Industrial park demand response potential assessment method and system

Abstract

The invention discloses an industrial park demand response potential assessment method and system. The method comprises the following steps: collecting basic data of each user in an industrial park; load characteristic key indexes are determined, and a typical daily load curve of each user is extracted through clustering analysis; the production characteristics of the user equipment are analyzed, and an interactive response model capable of translating, transferable and reducing loads is established; a multi-target optimization model with the minimum park load peak-valley difference and the minimum operation cost as targets is constructed; and carrying out statistics on the variable quantity of each user load in an optimization result, and evaluating the peak clipping and valley filling potential of the industrial park in different time periods. The method effectively improves the accuracy of industrial park demand response potential evaluation, can accurately describe the power regulation characteristics and time translation characteristics of the flexible load resources of industrial users in different periods of time within the day, fits the actual power consumption behaviors of the users, can accurately obtain the upper and lower adjustable potentials of the industrial park, and improves the evaluation accuracy of the industrial park demand response potential. And a powerful basis is provided for formulating a reasonable load interaction strategy for the industrial park.

Description

Technical Field

[0001] The present invention relates to the technical field of load-side response potential assessment of power systems, and in particular to an industrial park demand response potential assessment method and system. Background Art

[0002] Building a new power system with new energy as the main body is the only way to achieve the transformation of my country's energy structure. However, with the large-scale access of renewable energy, new challenges have been brought to the stable operation of the power system. Due to the intermittent and volatile characteristics of renewable energy power generation, it is difficult to effectively maintain the power balance of the power system by relying solely on the power supply side. The system will gradually shift from a "source follows load" to a "source-load interaction" model. Industrial load, as the most important demand-side resource, not only has considerable regulation potential, but also facilitates the implementation of centralized regulation. Therefore, conducting a demand response potential assessment study on industrial parks that have gathered a large number of industrial users can give full play to the regulation potential of industrial loads and provide a reliable basis for formulating effective demand response strategies.

[0003] At present, the research on the evaluation of demand response potential of industrial users is mostly based on the analysis of historical load data of users, focusing on the qualitative analysis of the theoretical demand response potential of industrial loads. The evaluation results of theoretical demand response potential are often rough, lacking consideration of the power consumption characteristics and response characteristics of different types of industrial users, and unable to accurately tap the adjustable potential of user loads; in addition, the existing industrial user demand response potential evaluation model mainly tends to focus on the peak shaving potential of users, and does not fully tap the valley filling potential of users, resulting in the evaluation results being unable to intuitively depict the adjustable capacity of loads in each period, thereby affecting the normal production electricity consumption of users after participating in demand response, and greatly increasing the production costs of enterprises. To this end, the present invention proposes a demand response potential evaluation method that fully considers the production characteristics and operating economic costs of industrial users, providing important support for industrial parks that gather a large number of industrial users to formulate practical and economically applicable demand response plans. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides an industrial park demand response potential assessment method to solve the problem of how to analyze the power consumption characteristics of different users in the industrial park.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for evaluating demand response potential of an industrial park, comprising:

[0008] Collect basic data of each user in the industrial park;

[0009] Based on the basic data, key indicators of load characteristics are determined, and typical daily load curves of each user are extracted through cluster analysis;

[0010] Based on the typical daily load curve, the production characteristics of the user equipment are analyzed, and an interactive response model for load translation, transfer and reduction is established;

[0011] Based on the interactive response model, a multi-objective optimization model with the goal of minimizing the peak-to-valley difference of the park load and minimizing the operating cost is constructed, and the multi-objective optimization model is solved to obtain an optimization result;

[0012] The load variation of each user in the optimization result is counted to evaluate the peak load shaving and valley load filling potential of the industrial park in different time periods.

[0013] As a preferred solution of the method for evaluating the demand response potential of an industrial park according to the present invention, the key indicators for determining load characteristics include:

[0014] The basic data of each user includes: the hourly historical load data of each user in the industrial park throughout the year, the installed capacity of distributed power sources, the installed capacity of energy storage, the proportion of flexible loads, and the operating parameters of major production equipment;

[0015] Based on the historical load data of 365 days throughout the year, the five key indicators are clustered. The key indicators include load rate, peak load rate, normal load rate, valley load rate and peak-valley difference rate, which are respectively expressed as:

[0016]

[0017] in, represents the load data of the i-th user in the industrial park on the n-th day, They represent the load data of the i-th user for 24 hours on the n-th day, and They represent the daily load rate, peak load rate, normal load rate, valley load rate and daily peak-valley difference rate of the i-th user on the nth day, respectively. and They represent the average, maximum and minimum load values ​​of the i-th user on the n-th day, respectively. and They represent the average load during the peak period, the average load during the normal period, and the average load during the valley period of the i-th user on the n-th day.

[0018] As a preferred solution of the method for evaluating the demand response potential of an industrial park according to the present invention, the method of extracting the typical daily load curve of each user through cluster analysis includes:

[0019] Based on the five key load characteristic indicators, the load characteristic indicator vector of the i-th user in the industrial park on the n-th day is obtained, and the load characteristic indicator matrix Y of each user is constructed by combining the historical load data throughout the year. 365*5 ;

[0020] Using K-means clustering method, the Y of the i-th user i As input, clustering is performed using the Euclidean distance between samples as the similarity criterion;

[0021] After completing K-means clustering, the atypical daily load curve is defined as the load curve with the farthest distance from the other categories, including ω daily load curves, and is removed from the historical load curves throughout the year;

[0022] The remaining daily load curves are averaged at each hour, and the obtained load average curve is used as the typical daily load curve of the user, expressed as:

[0023]

[0024] Among them, l i is the vector representation of the typical daily load curve of the i-th user in the industrial park, Represents the load data of the jth period in the typical daily load curve of the i-th user.

[0025] As a preferred solution of the method for evaluating the potential of industrial park demand response according to the present invention, the establishment of an interactive response model that can be translated, transferred and reduced loads includes:

[0026] According to the total amount of load that can be shifted by industrial users in time period t, a shiftable load interactive response model is established, including load shift amount constraint, shift start time constraint and shift number constraint;

[0027] According to the total amount of transferable load of industrial users in time period t, an interactive response model of transferable load is established, including load transfer amount constraint, transfer number constraint, minimum transfer-in and transfer-out duration constraint, transferable time interval constraint and transfer power unchanged constraint;

[0028] According to the total amount of load that can be reduced by industrial users in time period t, an interactive response model of load that can be reduced is established, including load reduction amount constraints, reduction number constraints, reduction duration constraints and reduction period interval constraints.

[0029] As a preferred solution of the method for evaluating the demand response potential of an industrial park according to the present invention, constructing the multi-objective optimization model includes:

[0030] The first optimization goal is to minimize the peak-to-valley difference of the daily load of the industrial park, and the second optimization goal is to minimize the operating economic cost of the industrial park. The operating economic cost includes the energy storage charging and discharging loss cost, the cost of purchasing electricity from the superior power grid, and the load interaction cost.

[0031] Two objective functions are established, namely, minimizing the peak-to-valley difference F1 of the industrial park's daily load and minimizing the total operating cost F2 of the industrial park, which are expressed as:

[0032]

[0033] minF2=C ESS +C BUY -C DR

[0034]

[0035] Among them, T is the scheduling period, N is the number of users in the industrial park, and L t represents the sum of the loads of all users in the industrial park after participating in demand response in period t, C ESS , C BUY and C DR are the energy storage charging and discharging loss cost of the industrial park, the electricity purchase cost from the upper power grid, and the flexible load interaction cost of all users. α and β are the charging and discharging loss cost coefficients of the energy storage device in the industrial park, respectively. t ch and P t dch They represent the charging and discharging power of the energy storage device in time period t, e p 、e f 、e v Respectively represent the electricity purchase price during peak, flat and valley periods, T p , T f , T v Respectively represent the peak, flat and valley periods in a scheduling cycle, P t buy represents the amount of electricity purchased by the industrial park from the upper power grid during period t, and They represent the unit capacity compensation price of the transferable load, shiftable load and curtailable load of the i-th user in the industrial park in period t, and They represent the response states of the transferable load, shiftable load and curtailable load of the i-th user in the industrial park in period t, and They represent the transferable load transfer-out amount, the shiftable load transfer-out amount and the curtailable load reduction amount of the i-th user in the industrial park in period t respectively;

[0036] When demand response regulation is performed through the multi-objective optimization model, the constraints of power balance constraints, renewable energy generation constraints and energy storage device constraints must be met.

[0037] As a preferred solution of the method for evaluating the demand response potential of an industrial park according to the present invention, solving the multi-objective optimization model includes:

[0038] Decompose the multi-objective optimization problem into single-objective optimization sub-problems, and optimize these sub-problems at the same time according to the neighborhood relationship between the sub-problems;

[0039] Each sub-problem after decomposition corresponds to a weight vector, and a set of approximate Pareto optimal solutions are obtained by synchronously optimizing the sub-problems, and the load response of the shiftable load, transferable load and reducible load of each user in the industrial park is obtained.

[0040] As a preferred solution of the method for evaluating the demand response potential of an industrial park according to the present invention, the method for evaluating the peak shaving and valley filling potential of an industrial park at different time periods includes:

[0041] Based on the optimization results, the load reduction of each user in each time period is added together to obtain the peak shaving potential of the industrial park in each time period.

[0042] The load increase of each user in each time period is added together to obtain the valley filling potential of the industrial park in each time period.

[0043] In a second aspect, the present invention provides an industrial park demand response potential assessment system, comprising:

[0044] The collection module is used to collect basic data of each user in the industrial park;

[0045] An extraction module, used to determine the key indicators of load characteristics based on the basic data, and extract the typical daily load curve of each user through cluster analysis;

[0046] The first building module is used to analyze the production characteristics of the user equipment based on the typical daily load curve and establish an interactive response model that can translate, transfer and reduce loads;

[0047] The second construction module is used to construct a multi-objective optimization model with the objectives of minimizing the peak-to-valley difference of the park load and minimizing the operating cost based on the interactive response model, and solve the multi-objective optimization model to obtain an optimization result;

[0048] The evaluation module is used to count the changes in the load of each user in the optimization result and evaluate the peak-shaving and valley-filling potential of the industrial park in different time periods.

[0049] In a third aspect, the present invention provides an electronic device, comprising:

[0050] Memory and processor;

[0051] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the industrial park demand response potential assessment method are implemented.

[0052] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the industrial park demand response potential assessment method.

[0053] Compared with the prior art, the present invention has the following beneficial effects: the present invention constructs a load characteristic index vector based on the historical load data of each user in the industrial park, and uses the K-means algorithm to perform cluster analysis on each index to obtain a typical daily load curve for each user. The proposed index can identify the typical electricity consumption pattern and habits of the user, and at the same time reflect the electricity consumption elasticity of the user, thereby effectively improving the accuracy of the demand response potential assessment of the industrial park; the present invention establishes an interactive response model of shiftable load, transferable load and reducible load for each user in the industrial park. The established model can accurately characterize the power regulation characteristics and time shift characteristics of the flexible load resources of the industrial user at different times of the day, which fits the actual electricity consumption behavior of the user; the present invention establishes an industrial park demand response potential assessment model based on the load interactive response mechanism of the flexible load resources and the power grid. The model takes the minimum peak-to-valley difference of the daily load of the industrial park and the minimum operating cost as the optimization goal, and uses the MOEA / D algorithm for solution, which can accurately obtain the upper and lower adjustable potential of the industrial park, and provide a strong basis for the industrial park to formulate a reasonable load interaction strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0055] Figure 1 A schematic diagram of the overall process of a method for evaluating the demand response potential of an industrial park according to an embodiment of the present invention;

[0056] Figure 2A schematic diagram of a photovoltaic output and total load demand curve in an industrial park according to an industrial park demand response potential assessment method according to an embodiment of the present invention;

[0057] Figure 3 A schematic diagram of typical daily curves of various flexible loads in an industrial park according to an industrial park demand response potential assessment method according to an embodiment of the present invention;

[0058] Figure 4 A schematic diagram of system load curves before and after optimization of an industrial park demand response potential assessment method according to an embodiment of the present invention;

[0059] Figure 5 A schematic diagram of typical flexible load adjustment amounts in each time period of an industrial park demand response potential assessment method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0063] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0064] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0065] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0066] Example 1

[0067] Reference Figure 1 , which is an embodiment of the present invention, provides an industrial park demand response potential assessment method, comprising:

[0068] S101, collect basic data of each user in the industrial park;

[0069] Preferably, the basic data of each user includes: the hourly historical load data of each user in the industrial park throughout the year, the installed capacity of distributed power sources, the installed capacity of energy storage, the proportion of flexible loads, and the operation parameters of major production equipment.

[0070] S102, based on the basic data, determine the key indicators of load characteristics, and extract the typical daily load curve of each user through cluster analysis.

[0071] Exemplarily, key indicators are determined based on historical load data of each user in the industrial park, the K-means method is used to perform cluster analysis on each indicator, and then the typical load curve of each user is determined by the average method.

[0072] Preferably, determining the key indicators of load characteristics includes:

[0073] In order to reflect the load characteristics and electricity consumption patterns of users in various periods from multiple perspectives, five key indicators are clustered based on the historical load data of 365 days a year. The key indicators include load rate, peak load rate, normal load rate, valley load rate and peak-valley difference rate, which are expressed as:

[0074]

[0075] in, represents the load data of the i-th user in the industrial park on the n-th day, They represent the load data of the i-th user for 24 hours on the n-th day, and They represent the daily load rate, peak load rate, normal load rate, valley load rate and daily peak-valley difference rate of the i-th user on the nth day, respectively. and They represent the average, maximum and minimum load values ​​of the i-th user on the n-th day, respectively. and They represent the average load during the peak period, the average load during the normal period, and the average load during the valley period of the i-th user on the n-th day.

[0076] Preferably, extracting the typical daily load curve of each user through cluster analysis includes:

[0077] Based on the five key load characteristic indicators, the load characteristic indicator vector of the i-th user in the industrial park on the nth day is obtained, which is expressed as:

[0078]

[0079] Combined with the historical load data throughout the year, the load characteristic index matrix Y of each user is constructed 365*5 ;

[0080] Specifically, a user load characteristic index vector is constructed, where each column element in the vector is the user's load rate, peak load rate, average load rate, valley load rate, and peak-valley difference rate on a certain day of the year. Then, based on the historical hourly load data of each user in the industrial park for 365 days throughout the year, the load characteristic index of each user on each day is calculated to form a load characteristic index matrix Y 365*5 .

[0081] Using K-means clustering method, the Y of the i-th user i As input, clustering is performed using the Euclidean distance between samples as the similarity criterion, that is, the closer the Euclidean distance between two samples, the greater the similarity;

[0082] After completing K-means clustering, the atypical daily load curve is defined as the load curve with the farthest distance from the other categories, including ω daily load curves, and is removed from the historical load curves throughout the year;

[0083] The remaining daily load curves are averaged at each hour, and the obtained load average curve is used as the typical daily load curve of the user, expressed as:

[0084]

[0085] Among them, li is the vector representation of the typical daily load curve of the i-th user in the industrial park, Represents the load data of the jth period in the typical daily load curve of the i-th user.

[0086] Specifically, the specific steps of using K-means clustering are as follows:

[0087] 1) The load characteristic index matrix Y of the i-th user i Randomly select k samples as the initial cluster centers;

[0088] 2) Calculate the distance between each sample and the initial cluster center, and assign each sample to the nearest cluster center according to the distance to form k cluster categories;

[0089] 3) Calculate the average value of all samples in each category as the new cluster center.

[0090] Repeat the above steps to update the Euclidean distance between each sample and the cluster center, then classify the samples, and determine the new cluster center based on the sample data in the new category, until the predetermined number of iterations is reached or the Euclidean distance between the samples in each category and the cluster center no longer changes, thus completing the clustering of k categories.

[0091] S103, based on the typical daily load curve, analyze the production characteristics of the user equipment and establish an interactive response model for load translation, transferability and load reduction.

[0092] It should be noted that by analyzing the equipment production characteristics of each user in the industrial park, a load interaction response model that can be shifted, transferred and reduced is established. The established model can accurately characterize the power regulation characteristics and time shift characteristics of the flexible load resources of industrial users at different times of the day, so as to ensure the accuracy of the evaluation results.

[0093] Preferably, establishing an interactive response model for load translation, transferability and load reduction includes:

[0094] According to the total amount of load that can be shifted by industrial users in time period t, a shiftable load interactive response model is established, including load shift amount constraint, shift start time constraint and shift number constraint;

[0095] For example, the total amount of load that can be shifted by an industrial user in time period t is expressed as:

[0096]

[0097] in, represents the total amount of load that can be translated in time period t, α t It represents the load transfer coefficient of the industrial user in time period t, 0≤α t≤1,λ t shift represents the shiftable load responsiveness of the industrial user in time period t, represents the total power consumption of the industrial user in time period t before participating in the response;

[0098] The load translation constraint is expressed as:

[0099]

[0100] The translation start period constraint is expressed as:

[0101]

[0102] The number of translation constraints can be expressed as:

[0103]

[0104] Where ΔP t shift,in and ΔP t shift,out They represent the amount of transferable load moving in and out during the scheduling period t, and is a 0-1 variable, indicating the moving-in and moving-out status of the movable load in the scheduling period t. is the acceptable load transfer time interval, t s is the starting time for the displacement of the movable load, T shift is the load shifting duration, and T is the scheduling period.

[0105] According to the total amount of transferable load of industrial users in time period t, an interactive response model of transferable load is established, including load transfer amount constraint, transfer number constraint, minimum transfer-in and transfer-out duration constraint, transferable time interval constraint and transfer power unchanged constraint;

[0106] For example, the total amount of transferable load of industrial users in time period t is expressed as:

[0107]

[0108] in, represents the total amount of transferable load in time period t, γ t represents the load transfer coefficient of the industrial user in time period t, 0≤γ t ≤1, represents the transferable load responsiveness of the industrial user in time period t;

[0109] The load transfer constraint is expressed as:

[0110]

[0111] The transfer count constraint is expressed as:

[0112]

[0113] The minimum transfer-in and transfer-out duration constraints are expressed as:

[0114]

[0115] The transferable time interval constraint is expressed as:

[0116]

[0117] The transfer energy invariance constraint is expressed as:

[0118]

[0119] Where ΔP t trans,in and ΔP t trans,out are the loads transferred in and out during the dispatch period t, and is a 0-1 variable, indicating the transfer-in and transfer-out status of the transferable load in the scheduling period t. and is the minimum load and maximum load that can be transferred in the dispatch period t, and is the maximum number of load transfers in and out allowed within the dispatch cycle, and are the minimum transfer-in duration and minimum transfer-out duration of the transferable load, respectively. and They are the load-acceptable transfer-in time period and load-out time period respectively.

[0120] Based on, an interactive response model of load reduction is established, including load reduction amount constraint, reduction number constraint, reduction duration constraint and reduction period interval constraint;

[0121] For example, the total amount of load that can be reduced by industrial users in time period t is expressed as:

[0122]

[0123] in, represents the total load that can be reduced in time period t, β t It represents the load reduction coefficient of the industrial user in time period t, 0≤β t ≤1, It represents the responsiveness of the industrial user's load reduction in time period t;

[0124] The load reduction constraint is expressed as:

[0125]

[0126] The reduction constraint is expressed as:

[0127]

[0128] The reduction duration constraint is expressed as:

[0129]

[0130] The cuttable time interval constraint is expressed as:

[0131]

[0132] in, is a 0-1 variable, indicating the reduction status of the load that can be reduced in the scheduling period t. is the maximum number of load reductions allowed in a dispatch cycle, and are the minimum load reduction duration and the maximum load reduction duration, respectively. Indicates the time period during which load can be reduced.

[0133] S104, based on the interactive response model, a multi-objective optimization model with the goal of minimizing the peak-to-valley difference of the park load and minimizing the operating cost is constructed, and the multi-objective optimization model is solved to obtain the optimization result.

[0134] It should be noted that demand response refers to users changing their electricity consumption behavior or patterns based on price signals or incentive policies to reduce or shift electricity demand during a certain period of time, thereby achieving the effect of shaving peaks and filling valleys and smoothing the load curve. Therefore, minimizing the daily load peak-to-valley difference in the industrial park is taken as the first optimization goal to balance the volatility of the system load and promote the balance of power supply and demand in the power grid. At the same time, in order to take into account the economic benefits of each user in the industrial park participating in demand response and to increase the enthusiasm of users to respond, minimizing the operating economic cost of the industrial park is taken as the second optimization goal, including the energy storage charging and discharging loss cost, the cost of purchasing electricity from the superior power grid, and the load interaction cost.

[0135] Preferably, constructing a multi-objective optimization model includes:

[0136] In order to evaluate the demand response potential of the industrial park, the first optimization goal is to minimize the daily load peak-valley difference of the industrial park, and the second optimization goal is to minimize the operating economic cost of the industrial park. The operating economic cost includes the energy storage charging and discharging loss cost, the cost of purchasing electricity from the upper power grid, and the load interaction cost.

[0137] Two objective functions are established, namely, minimizing the peak-to-valley difference F1 of the industrial park's daily load and minimizing the total operating cost F2 of the industrial park, which are expressed as:

[0138]

[0139] minF2=C ESS +C BUY -C DR

[0140]

[0141] Among them, T is the scheduling period, which is 24 hours, N is the number of users in the industrial park, and L t represents the sum of the loads of all users in the industrial park after participating in demand response in period t, C ESS , C BUY and C DR are the energy storage charging and discharging loss cost of the industrial park, the electricity purchase cost from the upper power grid, and the flexible load interaction cost of all users. α and β are the charging and discharging loss cost coefficients of the energy storage device in the industrial park, respectively. t ch and P t dch They represent the charging and discharging power of the energy storage device in time period t, e p 、e f 、e v Respectively represent the electricity purchase price during peak, flat and valley periods, T p , T f , T v Respectively represent the peak, flat and valley periods in a scheduling cycle, P t buy represents the amount of electricity purchased by the industrial park from the upper power grid during period t, and They represent the unit capacity compensation price of the transferable load, shiftable load and curtailable load of the i-th user in the industrial park in period t, and They represent the response states of the transferable load, shiftable load and reducible load of the i-th user in the industrial park in period t. When it is 1, it means participating in regulation, and when it is 0, it means not participating in regulation. and They represent the transferable load transfer-out amount, the shiftable load transfer-out amount and the curtailable load reduction amount of the i-th user in the industrial park in period t respectively;

[0142] When demand response regulation is performed through a multi-objective optimization model, the constraints of power balance constraints, renewable energy generation constraints, and energy storage device constraints must be met.

[0143] It should be noted that for each user, the shiftable load must satisfy the above-mentioned load shift amount constraint, shift start time period constraint and shift number constraint; the transferable load must satisfy the above-mentioned load transfer amount constraint, transfer number constraint, minimum transfer-in and transfer-out duration constraint, transfer time period interval constraint and transfer power unchanged constraint; the reduceable load must satisfy the above-mentioned load reduction amount constraint, reduction number constraint, reduction duration constraint and reduction time period interval constraint. In addition, each user in the industrial park must also satisfy the above-mentioned power balance constraint, renewable energy generation constraint and energy storage device constraint when participating in demand response regulation.

[0144] Exemplarily, the power balance constraint is expressed as:

[0145]

[0146] in, is the basic rigid load of the ith user in period t, i.e., the load that does not participate in interactive response, and are the total load that can be transferred, the load that can be translated, and the total load that can be reduced for the i-th user in period t, P t reg is the power generation of renewable energy in the industrial park during period t.

[0147] The renewable energy generation constraint is expressed as:

[0148] 0≤P t reg ≤P t reg,max

[0149] Among them, P t reg,max It is the maximum power that renewable energy can output in period t.

[0150] The energy storage device constraint is expressed as:

[0151] 0≤P t ch ≤P ch,max

[0152] 0≤P t dch ≤P dch,max

[0153] P t ch ·P t dch =0

[0154] SOC min ≤SOC t≤SOC max

[0155]

[0156] Among them, P ch,max and P dch,max are the upper limits of the charging and discharging power of the energy storage device in the industrial park, respectively. The third formula above indicates that the energy storage device cannot be in the charging and discharging state at the same time during the t period. SOC t is the state of charge of the energy storage device during period t, SOC min and SOC max are the lower and upper limits of the state of charge of the energy storage device. The fifth formula above is the updated expression of the state of charge of the energy storage device. δ is the self-discharge rate of the energy storage device, η ch and η dch are the charging and discharging efficiencies of the energy storage device, E bat It represents the rated capacity of the energy storage device, and Δt is the sampling time, which is set to 1h.

[0157] Preferably, solving the multi-objective optimization model includes:

[0158] Decompose the multi-objective optimization problem into single-objective optimization sub-problems, and optimize these sub-problems at the same time according to the neighborhood relationship between the sub-problems;

[0159] Each sub-problem after decomposition corresponds to a weight vector, and a set of approximate Pareto optimal solutions are obtained by synchronously optimizing the sub-problems, and the load response of the shiftable load, transferable load and reducible load of each user in the industrial park is obtained.

[0160] It should be noted that this embodiment establishes a dual-objective optimization model and adopts a decomposition-based multi-objective evolutionary algorithm (MOEA / D) to solve it. The algorithm can ensure the diversity and uniformity of the solution and has strong adaptability.

[0161] S105, counting the change in load of each user in the optimization result, and evaluating the peak shaving and valley filling potential of the industrial park in different time periods.

[0162] Preferably, based on the optimization result, the load reduction amount of each user in each time period is added together to obtain the peak shaving potential of the industrial park in each time period;

[0163] The load increase of each user in each time period is added together to obtain the valley filling potential of the industrial park in each time period.

[0164] It should be noted that the present invention constructs a load characteristic index vector based on the historical load data of each user in the industrial park, and uses the K-means algorithm to perform cluster analysis on each index to obtain the typical daily load curve of each user. The proposed index can identify the user's typical electricity consumption pattern and habits, and reflect the user's electricity consumption elasticity, thereby effectively improving the accuracy of the industrial park's demand response potential assessment.

[0165] The present invention establishes an interactive response model of shiftable loads, transferable loads and reducible loads for each user in the industrial park. The established model can accurately characterize the power regulation characteristics and time shift characteristics of the flexible load resources of industrial users at different time periods within the day, which is in line with the actual electricity consumption behavior of the users.

[0166] Based on the load interaction response mechanism of flexible load resources and power grid, the present invention establishes an industrial park demand response potential evaluation model. The model takes the minimum peak-to-valley difference of daily load in the industrial park and the minimum operating cost as the optimization objectives, and adopts MOEA / D algorithm for solving. It can accurately obtain the upper and lower adjustable potential of the industrial park, and provide a strong basis for the industrial park to formulate a reasonable load interaction strategy.

[0167] The above is a schematic scheme of a method for evaluating the potential for demand response in an industrial park in this embodiment. It should be noted that the technical scheme of the industrial park demand response potential evaluation system and the technical scheme of the above-mentioned method for evaluating the potential for demand response in an industrial park belong to the same concept. For details not described in detail in the technical scheme of the industrial park demand response potential evaluation system in this embodiment, please refer to the description of the technical scheme of the above-mentioned method for evaluating the potential for demand response in an industrial park.

[0168] The industrial park demand response potential assessment system in this embodiment includes:

[0169] The collection module is used to collect basic data of each user in the industrial park;

[0170] The extraction module is used to determine the key indicators of load characteristics based on basic data and extract the typical daily load curve of each user through cluster analysis;

[0171] The first building module is used to analyze the production characteristics of user equipment based on a typical daily load curve and establish an interactive response model that can translate, transfer and reduce loads;

[0172] The second construction module is used to construct a multi-objective optimization model based on the interactive response model with the goal of minimizing the peak-to-valley difference of the park load and minimizing the operating cost, and solve the multi-objective optimization model to obtain the optimization result;

[0173] The evaluation module is used to count the changes in the load of each user in the optimization results and evaluate the peak-shaving and valley-filling potential of the industrial park in different periods of time.

[0174] This embodiment also provides an electronic device, which is applicable to the situation of evaluating the demand response potential of an industrial park, including:

[0175] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method for evaluating the demand response potential of an industrial park as proposed in the above embodiment.

[0176] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for evaluating the demand response potential of an industrial park as proposed in the above embodiment is implemented.

[0177] The storage medium proposed in this embodiment and the method for implementing the industrial park demand response potential assessment proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0178] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0179] Example 2

[0180] Refer to Table 1 and Figure 2-Figure 5 , which is an embodiment of the present invention, provides an industrial park demand response potential assessment method. In order to verify its beneficial effects, comparison results of two schemes are provided.

[0181] According to the proposed method, the total load demand in a typical industrial park is fitted. The clustering results and the photovoltaic output and various flexible load demand prediction curves are shown in the figure. Figure 2 , Figure 3As shown. Among them, the proportion of flexible load in the overall load demand in the park is 16%, and the proportions of the three types of flexible loads that can be reduced, transferred, and translated are 10%, 4.8%, and 1.2% respectively. The overall load throughout the day is stable, but there is a trough in the morning, noon, and evening, and the trough in the morning and evening is relatively lower, showing obvious characteristics of daytime peaks and nighttime valleys.

[0182] Specific electricity prices are set for each time period in the active power distribution system of the industrial park. During the peak hours of 8:00-12:00 and 17:00-21:00, the electricity price is 0.9947 yuan / kWh; during the off-peak period of 0:00-8:00, the electricity price is 0.2989 yuan / kWh; the flat price for the remaining time periods is 0.5968 yuan / kWh.

[0183] Figure 4 In order to optimize the load curve of the front and rear industrial parks, Figure 4 It can be seen that compared with the original load curve, the response of the adjustable resources in the park has a certain peak-shaving effect on the active power distribution system of the industrial park. The total load characteristics of the system before and after optimization are shown in Table 1. The load curves before and after optimization are used to quantify the demand response potential of the industrial park. It can be seen from Table 1 that the peak-to-valley difference of the system load before optimization is 2689kW. By controlling multiple types of flexible loads in the system, the peak-to-valley difference is reduced to 2535kW, and the peak-to-valley difference of the system is reduced by 5.7%. The above results show that by coordinating the flexible loads in the park, the peak-to-valley difference of the system itself can be reduced to a certain extent, which is conducive to the safe and economic operation of the active power distribution system of the industrial park.

[0184] Table 1: Load characteristics of industrial parks before and after optimization

[0185] type Peak value (kW) Valley value (kW) Peak-to-valley difference (kW) Before optimization 5289 2600 2689 After optimization 4995 2460 2535

[0186] Figure 5 In order to optimize the adjustment of the three types of typical flexible loads in the post-industrial park at different time periods during the dispatching day, it can be seen that the reduction of the load that can be reduced is significantly higher during the peak period than during the trough period. The transferable load tends to transfer its own load power consumption from the peak to the trough period. The shiftable load will only shift backward in a small range due to its own characteristics and limitations. Through the dynamic adjustment of various types of flexible loads, the peak load regulation pressure of the power grid can be alleviated to a certain extent, and peak power valley can be achieved to a certain extent.

[0187] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the demand response potential of an industrial park, characterized in that: include: Collect basic data of each user in the industrial park; Based on the basic data, key indicators of load characteristics are determined, and typical daily load curves of each user are extracted through cluster analysis; Based on the typical daily load curve, the production characteristics of the user equipment are analyzed, and an interactive response model for load translation, transfer and reduction is established; Based on the interactive response model, a multi-objective optimization model with the goal of minimizing the peak-to-valley difference of the park load and minimizing the operating cost is constructed, and the multi-objective optimization model is solved to obtain an optimization result; The load variation of each user in the optimization result is counted to evaluate the peak load shaving and valley load filling potential of the industrial park in different time periods.

2. The method for evaluating the demand response potential of an industrial park according to claim 1, characterized in that: The key indicators for determining load characteristics include: The basic data of each user includes: the hourly historical load data of each user in the industrial park throughout the year, the installed capacity of distributed power sources, the installed capacity of energy storage, the proportion of flexible loads, and the operating parameters of major production equipment; Based on the historical load data of 365 days throughout the year, the five key indicators are clustered. The key indicators include load rate, peak load rate, normal load rate, valley load rate and peak-valley difference rate, which are respectively expressed as: in, represents the load data of the i-th user in the industrial park on the n-th day, They represent the load data of the i-th user for 24 hours on the n-th day, and They represent the daily load rate, peak load rate, normal load rate, valley load rate and daily peak-valley difference rate of the i-th user on the nth day, respectively. and They represent the average, maximum and minimum load values ​​of the i-th user on the n-th day, respectively. and They represent the average load during the peak period, the average load during the normal period, and the average load during the valley period of the i-th user on the n-th day.

3. The method for evaluating the demand response potential of an industrial park according to claim 2, characterized in that: The method of extracting the typical daily load curve of each user through cluster analysis includes: Based on the five key load characteristic indicators, the load characteristic indicator vector of the i-th user in the industrial park on the nth day is obtained, and the load characteristic indicator matrix Y of each user is constructed by combining the historical load data throughout the year. 365*5 ; Using K-means clustering method, the Y of the i-th user i As input, clustering is performed using the Euclidean distance between samples as the similarity criterion; After completing K-means clustering, the atypical daily load curve is defined as the load curve with the farthest distance from the other categories, including ω daily load curves, and is removed from the historical load curves throughout the year; The remaining daily load curves are averaged at each hour, and the obtained load average curve is used as the typical daily load curve of the user, expressed as: Among them, l i is the vector representation of the typical daily load curve of the i-th user in the industrial park, Represents the load data of the jth period in the typical daily load curve of the i-th user.

4. The method for evaluating the demand response potential of an industrial park according to claim 1 or 3, characterized in that: The interactive response model for establishing loads that can be translated, transferred and reduced includes: According to the total amount of load that can be shifted by industrial users in time period t, a shiftable load interactive response model is established, including load shift amount constraint, shift start time constraint and shift number constraint; According to the total amount of transferable load of industrial users in time period t, an interactive response model of transferable load is established, including load transfer amount constraint, transfer number constraint, minimum transfer-in and transfer-out duration constraint, transferable time interval constraint and transfer power unchanged constraint; According to the total amount of load that can be reduced by industrial users in time period t, an interactive response model of load that can be reduced is established, including load reduction amount constraints, reduction number constraints, reduction duration constraints and reduction period interval constraints.

5. The method for evaluating the demand response potential of an industrial park according to claim 4, characterized in that: Constructing the multi-objective optimization model includes: The first optimization goal is to minimize the peak-to-valley difference of the daily load of the industrial park, and the second optimization goal is to minimize the operating economic cost of the industrial park. The operating economic cost includes the energy storage charging and discharging loss cost, the cost of purchasing electricity from the superior power grid, and the load interaction cost. Two objective functions are established, namely, minimizing the peak-to-valley difference F1 of the industrial park's daily load and minimizing the total operating cost F2 of the industrial park, which are expressed as: minF2=C ESS +C BUY -C DR Among them, T is the scheduling period, N is the number of users in the industrial park, and L t represents the sum of the loads of all users in the industrial park after participating in demand response in period t, C ESS , C BUY and C DR are the energy storage charging and discharging loss cost of the industrial park, the electricity purchase cost from the upper grid, and the flexible load interaction cost of all users. α and β are the charging and discharging loss cost coefficients of the energy storage device in the industrial park, respectively. t ch and P t dch They represent the charging and discharging power of the energy storage device in time period t, e p 、e f 、e v Respectively represent the electricity purchase price during peak, flat and valley periods, T p , T f , T v Respectively represent the peak, flat and valley periods in a scheduling cycle, P t buy represents the amount of electricity purchased by the industrial park from the upper power grid during period t, and They represent the unit capacity compensation price of the transferable load, shiftable load and curtailable load of the i-th user in the industrial park in period t, and They represent the response states of the transferable load, shiftable load and curtailable load of the i-th user in the industrial park in period t, and They represent the transferable load transfer-out amount, the shiftable load transfer-out amount and the curtailable load reduction amount of the i-th user in the industrial park in period t respectively; When demand response regulation is performed through the multi-objective optimization model, the constraints of power balance constraints, renewable energy generation constraints and energy storage device constraints must be met.

6. The method for evaluating the demand response potential of an industrial park according to claim 5, characterized in that: Solving the multi-objective optimization model includes: Decompose the multi-objective optimization problem into single-objective optimization sub-problems, and optimize these sub-problems at the same time according to the neighborhood relationship between the sub-problems; Each sub-problem after decomposition corresponds to a weight vector, and a set of approximate Pareto optimal solutions are obtained by synchronously optimizing the sub-problems, and the load response of the shiftable load, transferable load and reducible load of each user in the industrial park is obtained.

7. The method for evaluating the demand response potential of an industrial park according to any one of claims 4 to 6, characterized in that: The evaluation of the peak-shaving and valley-filling potential of industrial parks at different times includes: Based on the optimization results, the load reduction of each user in each time period is added together to obtain the peak shaving potential of the industrial park in each time period. The load increase of each user in each time period is added together to obtain the valley filling potential of the industrial park in each time period.

8. An industrial park demand response potential assessment system, characterized in that: include, The collection module is used to collect basic data of each user in the industrial park; An extraction module, used to determine the key indicators of load characteristics based on the basic data, and extract the typical daily load curve of each user through cluster analysis; The first building module is used to analyze the production characteristics of the user equipment based on the typical daily load curve and establish an interactive response model that can translate, transfer and reduce loads; The second construction module is used to construct a multi-objective optimization model with the objectives of minimizing the peak-to-valley difference of the park load and minimizing the operating cost based on the interactive response model, and solve the multi-objective optimization model to obtain an optimization result; The evaluation module is used to count the changes in the load of each user in the optimization result and evaluate the peak-shaving and valley-filling potential of the industrial park in different time periods.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the industrial park demand response potential assessment method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, can implement the steps of the industrial park demand response potential assessment method described in any one of claims 1 to 7.

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