Typical resource multi-scale cost-benefit analysis method and system

Abstract

The invention discloses a typical resource multi-scale cost-benefit analysis method and system, and relates to the technical field of resource analysis, and the method comprises the following steps: firstly analyzing the correlation characteristics of typical resources, and obtaining basic data; based on the basic data, performing energy complementarity analysis, and making a demand response strategy; and constructing the multi-energy complementary system according to the demand response strategy. The invention provides a comprehensive and systematic way for evaluating typical resources, and can deeply research the internal correlation characteristics of various resources and the influence of the internal correlation characteristics on the cost and benefits, thereby realizing the optimal configuration and use of the resources. Through the analysis method, the energy utilization efficiency can be improved, the energy cost can be reduced, enterprises and policy formulators can be helped to formulate energy policies and management strategies more scientifically, and remarkable social benefits and economic benefits are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of resource analysis, and in particular to a typical resource multi-scale cost-benefit analysis method and system. Background Art

[0002] For different types of resources (such as photovoltaic power generation, wind power generation, etc.), their characteristics may vary greatly, which increases the complexity of the analysis. Cost-benefit analysis methods usually require predictions of future situations, but this is full of uncertainties and may lead to inaccurate analysis results. Cost-benefit analysis is usually based on existing resource quotas and policies, which may limit its application in improving resource quotas and proposing new policies. Dynamic market conditions may require rapid adjustments to cost-benefit analysis methods, but existing methods may not meet this need. Summary of the invention

[0003] In view of the problems existing in the existing typical resource multi-scale cost-benefit analysis and system, the present invention is proposed.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0005] In a first aspect, an embodiment of the present invention provides a typical resource multi-scale cost-benefit analysis method, which comprises the following steps:

[0006] First, analyze the correlation characteristics of typical resources to obtain basic data;

[0007] Based on basic data, conduct energy complementarity analysis and formulate demand response strategies;

[0008] Construct a multi-energy complementary system based on demand response strategy.

[0009] As a preferred solution of the typical resource multi-scale cost-benefit analysis method of the present invention, the analysis of the correlation characteristics of typical resources includes dependency analysis, correlation analysis and driving analysis;

[0010] The dependency analysis step includes: converting the value of the random variable into a rank according to the cumulative probability distribution of the random variable, calculating the rank correlation coefficient between the converted ranks, and judging the correlation between the random variables according to the value of the rank correlation coefficient, wherein the value range of the rank correlation coefficient is between -1 and 1;

[0011] The correlation analysis step includes: collecting average wind speed, average radiation and average runoff data, plotting the collected data in the same coordinate system, analyzing the height averages of the three groups of curves, and evaluating the complementarity of wind, solar and water resources based on seasonal changes;

[0012] The driving analysis step includes: determining the objective function, taking the minimum total electricity bill paid by users after participating in the incentive response as the goal, calculating the subsidies obtained by users participating in the demand-side response through government information, setting the demand response baseline as the criterion for judging effective demand response, and improving the user-side energy storage response electricity price policy and the economic benefits of participating in ancillary services.

[0013] As a preferred solution of the typical resource multi-scale cost-benefit analysis method of the present invention, wherein: the demand response setting constraint conditions specifically include:

[0014] To ensure that the peak load during the response period is not higher than the baseline peak load, the constraint formula is:

[0015]

[0016] In the formula, and are the original user load power and energy storage power in the baseline, and They are the original user load power and energy storage power during the incentive response period;

[0017] The difference between the average load during the response period and the average load of the baseline, that is, the average power of the response reduction, shall not be less than 0.8 times the agreed response power reported by the user. The formula is:

[0018]

[0019] Demand response power P DR It should be no less than 0.05 times and no more than 0.2 times of the user's peak load in the previous year. The formula is:

[0020] 0.05·P year,max ≤P DR ≤0.2·P year,max ;

[0021] Where P year，max It is expressed as the user's peak load in the previous year.

[0022] As a preferred solution of the typical resource multi-scale cost-effectiveness analysis method described in the present invention, wherein: the demand response setting constraint condition also includes energy storage operation constraints, wherein the energy storage operation constraints include energy storage charge state constraints, charge and discharge state constraints, charge and discharge power constraints, charge state continuity constraints and energy storage battery performance constraints;

[0023] Energy storage operation constraints include state of charge constraints, charge and discharge power constraints, and energy storage battery performance constraints;

[0024] The energy storage state of charge constraint is expressed as:

[0025] SOC t =SOC t-1 +η c ·P c,t-1 ·Δt / E rate -η d ·P d,t-1 ·Δt / E rate ,t∈T;

[0026] In the formula, SOC t It is expressed as the energy storage charge state at the tth moment, E rate Expressed as the rated capacity of energy storage, η c and η d Expressed as the energy storage rated charging and discharging efficiency;

[0027] The upper and lower limit constraints of energy storage SOC are expressed as:

[0028] SOC min ≤SOC t ≤SOC max ,t∈T;

[0029] In the formula, SOC max and SOC min They are respectively represented as the upper limit of SOC and the lower limit of SOC;

[0030] The energy storage charging and discharging power constraint can only be in one state of charging or discharging at the same time, and the formula is expressed as:

[0031]

[0032] In the formula, sw d,t and sw c,t A variable of 0 or 1, representing the discharge state and charge state of the energy storage at the tth moment, sw d,t When it is 1, it means the energy storage is in the discharge state. c,t 1 means the energy storage is in charging state, p rate Expressed as the rated power of the energy storage device;

[0033] The energy storage battery performance constraint limits the number of equivalent charge and discharge times of the energy storage to reduce the loss of the battery due to frequent charge and discharge. The formula is expressed as:

[0034]

[0035] In the formula, m represents the equivalent number of charge and discharge times of energy storage;

[0036] The user demand response model with energy storage based on electricity price policy is:

[0037]

[0038] The charging power and discharging power of energy storage are optimized to obtain the reported demand and minimize the sum of the user's electricity charge and the demand charge. The established model is a mixed integer linear programming.

[0039] The user demand response model with energy storage considering incentive response is expressed as:

[0040]

[0041] As a preferred solution of the typical resource multi-scale cost-benefit analysis method of the present invention, the complementary system consists of three parts: wind power, photovoltaic power and hydropower. The maximum target calculation formula for the total power generation of the complementary system is expressed as:

[0042]

[0043] Where E(N) represents the total power generation of the complementary system, N = [N1, N2, ..., NT] represents the output process of the complementary system, and N w,i,t It is expressed as the output of the i-th wind farm in the t-th period, N s,i,t It is expressed as the output of the jth photoelectric field in the tth period, N h,k,t It is represented by the output of the kth hydropower station in the tth period, M w 、M s and M h are the number of wind farms, photovoltaic farms and hydropower stations in the complementary system, T and Δt are the total number of time periods and the number of hours in a single time period during the dispatch period, respectively;

[0044] In the process of complementary system, the minimum coefficient of variation is the target quantitative output stability. The smaller the output variation coefficient, the higher its stability. The calculation formula is expressed as:

[0045]

[0046] Where CV(V) and std(N) are the coefficient of variation and standard deviation of the total output process of the complementary system, respectively, and N is the average value of the total output process of the complementary system.

[0047] As a preferred solution of the typical resource multi-scale cost-benefit analysis method of the present invention, the predicted value of the maximum power point P in each time period is used as the maximum output prediction, and the unit output can be continuously adjusted from 0 to gs* through power electronic devices. The upper and lower limits of the PV unit power and the cumulative power generation are:

[0048]

[0049] Where e and er are the upper bounds of the cumulative power generation of PV unit k at time 1 and time T, respectively, and gs represents the maximum output prediction value.

[0050] As a preferred solution of the typical resource multi-scale cost-benefit analysis method described in the present invention, the control characteristics of the WT unit connected to the virtual power plant can adjust the unit output from 0 to continuous, and the upper and lower limits of the power and cumulative power generation of the WT unit are:

[0051]

[0052] The output volatility of WT units is used to measure the hourly average output change rate of wind power, and the formula is expressed as:

[0053]

[0054] In the formula, Expressed as the wind power hourly output change rate, ΔP i,W,k Expressed as the hourly wind power output fluctuation, Indicates the rated installed capacity of wind power.

[0055] In a second aspect, an embodiment of the present invention provides a typical resource multi-scale cost-benefit analysis system, which includes an association module, an output module, and a construction module;

[0056] The association module is used to analyze the association characteristics of typical resources;

[0057] The output module is used to analyze energy complementarity;

[0058] The building blocks are used to construct a multi-energy complementary system.

[0059] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned typical resource multi-scale cost-benefit analysis method.

[0060] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned typical resource multi-scale cost-benefit analysis method is implemented.

[0061] The beneficial effects of the present invention are: providing a comprehensive and systematic way to evaluate typical resources, and being able to deeply study the inherent correlation characteristics of various resources and their impact on costs and benefits, so as to achieve optimal allocation and use of resources. Through this analysis method, it can not only improve energy utilization efficiency and reduce energy costs, but also help enterprises and policy makers to formulate energy policies and management strategies more scientifically, with significant social and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] 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 work. Among them:

[0063] Figure 1 Photovoltaic unit power diagram for the multi-scale cost-effectiveness analysis method for typical resources.

[0064] Figure 2 The photovoltaic daily output curve of the multi-scale cost-benefit analysis method for typical resources.

[0065] Figure 3 Schematic diagram of wind power generation characteristics for typical resource multi-scale cost-benefit analysis methods.

[0066] Figure 4 Schematic diagram of air conditioning load for typical resource multi-scale cost-benefit analysis method.

[0067] Figure 5 Schematic diagram of air conditioning on for a typical resource multi-scale cost-benefit analysis method.

[0068] Figure 6 Schematic diagram of air conditioning shutdown for a typical resource multi-scale cost-benefit analysis method.

[0069] Figure 7 EV charging power diagram for typical resource multi-scale cost-effectiveness analysis method. DETAILED DESCRIPTION

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] Example 1

[0077] Reference Figure 1 to Figure 7 , which is the first embodiment of the present invention, and provides a typical resource multi-scale cost-benefit analysis method, comprising the following steps:

[0078] S1. First, analyze the correlation characteristics of typical resources to obtain basic data.

[0079] 2. The analysis of the correlation characteristics of typical resources includes dependency analysis, correlation analysis and driving analysis;

[0080] The dependency analysis step includes: converting the value of the random variable into a rank according to the cumulative probability distribution of the random variable, calculating the rank correlation coefficient between the converted ranks, and judging the correlation between the random variables according to the value of the rank correlation coefficient, wherein the value range of the rank correlation coefficient is between -1 and 1;

[0081] The correlation analysis step includes: collecting average wind speed, average radiation and average runoff data, plotting the collected data in the same coordinate system, wherein the radiation and runoff share the left vertical coordinate system, and the wind speed uses the right coordinate system, analyzing the height averages of the three groups of curves, and evaluating the complementarity of wind, solar and water resources according to seasonal changes;

[0082] The driving analysis step includes: determining the objective function, taking the minimum total electricity bill paid by users after participating in the incentive response as the goal, calculating the subsidies obtained by users participating in the demand-side response through government information, setting the demand response baseline as the criterion for judging effective demand response, and improving the user-side energy storage response electricity price policy and the economic benefits of participating in ancillary services.

[0083] The goal is to minimize the total electricity charges paid by large industrial users after participating in the incentive response, as shown in the formula:

[0084] minF = C1 + C2 - C3;

[0085] Among them, 3C is the subsidy obtained by users participating in demand-side response, which is calculated according to the details of Jiangsu Province's implementation of incentive-based demand-side response in 2018, as shown in the formula:

[0086] C3=α·P DR

[0087] α=s·v;

[0088] Among them, α is the compensation per unit demand response power, DRP is the agreed response power reported to the power grid, s is the compensation electricity price standard corresponding to the participation in regulation time, and v is the response speed coefficient, which indicates the time from when the user receives the response invitation to when he or she formally participates in the response. The shorter the time, the larger v is.

[0089] 11. Demand response setting constraints, including:

[0090] To ensure that the peak load during the response period is not higher than the baseline peak load, the constraint formula is:

[0091]

[0092] In the formula, and are the original user load power and energy storage power in the baseline, P L,tDR and They are the original user load power and energy storage power during the incentive response period;

[0093] The difference between the average load during the response period and the average load of the baseline, that is, the average power of the response reduction, shall not be less than 0.8 times the agreed response power reported by the user. The formula is:

[0094]

[0095] Demand response power P DR It should be no less than 0.05 times and no more than 0.2 times of the user's peak load in the previous year. The formula is:

[0096] 0.05·P year,max ≤P DR ≤0.2·P year,max ;

[0097] Where P year，max It is expressed as the user's peak load in the previous year.

[0098] 12. The demand response setting constraints also include energy storage operation constraints, where the energy storage operation constraints include energy storage state of charge constraints, charge and discharge state constraints, charge and discharge power constraints, charge state continuity constraints and energy storage battery performance constraints;

[0099] Energy storage operation constraints include state of charge constraints, charge and discharge power constraints, and energy storage battery performance constraints;

[0100] The energy storage state of charge constraint is expressed as:

[0101] SOC t =SOC t-1 +η c ·P c,t-1 ·Δt / E rate -η d ·P d,t-1 ·Δt / E rate ,t∈T;

[0102] In the formula, SOC t It is expressed as the energy storage charge state at the tth moment, E rate Expressed as the rated capacity of energy storage, η c and η d Expressed as the energy storage rated charging and discharging efficiency;

[0103] The upper and lower limit constraints of energy storage SOC are expressed as:

[0104] SOC min ≤SOC t ≤SOC max ,t∈T;

[0105] In the formula, SOC max and SOC min They are respectively represented as the upper limit of SOC and the lower limit of SOC;

[0106] The energy storage charging and discharging power constraint can only be in one state of charging or discharging at the same time, and the formula is expressed as:

[0107]

[0108] In the formula, sw d,t and sw c,t A variable of 0 or 1, representing the discharge state and charge state of the energy storage at the tth moment, sw d,t When it is 1, it means the energy storage is in the discharge state. c,t 1 means the energy storage is in charging state, p rate Expressed as the rated power of the energy storage device;

[0109] The energy storage battery performance constraint limits the number of equivalent charge and discharge times of the energy storage to reduce the loss of the battery due to frequent charge and discharge. The formula is expressed as:

[0110]

[0111] In the formula, m represents the equivalent number of charge and discharge times of energy storage;

[0112] The user demand response model with energy storage based on electricity price policy is:

[0113]

[0114] The charging power and discharging power of energy storage are optimized to obtain the reported demand and minimize the sum of the user's electricity charge and the demand charge. The established model is a mixed integer linear programming.

[0115] The user demand response model with energy storage considering incentive response is expressed as:

[0116] Over:

[0117] P demand ,P DR ,b;

[0118] P ESS,t ,P c,t ,P d,t ,SOC t for t∈T;

[0119] sw d,t ,sw c,t ∈{0,1}for t∈T;.

[0120] The optimization model can set different dates and time periods for participating in incentive-based responses, optimize the agreed response power, maximum user demand, and energy storage charging and discharging power, so that large industrial users can obtain subsidies by completing effective responses and reduce the total electricity bills paid. The established model is a mixed integer linear programming model.

[0121] S2. Based on basic data, conduct energy complementarity analysis and formulate demand response strategies.

[0122] The complementary system consists of three parts: wind power, photovoltaic power and hydropower. The maximum target calculation formula for the total power generation of the complementary system is expressed as:

[0123]

[0124] Where E(N) represents the total power generation of the complementary system, N = [N1, N2, ..., NT] represents the output process of the complementary system, and N w,i,t It is expressed as the output of the i-th wind farm in the t-th period, N s,i,t It is expressed as the output of the jth photoelectric field in the tth period, N h,k,t It is represented by the output of the kth hydropower station in the tth period, M w 、M s and M h are the number of wind farms, photovoltaic farms and hydropower stations in the complementary system, T and Δt are the total number of time periods and the number of hours in a single time period during the dispatch period, respectively;

[0125] In the process of complementary system, the minimum coefficient of variation is the target quantitative output stability. The smaller the output variation coefficient, the higher its stability. The calculation formula is expressed as:

[0126]

[0127] Where CV(V) and std(N) are the coefficient of variation and standard deviation of the total output process of the complementary system, respectively, and N is the average value of the total output process of the complementary system.

[0128] Preliminary analysis shows that the main influencing factors of wind power, photovoltaic power and hydropower output are wind speed, solar radiation intensity and runoff. According to the temporal distribution of wind, light and water resources in the same research area, it can be analyzed that hydropower output has certain complementarity with wind power and photovoltaic power output at different time scales within the year or within the day. Generally speaking, in the annual time scale, the summer has sufficient sunlight and high solar radiation intensity, abundant water inflow and large runoff in the flood season, but the wind speed is low; the winter solar radiation intensity is low, the runoff in the dry season is small, but the wind speed is high. In the daily time scale, the daytime solar radiation intensity is high and the wind speed is low; there is no light at night but the wind speed is high. The power generation efficiency and stability of the wind, light and water complementary system are of great significance to the vigorous development of wind power, photovoltaic and hydropower hybrid energy.

[0129] The output power of photovoltaic cells is closely related to light intensity and ambient temperature. The operating voltage corresponding to the maximum power point of the solar cell increases exponentially with the increase of light intensity, and the power at the maximum point also increases accordingly. The operating voltage corresponding to the maximum power point decreases with the increase of temperature, and the power at the maximum point also decreases accordingly. The output power of the solar cell rises first and then falls with the increase of the battery operating voltage. Under certain external environmental conditions, the solar cell has a unique maximum power point.

[0130] From the analysis, we can see that since the maximum power point of solar cells is affected by temperature and light, in order to make the solar cells output the maximum power under different working environments, the parameters of the solar cells and the load must be optimally matched to obtain the maximum output power.

[0131] S3. Construct a multi-energy complementary system based on demand response strategy.

[0132] 13. As Figure 1 As shown, the predicted value of the maximum power point P in each period is used as the maximum output prediction. Through power electronic devices, the unit output can be continuously adjusted from 0 to gs*. The upper and lower limits of the PV unit power and cumulative power generation are:

[0133]

[0134] Where e and er are the upper bounds of the cumulative power generation of PV unit k at time 1 and time T, respectively, and gs represents the maximum output prediction value.

[0135] Figure 2 As shown in the figure, the PV output is determined by the total amount of solar radiation received by the surface of the photovoltaic array, and the conversion efficiency of the solar cell is affected by the ambient temperature. This is a distributed photovoltaic output curve with an installed capacity of 500kW under four different weather conditions, namely, typical sunny, rainy, cloudy and rainy to cloudy, excluding the photovoltaic day output curve during the night period. Except for the relatively smooth output power curve under sunny conditions, the output power curves under other weather conditions have great volatility and may show multi-peak characteristics. The overall output shows a pattern of increasing power in the morning and decreasing in the afternoon.

[0136] like Figure 3 As shown in Figure 1, the control characteristics of the WT units connected to the virtual power plant can adjust the unit output from 0 to continuous, and the upper and lower limits of the power and cumulative power generation of the WT units are:

[0137]

[0138] The output volatility of WT units is used to measure the hourly average output change rate of wind power, and the formula is expressed as:

[0139]

[0140] In the formula, Expressed as the wind power hourly output change rate, ΔP i,W,k Expressed as the hourly wind power output fluctuation, It is expressed as the rated installed capacity of wind power. According to the annual power generation curve of a wind power station in Dali, Yunnan Province, the distribution of power station output change rate is statistically analyzed, and the statistical results of the wind power hourly average output change rate are shown in the figure. Mainly distributed within 30%, The probability of exceeding 40% is less than 5%. The change trend of wind power output is weak in the short time scale, but its change is more obvious as the time scale increases.

[0141] It is also worth noting that the volatility of PV and WT power generation systems will directly affect the reliability of electricity use and even the safety of the power grid. The impact of the output fluctuation of the grid-connected DG on the power grid is reflected in the power quality. When the fluctuation reaches a certain level, the grid frequency will shift, the voltage near the grid connection point will fluctuate greatly, and a certain amount of transmission and distribution equipment capacity will be wasted.

[0142] like Figure 4 As shown in Figure 2, air conditioning load is a flexible load resource with great regulation potential: its adjustable potential comes from two points:

[0143] 1. The building where the air conditioner is located often has a heat storage function, and the impact caused by the short-term output change of the air conditioner itself is not significant;

[0144] 2. There is a range of comfortable ambient temperature for the human body, which is acceptable to users. This creates space for load control. According to the working status, there are two main types of air conditioners, fixed-frequency air conditioners and variable-frequency air conditioners. The two have different power consumption states and control methods when working. For variable-frequency air conditioners, the compressor frequency can be changed, so its output cooling capacity can also be freely adjusted according to the control target. The control process of temperature control is: when starting up, the compressor frequency is maintained at the maximum so that the air conditioner runs at full output, and the indoor temperature changes to the target temperature at the fastest speed until the temperature reaches the set target temperature. The compressor frequency is controlled to maintain the indoor temperature around the target temperature. It shows the simulation of the variable-frequency air conditioner cooling output and the indoor and outdoor temperature conditions at a certain summer outside temperature. As can be seen from the figure, the air conditioner is turned on at 0 o'clock, and the indoor temperature is equal to the outdoor temperature when it is turned on.

[0145] like Figure 5-Figure 6 As shown, since the compressor frequency of the fixed-frequency air conditioner cannot be changed, there are only two states for the control of the fixed-frequency air conditioner: on and off, and the corresponding power consumption also has only two states: rated power and 0.

[0146] Fixed-frequency air conditioning control requires pre-setting the air conditioning temperature control target and its allowable temperature fluctuation range. For the air conditioning cooling load, the control process is: when the air conditioning temperature detector detects that the temperature is higher than the upper limit of the preset temperature range, the air conditioning turns on the compressor and runs at rated power; when the air conditioning detects that the temperature is lower than the lower limit of the temperature range, the compressor is turned off and the air conditioning operating power is close to 0.

[0147] It represents the simulation of the cooling output of the fixed-frequency air conditioner and the indoor and outdoor temperature conditions under a certain summer external temperature. The temperature fluctuation range is set within 2°C. As can be seen from the figure, the air conditioner is turned on at 0 o'clock. When the air conditioner is turned on, the indoor temperature is equal to the outdoor temperature. The air conditioner is in the on state. The indoor temperature drops rapidly and reaches the temperature target range of the air conditioner (20+1)°C. Then the air conditioner is in a continuous on-off state to maintain the indoor temperature fluctuating within the target range. At 8 o'clock, the control target of the air conditioner is changed to the indoor temperature (15+1)°C. Since the indoor temperature is higher than the air conditioner temperature target at this time, the air conditioner is always in the on state. After reaching the predetermined temperature range, the air conditioner enters the on-off state again. At 20 o'clock, the indoor temperature target is increased from 15°C to 20°C again. At this time, the indoor temperature is lower than the air conditioner temperature target. The air conditioner is always in the off state. After reaching the predetermined temperature range, the air conditioner enters the on-off state again.

[0148] In recent years, the production and sales of electric vehicles have grown rapidly, and the EV charging load has increased year by year. Different from traditional loads, EV moves with users, and its charging load has a complex time-space distribution pattern; my country's enterprise standard "G / GDW397-2009 General Technical Requirements for Non-onboard Charging and Discharging Devices of Electric Vehicles" and IEC62196 standard have clearly required electric vehicle charging piles to have charging power adjustment functions, so it can be judged that EV charging loads have complex load-power dual characteristics; EV loads are subdivided into two categories: single electric vehicle users and electric vehicle charging stations, and the load characteristics of these two types of electric vehicle loads are studied separately.

[0149] like Figure 7 As shown in the figure, the load characteristics of a single electric vehicle. The load demand of a single EV is mainly affected by three factors: charging facilities, batteries and user behavior. Charging facilities are divided into DC charging facilities and AC charging facilities. Charging facilities limit the charging power of EVs. The current technical specifications of charging facilities are shown in the table below. DC charging facilities are fast charging mode, and the most common AC charging facilities are single-phase AC charging piles.

[0150] During actual charging, the charging power of EV needs to be determined based on the matching results of battery parameters and charging piles, and changes with the charging status. At present, EV is mainly charged in a two-stage charging method of constant current and constant voltage. The current-voltage waveform and charging power changes during the charging process are shown in the figure.

[0151] The actual energy storage system involves various energy storage forms such as electrochemical energy storage, pumped storage, flywheel energy storage and thermal energy storage. Among them, the EV and charging station introduced above are typical electrochemical energy storage. The above EV charging and discharging model can be used to describe the power generation characteristics of the energy storage resources. The upper and lower limits of the instantaneous power generation of the energy storage are its rated discharge power and the negative rated charging power, respectively. Since the energy storage resources do not have a minimum requirement to leave the state of charge in the last period, their state of charge can be located at any position between the minimum and maximum allowable states of charge; therefore, the upper and lower limits of their cumulative power generation correspond to ADFH and ABC in the figure above.

[0152] In addition to electrochemical energy storage, other forms of energy storage cover multi-energy coupling links. When analyzing their electrical load characteristics, we first analyze the energy conversion mechanism of the multi-energy coupling elements at the bottom of various types of energy, and build the physical basis of the multi-energy coupling link. The essence of flywheel energy storage and pumped storage is the conversion between electrical energy and mechanical energy. Both can use conversion efficiency to express the relationship between mechanical energy consumption and electrical energy.

[0153] In summary, this provides a comprehensive and systematic way to evaluate typical resources, which can deeply study the inherent correlation characteristics of various resources and their impact on costs and benefits, so as to achieve optimal allocation and use of resources. Through this analysis method, it can not only improve energy utilization efficiency and reduce energy costs, but also help enterprises and policy makers to formulate energy policies and management strategies more scientifically, with significant social and economic benefits.

[0154] Example 2

[0155] On the basis of the first embodiment, this embodiment further provides a typical resource multi-scale cost-benefit analysis system, including an association module, an output module, and a construction module;

[0156] The association module is used to analyze the association characteristics of typical resources;

[0157] The output module is used to analyze energy complementarity;

[0158] The building blocks are used to construct a multi-energy complementary system.

[0159] This embodiment also provides a computer device, which is suitable for the case of a typical resource multi-scale cost-benefit analysis method, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the typical resource multi-scale cost-benefit analysis method proposed in the above embodiment.

[0160] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0161] 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 implementing multi-scale cost-benefit analysis of typical resources as proposed in the above embodiment is implemented.

[0162] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0163] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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 typical resource multi-scale cost-benefit analysis method, characterized by: The following steps are included: First, analyze the correlation characteristics of typical resources to obtain basic data; Based on basic data, conduct energy complementarity analysis and formulate demand response strategies; Construct a multi-energy complementary system based on demand response strategy.

2. The typical resource multi-scale cost-benefit analysis method according to claim 1, characterized in that: The analysis of the correlation characteristics of typical resources includes dependency analysis, correlation analysis and driving analysis; The dependency analysis step includes: converting the value of the random variable into a rank according to the cumulative probability distribution of the random variable, calculating the rank correlation coefficient between the converted ranks, and judging the correlation between the random variables according to the value of the rank correlation coefficient, wherein the value range of the rank correlation coefficient is between -1 and 1; The correlation analysis step includes: collecting average wind speed, average radiation and average runoff data, plotting the collected data in the same coordinate system, analyzing the height averages of the three groups of curves, and evaluating the complementarity of wind, solar and water resources based on seasonal changes; The driving analysis step includes: determining the objective function, taking the minimum total electricity bill paid by users after participating in the incentive response as the goal, calculating the subsidies obtained by users participating in the demand-side response through government information, setting the demand response baseline as the criterion for judging effective demand response, and improving the user-side energy storage response electricity price policy and the economic benefits of participating in ancillary services.

3. The typical resource multi-scale cost-benefit analysis method according to claim 2, characterized in that: Demand response sets constraints, including: To ensure that the peak load during the response period is not higher than the baseline peak load, the constraint formula is: In the formula, and are the original user load power and energy storage power in the baseline, and They are the original user load power and energy storage power during the incentive response period; The difference between the average load during the response period and the average load of the baseline, that is, the average power of the response reduction, shall not be less than 0.8 times the agreed response power reported by the user. The formula is: Demand response power P DR It should be no less than 0.05 times and no more than 0.2 times of the user's peak load in the previous year. The formula is: 0.05·P year,max ≤P DR ≤0.2·P year,max ; Where P year，max It is expressed as the user's peak load in the previous year.

4. The typical resource multi-scale cost-benefit analysis method according to claim 3, characterized in that: The demand response setting constraint conditions also include energy storage operation constraints, wherein the energy storage operation constraints include energy storage charge state constraints, charge and discharge state constraints, charge and discharge power constraints, charge state continuity constraints and energy storage battery performance constraints; Energy storage operation constraints include state of charge constraints, charge and discharge power constraints, and energy storage battery performance constraints; The energy storage state of charge constraint is expressed as: SOCIETY t =SOC t-1 +η c ·P c,t-1 ·Δt / E rate -η d ·P d,t-1 ·Δt / E rate ,t∈T; In the formula, SOC t It is expressed as the energy storage charge state at the tth moment, E rate Expressed as the rated capacity of energy storage, η c and η d Expressed as the energy storage rated charging and discharging efficiency; The upper and lower limit constraints of energy storage SOC are expressed as: SOC min ≤SOC t ≤SOC max ,t∈T; In the formula, SOC max and SOC min They are respectively represented as the upper limit of SOC and the lower limit of SOC; The energy storage charging and discharging power constraint can only be in one state of charging or discharging at the same time, and the formula is expressed as: In the formula, sw d,t and sw c,t A variable of 0 or 1, representing the discharge state and charge state of the energy storage at the tth moment, sw d,t When it is 1, it means the energy storage is in the discharge state. c,t 1 means the energy storage is in charging state, p rate Expressed as the rated power of the energy storage device; The energy storage battery performance constraint limits the number of equivalent charge and discharge times of the energy storage to reduce the loss of the battery due to frequent charge and discharge. The formula is expressed as: In the formula, m represents the equivalent number of charge and discharge times of energy storage; The user demand response model with energy storage based on electricity price policy is: The charging power and discharging power of energy storage are optimized to obtain the reported demand and minimize the sum of the user's electricity charge and the demand charge. The established model is a mixed integer linear programming. The user demand response model with energy storage considering incentive response is expressed as: Over: P demand ,P DR ,b; P ESS,t ,P c,t ,P d,t ,SOC t for t∈T; sw d,t, sw c,t ∈{0,1}for t∈T;。 5. The typical resource multi-scale cost-benefit analysis method according to claim 4, characterized in that: The complementary system consists of three parts: wind power, photovoltaic power and hydropower. The maximum target calculation formula for the total power generation of the complementary system is expressed as: Where E(N) represents the total power generation of the complementary system, N = [N1, N2, ..., NT] represents the output process of the complementary system, and N w,i,t It is represented as the output of the i-th wind farm in the t-th period, N s,i,t It is expressed as the output of the jth photoelectric field in the tth period, N h,k,t It is represented by the output of the kth hydropower station in the tth period, M w 、M s and M h are the number of wind farms, photovoltaic farms and hydropower stations in the complementary system, T and Δt are the total number of time periods and the number of hours in a single time period during the dispatch period, respectively; In the process of complementary system, the minimum coefficient of variation is the target quantitative output stability. The smaller the output variation coefficient, the higher its stability. The calculation formula is expressed as: Where CV(V) and std(N) are the coefficient of variation and standard deviation of the total output process of the complementary system, respectively, and N is the average value of the total output process of the complementary system.

6. The typical resource multi-scale cost-benefit analysis method according to claim 5, characterized in that: The predicted value of the maximum power point P in each period is used as the maximum output prediction. Through power electronic devices, the unit output can be continuously adjusted from 0 to gs*. The upper and lower limits of the PV unit power and cumulative power generation are: Where e and er are the upper bounds of the cumulative power generation of PV unit k at time 1 and time T, respectively, and gs represents the maximum output prediction value.

7. The typical resource multi-scale cost-benefit analysis method according to claim 6, characterized in that: The control characteristics of the WT units connected to the virtual power plant can adjust the unit output from 0 to continuous. The upper and lower limits of the power and cumulative power generation of the WT units are: The output volatility of WT units is used to measure the hourly average output change rate of wind power, and the formula is expressed as: In the formula, Expressed as the wind power hourly output change rate, ΔP i,W,k Expressed as the hourly wind power output fluctuation, It is expressed as the rated installed capacity of wind power.

8. A typical resource multi-scale cost-benefit analysis system, based on the typical resource multi-scale cost-benefit analysis method according to any one of claims 1 to 7, characterized in that: Includes association modules, output modules, and construction modules; The association module is used to analyze the association characteristics of typical resources; The output module is used to analyze energy complementarity; The building blocks are used to construct a multi-energy complementary system.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the typical resource multi-scale cost-benefit analysis method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the typical resource multi-scale cost-benefit analysis method according to any one of claims 1 to 7 are implemented.

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