The multi-target optimization method of the multi-park comprehensive energy system according to claim 2, wherein when each single-park comprehensive energy system model is built, various energy conversion relations inside an energy station are described by adopting an energy hub model, and an energy station model is built; and establishing an energy storage process model of the energy storage equipment, wherein the energy storage equipment and the energy storage equipment are provided with capacity constraint.
5. The multi-objective optimization method of the multi-campus integrated energy system according to claim 1, wherein the operation cost of the multi-campus integrated energy system is the sum of products of various energy powers and corresponding energy prices.
6. The multi-objective optimization method of the multi-park comprehensive energy system according to claim 1, wherein the total carbon emission is the sum of carbon emission of each park, and the carbon emission of each park is the sum of products of electricity purchase quantity and electricity purchase quantity at each moment of the corresponding park and the corresponding carbon emission coefficient.
7. The multi-target optimization method for the multi-park comprehensive energy system according to claim 1, wherein the primary energy utilization rate is the highest and the lowest energy consumption of the power grid, the micro gas turbines and the gas boilers of each park.
8. The multi-target optimization method for the multi-park comprehensive energy system according to claim 1, wherein the specific process of adopting the co-evolution constraint multi-target optimization solving method comprises the following steps: using two populations that undergo different evolutionary pathways, population 2 is used as an auxiliary population for unconstrained evolution to explore the target space, find viable solutions in the viable domain, and then store these viable solutions in population 1, continually guided through environmental selection of population 1, to obtain the optimal solution set.
9. A multi-target optimization system of a multi-park comprehensive energy system is characterized by comprising:
a multi-park model building module configured to build a multi-park integrated energy system model;
the multi-objective function construction module is configured to establish a system economic operation objective function by taking the lowest operation cost of the multi-park comprehensive energy system as an optimization target, to establish a system low-carbon operation objective function by taking the lowest carbon emission of the multi-park comprehensive energy system as the optimization target, and to establish a system efficient operation objective function by taking the highest primary energy utilization rate of the multi-park comprehensive energy system as the optimization target;
and the collaborative solving module is configured to perform multi-objective optimization solving by adopting a collaborative evolution constraint multi-objective optimization solving method, and obtain an optimized scheduling strategy set after meeting convergence conditions.
10. A terminal device, comprising a processor and a computer readable storage medium, the processor configured to implement instructions; a computer readable storage medium for storing a plurality of instructions adapted to be loaded by a processor and to perform the steps of the method of any of claims 1-8.
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