A comprehensive energy system optimization method and device taking into account the support function of power grid

Abstract

The present application is applicable to the technical field of energy system optimization, and provides a method and device for optimizing an integrated energy system taking into account the supporting role of power grids, the method comprising: obtaining basic data of the integrated energy system; the basic data of the integrated energy system includes basic data of the integrated energy system; based on the basic data, determining the output strategy of the integrated energy system; under the output strategy of the integrated energy system, quantifying the power grid supporting capacity of the integrated energy system; based on the power grid supporting capacity of the integrated energy system, constructing an integrated energy system planning optimization model; solving the integrated energy system planning optimization model to obtain the optimized equipment capacity of the integrated energy system. The present application can quantify the power grid supporting capacity of the integrated energy system based on the characteristics of the integrated energy system, and construct a reasonable integrated energy system planning optimization model to provide support for the subsequent formulation of equipment capacity planning plans.

Description

Technical Field

[0001] The present application belongs to the technical field of energy system optimization, and in particular, relates to a comprehensive energy system optimization method and device taking into account the support role of power grid. Background Art

[0002] The Regional Integrated Energy System (RIES) organically couples multiple energy sources such as electricity, cooling, heat, and gas, and uses energy storage conversion and cascade utilization to improve the flexibility of the energy supply system, smooth out the fluctuations of renewable energy, and promote the consumption of clean energy. The operation optimization research of RIES, which includes multiple energy carriers and networks, will become a hot topic in future research, and there is still a lot of room for improvement in the operation optimization research of RIES.

[0003] The regional integrated energy system involves the coupling of multiple energy systems, including various energy conversion equipment such as combined heat and power generation (CHP), conventional gas turbine (CGT), power-to-gas (P2G), etc. P2G technology can convert clean energy abandoned power generation into natural gas that is easy to store on a large scale, realizing the deep coupling of power-gas networks.

[0004] P2G technology is widely used in integrated energy systems. However, in the process of building new power systems, improving the ability of new energy power generation equipment to actively support the power grid is one of the main challenges facing the power industry. How to quantify the supporting role of the integrated energy system to the power grid needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present application provide a method and device for optimizing an integrated energy system taking into account the supporting role of the power grid. The method and device can quantify the power grid supporting capacity of the integrated energy system based on the characteristics of the integrated energy system, and construct a reasonable integrated energy system planning optimization model to provide support for the subsequent formulation of equipment capacity planning plans.

[0006] This application is implemented through the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a comprehensive energy system optimization method taking into account the support function of a power grid, including:

[0008] Obtain basic data of the integrated energy system; the basic data of the integrated energy system includes the basic data of the integrated energy system; based on the basic data, determine the output strategy of the integrated energy system; under the output strategy of the integrated energy system, quantify the grid support capacity of the integrated energy system; based on the grid support capacity of the integrated energy system, construct an integrated energy system planning optimization model; solve the integrated energy system planning optimization model to obtain the optimized equipment capacity of the integrated energy system.

[0009] In a second aspect, an embodiment of the present application provides an integrated energy system optimization device taking into account the support function of a power grid, and executes the integrated energy system optimization method taking into account the support function of a power grid as in the first aspect, including:

[0010] An acquisition module, used for acquiring basic data of the integrated energy system; the basic data of the integrated energy system includes the basic data of the integrated energy system; an output strategy determination module, used for determining the output strategy of the integrated energy system based on the basic data;

[0011] The grid support capacity quantification module is used to quantify the grid support capacity of the integrated energy system under the output strategy of the integrated energy system; the model construction module is used to construct an integrated energy system planning optimization model based on the grid support capacity of the integrated energy system; the solution module is used to solve the integrated energy system planning optimization model to obtain the optimized equipment capacity of the integrated energy system.

[0012] Compared with the related art, the embodiments of the present application have the following beneficial effects:

[0013] The integrated energy system optimization method and device taking into account the supporting role of the power grid in the embodiment of the present application formulates the output strategy of the integrated energy system through the basic data of the integrated energy system, fully considers the actual situation and external environment of the system, and on this basis, quantifies the power grid supporting capacity of the integrated energy system, clearly understands the supporting role and contribution of the system to the power grid, and provides strong support for the planning and optimization of the integrated energy system. The integrated energy system takes the power grid supporting capacity as the optimization goal, constructs the integrated energy system planning optimization model taking into account the supporting role of the power grid, and solves the integrated energy system planning optimization model to obtain the optimized equipment capacity of the integrated energy system, which can provide scientific guidance for the equipment capacity configuration, energy selection, etc. of the system, and help to achieve the overall optimization of the system and improve the operating efficiency and economic benefits of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0015] Figure 1 It is a schematic diagram of the operation process of a typical structure of an integrated energy system provided in one embodiment of the present application;

[0016] Figure 2 It is a flow chart of a comprehensive energy system optimization method taking into account the support function of the power grid provided in one embodiment of the present application;

[0017] Figure 3 It is a specific schematic diagram of a comprehensive energy system optimization method taking into account the support function of the power grid provided in one embodiment of the present application;

[0018] Figure 4 It is a schematic diagram of a thermal system equipment output strategy provided by an embodiment of the present application;

[0019] Figure 5 It is a schematic diagram of an output strategy of an electrical system equipment provided in an embodiment of the present application;

[0020] Figure 6 It is a trend diagram of the power load of the integrated energy system and the user provided in one embodiment of the present application;

[0021] Figure 7 This is a flow chart of a density peak clustering algorithm considering single-dimensional data distribution provided by an embodiment of the present application;

[0022] Figure 8 It is a schematic diagram of a density peak clustering algorithm provided in an embodiment of the present application;

[0023] Fig. 9 It is a schematic diagram of the solution process of the improved two-stage butterfly optimization algorithm provided in an embodiment of the present application;

[0024] Fig.10 It is a Pareto chart of annualized total investment cost and comprehensive energy efficiency provided in one embodiment of the present application;

[0025] Fig.11 It is a schematic diagram of the peak shaving and valley filling rate clustering of different solutions provided in one embodiment of the present application;

[0026] Fig.12 It is a schematic diagram of the annualized total investment cost ratio of different solutions provided in an embodiment of the present application;

[0027] Fig.13It is a schematic diagram of the hourly peak shaving and valley filling rates and energy costs of various solutions provided in an embodiment of the present application on a typical day;

[0028] Fig.14 This is a performance comparison chart of algorithms provided in an embodiment of the present application; Fig.14 (a) is a comparison chart of the solution time of different algorithms; Fig.14 (b) is a schematic diagram of the algorithm's target value solution process at different iteration times;

[0029] Fig.15 It is a structural schematic diagram of a comprehensive energy system optimization device taking into account the support function of the power grid provided in one embodiment of the present application. DETAILED DESCRIPTION

[0030] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0031] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0032] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0033] References to "an embodiment", "one embodiment" or "some embodiments" described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0034] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0035] Power-to-gas technology can convert electrical energy into gaseous fuel, which can produce hydrogen for direct use, or synthesize hydrogen into methane and other gases for easy storage. In the context of the construction of new power systems, the access of a high proportion of new energy has led to a surge in demand for electrical energy storage. Power-to-gas energy storage technology equipped with gas storage tanks can convert electrical energy into gas for storage, realizing the transmission of energy from the power system to the natural gas system and the large-scale, long-term storage of energy, providing new ideas for the consumption of new energy in new power systems.

[0036] Since natural gas is more stable and more suitable for the integrated energy system constructed by this application, the power-to-gas technology used in this application is to convert electrical energy into natural gas. First, water is used as a reactant to produce hydrogen by electrolysis of water, as shown in formula (1); secondly, hydrogen is methanated to produce methane by reacting hydrogen and carbon dioxide under certain conditions, as shown in formula (2). The conversion efficiency of power-to-natural gas methane is about 50% to 64% on average.

[0037]

[0038] In this application, the P2G equipment can be powered by wind turbines or directly by the power grid when the electricity price is low. The generated natural gas is stored in a gas tank. The cogeneration system consumes the reserved natural gas to supply electricity and heat to the system, forming a closed-loop coupling of the electricity system, the natural gas system, and the heat system. The operation process of the typical structure of the constructed integrated energy system is as follows: Figure 1 shown.

[0039] The introduction of power-to-gas technology in the integrated energy system can improve the coupling relationship between the three subsystems of electricity, heat and gas, while providing natural gas resources for the cogeneration system, reducing the cost of purchasing electricity and gas during the operation of the system. It can absorb surplus wind power and convert it into gas fuel for storage, thereby improving the grid's ability to absorb wind power and reducing the impact of wind power fluctuations on the grid. It can also provide power generation energy during peak electricity consumption periods, alleviating the power generation pressure on the source side of the grid.

[0040] Under the above-mentioned integrated energy system, this application optimizes the integrated energy system by considering the supporting role of the power grid.

[0041] Figure 2is a flow chart of a comprehensive energy system optimization method taking into account the support function of the power grid provided in one embodiment of the present application. Figure 3 is a specific schematic diagram of a comprehensive energy system optimization method taking into account the power grid support function provided by an embodiment of the present application, with reference to Figure 2 and Figure 3 The integrated energy system optimization method taking into account the supporting role of the power grid includes:

[0042] Step 101, obtaining basic data of the integrated energy system.

[0043] The basic data required for comprehensive energy system planning include basic park data, regional natural resource data, park electric and thermal load data, park energy prices, and parameters of the equipment to be selected. The basic park data mainly include the available land area and area of ​​the park, the investment capacity of the park builders, the park pipe network structure, etc., providing physical information for planning. Regional natural resource data refers to the wind speed and light intensity data of the area where the park is located throughout the year. This data is generally obtained through the local meteorological bureau. The accuracy of this data has a great impact on the rationality of the planned capacity of wind turbines and photovoltaic systems. The park electric and thermal load data refers to the hourly electric and thermal load data of park users throughout the year. Generally, it is necessary to predict the future load situation based on the electric and thermal load data of previous years through medium- and long-term load forecasting technology. However, since the electric and thermal load situation of the residential park selected in this embodiment is relatively stable every year, the hourly electric and thermal load data of the previous year can be directly used as the electric and thermal energy demand data of the future park for input. The park energy price refers to the regional electricity price and natural gas price, among which the time-of-use electricity price is based on the latest local electricity price policy, and the natural gas price refers to the actual energy price in the region. The parameters of the equipment to be selected refer to the cost and operating data of the equipment to be planned for the comprehensive energy system. The energy supply equipment needs to determine its operating efficiency, minimum load rate, investment and construction cost, operation and maintenance cost, service life and other parameters. The energy storage equipment needs to determine its self-damage rate, operating efficiency, minimum state inventory, maximum state inventory, investment and construction cost, operation and maintenance cost, service life and other parameters.

[0044] Step 102, based on the basic data, determine the output strategy of the integrated energy system.

[0045] Generally speaking, there is a strong positive correlation between time-of-use electricity prices and grid loads. During peak and normal electricity price periods, the electricity load is high, and the grid power supply pressure is high; during valley electricity price periods, the electricity load is low, and the grid has a large power supply space. At present, in order to alleviate the grid power supply pressure during peak load periods, the integrated energy system (referred to as the "system") mainly adopts the demand response method to divide the user load into loads that can be reduced and transferred. However, due to the diverse forms of user loads and the large uncertainty, the system's demand response degree has a fuzzy range. There are a variety of power generation equipment and energy coupling equipment within the system. By setting equipment output strategies in different time periods, the system's demand for power purchases from the grid can be reduced during normal and peak electricity price periods, and its internal power generation equipment can be used first, achieving the "peak shaving" effect while reducing the system's electricity cost; during valley electricity price periods, the system's power demand is reasonably increased to charge the energy storage equipment, achieving the "valley filling" effect while providing energy reserves for the system.

[0046] Exemplarily, the integrated energy system includes a thermal system and an electrical system. The thermal system equipment includes an air source heat pump AHP, a combined heat and power system CHP, and a heat storage tank HST. The electrical system equipment includes a wind turbine, a photovoltaic power generation system, a combined heat and power system CHP, an air source heat pump AHP, P2G, and an energy storage battery.

[0047] Based on the basic data, determine the output strategy of the integrated energy system, including:

[0048] Based on basic data and the principle of peak shaving and valley filling, determine the equipment output strategy of the thermal system.

[0049] Exemplarily, the equipment output strategy of the thermal system includes a first peak-time strategy, a first normal-time strategy, and a first valley-time strategy.

[0050] The first peak-time strategy includes: when the electricity price peaks, the thermal system gives priority to using the CHP system for heating in order to reduce the system's demand for electricity purchases. If the CHP system's heating cannot meet the heat load demand, the heat storage tank HST releases heat. If the heat storage tank HST still cannot meet the heat load demand, the air source heat pump AHP is used for heating.

[0051] The first normal strategy includes: when the electricity price is normal, the combined heat and power system CHP is used for heating first. If the combined heat and power system CHP heating cannot meet the heat load demand, the air source heat pump AHP is used for heating;

[0052] The first valley-time strategy includes: during the valley-time of electricity prices, the air source heat pump AHP is used for heating first, and the heat storage tank HST is charged with heat at rated power until the upper limit. If the output of the air source heat pump AHP cannot meet the user's heat load demand and the heat storage tank HST charging demand, it will be supplemented by the cogeneration system CHP.

[0053] When the electricity price is normal, the heat storage tank HST neither charges nor releases heat. The reason for not charging heat is that the heat storage cost at this time is higher than when the electricity price is low, which will reduce the economy of the system. The reason for not releasing heat is that releasing heat at this time may cause the heat storage tank HST to have insufficient heat energy reserves when the electricity price is high, and the air source heat pump AHP needs to supply more heat energy, which increases the power supply pressure of the power grid and increases the energy cost of the system. In the above three cases, it is necessary to ensure that the output of the heating equipment can meet the overall heat load demand of the system. The output strategy of the thermal system equipment is as follows: Figure 4 T represents hours. By setting the end condition, for example, T=8760, the end time is set to 1 year to ensure that the output of the heating equipment can meet the overall heat load demand of the system for one year.

[0054] Based on the equipment output strategy of the thermal system, determine the equipment output strategy of the electrical system.

[0055] For example, according to "determining electricity by heat", when the output of the thermal system equipment is known, the power generation of the combined heat and power system CHP and the power consumption of the air source heat pump AHP can be determined.

[0056] The equipment output strategy of the power system is: if the power generation of the cogeneration system CHP and the power consumption of the air source heat pump AHP can meet the power load demand of users and air source heat pumps, the remaining output is used to charge the energy storage battery BES and support the power consumption of P2G. If the energy storage battery BES and P2G cannot reach the rated power consumption, they will be supplemented by wind turbines WT and photovoltaic PV. If the cogeneration system cannot meet the power load demand of users and air source heat pumps, wind turbines WT and photovoltaic PV will be used for power generation. If the new energy power generation equipment wind turbines WT and photovoltaic PV power generation can meet the power load, the remaining output will support the power consumption of energy storage batteries BES and P2G. If the power load is still not met, electricity can be purchased directly during normal and valley electricity prices. During peak electricity prices, energy storage batteries will be used for discharge first, and the power grid will make up for the power gap. When the electricity price is low, if the remaining power output in the system cannot meet the charging or energy consumption of the energy storage battery BES and P2G at the rated power, it can be directly supplemented by purchasing electricity from the power grid until the energy storage battery and gas tank reach the upper limit of the energy storage. When the electricity price is normal, the energy storage battery and P2G do not operate. Figure 5 shown.

[0057] Based on the equipment characteristics of the integrated energy system, this embodiment sets an equipment output strategy that takes into account the effect of peak shaving and valley filling. During peak load periods, the output of non-power-consuming equipment is used first, and the generated electrical energy and thermal energy are transmitted to the user side. If the user's energy demand cannot be met, electricity is purchased from the power grid; during low load periods, the integrated energy system needs to first meet the user's energy demand, and then make the energy storage device reach the energy storage limit as much as possible to reduce the energy supply pressure during subsequent peak load periods. Considering that there is a conversion from electrical energy to thermal energy in the basic structure of the constructed integrated energy system, the equipment output strategy of the thermal system is determined first, and then the equipment output strategy of the electrical system is determined, that is, "determining electricity by heat" is achieved.

[0058] Step 103, quantifying the power grid support capacity of the integrated energy system under the output strategy of the integrated energy system.

[0059] Exemplarily, step 103 includes:

[0060] Step 1031, under the output strategy of the integrated energy system, based on the difference between the power purchase amount of the integrated energy system and the power load of the user, define the peak shaving and valley filling rate of the integrated energy system at each time node.

[0061] exist Figure 6 In the figure, the solid line is the user's own electricity load in each period, the dashed line is the overall electricity purchase amount of the integrated energy system, and the dotted line is the time-of-use electricity price. It can be seen that the time-of-use electricity price is basically consistent with the change trend of the user's own electricity load. The electricity load is high during peak and normal times, and low during valley times. In the period of 00:00-8:00, when the electricity price is valley, the user's electricity load is at a low point while the electricity purchase amount of the integrated energy system is at a peak point. Therefore, the area A formed by the solid line and the dashed line can be regarded as the "valley filling" area of ​​the integrated energy system; in the period of 8:00-20:00, when the electricity price is normal and peak, the user's electricity load is at a peak point while the electricity purchase amount of the integrated energy system is at a low point. Therefore, the area B formed by the solid line and the dashed line can be regarded as the "peak shaving" area of ​​the integrated energy system. Based on this, the peak shaving and valley filling rate of the integrated energy system at each time node can be defined based on the difference between the electricity purchase amount of the integrated energy system and the electricity load of the user, as shown in formula (3):

[0062]

[0063] Among them, ω i It represents the peak shaving and valley filling rate of the comprehensive energy system at the i-th time node. represents the user's own electricity load at the i-th time node; It represents the electricity purchase amount of the comprehensive energy system at the i-th time node; k represents 1 or -1, when it is at the peak of electricity price, k is 1 at normal times, and when it is at the valley of electricity price, k is -1.

[0064] This embodiment takes into account the peak-shaving and valley-filling effect of the integrated energy system on the power grid, and quantifies the power grid support capacity of the integrated energy system based on the peak-shaving and valley-filling rate of the integrated energy system at each time point. By executing the equipment output strategy that takes into account the peak-shaving and valley-filling effect, the overall power purchase trend of the integrated energy system is exactly the opposite of the user's own power load trend.

[0065] Step 1032, based on the peak shaving and valley filling rate of the integrated energy system, a multi-density peak clustering algorithm is used to extract characteristic values ​​of basic data of the integrated energy system.

[0066] In order to reasonably quantify and evaluate the grid support capacity of the integrated energy system, it is necessary to analyze the basic data of the integrated energy system and select representative characteristic values ​​from it. The Density Peak Clustering Algorithm (DPC) can efficiently allocate sample points and remove outliers in the samples by calculating the local density and relative distance of the data set, thereby obtaining the characteristic values ​​of the data set. It has low requirements on data distribution and is suitable for clustering low-dimensional data sets. Taking into account that the peak shaving and valley filling rate of the integrated energy system only has a numerical dimension but not a time dimension, and peak shaving and valley filling have different representative meanings, this embodiment adaptively improves the density peak clustering algorithm and proposes a multi-density peak clustering algorithm that considers single-dimensional data, such as Figure 7 shown.

[0067] exist Figure 7 In , the process of obtaining more reasonable eigenvalues ​​by using the multi-density peak clustering algorithm considering single-dimensional data is as follows:

[0068] (1) Select a certain period and classify the peak shaving and valley filling rates based on the electricity price period. Aggregate the "peak shaving" value and the "valley filling" value to the same coordinate axis. The horizontal axis is the value of the peak shaving and valley filling rate, and the vertical axis is the number of hours with the same peak shaving and valley filling rate, such as Figure 8 shown.

[0069] (2) Set the cutoff distance according to the numerical range and calculate the local density and relative distance of the data points. The local density is expressed as the data point x i The number of data points within a certain radius as the center. The relative distance indicates that the local density is greater than x i , and the distance to its nearest point is calculated as shown in formula (4) and formula (5). Based on the calculation results, the decision diagram is drawn. Since the local density and relative distance of the overlapping points are the same, the overlapping points are regarded as one point when drawing the decision diagram, such as Figure 8 shown.

[0070]

[0071]

[0072] Among them, ρ i represents the local density; δ i Indicates relative distance; I S represents the peak shaving and valley filling rate data point set; χ(x) represents the piecewise function about x, which is 1 when x<0 and 0 when x≥0; D ij Represents data point x i With x j Distance; D c Represents the cutoff distance.

[0073] (3) Select the point in the upper right corner of the decision diagram as the cluster center of the data set to obtain the cluster center, which includes the cluster center of the "peak clipping" data and the cluster center of the "valley filling" data.

[0074] (4) Calculate the weight of the cluster center. The weight of the cluster center is equal to the local density of the point divided by the sum of the local densities of the two cluster centers, as shown in formula (6).

[0075]

[0076] Among them, W j represents the weight of the jth cluster center; ρ j represents the local density of the jth cluster center.

[0077] Since there is a significant difference in the power consumption of users during peak and valley periods, which may be twice as much, and the output mode of the internal equipment of the integrated energy system is also different during peak and valley periods, the multi-density peak clustering algorithm considering single-dimensional data can cluster the peak-shaving and valley-filling rate data with only numerical attributes, thereby clustering the peak and valley data separately, and better extracting the characteristic values ​​of the power grid support capacity of the integrated energy system. While ensuring that the parameter settings are the same, the power grid support capacity of multiple integrated energy systems can be compared horizontally, reflecting the peak-shaving and valley-filling effects of the integrated energy system.

[0078] In this embodiment, the "valley filling" data during valley hours and the "peak shaving" data during normal and peak hours are clustered based on the electricity price time period, and the local density of the cluster center is comprehensively weighted to more representatively quantify the grid support capacity of the integrated energy system. Classification and clustering can be performed based on data characteristics to avoid the loss of a large number of data points due to different categories and magnitudes, thereby obtaining more reasonable feature values.

[0079] Step 1033, quantifying the power grid support capability of the integrated energy system based on the characteristic values ​​of the basic data of the integrated energy system.

[0080] Exemplary, the grid support capacity of the integrated energy system ωIES The expression is:

[0081]

[0082] Among them, ω j represents the peak-cutting and valley-filling rate corresponding to the jth cluster center, J represents the total number of cluster centers, J = 2; W j Represents the weight of the j-th cluster center.

[0083] Based on the regulation demand of the power system and the characteristics of the integrated energy system, this embodiment analyzes the grid support role of the integrated energy system in peak shaving and valley filling, wind power consumption, voltage regulation, frequency regulation, fault recovery, etc.; secondly, a multi-dimensional improvement strategy based on power-to-gas technology is formulated, which is helpful to improve the energy coupling degree and grid support role of the integrated energy system; then, with peak shaving and valley filling as the core, based on the equipment output characteristics, equipment output strategies for the thermal system and the electrical system are formulated respectively; finally, the peak shaving and valley filling rate of the integrated energy system is defined, and on this basis, the grid support capacity of the integrated energy system is quantified through an improved density peak clustering algorithm.

[0084] The integrated energy system planning needs to build optimization goals based on the main needs, consider the actual constraints in the planning and operation stages, and build a reasonable integrated energy system planning optimization model to provide support for the subsequent formulation of equipment capacity planning plans. Therefore, next, based on the integrated energy system structure in the above embodiment and the power grid support capacity of the integrated energy system, a comprehensive energy system planning optimization model is constructed.

[0085] Step 104, constructing an integrated energy system planning optimization model based on the power grid support capability of the integrated energy system.

[0086] The planning and optimization of the integrated energy system is to set a reasonable integrated energy system architecture and equipment combination based on the analysis of the needs of multiple types of users such as industry, commerce, and residents, and then optimize the planned capacity of each type of equipment.

[0087] The planning object of this embodiment is a residential park. In the early stage of planning, it is necessary to consider the residents' energy consumption methods and optimization intentions, analyze the actual operation effect of the integrated energy system, and the constructed integrated energy system planning optimization model takes the highest grid support capacity and the lowest annualized economic total cost as the optimization goals. It clarifies the supply and demand balance constraints, pipeline network constraints, and equipment operation constraints in the actual operation of the system to facilitate the solution of subsequent models.

[0088] Exemplarily, the optimization objectives of the integrated energy system planning optimization model include maximizing the grid support capacity and minimizing the annualized total economic cost.

[0089] For example, the maximum grid support capacity is maxF supThe expression is:

[0090]

[0091] Among them, ω IES represents the grid support capacity of the integrated energy system; DPC[x] represents the density peak clustering algorithm; It represents the peak shaving and valley filling rate at T time points in the whole set.

[0092] The economic efficiency aims to minimize the annualized total economic cost of the integrated energy system, including the purchase cost of various types of equipment during the system construction period, the maintenance cost of equipment operation during the system operation period, and the cost of electricity and natural gas purchased from outside due to insufficient internal energy supply capacity of the system. The expression for the lowest annualized total economic cost is:

[0093] minC=C inv +C gas +C grid +C EF (9)

[0094] Where C represents the annualized total investment cost; C inv Represents the annualized equipment acquisition cost; C gas Indicates the system gas purchase cost; C grid Represents the system electricity purchase cost; C EF Indicates the equipment operation and maintenance cost.

[0095] The specific calculation formulas for the annualized equipment purchase cost, system gas purchase cost, system electricity purchase cost, and equipment operation and maintenance cost are as follows:

[0096]

[0097] Among them, Q j represents the planned capacity of the jth type of equipment (kW); S j represents the unit capacity construction cost of the jth type of equipment (yuan); j represents the proportion of annual equipment operation and maintenance cost to construction cost; m represents system life (year); r represents the base discount rate; P CHP,t represents the operating power of the cogeneration system at time t (kW); η represents the power conversion coefficient; c gas Indicates natural gas price (yuan / m 3 );P grid,t represents the total amount of electric energy purchased from the power grid at time t (kW); c grid,t represents the grid electricity price at time t (yuan / kWh); P j,t represents the output power (kW) of device j at time t; w j represents the operation and maintenance cost coefficient of equipment j; T represents the calculation period.

[0098] Exemplarily, the constraints of the integrated energy system planning optimization model include supply and demand balance constraints, pipeline network constraints and equipment operation constraints. The supply and demand balance constraints include power balance constraints and thermal energy balance constraints.

[0099] The expression of the power balance constraint is:

[0100] P grid,t +P pv,t +P wt,t +P CHP,t +P BES,t =L user,t +L AHP,t +L BES,t +L P2G,t (14)

[0101] Among them, P grid,t P represents the power supply of the power grid at time t (kW); pv,t P represents the power supply of the fan at time t (kW); wt,t represents the photovoltaic power supply at time t (kW); P CHP,t P represents the power supply of the cogeneration system at time t (kW); BES,t Indicates the discharge capacity of the energy storage battery at time t (kW); L user,t represents the power consumption of users in the park at time t (kW); L AHP,t represents the power consumption of the air source heat pump at time t (kW); L BES,t Indicates the energy storage battery charge at time t (kW); L P2G,t Indicates the P2G power consumption at time t (kW).

[0102] The expression of thermal energy balance constraint is:

[0103] T AHP,t +T CHP,t +T HST,t =TL user,t +TL HST,t (15)

[0104] Among them, T AHP,t represents the heating capacity of the air source heat pump at time t (kW); T CHP,t represents the heat supply of the cogeneration system at time t (kW); T HST,t Indicates the heat released by the heat storage tank at time t (kW); TL user,t Indicates the heat consumption of users in the park at time t (kW); TL user,t Indicates the heat capacity of the energy storage tank at time t (kW).

[0105] Exemplarily, the pipeline network constraints include node voltage constraints, grid transmission power constraints, and heating pipeline temperature constraints.

[0106] The expression of node voltage constraint is:

[0107]

[0108] Among them, U i represents the voltage of the power network node i; represents the lower voltage limit of power network node i; Represents the voltage upper limit of power network node i.

[0109] The expression of the grid transmission power constraint is:

[0110]

[0111] Among them, P l min Indicates the lower limit of the transmission power of line l; P l max represents the upper limit of the transmission power of line l; θ ij Represents the node voltage phase angle difference; G ij represents the real part of the node derivative matrix; B ij Represents the imaginary part of the node derivative matrix.

[0112] The expression of the heating pipe temperature constraint is:

[0113]

[0114] Among them, T t supply Indicates the temperature of the heating pipe at time t (℃); Indicates the lower limit of the heating pipe temperature (℃); Indicates the upper limit of the heating pipe temperature (°C).

[0115] Exemplarily, the equipment operation constraints include energy supply equipment output constraints and energy storage equipment operation constraints.

[0116] The expression of the energy supply equipment output constraint is:

[0117]

[0118] P i_dn ≤P i t -P i t-1 ≤P i_up (20)

[0119] Among them, P i t represents the output power (kW) of device i at time t; P i_max Indicates the maximum output power of device i (kW); Pi_up , P i_dn Represents the uphill and downhill constraints of the output of equipment i (kW).

[0120] The expression of energy storage equipment operation constraint is:

[0121]

[0122] in, represents the charging power of the i-th energy storage device at time t, represents the energy release power of the i-th energy storage device at time t, H i (t) represents the energy storage of the i-th energy storage device at time t; represents the maximum charging power of the i-th energy storage device (kW), represents the maximum energy release power of the i-th energy storage device (kW); H i,min represents the minimum energy storage of the i-th energy storage device, H i,max represents the maximum energy storage capacity of the i-th energy storage device; represents the charging efficiency of the i-th energy storage device, Represents the energy release efficiency of the i-th energy storage device.

[0123] Step 105, solving the integrated energy system planning optimization model to obtain the optimized equipment capacity of the integrated energy system.

[0124] In the above embodiment, the integrated energy system planning optimization model includes multiple objective functions and multi-faceted constraints, and the model parameters include multiple integer variables and continuous variables. Many nonlinear, discrete and uncertain factors are involved in the solution process, which is a typical multi-objective mixed integer nonlinear optimization problem. At present, heuristic algorithms such as genetic algorithms, particle swarm algorithms, and ant colony algorithms are generally used to solve such problems. There are large differences in the optimization process and solution performance of different algorithms. Butterfly Optimization Algorithm (BOA) is an intelligent optimization algorithm with heuristic information. Compared with other intelligent algorithms, butterfly optimization has strong portability and robustness, and the algorithm has few adjustment parameters, but it is easy to fall into local optimality like common intelligent optimization algorithms, and the convergence speed of later iterations is slow.

[0125] Therefore, this embodiment uses the Good Point Set strategy (GPS) as an optimization strategy for generating uniform spatial points in the solution space, which can make the initial population of the intelligent algorithm evenly distributed, thereby ensuring the diversity of the initial population, and can greatly improve the algorithm's optimization ability in the early stage of the solution, avoiding the obvious differentiation of the optimization results caused by the randomization of the initial population. In addition, using the Gaussian Random Walk strategy (GRW), it is possible to generate new individuals when the dominant population stops iterating, and further in-depth optimization is carried out on the solution results, effectively avoiding the problem of the butterfly optimization algorithm falling into the local optimum during the solution process. Based on this, the butterfly optimization algorithm is improved by the good point set strategy and the Gaussian random walk strategy, and an improved two-stage butterfly optimization algorithm (ITBOA) is constructed to solve the integrated energy system planning optimization model.

[0126] Exemplarily, step 105 includes:

[0127] The double-order butterfly optimization algorithm is used to obtain the Pareto solution set of multiple feasible solutions for the integrated energy system planning optimization model.

[0128] The butterfly optimization algorithm is optimized by simulating the foraging and mating behaviors of butterflies. It is assumed that each butterfly has a scent that spreads and is sensed by other butterflies. The scent released by each butterfly is related to its fitness. The search phase of the butterfly optimization algorithm is divided into global search and local search. Global search means that when a butterfly senses that other butterflies emit more scent, it will move closer, and local search means that when a butterfly cannot sense a scent greater than its own, it will move randomly.

[0129] In the butterfly optimization algorithm, the taste of each butterfly is calculated by formula (22).

[0130] F=cI a (twenty two)

[0131] Among them, F represents the size of the perceived fragrance; c represents the sensory modality; I represents the stimulus intensity; and a represents the power exponent that depends on the modality, which describes the different degrees of absorption.

[0132] The search phase of the butterfly optimization algorithm is divided into global search and local search. The transition between the two phases is controlled by the transition probability p. In the global phase, formula (23) is used to update the butterfly to move to the best butterfly in the world; in the local phase, formula (24) is used to update the position of the individual, and the position of the individual is updated by the position of other individuals in the population.

[0133] x i (t+1)=x i (t)+(r 2 ×Gbest (t)-x i (t))×bF i (t) (23)

[0134] x i (t+1)=x i (t)+(r 2 × j (t)-x k (t))×bF i (t) (24)

[0135] Where Gbest(t) represents the position of the global optimal individual in the iteration so far; r represents a random number between [0,1] that follows a uniform distribution; x i (t) represents the position of the i-th individual in the t-th generation; bF i (t) represents the fitness value of the i-th individual in the t-th generation; x j (t), x k (t) represents randomly selected different individuals in the population, i≠j≠k.

[0136] The principle of the good point set strategy is: let G S is a unit cube in S-dimensional Euclidean space, if r∈G S , the form is:

[0137]

[0138] Its deviation Then it is called P n (k) is a set of good points, r is a good point, where C(r,ε) is a constant that is only related to r,ε (ε>0), and r is taken as i =2cos(2πi / p), 1≤i≤s, p is the smallest prime number satisfying (p-3) / 2≥s.

[0139] The Gaussian random walk strategy guides individual random walks by using the optimal individual and adaptive variance, and introduces the cosine function to adjust the step size of the Gaussian random walk. A larger variance value is selected at the beginning of the iteration so that the algorithm has a stronger exploration function. As the number of iterations increases, the variance gradually decreases, thereby ensuring the convergence efficiency of the algorithm. The strategy is shown in formulas (26) and (27).

[0140] x i (t+1) = Gaussian[x i (t),τ] (26)

[0141]

[0142] where x best (t)——the optimal individual in the t-th generation population; τ represents variance.

[0143] Exemplarily, an improved two-stage butterfly optimization algorithm is used to solve the integrated energy system planning and optimization model. See Fig. 9 , and the specific steps of the algorithm solving process are as follows:

[0144] 1) System initialization. Input system parameters, algorithms, etc., and set parameters such as the number of iterations T, population size N, conversion probability p, etc.

[0145] 2) Generate the initial population. Based on the system search space, generate the initial population through the good point set strategy.

[0146] 3) Calculate the individual fitness. Based on the individual position, calculate the fitness value of each individual, and find the current optimal value and optimal solution.

[0147] 4) Update the population position. Calculate the fragrance perception amount. If rand < p, update the population position through global search; otherwise, update the population position through local search.

[0148] 5) Update the individual fitness. Calculate the fitness value of the individuals in the new population. If the fitness value of the new individual is higher than that of the original individual, replace it; otherwise, continue to use the original individual.

[0149] 6) Determine whether the algorithm is stagnant. When the average value of the dominant population does not change in two consecutive iteration processes, it is considered that the algorithm is stagnant. At this time, use the Gaussian random walk strategy to generate new individuals and return to step 5); otherwise, return to step 4).

[0150] 7) Termination condition. Judge whether the termination condition is satisfied. If so, output the optimal solution; otherwise, return to step 4).

[0151] Based on the inferior-superior solution distance method, select the optimal solution from the Pareto solution set of multiple feasible solutions of the integrated energy system planning and optimization model to obtain the optimized equipment capacity of the integrated energy system.

[0152] Using the improved two-stage butterfly optimization algorithm to solve the planning model can obtain a Pareto solution set containing multiple model feasible solutions. Determine the weights of each objective function based on the decision maker's意愿, and then select the optimal compromise solution from the Pareto solution set through the Topsis method as the optimal equipment capacity planning scheme for the integrated energy system. The strategy of determining the weights of each objective function based on the decision maker's意愿 and then selecting the optimal compromise solution from the Pareto solution set through the Topsis method is as follows:

[0153]

[0154] P=(p ij ) n×m =(w j y ij ) n×m (29)

[0155]

[0156] Among them, x ij represents the jth indicator value of the ith solution; Y represents the standardized decision matrix; w j represents the comprehensive weight of the jth indicator; represents the positive ideal solution of the jth index, represents the negative ideal solution of the jth index; represents the distance from the target to the positive ideal solution, Represents the distance from the target to the negative ideal solution.

[0157]

[0158] Among them, C i Indicates closeness, 0≤C i ≤1. The target with the greatest closeness is the optimal evaluation target.

[0159] After solving the multi-objective mixed integer nonlinear optimization problem based on the heuristic algorithm, a Pareto solution set containing multiple feasible solutions can be obtained, all of which are feasible integrated energy system equipment capacity planning solutions. Therefore, it is necessary to set the weights of the optimization objectives in the model according to the decision maker's wishes, and then rank the multiple solutions in the Pareto solution set based on the multi-objective decision theory and the optimal sorting method, so as to select the optimal integrated energy system equipment capacity planning solution in the solution set under given conditions.

[0160] Considering that the Topsis method can directly use the original data for calculation, and has no strict requirements on data category, format, and sample content, the sorting results can quantitatively reflect the advantages and disadvantages of different schemes, and can eliminate the influence of different dimensions, etc., and have good intuitiveness, reliability, and objectivity. Therefore, this embodiment uses the Topsis method to comprehensively sort the Pareto solution set, which can select a more scientific, reasonable, and effective comprehensive energy system equipment capacity planning scheme, and provide decision makers with a strong basis for scheme selection.

[0161] In order to better describe the present solution, an embodiment verifies the superiority of the theory and the rationality of the model through simulation.

[0162] In this example, a residential park in northern China with electricity and heat energy demand is selected to verify the rationality and superiority of the integrated energy system planning optimization method taking into account the support of the power grid. The planning simulation optimization step is 1 hour, and there are 8760 optimization nodes in a year. Based on the actual engineering situation, the planning life cycle of the integrated energy system is set to 20 years.

[0163] Natural resource data include wind speed and light intensity data for the region. Wind speed data and light intensity data are provided by local wind towers and meteorological stations, respectively. Electric and heat load data for the region are provided by local power-related departments. The specific conditions of all basic data are not listed in detail here. For example, a set of basic data for the park can be provided, among which wind speed and light intensity are affected by seasonality, with the largest fluctuation in wind speed in spring, followed by winter, and less change in wind speed in summer; light intensity is higher in summer and weakest in winter, and light intensity has significant diurnal characteristics, with the highest light intensity at noon and lower light intensity in the morning and evening. The park's electrical load is relatively uniform throughout the year; heat load demand is higher in winter, the lowest in summer, and the heat load is at an average level in spring and autumn.

[0164] The integrated energy system constructed in this embodiment is a grid-connected but not connected mode, and can purchase electricity and natural gas from outside. The electricity price adopts the time-of-use electricity price recently released by the local power management department, among which the peak time (9:00-12:00, 17:00-22:00) electricity price is 1.18 yuan / kWh, the valley time (23:00-7:00) electricity price is 0.55 yuan / kWh, and the normal time (7:00-9:00, 12:00-17:00) electricity price is 0.85 yuan / kWh. The gas price is the average gas price released by the local energy agency, which is 3.27 yuan / m 3 .

[0165] The equipment involved in this embodiment includes wind turbines, photovoltaics, cogeneration systems, P2G equipment, air source heat pumps, energy storage batteries, heat storage tanks, and gas storage tanks. The technical parameters and economic parameters of the equipment are shown in Tables 1 and 2.

[0166] Table 1 Energy conversion equipment related parameters

[0167]

[0168] Table 2 Storage device related parameters

[0169]

[0170] In order to verify the effectiveness of the wind power consumption strategy based on power-to-gas technology proposed in this application and the effectiveness of the grid support capacity target in improving the performance of the integrated energy system, this section sets up three different planning schemes, sets the planning optimization method proposed in this application as Scheme 3, and sets up the other two schemes as comparison schemes. The integrated energy system collaborative planning process is executed under the three schemes, and the equipment output strategy considering the peak shaving and valley filling effect is adopted to obtain the optimal planning capacity of the equipment in different schemes. The specific settings of the schemes are as follows:

[0171] Solution 1: This solution takes the optimization of system economy as the planning goal, does not apply power-to-gas technology, and implements the equipment output strategy that takes into account the peak-shaving and valley-filling effect. The system equipment combination is wind turbine-photovoltaic-air source heat pump-cogeneration system-energy storage battery-heat storage tank.

[0172] Solution 2: This solution takes the optimization of system economy as the planning goal, applies power-to-gas technology, and implements the equipment output strategy that takes into account the peak-shaving and valley-filling effect. The system equipment combination is wind turbine-photovoltaic-air source heat pump-cogeneration system-P2G-energy storage battery-heat storage tank-gas storage tank.

[0173] Plan 3: This plan takes the comprehensive optimization of the system grid support capacity and economic efficiency as the planning goal, applies power-to-gas technology, and implements the equipment output strategy that takes into account the peak-shaving and valley-filling effect. The system equipment combination is wind turbine-photovoltaic-air source heat pump-cogeneration system-P2G-energy storage battery-heat storage tank-gas storage tank.

[0174] Taking Scheme 3 as an example for analysis, based on the integrated energy system planning optimization model and solution algorithm constructed in the previous article, the model is simulated and solved by MATLAB software, and the Pareto frontier solution set of the model is obtained, with a total of 60 feasible solutions, all of which are available planning solutions for the integrated energy system of Scheme 3, such as Fig.10 shown.

[0175] Depend on Fig.10It can be seen that there is a close positive correlation between the two goals of grid support capacity and annualized total investment cost, and the grid support capacity of the system increases with the increase of annualized total investment cost. In the early stage of the increase of annualized total investment cost, its relative change rate with the grid support capacity is large, and the grid support capacity increases by 8.78% for every 10% increase in annualized total investment cost; in the later stage of the increase of annualized total investment cost, its relative change rate with the grid support capacity gradually decreases, and the grid support capacity increases by 3.05% for every 10% increase in annualized total investment cost. This is because in the early stage of the increase of annualized total investment cost, the increase is mainly used to expand the planned capacity of power generation equipment such as wind turbines, photovoltaics, and cogeneration systems in the system, thereby greatly improving the grid support capacity of the system. However, due to the constant load of park users, in the later stage of the increase of annualized total investment cost, the grid support capacity of the system basically reaches its peak, and the improvement brought by increasing the planned capacity of power generation equipment gradually decreases, and it will cause a waste of part of the equipment capacity. Through the above analysis, it can be concluded that the increase of annualized total investment cost can improve the grid support capacity of the integrated energy system, but there is an upper limit.

[0176] On the basis of obtaining the Pareto solution set of Scheme 3, the solution set is ranked by the Topsis method, and the weights of the two objectives of grid support capacity and economy are set to 0.5 and 0.5 respectively, considering the decision maker's willingness. Thus, the optimal equipment planning capacity of the integrated energy system under Scheme 3 is obtained. The planning optimization steps of Schemes 1 and 2 are consistent with those of Scheme 3, and the optimal equipment planning capacity of Schemes 1 and 2 is obtained. The optimal planning capacity of the integrated energy system equipment of different schemes is shown in Table 3.

[0177] Table 3 Configuration planning of various devices under different solutions

[0178]

[0179] The optimal equipment capacity planning results of each scheme are analyzed. The difference between Scheme 1 and Scheme 2 is whether the power-to-gas technology is applied. Scheme 2 applies the power-to-gas technology, and the planning results include the capacity of P2G and gas storage tanks. Compared with Scheme 1, the planned capacity of the wind turbine, cogeneration system, and heat storage tank in Scheme 2 increased by 18.90%, 12.74%, and 17.59%, respectively, and the planned capacity of the air source heat pump decreased by 5.95%. This is because the power-to-gas technology is applied under a separate economic goal. The system converts wind power into natural gas through the power-to-gas technology and stores it in the gas tank, which can provide energy input for the cogeneration system and reduce the gas purchase cost during system operation, thereby increasing the planned capacity of the wind turbine and cogeneration system. Since the heating efficiency of the cogeneration system is low, the system also needs other heating equipment for heating. Considering that the planning cost of the heat storage tank is much lower than that of the air source heat pump, the system is more inclined to increase the planning capacity of the heat storage tank to assist the cogeneration system in heating the park. As a result, Plan 2 increases the planning capacity of the heat storage tank and reduces the planning capacity of the air source heat pump.

[0180] The difference between Scheme 2 and Scheme 3 is the different planning optimization objectives. Scheme 3 comprehensively considers the system's grid support capacity and economy. Compared with Scheme 2, the planned capacities of wind turbines, photovoltaics, cogeneration systems, P2G, energy storage batteries, heat storage tanks, and gas storage tanks in Scheme 2 increased by 34.43%, 34.82%, 39.37%, 82.45%, 43.11%, 20.58%, and 76.05%, respectively, and the planned capacity of air source heat pumps decreased by 8.64%. This is because under the influence of the grid support objective, the system will increase the planned capacity of power generation equipment such as wind turbines, photovoltaics, and cogeneration systems, thereby alleviating the grid power supply pressure during peak power supply periods, improving the system's "peak shaving" capability, and reducing the cost of purchasing electricity during system simulation operation. Under the set comprehensive energy system equipment output strategy, the energy storage battery discharges during the peak power supply period and charges during the low power supply period. It has a strong "peak shaving and valley filling" capability and can relatively reduce the system's electricity purchase cost. Therefore, taking into account the grid support role and economic goals, the system will greatly increase the planned capacity of the energy storage battery. The air source heat pump will increase the system's grid purchase of electricity to meet the park's heat load demand, which is not conducive to the improvement of the system's grid support capacity. Therefore, the planned capacity of the air source heat pump in Plan 3 is reduced, and the planned capacity of the heat storage tank is further increased. The power-to-gas technology can absorb surplus wind energy, provide natural gas energy for the cogeneration system, reduce gas purchase costs, and take into account the system's grid support capacity and economy. Therefore, Plan 3 greatly increases the planned capacity of P2G and gas storage tanks.

[0181] According to the above analysis, it can be concluded that the application of power-to-gas technology will increase the planned capacity of wind turbines, cogeneration systems, and thermal storage tanks, and reduce the planned capacity of air source heat pumps. Considering the optimization goal of grid support capacity will more significantly increase the planned capacity of power generation equipment such as wind turbines, photovoltaics, and cogeneration systems, and will also more frequently apply power-to-gas technology, which will further increase the planned capacity of P2G and gas storage tanks.

[0182] While obtaining the optimal equipment planning capacity of each scheme, the optimal objective function value of each scheme can also be obtained, as shown in Table 4. By comparing the grid support capacity and annualized total investment cost of different schemes, analyzing the correlation between planning objectives and equipment capacity, the effectiveness of the optimization theory proposed in this application is verified.

[0183] Table 4 Optimal planning target results under different schemes

[0184]

[0185] As can be seen from Table 4, under the same planning process, the target results of each scheme are different due to different scheme settings. The grid support capacity of Scheme 2 is 0.3625, which is 2.52% higher than that of Scheme 1, and the annualized total investment cost is slightly reduced. This is because the grid is in the low load and low electricity price period when the power-to-gas technology absorbs the surplus wind power. The grid support capacity of the system is improved by "filling the valley" and the energy cost of the system is reduced. The grid support capacity of Scheme 3 is the largest, which is 0.4518, which is 27.78% and 24.63% higher than Scheme 1 and Scheme 2 respectively; the annualized total investment cost of Scheme 3 is the highest, which is 873875.70 yuan, which is 2.43% and 2.52% higher than Scheme 1 and Scheme 2 respectively. This is because the grid support capacity target will increase the planned capacity of the system's power generation equipment, resulting in an increase in equipment investment costs, and the increase in equipment investment costs is significantly higher than the reduction in energy costs during system operation. From this, it can be seen that the application of power-to-gas technology can improve the system's grid support capacity and economy to a certain extent, but the impact on the economic target is relatively small; a planning scheme that takes into account both the grid support capacity and economic targets can greatly improve the system's grid support capacity.

[0186] In order to describe the quantification process of the power grid support capacity of the integrated energy system in detail, the operation data of 8760 optimization nodes of the integrated energy system in one year are derived to obtain the peak shaving and valley filling rate at each time point. Based on the value and frequency of the peak shaving and valley filling rate, the local density and relative distance corresponding to each value point can be obtained, and the decision diagram can be drawn, such as Fig.11 shown.

[0187] Table 5 shows the relative distance, local density and peak-cutting and valley-filling ratio of the cluster centers of each scheme. Cluster center 1 is the “peak-cutting” cluster center, and cluster center 2 is the “valley-filling” cluster center.

[0188] Table 5 System operation target results of different schemes on a typical day

[0189]

[0190] Depend on Fig.11 As can be seen from Table 5, the "peak shaving" cluster center of Scheme 1 is 0.3462, and the "valley filling" cluster center is 0.3637. The "peak shaving" cluster center of Scheme 2 is 0.3504, and the "valley filling" cluster center is 0.3792. The peak shaving and valley filling effects of Scheme 2 are stronger than those of Scheme 1, indicating that the application of power-to-gas technology can improve the grid support capacity of the system. At the same time, due to the low user load during the valley period of electricity prices, the peak shaving and valley filling rates of the "valley filling" cluster centers of Schemes 1 and 2 are higher than their "peak shaving" cluster centers. The "peak shaving" cluster center of Scheme 3 is 0.5451, and the "valley filling" cluster center is 0.3093. Due to the increase in the planned capacity of power generation equipment and energy storage equipment in Scheme 3, the system can play a greater role in peak shaving during normal and peak periods of electricity prices. At the same time, the system can rely more on new energy equipment such as wind turbines and photovoltaics for power supply during the valley period of electricity prices, reducing the amount of electricity purchased during the valley period of electricity prices and reducing the valley filling effect of the system. For the power grid, it is more important to reduce its power supply pressure during peak power supply periods, which shows the effectiveness of planning and optimizing the equipment capacity of the integrated energy system with the power grid support capacity as the goal.

[0191] Based on the overall analysis of the objective function values ​​and equipment planning capacity of Schemes 1 to 3, a detailed analysis of the annualized total investment cost of each scheme is conducted to compare the annualized equipment cost, electricity purchase cost, gas purchase cost, and operation and maintenance cost of different schemes. Fig.12 shown.

[0192] Fig.12 The figure shows the annualized total investment cost ratio of different solutions. Fig.12It can be seen that due to the application of power-to-gas technology, the annualized equipment cost of Scheme 2 is slightly higher than that of Scheme 1, but the annual electricity and gas purchase costs are reduced, and the decline in energy costs is greater than the increase in annualized equipment costs, resulting in the annualized total investment cost of Scheme 2 being lower than that of Scheme 1. Considering the grid support capacity target, the annualized equipment cost of Scheme 3 has increased significantly, mainly used to purchase power generation equipment and energy storage equipment such as wind turbines, photovoltaics, and cogeneration systems, which also leads to an increase in equipment operation and maintenance costs. The annual electricity and gas purchase costs of Scheme 3 have been significantly reduced, and the decline in energy costs is less than the increase in annualized equipment costs, resulting in the annualized total investment cost of Scheme 3 being higher than that of Scheme 2. After calculation, assuming that all natural gas generated by power-to-gas technology is purchased from outside, the gas purchase costs of Schemes 2 and 3 will increase by 10,441.41 yuan and 22,035.78 yuan. Based on the above analysis, it can be concluded that the application of power-to-gas technology can reduce the system's energy cost while increasing the system equipment purchase cost. Under the influence of the grid support capacity target, the system will significantly increase the purchase cost of power generation and storage equipment to reduce the energy cost of the system during operation.

[0193] The above embodiments analyze the planning results of the integrated energy system under different schemes, and verify the superiority of the electric-heat integrated energy system planning optimization scheme considering the support of the power grid proposed in this application. In order to verify the superiority of the proposed optimization theory, a typical day operation, a typical day thermal system, a typical electric power system and a typical day target result are selected as an example for analysis on a winter day with high electricity and heat energy demand.

[0194] For example, the objective function values ​​of the three schemes in the typical day operation results are analyzed. Since the equipment cost cannot be reflected in the typical day operation results, this section conducts a comparative analysis of the schemes from the two dimensions of grid support and energy cost. The target values ​​of the operation results of each scheme on a typical day are shown in Table 6, and the hourly peak shaving and valley filling rate and energy cost of each scheme on a typical day are shown in Table 6. Fig.13 shown.

[0195] Table 6 System operation target results of different schemes on a typical day

[0196]

[0197] As can be seen from Table 6, Scheme 3 has the strongest grid support capacity on a typical day, which is 0.4683, and the lowest energy cost, which is 1703.01 yuan, which is 36.62% and 30.27% lower than Scheme 1 and Scheme 2, respectively. This shows that the application of power-to-gas technology can reduce the typical day energy cost to a certain extent, and the planning target considering the grid support capacity can significantly reduce the typical day energy cost. Combined with Table 4, it can be seen that the grid support capacity target values ​​of Schemes 1 and 2 on a typical day are lower than the annual target. This is because the heat load demand in winter is high, and the planned capacity of the cogeneration system of Schemes 1 and 2 is low, which causes the system to need to heat more frequently through air source heat pumps, thereby increasing the power grid purchase power during peak and normal times. At the same time, Scheme 2 applies power-to-gas technology and increases the planned capacity of the cogeneration system. Therefore, the decrease in the grid support capacity target value of Scheme 2 on a typical day is slightly lower than that of Scheme 1. The planned capacity of the cogeneration system in Plan 3 is higher, and the grid support function of the system can be better played in winter compared with other seasons. Therefore, the grid support capacity value of Plan 3 on a typical winter day is higher than the annual level.

[0198] Depend on Fig.13 It can be seen that the peak-shaving and valley-filling rate of Scheme 3 is generally lower than that of Scheme 1 and Scheme 2 during the electricity price valley. This is because Scheme 3 plans more wind turbines, photovoltaic and other power generation equipment, which can effectively reduce the overall power purchase level of the system, resulting in lower power purchases during the electricity price valley than Scheme 1 and Scheme 2, reducing the system's "valley-filling" capacity during the electricity price valley. However, due to the increase in power generation equipment, the system relies more on internal power generation equipment for power supply during normal and peak electricity prices, reducing external power purchases. The peak-shaving and valley-filling rate at 10:00 even reached 100%, greatly improving the system's "peak-shaving" capacity. Overall, the system's power grid support capacity is improved in Scheme 3, and the system's energy cost is reduced, which verifies the effectiveness of planning goals that consider power grid support capacity.

[0199] In the process of model solving, it is necessary to verify that the algorithm used has advantages in speed and accuracy when solving mixed integer nonlinear programming problems such as integrated energy system planning and optimization, so as to ensure the reliability of the solution results. Therefore, in order to verify the superiority of the improved two-stage butterfly optimization algorithm proposed in this application, the genetic algorithm (Non-dominated Sorting Genetic Algorithms, NSGA) and the traditional butterfly optimization algorithm are selected to compare with the improved two-stage butterfly optimization algorithm ITBOA of this application, and the model is solved by the three algorithms respectively. The solution results are as follows: Fig.14 shown.

[0200] Fig.14(a) is a comparison chart of the solution time of different algorithms, which shows the solution time of the algorithm under different population sizes when the number of iterations is 200. It can be seen from the figure that the solution time of BOA and ITBOA is significantly lower than that of NSGA algorithm. When the population size is 200, the solution time of ITBOA is reduced by 14.04% and 35.61% compared with that of BOA and NSGA respectively. As the population size increases, the proportion of solution time reduction increases. This is because NSGA needs to exchange and mutate individuals during the solution process, which leads to a longer solution time. At the same time, due to the setting of the good point set and Gaussian random walk strategy, ITBOA can quickly find the optimal solution before reaching the upper limit of the number of iterations, which greatly shortens the solution time of the algorithm. Fig.14 (b) shows the target value solving process of the algorithm under different iteration numbers when the population size is 500. It can be seen from the figure that the target value of ITBOA rises quickly in the early stage of iteration. This is because the setting of the good point set enables the algorithm to have the ability to quickly find the best in the early stage of iteration. At the same time, the setting of the Gaussian random walk strategy enables the algorithm to further improve the target value in the middle of iteration. The target value of the algorithm is constant at 0.4518 when it iterates to 108 generations. The target value of NSGA is constant at 0.3771 when it iterates to 120 generations. The target value of BOA is constant at 0.3252 when it iterates to 128 generations, indicating that the algorithm has poor convergence and is easy to fall into local optimality. It can be seen that the convergence and optimization ability of ITBOA are significantly stronger than those of NSGA and BOA. The convergence of ITBOA is 10% and 15.63% higher than that of NSGA and BOA, respectively, and the optimization ability of ITBOA is 19.80% and 38.94% higher than that of NSGA and BOA, respectively.

[0201] comprehensive Fig.14 The various information reflected in it shows that the improved two-stage butterfly optimization algorithm is significantly superior to the genetic algorithm and the traditional butterfly optimization algorithm in terms of solution speed, optimization ability and convergence, and is more suitable for solving the comprehensive energy system planning optimization model constructed in this application that takes into account the support role of the power grid.

[0202] In summary, the integrated energy system optimization method provided in the embodiment of the present application that takes into account the supporting role of the power grid formulates the output strategy of the integrated energy system through the basic data of the integrated energy system, fully considers the actual situation and external environment of the system, and on this basis, quantifies the power grid support capacity of the integrated energy system, clearly understands the supporting role and contribution of the system to the power grid, and provides strong support for the planning and optimization of the integrated energy system. The integrated energy system takes the power grid support capacity as the optimization goal, constructs an integrated energy system planning optimization model that takes into account the supporting role of the power grid, and solves the integrated energy system planning optimization model to obtain the optimized equipment capacity of the integrated energy system, which can provide scientific guidance for the equipment capacity configuration, energy selection, etc. of the system, and help to achieve the overall optimization of the system and improve the operating efficiency and economic benefits of the system.

[0203] See also Fig.15 An embodiment of the present application provides a comprehensive energy system optimization device that takes into account the support role of the power grid, and executes the comprehensive energy system optimization method that takes into account the support role of the power grid as mentioned above. The device includes an acquisition module 201, an output strategy determination module 202, a power grid support capacity quantification module 203, a model construction module 204 and a result solution module 205.

[0204] The acquisition module 201 is used to acquire basic data of the integrated energy system; the basic data of the integrated energy system includes the basic data of the integrated energy system.

[0205] The output strategy determination module 202 is used to determine the output strategy of the integrated energy system based on basic data.

[0206] The power grid support capability quantification module 203 is used to quantify the power grid support capability of the integrated energy system under the output strategy of the integrated energy system.

[0207] The model building module 204 is used to build an integrated energy system planning optimization model based on the power grid support capability of the integrated energy system.

[0208] The solution module 205 is used to solve the integrated energy system planning optimization model to obtain the optimized equipment capacity of the integrated energy system.

[0209] It should be noted that, although several units / modules or sub-units / modules of the integrated energy system optimization device taking into account the grid support role are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiment of the present application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0210] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0211] The present application also provides a computer-readable storage medium, in which computer execution instructions are stored. When a processor executes the computer execution instructions, the comprehensive energy system optimization method taking into account the support role of the power grid provided in the above-mentioned embodiments of the present application is implemented.

[0212] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A comprehensive energy system optimization method taking into account the support function of the power grid, characterized in that: include: Obtain basic data on integrated energy systems; Based on the basic data, determining an output strategy of the integrated energy system; quantifying the grid support capacity of the integrated energy system under the output strategy of the integrated energy system; The quantification of the power grid support capacity of the integrated energy system under the output strategy of the integrated energy system includes: Under the output strategy of the integrated energy system, based on the difference between the power purchase amount of the integrated energy system and the power load of the user, the peak shaving and valley filling rate of the integrated energy system at each time node is defined; Based on the peak shaving and valley filling rate of the integrated energy system, a multi-density peak clustering algorithm is used to calculate the local density and relative distance of the data set of the peak shaving and valley filling rate, distribute sample points and remove outliers in the samples, and extract the characteristic values ​​of the basic data of the integrated energy system; quantifying the grid support capacity of the integrated energy system based on the characteristic values ​​of the basic data of the integrated energy system; Grid support capacity of integrated energy system IES The expression is: Among them, ω j represents the peak-cutting and valley-filling rate corresponding to the jth cluster center, J represents the total number of cluster centers; W j Represents the weight of the jth cluster center; Based on the grid support capability of the integrated energy system, a comprehensive energy system planning optimization model is constructed; Solve the integrated energy system planning optimization model to obtain the optimized equipment capacity of the integrated energy system.

2. The comprehensive energy system optimization method taking into account the power grid support function as claimed in claim 1, characterized in that: The optimization objectives of the integrated energy system planning optimization model include the highest power grid support capacity and the lowest annualized total economic cost; The constraints of the comprehensive energy system planning optimization model include supply and demand balance constraints, pipeline network constraints and equipment operation constraints.

3. The comprehensive energy system optimization method taking into account the power grid support function as claimed in claim 2, characterized in that: The maximum support capacity of the power grid is maxF sup The expression is: Among them, ω IES represents the grid support capacity of the integrated energy system; DPC[x] represents the density peak clustering algorithm; It represents the peak shaving and valley filling rate at T time points in the whole set.

4. The comprehensive energy system optimization method taking into account the power grid support function as claimed in claim 2, characterized in that: The supply and demand balance constraints include electric energy balance constraints and thermal energy balance constraints; The expression of the power balance constraint is: P grid,t +P pv,t +P wt,t +P CHP,t +P BES,t =L user,t +L AHP,t +L BES,t +L P2G,t Among them, P grid,t P represents the power supply of the power grid at time t; pv,t P represents the power supply of the fan at time t; wt,t represents the photovoltaic power supply at time t; P CHP,t represents the power supply of the cogeneration system at time t; P BES,t Indicates the discharge amount of the energy storage battery at time t; L user,t represents the power consumption of users in the park at time t; L AHP,t represents the power consumption of the air source heat pump at time t; L BES,t Indicates the charge capacity of the energy storage battery at time t; L P2G,t represents the P2G power consumption at time t; The expression of the thermal energy balance constraint is: T AHP,t +T CHP,t +T HST,t =TL user,t +TL HST,t Among them, T AHP,t represents the heat supply of the air source heat pump at time t; T CHP,t represents the heat supply of the cogeneration system at time t; T HST,t Indicates the heat released by the heat storage tank at time t; TL user,t Indicates the heat consumption of users in the park at time t; TL user,t Indicates the amount of heat charged in the energy storage tank at time t.

5. The comprehensive energy system optimization method taking into account the power grid support function as claimed in claim 1, characterized in that: The integrated energy system includes a thermal system and an electrical system; Determining the output strategy of the integrated energy system based on the basic data includes: Determine the equipment output strategy of the thermal system based on the basic data and the peak shaving and valley filling principle; Based on the equipment output strategy of the thermal system, the equipment output strategy of the electrical system is determined.

6. The method for optimizing the integrated energy system taking into account the supporting role of the power grid as claimed in claim 5, characterized in that: The equipment output strategy of the thermal system includes a first peak time strategy, a first normal time strategy and a first valley time strategy; The first peak-time strategy includes: when the electricity price is at a peak, the thermal system gives priority to using the cogeneration system for heating in order to reduce the system's demand for electricity purchases. If the cogeneration system cannot meet the heat load demand, the heat storage tank releases heat. If the heat storage tank still cannot meet the heat load demand, the air source heat pump is used for heating. The first normal strategy includes: when the electricity price is normal, the combined heat and power system is used for heating first, and if the combined heat and power system cannot meet the heat load demand, the air source heat pump is used for heating; The first valley-time strategy includes: during valley-time of electricity prices, air source heat pumps are used for heating first, and the heat storage tank is charged with heat at rated power until the upper limit. If the output of the air source heat pump cannot meet the user's heat load demand and the heat storage tank charging demand, the combined heat and power system will make up for it.

7. The method for optimizing a comprehensive energy system taking into account the support function of a power grid as claimed in any one of claims 1 to 6, characterized in that: The step of solving the integrated energy system planning optimization model to obtain the optimized equipment capacity of the integrated energy system includes: Using a double-order butterfly optimization algorithm, solving the Pareto solution set of multiple feasible solutions of the integrated energy system planning optimization model; Based on the inferior-to-excellent solution distance method, the optimal solution is selected from the Pareto solution set of multiple feasible solutions of the integrated energy system planning optimization model to obtain the optimized equipment capacity of the integrated energy system.

8. An integrated energy system optimization device taking into account the support role of a power grid, executing the integrated energy system optimization method taking into account the support role of a power grid as claimed in any one of claims 1 to 7, characterized in that: include: Acquisition module, used to obtain basic data of the integrated energy system; An output strategy determination module, used to determine the output strategy of the integrated energy system based on the basic data; A power grid support capacity quantification module, used to quantify the power grid support capacity of the integrated energy system under the output strategy of the integrated energy system; The power grid support capacity quantification module is specifically used for: Under the output strategy of the integrated energy system, based on the difference between the power purchase amount of the integrated energy system and the power load of the user, the peak shaving and valley filling rate of the integrated energy system at each time node is defined; Based on the peak shaving and valley filling rate of the integrated energy system, a multi-density peak clustering algorithm is used to calculate the local density and relative distance of the data set of the basic data of the integrated energy system, distribute the sample points and remove the outliers in the samples, and extract the characteristic values ​​of the basic data of the integrated energy system; quantifying the grid support capacity of the integrated energy system based on the characteristic values ​​of the basic data of the integrated energy system; Grid support capacity of integrated energy system IES The expression is: Among them, ω j represents the peak-cutting and valley-filling rate corresponding to the jth cluster center, J represents the total number of cluster centers; W j Represents the weight of the jth cluster center; A model building module, used to build a comprehensive energy system planning optimization model based on the power grid support capability of the comprehensive energy system; The solution module is used to solve the integrated energy system planning optimization model to obtain the optimized equipment capacity of the integrated energy system.

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