CN116227167A - A multi-objective optimization method and system for a multi-park integrated energy system - Google Patents

Abstract

The present invention provides a multi-objective optimization method and system for a multi-park comprehensive energy system, which establishes a multi-park comprehensive energy system model; takes the lowest operating cost of a multi-park comprehensive energy system as the optimization goal, establishes a system economic operation objective function, and uses multi-park comprehensive The lowest carbon emission of the energy system is the optimization goal, and the low-carbon operation objective function of the system is established, and the highest primary energy utilization rate of the multi-park comprehensive energy system is the optimization objective, and the efficient operation objective function of the system is established; using the multi-objective optimization solution method of co-evolution constraints, the Multi-objective optimization solution, when the convergence conditions are met, the optimal scheduling strategy set is obtained. The invention realizes low-carbon, economical and efficient operation of the system.

Description

A multi-objective optimization method and system for multi-park integrated energy system

Technical Field

The present invention belongs to the technical field of integrated energy systems, and relates to a multi-objective optimization method and system for a multi-park integrated energy system.

Background Art

The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

The Multi Park Integrated Energy System (MPIES) has the characteristics of multi-energy synergy, mutual complementation and high energy utilization efficiency, and is an effective aid in promoting synergistic efficiency in pollution reduction and carbon reduction.

In the existing technology, most of the research focuses on the modeling and optimization scheduling of a single integrated energy system or micro-energy network, without considering the energy interaction between multiple energy systems, and cannot effectively achieve the energy complementarity and mutual assistance between multiple systems. For different subjects in the system, the indicators and application scenarios they require are not consistent and single, and there are different interest requirements in different application scenarios. In particular, the indicator of low-carbon operation of the system has attracted much attention. How to achieve efficient and economical operation of the system while reducing the carbon emissions of the system is currently lacking a relatively complete method and means.

In summary, traditional methods are unable to model and analyze systems in multiple interconnected parks and perform multi-scenario and multi-objective optimization scheduling, and cannot meet the multi-scenario and multi-objective operation requirements of the system.

Summary of the invention

In order to solve the above problems, the present invention proposes a multi-objective optimization method and system for a multi-park integrated energy system. On the basis of traditional economic operation, the present invention introduces the carbon emissions indicator, constructs a multi-objective optimization model with the lowest overall system operating cost and the least carbon emissions, takes into account the safe operation constraints of each device in the system, and uses a collaborative evolutionary constrained multi-objective optimization solution method to solve the problem, thereby achieving low-carbon, economical and efficient operation of the system.

According to some embodiments, the present invention adopts the following technical solutions:

A multi-objective optimization method for a multi-park integrated energy system comprises the following steps:

Establish a multi-park integrated energy system model;

Taking the lowest operating cost of the multi-park integrated energy system as the optimization goal, establish the system economic operation objective function; taking the lowest carbon emission of the multi-park integrated energy system as the optimization goal, establish the system low-carbon operation objective function; taking the highest primary energy utilization rate of the multi-park integrated energy system as the optimization goal, establish the system efficient operation objective function;

The collaborative evolution constrained multi-objective optimization solution method is adopted to carry out multi-objective optimization solution. When the convergence conditions are met, the optimal scheduling strategy set is obtained.

As an optional implementation method, the specific process of establishing a multi-park integrated energy system model includes establishing a comprehensive energy system model for each single park, interconnecting the energy subsystems of the comprehensive energy system models of each single park, and forming a multi-park integrated energy system model.

Furthermore, the energy supply equipment of each single-park integrated energy system model includes distributed energy such as wind power and photovoltaic power, gas boilers, electric refrigeration units, cogeneration units and absorption refrigeration units, and the energy storage includes electric energy storage systems and thermal energy storage systems.

As a further step, when establishing the comprehensive energy system model of each single park, the energy hub model is used to describe the various energy conversion relationships within the energy station and build the energy station model; the energy storage process model of the energy storage equipment is established, and both the energy supply equipment and the energy storage equipment have capacity constraints.

As an optional implementation, the operating cost of the multi-park integrated energy system is the sum of the products of various energy powers and corresponding energy prices.

As an optional implementation, the total carbon emissions are the sum of the carbon emissions of each park, and the carbon emissions of each park are the sum of the products of the electricity and gas purchases of the corresponding park at each moment and their corresponding carbon emission coefficients.

As an optional implementation, the primary energy utilization rate is the highest when the total energy consumption of the power grid, micro gas turbines and gas boilers in each park is the lowest.

As an optional implementation method, the specific process of using the collaborative evolution constrained multi-objective optimization solution method includes: using two populations, the two populations undergo different evolutionary paths, population 2 is used as an auxiliary population for unconstrained evolution to explore the target space and find feasible solutions in the feasible domain, and then these feasible solutions are stored in population 1 through environmental selection of population 1, and they continuously guide each other to obtain the optimal solution set.

A multi-park integrated energy system multi-objective optimization system, comprising:

a multi-park model building module, configured to build a multi-park integrated energy system model;

The multi-objective function building module is configured to establish the system economic operation objective function with the lowest operation cost of the multi-park integrated energy system as the optimization goal, establish the system low-carbon operation objective function with the lowest carbon emission of the multi-park integrated energy system as the optimization goal, and establish the system efficient operation objective function with the highest primary energy utilization rate of the multi-park integrated energy system as the optimization goal;

The collaborative solution module is configured to adopt a collaborative evolutionary constrained multi-objective optimization solution method to perform multi-objective optimization solution, and when the convergence conditions are met, an optimized scheduling strategy set is obtained.

A terminal device includes a processor and a computer-readable storage medium, wherein the processor is used to implement various instructions; and the computer-readable storage medium is used to store multiple instructions, wherein the instructions are suitable for being loaded by the processor and executing the steps in the described method.

Compared with the prior art, the present invention has the following beneficial effects:

The present invention comprehensively considers the different operating indicators of the multi-park integrated energy system under different operating scenarios, and adopts a collaborative evolutionary constrained multi-objective optimization solution method to obtain its low-carbon, economical and efficient operation plan, taking into account the system's economy, low carbon and high efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

FIG1 is a system structure diagram of the present invention;

FIG2 is a schematic diagram of a single park integrated energy system in the system of the present invention;

Figure 3 is a Pareto frontier diagram of the solution algorithm;

FIG4 is an electric energy dispatch diagram of Park 3 in the example analysis of the present invention;

FIG5 is a thermal energy dispatch diagram of Park 3 in the example analysis of the present invention;

FIG6 is a diagram of electric energy interaction in an example analysis of the present invention;

FIG. 7 is a diagram of thermal energy interaction in an example analysis of the present invention.

DETAILED DESCRIPTION

The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and/or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and/or combinations thereof.

This embodiment provides an economical, low-carbon and high-efficiency multi-objective optimization method for a multi-park integrated energy system.

First, this embodiment provides a system structure in which several parks in a multi-park integrated energy system are interconnected and mutually complementary in energy through energy interaction, as shown in FIG1 . As shown in FIG1 .

First, model each part of the system according to the system structure diagram.

Single park integrated energy system model:

The energy supply equipment in a single park includes distributed energy such as wind power and photovoltaic power, gas boilers, electric refrigeration units, cogeneration units and absorption refrigeration units, etc. Energy storage includes electric energy storage system and thermal energy storage system, as shown in Figure 2.

This embodiment uses the energy hub model to describe various energy conversion relationships within the energy station and builds an energy station model:

The energy hub model is used to describe the energy balance relationship within the system:

Wherein: _Le , _Lh and _Lc are electric load, heat load and cooling load respectively; _ηT , _ηgee , _ηgeh and _ηgh are transformer efficiency, CHP unit power generation efficiency, CHP unit heat generation efficiency and gas boiler efficiency respectively; _Pe , _Pge and _Pgh are electricity purchase amount, CHP unit gas consumption and gas boiler gas consumption respectively; _Pechar , _Pedis , _Phchar and _Phdis are charging and discharging power of electric energy storage system and charging and discharging power of thermal energy storage system respectively; _PPVe and _PPVh are distributed energy power generation and heating power respectively; _Pei is electric power consumption of electric refrigeration unit and _Phi is thermal power consumption of absorption refrigeration unit; _Peic and _Phic are cooling power of electric refrigeration unit and absorption refrigeration unit respectively; _Pexe and _Pexh are electric power and thermal power flowing into energy station.

For the energy storage process of energy storage equipment:

At the same time, the initial state of energy storage should be consistent:

分别为充能与释能的能量效率,t₀、t_e分别为起始时刻和终止时刻。Where: S _*,t is the energy storage state of the energy storage device at time t, δ _* is the static energy storage efficiency, P _*char,t and P _*dis,t are the charging and discharging powers at time t, respectively.

are the energy efficiencies of charging and releasing, t ₀ and t _e are the starting and ending times, respectively.

For energy supply and storage equipment, their capacities 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)

Where: P _chp , P _qg are the gas power of the CHP unit and the gas boiler respectively; P _*,max , P _*,min are the upper and lower limits of the power of each device respectively.

Energy storage equipment 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)

Where: S _*,max and S _*,min are the upper and lower limits of the energy storage state of the energy storage device respectively.

System multi-objective optimization scheduling model:

Multiple single-park energy subsystems in the multi-park integrated energy system are interconnected, and the economic operation objective function is established with the lowest total operating cost as the optimization goal:

Where: i∈{1,2,...,N}, N is the number of parks, and C _*,t is the price of various energy sources at time t.

At the same time, considering that the total carbon emissions of the integrated energy system are the lowest, the system carbon emission model is established as follows:

Where: i∈{1,2,...,N}, N is the number of parks, a _* , b _* , c _* are carbon emission coefficients, P _e,i (t) is the electricity purchase amount of the i park at time t, and P _ge,i (t) is the gas purchase amount of the i park at time t.

The energy saving of the i-park is mainly reflected in the primary energy consumption of the system, that is, the less the total primary energy consumption of the system, the better the energy saving of the system. The primary energy consumption in the i-park mainly depends on the working conditions of the power grid, micro gas turbines and gas boilers, that is, the total primary energy consumption PEC is expressed as:

Where G _mt,i (t), G _gb,i (t), and G _grid,i (t) represent the gas consumption of gas turbines and gas boilers at time t and the coal and other fossil fuel energy consumption converted from the interactive electric energy between park i and the power grid.

In order to evaluate the three aspects of the system's operating performance, it is necessary to establish different objective functions under the three indicators and optimize the operating results for analysis. Since different decision makers have different emphasis on the three indicators, the three objective functions are comprehensively considered for multi-objective optimization, and finally a compromise optimal solution is obtained.

Based on the three performance indicators, the optimization objective function is to minimize the total system operating cost, minimize the total primary energy consumption, and minimize the total pollutant emissions, which can be expressed as:

For the above model, this embodiment also provides a collaborative evolutionary constrained multi-objective optimization solution method.

The central idea of the co-evolutionary constrained multi-objective optimization method is to use two populations that undergo different evolutionary paths and then guide each other in some way to obtain the optimal solution set. The specific steps are as follows:

The input is the maximum number of iterations T, population size N, mutation rate F, and crossover rate CR.

Initialization is performed first, and the dominant population is randomly generated as population 1. The auxiliary population is randomly generated as population 2.

When the number of iterations t is less than T, the loop executes the following steps:

Father line 1 ← selects 2N individuals from population 1 through mating selection.

Father line 2 ← select 2N individuals from population 2 through mating selection.

Off 1←Generate N individuals based on parent 1 through differential operation.

Off 2←Generate N individuals based on parent 2 through differential operation.

Population 1←Combine Population 1, Off 1 and Off 2 to perform constrained environment selection to produce N individuals.

Population 2←Combine Population 2, Off 1 and Off 2 to perform unconstrained environmental selection to produce N individuals.

t increases by 1 until the condition that ends the loop is reached.

Output population 1 when the conditions are met.

In the above steps, population 2 is used as an auxiliary population for unconstrained evolution to explore the target space to find feasible solutions in the feasible domain, and then these feasible solutions are stored in population 1 through environmental selection of population 1. At the same time, individuals in population 1 are able to guide population 2 to find feasible solutions to a certain extent.

According to the solution results, a low-carbon, economical and efficient operation strategy for multi-park integrated energy systems is proposed:

By comprehensively considering the impact of multiple indicators on system operation, the optimal operation strategy set of the system is obtained. The multi-park integrated energy system in this example analysis includes three parks, which are connected to the large power grid and natural gas grid. Energy is exchanged between the parks through interconnection lines. The load of each park is the typical winter load of three different parks in the north.

In the case analysis, the proposed optimization model was solved, taking into account the economy, low carbon and high efficiency of system operation, and the optimal solution for the comprehensive energy system of each single park was obtained.

Figure 3 is the Pareto frontier diagram of the solution algorithm in the example analysis. As can be seen from the figure, in the multi-park integrated energy system, the three target values set are mutually coupled and influence each other, and the efficient and low-carbon operation of the system will inevitably bring higher operating costs.

Taking Park 3 as an example, it can be seen from Figure 4 that in terms of power load supply, it is mainly supplied through power purchase from the power grid, renewable energy, and gas-fired power generation of CHP units. Among them, during the period from 10 am to 6 pm, renewable energy such as wind power and photovoltaic power generation is abundant, and carbon emissions and power generation costs are lower than those of power purchase from the power grid and power generation of CHP units, so the system gives priority to using this part of energy for supply. In the peak electricity price area, if there is a shortage of power supply, CHP units are given priority for power supply, and the low-carbon economic operation of the system is achieved by reasonably selecting the power supply method. At the same time, the system is equipped with an electric energy storage system to store electricity when the power grid electricity price is at a valley value, and discharge it in the peak electricity price area to achieve time shift of electricity, which not only reduces the power supply pressure of the power grid but also reduces the operating cost of the system. It can be seen from Figure 5 that in terms of heat load supply, it is mainly supplied through solar collectors, gas filtration, and waste heat recovery of CHP units. When the light intensity is high during the day, collectors with low carbon emissions and low heating costs are given priority for heat supply. The CHP units in the system operate in the "electricity-based heat" mode. While generating electricity, the CHP units supply heat load through the waste heat recovery device.

In the case analysis, during joint operation, the energy between the parks can be complemented and mutually beneficial, and the optimization results are shown in Figures 6 and 7. Through the transfer and mutual assistance of energy between the parks, the system operation cost and carbon emissions can be further reduced.

In the case analysis, the carbon emissions and operating costs of each park under separate operation and joint operation are compared and analyzed. The carbon emissions in the process of natural gas entering CHP to generate electricity and heat energy and the carbon emissions in the process of wind power and photovoltaic units generating electricity are less than the carbon emissions of electricity purchased from the power grid. By interconnecting the three parks to achieve energy interaction, the park with large CHP units and wind power and photovoltaic installed capacity can generate electricity and heat energy through natural gas and interact with other parks, thereby reducing the carbon emissions of parks with smaller capacity and realizing low-carbon operation of multi-park integrated energy systems. The carbon emissions and energy consumption of the three parks operating separately and interconnected are solved four times and compared as shown in Tables 1 and 2. It can be seen from the table that the total carbon emissions per day can be reduced by about 7% and energy consumption by about 3.8% by adopting the operation mode of interconnecting the three parks.

Table 1 Comparison of carbon emissions

Table 2 Comparison of energy consumption throughout the day

The present invention also provides the following product embodiments:

A multi-park integrated energy system multi-objective optimization system, comprising:

a multi-park model building module, configured to build a multi-park integrated energy system model;

The multi-objective function building module is configured to establish the system economic operation objective function with the lowest operation cost of the multi-park integrated energy system as the optimization goal, establish the system low-carbon operation objective function with the lowest carbon emission of the multi-park integrated energy system as the optimization goal, and establish the system efficient operation objective function with the highest primary energy utilization rate of the multi-park integrated energy system as the optimization goal;

The collaborative solution module is configured to adopt a collaborative evolutionary constrained multi-objective optimization solution method to perform multi-objective optimization solution, and when the convergence conditions are met, an optimized scheduling strategy set is obtained.

A terminal device includes a processor and a computer-readable storage medium, wherein the processor is used to implement various instructions; and the computer-readable storage medium is used to store multiple instructions, wherein the instructions are suitable for being loaded by the processor and executing the steps in the described method.

Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

The present invention is described with reference to the flowchart and/or block diagram of the method, device (system), and computer program product according to the embodiment of the present invention. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the process and/or box in the flowchart and/or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Technical personnel in the relevant field should understand that various modifications or variations that can be made by technical personnel in the field without creative work on the basis of the technical solution of the present invention are still within the scope of protection of the present invention.

Claims (10)

1. A multi-objective optimization method for multi-park integrated energy systems, characterized in that it comprises the following steps: Establish a multi-park comprehensive energy system model; Taking the lowest operating cost of the multi-park integrated energy system as the optimization goal, establish the system economic operation objective function, and take the lowest carbon emission of the multi-park integrated energy system as the optimization goal, establish the system low-carbon operation objective function, and use the primary energy utilization of the multi-park integrated energy system The highest rate is the optimization goal, and the objective function of establishing a system to run efficiently; The co-evolution constrained multi-objective optimization solution method is used to solve the multi-objective optimization solution. When the convergence condition is satisfied, the optimal scheduling strategy set is obtained. 2. A kind of multi-objective optimization method for multi-park integrated energy systems as claimed in claim 1, characterized in that, the specific process of setting up multi-park integrated energy system models includes setting up each single park comprehensive energy system model, each single park comprehensive energy system The energy subsystems of the system model are interconnected to form a multi-park comprehensive energy system model. 3. A multi-objective optimization method for a multi-park integrated energy system as claimed in claim 2, wherein the energy supply equipment of each single park integrated energy system model includes distributed energy such as wind power photovoltaics, gas boilers, electric refrigeration Units, cogeneration units and absorption refrigeration units, and energy storage includes electric energy storage systems and thermal energy storage systems. 4. A multi-objective optimization method for a multi-park integrated energy system as claimed in claim 2, characterized in that, when the model of each single park integrated energy system is established, the energy hub model is used to describe various energy conversion relationships inside the energy station, Build the energy station model; build the energy storage process model of the energy storage equipment, and both the energy supply equipment and the energy storage equipment have capacity constraints. 5. A multi-objective optimization method for a multi-park integrated energy system according to claim 1, wherein the operating cost of the multi-park integrated energy system is the sum of the products of various energy powers and corresponding energy prices. 6. a kind of multi-objective optimization method for multi-park integrated energy system as claimed in claim 1, is characterized in that, described total carbon discharge is the carbon discharge summation of each park, and the carbon discharge of each park is corresponding park each The sum of the products of electricity purchases and gas purchases at each moment and their corresponding carbon emission coefficients. 7. A multi-objective optimization method for a multi-park integrated energy system as claimed in claim 1, wherein the highest utilization rate of primary energy is the minimum total energy consumption of power grids, micro gas turbines and gas boilers in each park. 8. A multi-objective optimization method for a multi-park integrated energy system as claimed in claim 1, wherein the specific process of adopting the co-evolutionary constrained multi-objective optimization solution method comprises: using two populations with different experiences The evolutionary pathway of , population 2 is used as an auxiliary population for unconstrained evolution to explore the target space, find feasible solutions in the feasible domain, and then store these feasible solutions in population 1 through the environmental selection of population 1, Continuously guide each other to obtain the optimal solution set. 9. A multi-objective optimization system for a multi-park integrated energy system, characterized by comprising: a multi-park model building block configured to model a multi-park integrated energy system; The multi-objective function building block is configured to take the lowest operating cost of the multi-park integrated energy system as the optimization goal, establish the system economic operation objective function, and take the lowest carbon emission of the multi-park integrated energy system as the optimization goal, establish the system low-carbon operation objective function, Taking the highest primary energy utilization rate of the multi-park integrated energy system as the optimization goal, establish an objective function for efficient operation of the system; The collaborative solution module is configured to use the multi-objective optimization solution method with co-evolution constraints to perform multi-objective optimization solution, and obtain an optimal scheduling strategy set when the convergence condition is met. 10. A terminal device, characterized in that it includes a processor and a computer-readable storage medium, the processor is used to implement instructions; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and Performing the steps in the method of any one of claims 1-8.

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