Comprehensive benefit quantitative analysis method, device, electronic device and storage medium

Abstract

The present application discloses a method, device, electronic device and storage medium for quantitative analysis of comprehensive benefits. The method and device are applied to electronic devices and are used to quantitatively analyze the comprehensive benefits of the distribution network. Specifically, the method comprises the following steps: constructing a distribution network operation model based on producer and consumer demand response; selecting multiple evaluation indicators based on the distribution network operation model; calculating the comprehensive weight of each evaluation indicator; scoring each evaluation indicator to obtain an indicator score for each evaluation indicator; and calculating based on the comprehensive weight of each evaluation indicator and the comprehensive weight of each indicator score to obtain the comprehensive benefits of the distribution network. The above scheme can be used to quantitatively analyze the comprehensive benefits between the distribution network and producers and consumers, thereby providing a data basis for improving the comprehensive energy efficiency and operation level of the power system.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and more specifically, to a method, device, electronic device and storage medium for quantitative analysis of comprehensive benefits. Background Art

[0002] As the penetration rate of renewable energy such as photovoltaics in the distribution network continues to increase, the load side of the distribution network has gradually transformed from a single electricity user to a prosumer with dual characteristics of electricity generation and consumption. Therefore, it is necessary to quantify the comprehensive benefits between the distribution network and prosumers in order to provide data basis for improving the comprehensive energy efficiency and operation level of the power system. Summary of the Invention

[0003] In view of this, the present application provides a comprehensive benefit quantitative analysis method, device, electronic device and storage medium for quantitative analysis of the comprehensive benefits of the distribution network, so as to provide data basis for improving the comprehensive energy efficiency and operation level of the power system.

[0004] In order to achieve the above objectives, the following solutions are proposed:

[0005] A comprehensive benefit quantitative analysis method is applied to electronic equipment and is used to quantitatively analyze the comprehensive benefits of a distribution network. The quantitative analysis method comprises the following steps:

[0006] Construct a distribution network operation model based on producer and consumer demand response;

[0007] Selecting a plurality of evaluation indicators based on the distribution network operation model;

[0008] Calculating the comprehensive weight of each evaluation indicator;

[0009] Scoring each evaluation indicator to obtain an indicator score for each evaluation indicator;

[0010] The comprehensive benefit of the distribution network is obtained by calculating based on the comprehensive weight of each evaluation indicator and the comprehensive weight of each indicator score.

[0011] Optionally, the distribution network operation model is configured with energy storage constraints, power purchase and sales constraints, and price constraints.

[0012] Optionally, the comprehensive weight includes a subjective weight and an objective weight.

[0013] Optionally, the calculation of the comprehensive weight of each indicator includes the steps of:

[0014] Calculating the evaluation index based on the geometric mean and normalization method to obtain the subjective weight;

[0015] The evaluation index is calculated based on the entropy weight method to obtain the objective weight.

[0016] A comprehensive benefit quantitative analysis device, applied to electronic equipment, for quantitatively analyzing the comprehensive benefits of a distribution network, comprising:

[0017] a model building module configured to build a distribution network operation model based on producer and consumer demand responses;

[0018] An indicator selection module is configured to select a plurality of evaluation indicators based on the distribution network operation model;

[0019] A weight calculation module is configured to calculate the comprehensive weight of each evaluation indicator;

[0020] A scoring processing module is configured to perform scoring processing on each of the evaluation indicators to obtain an indicator score for each of the evaluation indicators;

[0021] The analysis execution module is configured to calculate according to the comprehensive weight of each evaluation indicator and the comprehensive weight of each indicator score to obtain the comprehensive benefit of the distribution network.

[0022] Optionally, the distribution network operation model is configured with energy storage constraints, power purchase and sales constraints, and price constraints.

[0023] Optionally, the comprehensive weight includes a subjective weight and an objective weight.

[0024] Optionally, the weight calculation module includes:

[0025] A first calculation unit is configured to calculate the evaluation index based on a geometric mean and a normalization method to obtain the subjective weight;

[0026] The second calculation unit is configured to calculate the evaluation index based on the entropy weight method to obtain the objective weight.

[0027] An electronic device, optionally comprising at least one processor and a memory connected to the processor, wherein:

[0028] The memory is used to store computer programs or instructions;

[0029] The processor is used to execute the computer program or instruction so that the electronic device implements the quantitative analysis method of comprehensive benefits as described above.

[0030] A storage medium is applied to an electronic device, wherein the storage medium carries one or more computer programs. When the electronic device executes the one or more computer programs, the electronic device can implement the quantitative analysis method of comprehensive benefits as described above.

[0031] It can be seen from the above technical solutions that the present application discloses a method, device, electronic device and storage medium for quantitative analysis of comprehensive benefits. The method and device are applied to electronic devices for quantitative analysis of the comprehensive benefits of the distribution network, specifically to construct a distribution network operation model based on producer and consumer demand response; select multiple evaluation indicators based on the distribution network operation model; calculate the comprehensive weight of each evaluation indicator; score each evaluation indicator to obtain an indicator score for each evaluation indicator; calculate based on the comprehensive weight of each evaluation indicator and the comprehensive weight of each indicator score to obtain the comprehensive benefit of the distribution network. The above solution can be used to quantitatively analyze the comprehensive benefits between the distribution network and producers and consumers, thereby providing a data basis for improving the comprehensive energy efficiency and operation level of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0033] Figure 1 A flowchart of a method for quantitatively analyzing comprehensive benefits according to an embodiment of the present application;

[0034] Figure 2 A load diagram for a prosumer according to an embodiment of the present application;

[0035] Figure 3 This is a schematic diagram of a distribution network comprehensive benefit score simulation device according to an embodiment of the present application;

[0036] Figure 4 A block diagram of a device for quantitatively analyzing comprehensive benefits according to an embodiment of the present application;

[0037] Figure 5 This is a schematic structural diagram of an electronic device according to the present embodiment. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0039] Example 1

[0040] Figure 1 This is a flow chart of a method for quantitatively analyzing comprehensive benefits according to an embodiment of the present application.

[0041] like Figure 1 As shown, the quantitative analysis method provided in this embodiment is applied to electronic devices to quantitatively analyze the comprehensive benefits of the distribution network. The electronic devices here can be understood as computers or servers with data computing and information processing capabilities. The quantitative analysis method includes the following steps:

[0042] S1. Construct a distribution network operation model based on producer and consumer demand response.

[0043] A comprehensive analysis of the main objectives and main characteristics of my country's smart distribution network construction is conducted. Combined with the latest development trends of smart distribution network research and construction at home and abroad, a distribution network operation model based on producer and consumer demand response is developed, and a quantitative analysis of the electricity utility of producers and consumers in the distribution network is conducted.

[0044] Distribution network operators can trade with members within the distribution network and participate in the upper-level market. Their objective function is to minimize the overall economic cost of distribution network operation through bidirectional interaction and energy storage deployment. Based on this, a distribution network operation model can be constructed, which can be expressed as:

[0045]

[0046] Where: It represents the overall economic cost of the distribution network operation throughout the day; and are the electricity purchase price and electricity selling price of the distribution network from the power grid at time t; P t grid,b and P t grid,s are the amount of electricity bought / sold by the distribution network from the market at time t, which is used to compensate for the imbalance between supply and demand of shared electricity between producers and consumers; is the energy storage deployment cost of the distribution network at time t.

[0047] To ensure the consistency of the energy storage state at the beginning and end, the energy storage constraints configured in this model are as follows:

[0048] 0≤P t c ≤P cmax

[0049] 0≤P t d ≤P dmax

[0050] SOC t+1 =SOC t +P tc η c -P t d / η d

[0051] SOC min ≤SOC t ≤SOC max

[0052] SOC0=SOC 24

[0053]

[0054] Where: P t c and P t d are the charging and discharging power of energy storage at time t; P cmax and P dmax are the upper limit of charge and discharge power respectively; SOC t is the state of charge of the energy storage at time t; SOC min and SOC max are the upper and lower limits of the state of charge respectively; σ ESS It is the cost coefficient when the distribution network calls on energy storage.

[0055] To ensure the balance of power supply and demand at all times, the power purchase and sales constraints configured in this model are as follows:

[0056]

[0057] 0≤P t grid,s ≤μ g P grid,smax

[0058] 0≤P t grid,b ≤(1-μ g )P grid,bmax

[0059] μ g ∈{0,1}

[0060] Where: P t grid,smax P represents the maximum power of the distribution network sold to the main market at time t. t grid,bmax It represents the maximum power that the distribution network buys from the main market at time t. g =0, it means the distribution network buys electricity from the main market; when μ g =1, the distribution network sells electricity to the main market.

[0061] In order to ensure the benefits of producers and consumers, Should be higher than the price of selling directly to the mainnet To prevent The price is too high, while ensuring the benefit of the distribution network, Should be lower than the electricity purchase price from the grid during the current period Set the price constraint. The price constraint configured in this model is as follows:

[0062]

[0063] Consumer electricity utility:

[0064] Consumers' electricity consumption will increase their satisfaction, that is, consumers' electricity efficiency will increase as their electricity consumption increases, which can be expressed by the following piecewise benefit function:

[0065]

[0066] Where: P t load is the actual electricity consumption of prosumer i in period t; α is the preset parameter of prosumer i; w is the elasticity coefficient of prosumer, which represents the rate at which the benefit of prosumer increases with electricity consumption.

[0067] While prosumers gain benefits from electricity use, they also need to pay a certain cost. This cost consists of two parts: one is the economic cost paid by prosumers for purchasing electricity, and the other is the loss of comfort caused by prosumers adjusting their electricity use behavior according to electricity prices.

[0068] The prosumer will have the most appropriate electricity consumption in each period, which is the prosumer's baseline load. When the electricity consumption of prosumers deviates When the It represents the comfort loss function of prosumer i in period t, as shown in the following formula:

[0069]

[0070] in: and is the parameter of the prosumer comfort loss function; is the load adjustment of prosumer i in period t, which is equal to the difference between the actual load and the baseline load:

[0071]

[0072] in: is the baseline load of prosumer i in period t; is the actual load of prosumer i in period t.

[0073] In summary, the benefits generated by electricity consumption by prosumer i in period t are as follows:

[0074]

[0075] Where: Y i t is the amount of electricity purchased by prosumer i during period t; is the amount of electricity sold by prosumer i in period t; is the unit electricity price purchased by the prosumer during period t; is the unit electricity price sold by the prosumer during period t.

[0076] S2. Select multiple evaluation indicators based on the distribution network operation model.

[0077] The comprehensive benefit evaluation of distribution networks is related to factors such as network economics, operational stability, and user comfort. Distribution network economics refers to the economic principles that operators should follow when dispatching energy storage and determining the energy sharing price between producers and consumers. These indicators include distribution network operator revenue, user electricity efficiency, and the total cost of distribution network operation. To this end, multiple evaluation indicators can be selected for the model, such as operational stability and user comfort.

[0078] The operational stability indicator refers to the operator's implementation of energy storage scheduling based on stability principles, setting energy sharing prices for inter-consumer peak-to-valley load shifting. This includes basic indicators such as the peak-to-valley difference in distribution network load, distribution network load variation, and distribution network peak load reduction rate. The user comfort indicator refers to ensuring that consumers have optimal electricity consumption in each time period. Comfort indicates the contribution of the distribution network scheduling process to consumer comfort, including consumer comfort indicators.

[0079] The Analytic Hierarchy Process (AHP) is a commonly used decision-making method that combines qualitative and quantitative analysis. It primarily uses the decision-maker's experience to quantitatively evaluate indicators. The comprehensive evaluation indicator system for distribution networks is divided into three levels, forming a hierarchical model. The top level is the target level, the second level is the criteria level, and the third level is the indicator level. Within each level of the structural model, indicators are compared pairwise using the importance scale method, and a judgment matrix is ​​formed based on the relative importance of the indicators, as shown below:

[0080] A=[a ij ] m×m

[0081] Where: m is the number of evaluation indicators; a ij Indicates the relative importance of the two evaluation indicators.

[0082] Since the judgment matrix is ​​affected by the subjective judgment of the decision maker and has errors with objective facts, a consistency test must be performed. The consistency ratio CR is defined as:

[0083] CR=CI / RI

[0084] Where: CI is the consistency index; CI = (λ max -m) / (m-1);λ max is the maximum eigenvalue of the judgment matrix A; RI is defined as the mean random consistency index, which changes with the change of the evaluation index m.

[0085] When the consistency ratio CR<0.1, the consistency of the breaking matrix A is considered acceptable; otherwise, the judgment matrix needs to be modified accordingly to meet the consistency requirements.

[0086] S3. Calculate the comprehensive weight of each evaluation indicator.

[0087] That is, the comprehensive weight of the above-mentioned operation stability index and electricity comfort index is calculated. The comprehensive weight here includes subjective weight and objective weight.

[0088] First, the subjective weight of each evaluation indicator can be calculated based on the geometric mean method and normalization method:

[0089]

[0090] Then, the entropy weight method is applied to calculate the objective weights of the above evaluation indicators.

[0091]

[0092] k=1 / ln m

[0093]

[0094] Where: p ij It is the ratio of the score of the i-th evaluation object on the j-th indicator to the scores of all the objects to be evaluated on the j-th indicator.

[0095] The entropy weight calculation formula for the jth evaluation index is:

[0096]

[0097] Where: 1-e j is the degree of dispersion of the j-th evaluation indicator.

[0098] The linear weighting method is used to calculate the composite weight. Considering the subjectivity of the hierarchical analysis method and the objectivity of the entropy weight method, appropriate coefficients are selected to determine the corresponding linear combination, namely:

[0099] w i=αw' i +(1-α)w” j

[0100] Where: α is the weight of the hierarchical analysis method in the comprehensive coefficient.

[0101] S4. Score each evaluation indicator.

[0102] By scoring each evaluation indicator, we can get the indicator score of each evaluation indicator. The indicator score of each evaluation indicator at each level reflects the status of the evaluation indicator, and the connection weight between the upper and lower indicators indicates the degree of influence of the lower-level evaluation indicator on the upper-level evaluation indicator. Therefore, we can get the calculation formula for the comprehensive benefit score of the distribution network:

[0103]

[0104] Where: r i Represents the comprehensive benefit score of the distribution network.

[0105] According to the hierarchical structure of distribution network dispatching management evaluation indicators, the steps for calculating the upper indicators from bottom to top are as follows:

[0106] (1) Starting from the basic indicator layer, calculate the evaluation results of each basic indicator;

[0107] (2) Calculate the weights between the evaluation layer indicators and the corresponding basic indicators;

[0108] (3) Calculate the evaluation results of the evaluation layer indicators.

[0109] S5. Based on the comprehensive weight, the comprehensive benefits of the distribution network are obtained.

[0110] The comprehensive energy efficiency score of the distribution network under various working modes is obtained through the comprehensive weight of each evaluation index and the comprehensive weight of each index score.

[0111] As can be seen from the above technical solution, this embodiment provides a method for quantitative analysis of comprehensive benefits. This method is applied to electronic equipment and is used to quantitatively analyze the comprehensive benefits of the distribution network. Specifically, it includes constructing a distribution network operation model based on producer and consumer demand response; selecting multiple evaluation indicators based on the distribution network operation model; calculating the comprehensive weight of each evaluation indicator; scoring each evaluation indicator to obtain an indicator score for each evaluation indicator; and calculating based on the comprehensive weight of each evaluation indicator and the comprehensive weight of each indicator score to obtain the comprehensive benefits of the distribution network. The above solution can be used to quantitatively analyze the comprehensive benefits between the distribution network and producers and consumers, thereby providing a data basis for improving the comprehensive energy efficiency and operation level of the power system.

[0112] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0113] Although the operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0114] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0115] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0116] In a specific embodiment of the present application, a specific example of quantitative analysis of comprehensive benefits is disclosed, and the specific content is as follows:

[0117] A distribution network consisting of 6 power generators and 1 energy storage system was established as an example for analysis. The operation structure is as follows: Figure 2As shown in Figure 2. At different times, due to differences in electricity consumption behavior, load levels, energy storage space, and new energy output exhibit asynchronous characteristics, and resource complementarity is achieved through energy sharing. Among them, consumer load data is the actual data of an industrial park in a certain province in China, as shown in Figure 2. Figure 2 As shown in the figure, generators 1-3 are photovoltaic generators with the same power generation capacity, and generators 4-6 are wind turbines with the same power generation capacity. The upper limit of the virtual power plant's electricity sharing price is the grid purchase price, and the lower limit is the grid price for distributed energy. The energy storage cost coefficient is set to 0.05 yuan / (kW·h), the upper and lower states of charge (SOC) are 0.95 and 0.1, respectively, the energy storage system charge and discharge efficiency is 95%, and the maximum charge and discharge power is 3MW.

[0118] The following comparison gives the comprehensive benefits of the distribution network under four different dispatching modes.

[0119] Mode 1: This setting mode minimizes the operating cost of the distribution network;

[0120] Mode 2: This model takes into account the consumer's demand response and aims to minimize the distribution network operation cost;

[0121] Mode 3: This model takes into account the sharing of electricity among users and aims to minimize the operating cost of the distribution network;

[0122] Mode 4: This model takes into account consumer demand response and electricity sharing, with the goal of minimizing the operating cost of the distribution network.

[0123] According to the established hierarchical indicator system, the comprehensive weight of each indicator is calculated, and the weight of each dimension is shown in Table 1. In order to intuitively compare the scores of each mode, the indicator scores in Table 2 are the percentages of the maximum value of each mode to the full base.

[0124] Table 1

[0125]

[0126] Table 2

[0127]

[0128] Table 2 shows that Model 4 has the highest comprehensive benefit score. Furthermore, Table 1 shows that economic indicators have the highest weight among the evaluation indicators, followed by stability indicators. Table 2 shows that Model 1 has a significantly lower overall score than the other models, but it has the highest comfort score. Therefore, the rationality and consistency of these results verify the effectiveness of the method.

[0129] Figure 3This is a schematic diagram of a comprehensive benefit evaluation model device based on the interaction between a distribution network and prosumers, provided in an embodiment of the present invention. This embodiment is applicable to simulating a comprehensive benefit evaluation model based on the interaction between a distribution network and prosumers. The device can be implemented using software and / or hardware and can be configured in a terminal device. The comprehensive benefit evaluation model device based on the interaction between a distribution network and prosumers includes a distribution network operation mode input module and a distribution network comprehensive benefit score output module.

[0130] Example 2

[0131] Figure 4 This is a block diagram of a comprehensive benefit quantitative analysis device according to an embodiment of the present application.

[0132] like Figure 4 As shown, the quantitative analysis device provided in this embodiment is applied to electronic equipment to quantitatively analyze the comprehensive benefits of a distribution network. The electronic equipment here can be understood as a computer or server with data computing and information processing capabilities. The quantitative analysis device includes a model construction module 10, an indicator selection module 20, a weight calculation module 30, a scoring processing module 40, and an analysis execution module 50.

[0133] The model building module is used to build a distribution network operation model based on producer and consumer demand response.

[0134] A comprehensive analysis of the main objectives and main characteristics of my country's smart distribution network construction is conducted. Combined with the latest development trends of smart distribution network research and construction at home and abroad, a distribution network operation model based on producer and consumer demand response is developed, and a quantitative analysis of the electricity utility of producers and consumers in the distribution network is conducted.

[0135] Distribution network operators can trade with members within the distribution network and participate in the upper-level market. Their objective function is to minimize the overall economic cost of distribution network operation through bidirectional interaction and energy storage deployment. Based on this, a distribution network operation model can be constructed, which can be expressed as:

[0136]

[0137] Where: It represents the overall economic cost of the distribution network operation throughout the day; and are the electricity purchase price and electricity selling price of the distribution network from the power grid at time t; P t grid,b and P t grid,s are the amount of electricity bought / sold by the distribution network from the market at time t, which is used to compensate for the imbalance between supply and demand of shared electricity between producers and consumers; is the energy storage deployment cost of the distribution network at time t.

[0138] To ensure the consistency of the energy storage state at the beginning and end, the energy storage constraints configured in this model are as follows:

[0139] 0≤P t c ≤P cmax

[0140] 0≤P t d ≤P dmax

[0141] SOC t+1 =SOC t +P t c η c -P t d / η d

[0142] SOC min ≤SOC t ≤SOC max

[0143] SOC0=SOC 24

[0144]

[0145] Where: P t c and P t d are the charging and discharging power of energy storage at time t; P cmax and P dmax are the upper limit of charge and discharge power respectively; SOC t is the state of charge of the energy storage at time t; SOC min and SOC max are the upper and lower limits of the state of charge respectively; σ ESS It is the cost coefficient when the distribution network calls on energy storage.

[0146] To ensure the balance of power supply and demand at all times, the power purchase and sales constraints configured in this model are as follows:

[0147]

[0148] 0≤P t grid,s ≤μ g P grid,smax

[0149] 0≤P t grid,b ≤(1-μ g )P grid,bmax

[0150] μ g ∈{0,1}

[0151] Where: P t grid,smax P represents the maximum power of the distribution network sold to the main market at time t. t grid,bmax It represents the maximum power that the distribution network buys from the main market at time t. g =0, it means the distribution network buys electricity from the main market; when μ g =1, the distribution network sells electricity to the main market.

[0152] In order to ensure the benefits of producers and consumers, Should be higher than the price of selling directly to the mainnet To prevent The price is too high, while ensuring the benefit of the distribution network, Should be lower than the electricity purchase price from the grid during the current period Set the price constraint. The price constraint configured in this model is as follows:

[0153]

[0154] Consumer electricity utility:

[0155] Consumers' electricity consumption will increase their satisfaction, that is, consumers' electricity efficiency will increase as their electricity consumption increases, which can be expressed by the following piecewise benefit function:

[0156]

[0157] Where: P t load is the actual electricity consumption of prosumer i in period t; α is the preset parameter of prosumer i; w is the elasticity coefficient of prosumer, which represents the rate at which the benefit of prosumer increases with electricity consumption.

[0158] While prosumers gain benefits from electricity use, they also need to pay a certain cost. This cost consists of two parts: one is the economic cost paid by prosumers for purchasing electricity, and the other is the loss of comfort caused by prosumers adjusting their electricity use behavior according to electricity prices.

[0159] The prosumer will have the most appropriate electricity consumption in each period, which is the prosumer's baseline load. When the electricity consumption of prosumers deviates When the It represents the comfort loss function of prosumer i in period t, as shown in the following formula:

[0160]

[0161] in: and is the parameter of the prosumer comfort loss function; is the load adjustment of prosumer i in period t, which is equal to the difference between the actual load and the baseline load:

[0162]

[0163] in: is the baseline load of prosumer i in period t; is the actual load of prosumer i in period t.

[0164] In summary, the benefits generated by electricity consumption by prosumer i in period t are as follows:

[0165]

[0166] Where: Y i t is the amount of electricity purchased by prosumer i during period t; is the amount of electricity sold by prosumer i in period t; is the unit electricity price purchased by the prosumer during period t; is the unit electricity price sold by the prosumer during period t.

[0167] The indicator selection module is used to select multiple evaluation indicators based on the distribution network operation model.

[0168] The comprehensive benefit evaluation of distribution networks is related to factors such as network economics, operational stability, and user comfort. Distribution network economics refers to the economic principles that operators should follow when dispatching energy storage and determining the energy sharing price between producers and consumers. These indicators include distribution network operator revenue, user electricity efficiency, and the total cost of distribution network operation. To this end, multiple evaluation indicators can be selected for the model, such as operational stability and user comfort.

[0169] The operational stability indicator refers to the operator's implementation of energy storage scheduling based on stability principles, setting energy sharing prices for inter-consumer peak-to-valley load shifting. This includes basic indicators such as the peak-to-valley difference in distribution network load, distribution network load variation, and distribution network peak load reduction rate. The user comfort indicator refers to ensuring that consumers have optimal electricity consumption in each time period. Comfort indicates the contribution of the distribution network scheduling process to consumer comfort, including consumer comfort indicators.

[0170] The Analytic Hierarchy Process (AHP) is a commonly used decision-making method that combines qualitative and quantitative analysis. It primarily uses the decision-maker's experience to quantitatively evaluate indicators. The comprehensive evaluation indicator system for distribution networks is divided into three levels, forming a hierarchical model. The top level is the target level, the second level is the criteria level, and the third level is the indicator level. Within each level of the structural model, indicators are compared pairwise using the importance scale method, and a judgment matrix is ​​formed based on the relative importance of the indicators, as shown below:

[0171] A=[a ij ] m×m

[0172] Where: m is the number of evaluation indicators; a ij Indicates the relative importance of the two evaluation indicators.

[0173] Since the judgment matrix is ​​affected by the subjective judgment of the decision maker and has errors with objective facts, a consistency test must be performed. The consistency ratio CR is defined as:

[0174] CR=CI / RI

[0175] Where: CI is the consistency index; CI = (λ max -m) / (m-1);λ max is the maximum eigenvalue of the judgment matrix A; RI is defined as the mean random consistency index, which changes with the change of the evaluation index m.

[0176] When the consistency ratio CR<0.1, the consistency of the breaking matrix A is considered acceptable; otherwise, the judgment matrix needs to be modified accordingly to meet the consistency requirements.

[0177] The weight calculation module is used to calculate the comprehensive weight of each evaluation indicator.

[0178] That is, the comprehensive weight of the above-mentioned operation stability index and power comfort index is calculated, where the comprehensive weight includes subjective weight and objective weight. This module includes a first calculation unit and a second calculation unit.

[0179] The first calculation unit is used to calculate the subjective weight of each evaluation indicator based on the geometric mean method and normalization method:

[0180]

[0181] The second calculation unit is used to calculate the objective weight of the above evaluation index by applying the entropy weight method.

[0182]

[0183] k=1 / ln m

[0184]

[0185] Where: p ij It is the ratio of the score of the i-th evaluation object on the j-th indicator to the scores of all the objects to be evaluated on the j-th indicator.

[0186] The entropy weight calculation formula for the jth evaluation index is:

[0187]

[0188] Where: 1-e j is the degree of dispersion of the j-th evaluation indicator.

[0189] The linear weighting method is used to calculate the composite weight. Considering the subjectivity of the hierarchical analysis method and the objectivity of the entropy weight method, appropriate coefficients are selected to determine the corresponding linear combination, namely:

[0190] w i =αw' i +(1-α)w” j

[0191] Where: α is the weight of the hierarchical analysis method in the comprehensive coefficient.

[0192] The scoring processing module is used to score each evaluation indicator.

[0193] By scoring each evaluation indicator, we can get the indicator score of each evaluation indicator. The indicator score of each evaluation indicator at each level reflects the status of the evaluation indicator, and the connection weight between the upper and lower indicators indicates the degree of influence of the lower-level evaluation indicator on the upper-level evaluation indicator. Therefore, we can get the calculation formula for the comprehensive benefit score of the distribution network:

[0194]

[0195] Where: r i Represents the comprehensive benefit score of the distribution network.

[0196] According to the hierarchical structure of distribution network dispatching management evaluation indicators, the steps for calculating the upper indicators from bottom to top are as follows:

[0197] (1) Starting from the basic indicator layer, calculate the evaluation results of each basic indicator;

[0198] (2) Calculate the weights between the evaluation layer indicators and the corresponding basic indicators;

[0199] (3) Calculate the evaluation results of the evaluation layer indicators.

[0200] The analysis execution module is used to perform calculations based on comprehensive weights to obtain the comprehensive benefits of the distribution network.

[0201] The comprehensive energy efficiency score of the distribution network under various working modes is obtained through the comprehensive weight of each evaluation index and the comprehensive weight of each index score.

[0202] As can be seen from the above technical solution, this embodiment provides a comprehensive benefit quantitative analysis device, which is applied to electronic equipment and is used to quantitatively analyze the comprehensive benefits of the distribution network. Specifically, it constructs a distribution network operation model based on the response of producer and consumer demand; selects multiple evaluation indicators based on the distribution network operation model; calculates the comprehensive weight of each evaluation indicator; scores each evaluation indicator to obtain an indicator score for each evaluation indicator; and calculates the comprehensive benefit of the distribution network based on the comprehensive weight of each evaluation indicator and the comprehensive weight of each indicator score. The above solution can be used to quantitatively analyze the comprehensive benefits between the distribution network and producers and consumers, thereby providing a data basis for improving the comprehensive energy efficiency and operation level of the power system.

[0203] The units involved in the embodiments described in this disclosure may be implemented in software or hardware. In some cases, the name of a unit does not limit the unit itself. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses."

[0204] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0205] Example 3

[0206] Figure 5 This is a block diagram of an electronic device according to an embodiment of the present application.

[0207] like Figure 5 As shown, the electronic device is used to implement the quantitative analysis method of comprehensive benefits of embodiment 1, and can specifically provide services based on the implementation of a comprehensive benefit evaluation model for the interaction between the distribution network and the prosumers.

[0208] The electronic device 12 is implemented as a general-purpose computing device. Components of the electronic device 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0209] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0210] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0211] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be configured to read and write non-removable, non-volatile magnetic media. Each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0212] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 42 generally implement the functions and / or methods of the embodiments described herein.

[0213] The electronic device 12 may also communicate with one or more external devices 14 (e.g., a keyboard, a pointing device, a display 24, etc.), one or more devices that enable a user to interact with the electronic device 12, and / or any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed through an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20. Figure 4As shown, the network adapter 20 communicates with the other modules of the electronic device 12 via the bus 18. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0214] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the comprehensive benefit evaluation model based on the interaction between the distribution network and the prosumers provided in the embodiment of the present invention.

[0215] Example 4

[0216] This embodiment provides a computer-readable storage medium

[0217] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device can implement the quantitative analysis method of comprehensive benefits in the first embodiment.

[0218] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0219] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0220] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0221] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0222] The technical solution provided by the present invention is introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A comprehensive benefit quantitative analysis method, applied to electronic equipment, for quantitatively analyzing the comprehensive benefits of a distribution network, characterized in that: The quantitative analysis method comprises the steps of: Construct a distribution network operation model based on producer and consumer demand response; The distribution network operation model is expressed as: in, represents the overall economic cost of the distribution network throughout the day; t grid,b and λ t grid,s are the electricity purchase price and electricity selling price of the distribution network from the power grid at time t; P t grid,b and P t grid,s are the amount of electricity bought / sold by the distribution network from the market at time t, which is used to compensate for the imbalance between supply and demand of shared electricity between producers and consumers; is the energy storage deployment cost of the distribution network at time t; The distribution network operation model is configured with energy storage constraints, power purchase and sales constraints, and price constraints; The energy storage constraint is set based on the upper limit of the charge and discharge power, the state of charge range and the charge and discharge efficiency of the energy storage; The energy storage constraint is expressed as: 0≤P t c ≤P cmax 0≤P t d ≤P dmax SOCIETY t+1 =SOC t +P t c the c -P t d / η d SOC min ≤SOC t ≤SOC max SOC0=SOC 24 Among them, P t c and P t d are the charging and discharging power of energy storage at time t; P cmax and P dmax They are respectively the upper limit of charge and discharge power; SOC t is the state of charge of the energy storage at time t; SOC min and SOC max are the upper and lower limits of the state of charge respectively; σ ESS is the cost coefficient when the distribution network calls on energy storage; The power purchase and sale constraints are expressed as: 0≤P t grid,s ≤μ g P grid,smax 0≤P t grid,b ≤(1-μ g )P grid,bmax m g ∈{0,1}; Among them, P t grid,smax P represents the maximum power of the distribution network sold to the main market at time t. t grid,bmax It represents the maximum power that the distribution network buys from the main market at time t; when μ g =0, it means the distribution network buys electricity from the main market; when μ g =1, the distribution network sells electricity to the main market; The price constraint is expressed as: Selecting a plurality of evaluation indicators based on the distribution network operation model; the evaluation indicators include economic indicators, operation stability indicators and user comfort indicators; Calculating the comprehensive weight of each evaluation indicator; the comprehensive weight includes a subjective weight and an objective weight; wherein the calculating the comprehensive weight of each evaluation indicator includes: calculating the evaluation indicator based on the geometric mean and normalization method to obtain the subjective weight; calculating the evaluation indicator based on the entropy weight method to obtain the objective weight; and using a linear weighting method to calculate a synthetic weight of the subjective weight and the objective weight to obtain the comprehensive weight; Scoring each evaluation indicator to obtain an indicator score for each evaluation indicator; The comprehensive benefit of the distribution network is obtained by calculating based on the comprehensive weight of each evaluation indicator and the comprehensive weight of each indicator score.

2. A comprehensive benefit quantitative analysis device, applied to electronic equipment, for quantitatively analyzing the comprehensive benefits of a distribution network, characterized in that: The quantitative analysis device comprises: a model building module configured to build a distribution network operation model based on producer and consumer demand responses; The distribution network operation model is expressed as: in, represents the overall economic cost of the distribution network throughout the day; t grid,b and λ t grid,s are the electricity purchase price and electricity selling price of the distribution network from the power grid at time t; P t grid,b and P t grid,s are the amount of electricity bought / sold by the distribution network from the market at time t, which is used to compensate for the imbalance between supply and demand of shared electricity between producers and consumers; is the energy storage deployment cost of the distribution network at time t; The distribution network operation model is configured with energy storage constraints, power purchase and sales constraints, and price constraints; The energy storage constraint is set based on the upper limit of the charge and discharge power, the state of charge range and the charge and discharge efficiency of the energy storage; The energy storage constraint is expressed as: 0≤P t c ≤P cmax 0≤P t d ≤P dmax SOCIETY t+1 =SOC t +P t c the c -P t d / η d SOC min ≤SOC t ≤SOC max SOC0=SOC 24 Among them, P t c and P t d are the charging and discharging power of energy storage at time t; P cmax and P dmax They are respectively the upper limit of charge and discharge power; SOC t is the state of charge of the energy storage at time t; SOC min and SOC max are the upper and lower limits of the state of charge respectively; σ ESS is the cost coefficient when the distribution network calls on energy storage; The power purchase and sale constraints are expressed as: 0≤P t grid,s ≤μ g P grid,smax 0≤P t grid,b ≤(1-μ g )P grid,bmax m g ∈{0,1}; Among them, P t grid,smax P represents the maximum power of the distribution network sold to the main market at time t. t grid,bmax It represents the maximum power that the distribution network buys from the main market at time t; when μ g =0, it means the distribution network buys electricity from the main market; when μ g =1, the distribution network sells electricity to the main market; The price constraint is expressed as: An indicator selection module is configured to select a plurality of evaluation indicators based on the distribution network operation model; the evaluation indicators include an economic indicator, an operation stability indicator, and a user comfort indicator; A weight calculation module is configured to calculate the comprehensive weight of each of the evaluation indicators; the comprehensive weight includes a subjective weight and an objective weight; the weight calculation module includes: a first calculation unit, configured to calculate the evaluation indicators based on the geometric mean and normalization method to obtain the subjective weight; a second calculation unit, configured to calculate the evaluation indicators based on the entropy weight method to obtain the objective weight; a linear weighted method is used to calculate the combined weight of the subjective weight and the objective weight to obtain the comprehensive weight; A scoring processing module is configured to perform scoring processing on each of the evaluation indicators to obtain an indicator score for each of the evaluation indicators; The analysis execution module is configured to calculate according to the comprehensive weight of each evaluation indicator and the comprehensive weight of each indicator score to obtain the comprehensive benefit of the distribution network.

3. An electronic device, characterized in that: comprising at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is configured to execute the computer program or instruction so as to enable the electronic device to implement the comprehensive benefit quantitative analysis method according to claim 1 .

4. A storage medium, applied to an electronic device, characterized in that: The storage medium carries one or more computer programs. When the electronic device executes the one or more computer programs, the electronic device can implement the comprehensive benefit quantitative analysis method as claimed in claim 1.

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