Large customer side energy storage configuration method for demand and peak valley regulation

Abstract

The application discloses a large user side energy storage configuration method for demand and peak-valley regulation. The large user side energy storage configuration method mainly comprises two processes of basic electricity cost regulation energy storage configuration and electricity quantity and electricity cost regulation energy storage configuration. The basic electricity cost regulation energy storage configuration process comprises the following steps: forming a monthly power distribution curve, forming a monthly energy distribution curve, forming a sequence for normalized variance calculation, determining monthly demand regulation demand, determining annual demand regulation demand and the like. The electricity quantity and electricity cost regulation energy storage configuration process comprises the following steps: calculating daily average valley filling space, calculating daily average peak cutting space, calculating monthly electricity quantity regulation demand, calculating annual electricity quantity regulation demand, determining final energy storage configuration of the large industrial user side energy storage and the like. The application solves the problem in the prior art that an optimization model is constructed based on annual user load data, thereby a large amount of calculation is required, a long calculation time is caused, and the configuration efficiency is affected.

Description

Technical Field

[0001] This invention relates to the field of user-side energy storage planning and configuration technology, and more specifically, to a method for configuring large user-side energy storage for demand and peak-valley regulation. Background Technology

[0002] Under the current electricity pricing policy, the main demand of large users for energy storage is to reduce electricity costs. This reduction is achieved primarily by lowering basic electricity charges and electricity consumption charges.

[0003] Optimizing the configuration of energy storage on the large user side requires considering both reducing basic electricity costs and demand-based electricity costs. For basic electricity costs, users can choose capacity-based billing or demand-based billing. If a user has a high load factor, they typically choose capacity-based billing, paying a fixed monthly capacity fee. If a user has a low load factor, they can choose demand-based billing. Utilizing the regulation capabilities of energy storage, users can choose to "convert capacity to demand" or reduce basic electricity costs by lowering demand-based electricity costs. For demand-based electricity costs, energy storage systems primarily generate revenue through peak-valley and peak-office price differences, i.e., shifting peak-hour electricity consumption to off-peak hours, or reducing peak-hour electricity consumption and increasing off-peak-hour electricity consumption.

[0004] As can be seen from the process of energy storage regulating basic electricity charges and electricity charges, when energy storage is used for peak shaving and valley filling, it may increase demand charges when the load power is high during valley hours. When energy storage is used for demand charge regulation, it may also affect the revenue from electricity charge regulation. Therefore, these two processes are coupled and need to be considered comprehensively.

[0005] If a user's load factor is high and relatively stable, it's difficult to regulate demand through energy storage. Regulating electricity consumption will still increase demand costs, reducing the profitability of energy storage. However, in cases of short-term, high-load or impulsive loads, energy storage systems can be used for demand mitigation, reducing demand-related electricity costs. On the other hand, although demand-related electricity costs and capacity-related electricity costs are related, if the main factor influencing demand-related electricity cost regulation is impulsive loads, the proportion of electricity stored for demand regulation will be small, thus having little impact on electricity-related cost adjustments.

[0006] For impulsive loads, which are generally random and difficult to predict when they will occur, and whose capacity regulation is crucial to the profitability of the energy storage system, a separate configuration is considered for energy storage used for demand-fee regulation, treating it as a separate process from energy storage used for electricity-fee regulation. During actual operation, a portion of capacity and energy should be reserved for demand regulation to avoid incurring performance-based costs. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a method for configuring large user-side energy storage for demand and peak-valley regulation.

[0008] This invention discloses a method for configuring large-user-side energy storage for demand and peak-valley regulation, which is implemented through the following technical solution and consists of two parts: a basic electricity charge regulation configuration and an electricity charge regulation configuration. (1) Basic electricity fee regulation energy storage configuration This includes generating monthly power distribution curves, generating monthly energy distribution curves, determining monthly demand adjustment needs, and determining annual adjustment needs; ① Generate monthly power distribution curves Obtain annual metering / measurement power data from key metering points for large users to be configured with energy storage, with a time resolution of 30s, 1min, or 5min time intervals, and monthly demand power calculated as 15min average power. Take monthly power data calculated over 30 days, starting from 0MW, and calculate the number of data points greater than each power value at 30s, 1min, or 5min intervals until the number of data points for that power value is 0. Plot power as the x-axis and the number of data points as the y-axis to form a power distribution curve, as shown in the following formula. (1) In the formula, n ( p () indicates that the power is greater than the power value p The number of data points P max This represents the maximum monthly load power. ② Form a monthly energy distribution curve The energy value within the sampling interval is calculated by each power point and the sampling time interval. Therefore, the monthly energy distribution curve is formed based on the monthly power distribution curve, as shown in equation (2). Combining the monthly power distribution curve and the monthly energy distribution curve, it can be seen that the power value is greater than the power value. p If the number of data points and the load energy exceeding that tier value are considered, then from a monthly perspective, if the energy storage configuration is based on the maximum load power and the maximum power value... p If the difference between the power and the energy is greater than the power value, then the energy storage can be considered to control the monthly power of the load below the threshold value, that is, to control the maximum demand power.

[0009] (2) In the formula, e ( p () indicates that the power is greater than the power value p The load energy value; It should be noted that the monthly maximum demand power is the 15-minute average power. The 15-minute average power is a smoothed version of the monthly power data. Therefore, using the monthly power data to form power distribution curves and energy distribution curves, and then configuring energy storage, effectively raises the requirements for energy storage configuration. The configuration result will inevitably meet the monthly maximum demand control requirements. In actual operation, the 15-minute average power, as the demand power, provides room for energy storage to defend against demand fluctuations. It is not necessary to control the power below the maximum demand at every moment; it is sufficient to ensure that the 15-minute average power is less than the maximum demand.

[0010] ③ Form a sequence for calculating normalized variance. The monthly power distribution curve and monthly energy distribution curve yield the power value above the load. p The data point number and the energy required to reduce the load power, wherein the data point number represents the time when the load point occurred within the month; above the load power value p The concentration of data points is directly related to the energy required to mitigate loads. If the concentration of data points is high, it means that these data points occur within a concentrated period of time, which requires high energy storage configuration to mitigate these loads and continuously mitigate these loads within a certain period of time. If the concentration of data points is low, that is, the dispersion is high, it means that these data points occur within a dispersed time range, and these loads are short-term impact loads, which requires low energy storage configuration to mitigate these loads. Daily power values ​​exceeding the target within the month are statistically analyzed. p The load energy of 0, assuming a month is calculated as 30 days, forms the energy sequence for the whole month used for normalized variance calculation, as shown in the following formula; (3) ④ Determine monthly demand adjustment needs The above-mentioned monthly above-load power values ​​were measured using normalized variance. p The concentration of energy value 0 is used as an energy selection coefficient to calculate the energy storage capacity used for demand adjustment in that month, which is higher than the load power value. p The difference between the maximum value of the data point with a value of 0 and the load power is used as the calculated value of the energy storage power for demand adjustment in that month. The normalized standard deviation is calculated as follows: (4) In the formula, R mon To normalize the standard deviation, Was ( E mon ) represents the variance of the energy series within the month. Was maxThe maximum variance within the month is the variance of the energy series calculated by concentrating the maximum power of the energy series within the month on a certain day or several consecutive days. This is used to limit demand to power values. p At time 0, the calculated energy storage required for demand regulation is... (5) Used to limit demand to power value p At time 0, the calculated energy storage power required for demand regulation is... (6) In the formula, p max This represents the maximum load power within the month. ⑤ Determine the annual demand adjustment needs Demand regulation must consider economic efficiency. As described in the above scheme, when a certain load power is selected, there is a corresponding set of calculated energy storage power and energy storage capacity for demand regulation. Simultaneously, this load power is the demand defense value for that month. Energy storage power and energy storage capacity incur investment costs, while the demand defense value generates regulation revenue. Therefore, a series of load power points should correspond to a series of regulation revenues and energy storage investments. The maximum difference between the monthly regulation revenue and the monthly energy storage investment is the energy storage power and energy storage capacity configured for demand regulation at that defense value.

[0011] (7) In the formula, FOX ( p 0) To limit demand to power values p The gains obtained at time 0, P D For demand-based electricity pricing, C E The unit energy cost of energy storage C P Cost per unit power of energy storage.

[0012] Take the value that makes equation (7) reach its maximum value, that is, take it as Max( FOX ( p 0)) p 0 is the optimal demand limit value, and then substitute it into equation (5) and equation (6) to calculate the monthly energy storage configuration used to adjust demand.

[0013] Electricity demand charges are calculated and collected monthly, with a total of 12 months in a year. Following steps ①-④, the optimal energy storage configuration for demand regulation is calculated each month, resulting in 12 energy storage configurations. Therefore, the expected value of the calculated monthly energy storage power configuration is used as the calculated energy storage power. P cal.basThe expected value of energy storage configuration for each month is calculated as the energy storage calculation energy. E cal.bas ; (2) Energy storage configuration for power consumption and electricity price adjustment The process for configuring energy storage to regulate electricity consumption and electricity costs includes calculating the average daily valley filling / flattening space, calculating the average daily peak shaving space, calculating the monthly electricity regulation demand, and calculating the annual electricity regulation demand. ⑥ Calculate the daily average filling / leveling space. Steps ①-⑤ have determined the monthly demand defense value (after selecting the basic electricity charge adjustment energy storage configuration, the monthly demand defense value is different for each month and depends on the monthly load). Based on this defense value, that is, the 15-minute average monthly electricity load cannot be higher than this load value. At the same time, considering that user-side energy storage usually determines the operation curve on a daily basis according to the peak-valley time-of-use electricity price, calculate the average daily valley filling / flattening space within the month. The daily valley filling space is the time integral of load power below the monthly demand protection value during the valley period on a given day, i.e., the load electrical energy. The load electrical energy above the monthly demand protection value is considered to have been smoothed by the basic electricity tariff regulation energy storage configuration and controlled below the demand protection value. This load electrical energy is the monthly energy value used for valley filling. It should be noted that the daily valley filling space is the maximum amount of electricity available for valley filling, not the actual valley filling electricity, and it varies from day to day. The leveling space is the time integral of load power below the monthly demand protection value during the normal period on a given day, i.e., the load electrical energy. This load electrical energy is the monthly energy value used for valley filling, and it is also the maximum amount of electricity used for leveling, not the actual leveling electricity, and it varies from day to day. The daily valley filling / leveling space is calculated using the following two formulas.

[0014] (8) (9) In the formula, E vall.i To fill the valley space on a certain day E flat.i To fill the space for a certain day P 0 represents the monthly demand defense value. p L This represents the load power.

[0015] Generally, large industrial users' side-by-side energy storage operates in a "one charge, one discharge," "two charge, two discharge," or a mode in between. To maximize energy storage revenue or shorten the project payback period, the "two charge, two discharge" mode is more commonly used. For provinces where midday is a valley period, two peak-valley price difference revenues are obtained. For provinces where midday is a normal period, one peak-valley price difference and one peak-flat price difference are obtained. In the case of obtaining two peak-valley price differences, two daily average valley filling space should be calculated, rather than daily valley filling / flat filling space. The daily average valley filling / flat filling space is the expected value of the valley filling / flat filling space for each day within the month. ⑦ Calculate the daily average peak reduction space The daily peak shaving capacity is the time integral of the load power below the monthly demand defense value during peak hours on a given day. In other words, it represents the electrical energy required to reduce the entire load to zero. Load power exceeding the monthly demand defense value is considered to have been mitigated by the basic electricity tariff-regulated energy storage configuration. It should be noted that the daily peak shaving capacity is the maximum amount of electricity used for peak shaving, not the actual amount of electricity shaving, and it is impossible to completely reduce the peak load to zero. Generally, peak hours consist of two periods: the morning peak period and the evening peak period. The daily peak shaving capacity is the sum of the peak shaving capacities for both periods. If peak electricity prices exist in certain months, their impact should be considered. The daily average peak shaving capacity is the expected value of the daily peak shaving capacity within the month. (10) In the formula, is E peak.i For peak reduction space on a certain day; ⑧ Calculate monthly electricity demand adjustment The smaller of the monthly average daily valley filling / flattening space and the daily average peak shaving space is taken as the upper limit of energy storage configuration for large industrial users. Several energy storage configurations are selected from zero to the upper limit of energy storage configuration, distinguishing the number of energy storage hours (such as 1h, 2h, 4h and other commonly used energy storage configurations). The monthly power regulation revenue of each energy storage configuration is obtained by using a daily optimization method. The equivalent static payback period and internal rate of return are calculated based on the monthly data. Thus, each energy storage configuration can obtain a set of monthly power regulation revenue, equivalent static payback period and equivalent internal rate of return indicators. According to the project's requirements for the above indicators, a certain energy storage configuration is selected as the monthly power regulation demand. ⑨ Calculate annual electricity regulation demand Considering the varying electricity demand across different months, configuring the system based on months with lower demand would fail to fully utilize the peak-shaving / valley-filling capacity of each month. Conversely, configuring it based on months with higher demand would result in underutilization of the energy storage system, impacting its profitability. After obtaining the monthly energy storage configuration demand from step ⑧, the calculated power value for the annual energy storage configuration demand is derived. P cal.qua and energy calculation value E cal.quaThe expected value can be obtained from the adjustment demand for each month. If the historical data has a large time scale, such as multi-year historical data, then the expected value of the adjustment demand for each month should be obtained from the multi-year historical data. ⑩ Determine the final energy storage configuration for large industrial users. Combining the annual basic electricity price-regulated energy storage demand obtained from steps ①-⑤ with the annual electricity price-regulated energy storage demand obtained from steps ⑥-⑨, the calculated value of the energy storage scale on the large industrial user side is obtained. The final energy storage configuration values ​​are determined based on the following principles: First, the larger value of the annual basic electricity price-regulated energy storage demand and the electricity price-regulated energy storage demand is taken; second, the superposition value of the annual basic electricity price-regulated energy storage demand and the electricity price-regulated energy storage demand is taken, as shown in the following formula. (11) The above-mentioned value selection principles are based on the following considerations: For the power portion of the energy storage configuration, a larger value is chosen to simultaneously meet both basic electricity tariff adjustment needs and electricity consumption tariff adjustment needs. For the energy portion of the energy storage configuration, storage capacity needs to be reserved for demand mitigation or mitigation of impact loads; therefore, the sum of the two energy values ​​is required. Ultimately, the energy storage configuration for large users will be a commonly used energy storage power capacity and storage duration that is not lower than the calculated energy storage scale value.

[0016] It should be noted that this technical solution configures basic electricity fee adjustment demand and electricity fee adjustment demand separately, and weakens the mutual influence between the two. It can be used as an engineering application method to obtain a more accurate energy storage configuration for large industrial users.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention addresses the issue in existing technologies where the energy storage capacity required for demand management is not fully considered in large user-side energy storage scenarios, enabling energy storage configurations to simultaneously meet demand and energy regulation needs.

[0018] This invention addresses the problem in existing technologies where building optimization models based on year-round user load data requires a large amount of computation, thereby reducing computation time and improving configuration efficiency.

[0019] This invention addresses the problem that existing technical solutions do not take into account the coupling relationship between energy storage demand regulation and energy regulation, thus avoiding excessively large or small energy storage configurations for large users.

[0020] This invention addresses the problem in existing technologies that fail to consider the priority distinction between energy storage demand regulation and energy regulation, thereby reducing the complexity of the configuration process and improving interpretability. Attached Figure Description

[0021] Figure 1 This is a flowchart of a large user-side energy storage configuration for demand and peak-valley regulation. Detailed Implementation

[0022] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be described in detail below with reference to specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. The described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the present invention.

[0023] like Figure 1 As shown, a method for configuring large user-side energy storage for demand and peak-valley regulation consists of two parts: a basic electricity tariff regulation configuration and an energy consumption tariff regulation configuration. (1) Basic electricity fee regulation energy storage configuration This includes generating monthly power distribution curves, generating monthly energy distribution curves, determining monthly demand adjustment needs, and determining annual adjustment needs; ① Generate monthly power distribution curves Obtain annual metering / measurement power data from key metering points for large users to be configured with energy storage, with a time resolution of 30s, 1min, or 5min time intervals, and monthly demand power calculated as 15min average power. Take monthly power data calculated over 30 days, starting from 0MW, and calculate the number of data points greater than each power value at 30s, 1min, or 5min intervals until the number of data points for that power value is 0. Plot power as the x-axis and the number of data points as the y-axis to form a power distribution curve, as shown in the following formula. (1) In the formula, n ( p () indicates that the power is greater than the power value p The number of data points P max This represents the maximum monthly load power. ② Form a monthly energy distribution curve The energy value within the sampling interval is calculated by each power point and the sampling time interval. Therefore, the monthly energy distribution curve is formed based on the monthly power distribution curve, as shown in equation (2). Combining the monthly power distribution curve and the monthly energy distribution curve, it can be seen that the power value is greater than the power value. pIf the number of data points and the load energy exceeding that tier value are considered, then from a monthly perspective, if the energy storage configuration is based on the maximum load power and the maximum power value... p If the difference between the power and the energy is greater than the power value, then the energy storage can be considered to control the monthly power of the load below the threshold value, that is, to control the maximum demand power.

[0024] (2) In the formula, e ( p () indicates that the power is greater than the power value p The load energy value; It should be noted that the monthly maximum demand power is the 15-minute average power. The 15-minute average power is a smoothed version of the monthly power data. Therefore, using the monthly power data to form power distribution curves and energy distribution curves, and then configuring energy storage, effectively raises the requirements for energy storage configuration. The configuration result will inevitably meet the monthly maximum demand control requirements. In actual operation, the 15-minute average power, as the demand power, provides room for energy storage to defend against demand fluctuations. It is not necessary to control the power below the maximum demand at every moment; it is sufficient to ensure that the 15-minute average power is less than the maximum demand.

[0025] ③ Form a sequence for calculating normalized variance. The monthly power distribution curve and monthly energy distribution curve yield the power value above the load. p The data point number and the energy required to reduce the load power, wherein the data point number represents the time when the load point occurred within the month; above the load power value p The concentration of data points is directly related to the energy required to mitigate loads. If the concentration of data points is high, it means that these data points occur within a concentrated period of time, which requires high energy storage configuration to mitigate these loads and continuously mitigate these loads within a certain period of time. If the concentration of data points is low, that is, the dispersion is high, it means that these data points occur within a dispersed time range, and these loads are short-term impact loads, which requires low energy storage configuration to mitigate these loads. Daily power values ​​exceeding the target within the month are statistically analyzed. p The load energy of 0, assuming a month is calculated as 30 days, forms the energy sequence for the whole month used for normalized variance calculation, as shown in the following formula; (3) ④ Determine monthly demand adjustment needs Normalized variance was used to measure the above-mentioned above-load power during the month. p The concentration of energy value of 0 is used as an energy selection coefficient to calculate the energy storage capacity used for demand adjustment in that month, which is higher than the load power. pThe difference between the maximum value of the data point with a value of 0 and the load power is used as the calculated value of the energy storage power for demand adjustment in that month. The normalized standard deviation is calculated as follows: (4) In the formula, R mon To normalize the standard deviation, Was ( E mon ) represents the variance of the energy series within the month. Was max The maximum variance within the month is the variance of the energy series calculated by concentrating the maximum power of the energy series within the month on a certain day or several consecutive days. This is used to limit demand to power values. p At time 0, the calculated energy storage required for demand regulation is... (5) Used to limit demand to power value p At time 0, the calculated energy storage power required for demand regulation is... (6) In the formula, p max This represents the maximum load power within the month. Demand regulation needs to consider economic efficiency. As described in the above scheme, when taking the load power value... p At 0, there is a set of calculated values ​​for energy storage power and energy storage capacity used for demand regulation. At the same time, this load power is the demand defense value for that month. Energy storage power and energy storage capacity generate investment costs, while the demand defense value generates regulation revenue. Therefore, a series of load power points should correspond to a series of regulation revenues and energy storage investments. The maximum value of the difference between the monthly regulation revenue and the monthly energy storage investment is the energy storage power and energy storage capacity configured for demand regulation at the defense value.

[0026] When the monthly demand defense value is high, the energy storage configuration is small, the investment is low, and the regulation revenue is also low. Therefore, the total revenue is low. When the monthly demand defense value is moderate, the energy storage configuration and investment increase, but the demand regulation revenue increases significantly, and the total revenue will be high. When the monthly demand defense value is low, the energy storage configuration and investment are high, but the increase in demand regulation revenue has become saturated, which will affect the total revenue.

[0027] ⑤ Determine the annual demand adjustment needs Demand regulation must consider economic efficiency. As described in the above scheme, when a certain load power is selected, there is a corresponding set of calculated energy storage power and energy storage capacity for demand regulation. Simultaneously, this load power is the demand defense value for that month. Energy storage power and energy storage capacity incur investment costs, while the demand defense value generates regulation revenue. Therefore, a series of load power points should correspond to a series of regulation revenues and energy storage investments. The maximum difference between the monthly regulation revenue and the monthly energy storage investment is the energy storage power and energy storage capacity configured for demand regulation at that defense value.

[0028] (7) In the formula, FOX ( p 0) To limit demand to power values p The gains obtained at time 0, P D For demand-based electricity pricing, C E The unit energy cost of energy storage C P Cost per unit power of energy storage.

[0029] Take the value that makes equation (7) reach its maximum value, that is, take it as Max( FOX ( p 0)) p 0 is the optimal demand limit value, and then substitute it into equation (5) and equation (6) to calculate the monthly energy storage configuration used to adjust demand.

[0030] Electricity demand charges are calculated and collected monthly, with a total of 12 months in a year. Following steps ①-④, the optimal energy storage configuration for demand regulation is calculated each month, resulting in 12 energy storage configurations. Therefore, the expected value of the calculated monthly energy storage power configuration is used as the calculated energy storage power. P cal.bas The expected value of energy storage configuration for each month is calculated as the energy storage calculation energy. E cal.bas ; It should be noted that the calculated energy storage power and energy storage energy (calculated energy storage power and calculated energy storage energy) obtained in step ⑤ are not the final energy storage configuration results. The final energy storage configuration needs to be combined with the calculated energy storage configuration for electricity and electricity bill adjustment, that is, the calculated energy storage power and energy storage energy (calculated energy storage power and calculated energy storage energy) for electricity adjustment. The two parts are added together and rounded to obtain the final configuration. (2) Energy storage configuration for power consumption and electricity price adjustment The process for configuring energy storage to regulate electricity consumption and electricity costs includes calculating the average daily valley filling / flattening space, calculating the average daily peak shaving space, calculating the monthly electricity regulation demand, and calculating the annual electricity regulation demand. ⑥ Calculate the daily average filling / leveling space. Steps ①-⑤ have determined the monthly demand defense value (after selecting the basic electricity charge adjustment energy storage configuration, the monthly demand defense value is different for each month and depends on the monthly load). Based on this defense value, that is, the 15-minute average monthly electricity load cannot be higher than this load value. At the same time, considering that user-side energy storage usually determines the operation curve on a daily basis according to the peak-valley time-of-use electricity price, calculate the average daily valley filling / flattening space within the month. The daily valley filling space is the time integral of load power below the monthly demand protection value during the valley period on a given day, i.e., the load electrical energy. The load electrical energy above the monthly demand protection value is considered to have been smoothed by the basic electricity tariff regulation energy storage configuration and controlled below the demand protection value. This load electrical energy is the monthly energy value used for valley filling. It should be noted that the daily valley filling space is the maximum amount of electricity available for valley filling, not the actual valley filling electricity, and it varies from day to day. The leveling space is the time integral of load power below the monthly demand protection value during the normal period on a given day, i.e., the load electrical energy. This load electrical energy is the monthly energy value used for valley filling, and it is also the maximum amount of electricity used for leveling, not the actual leveling electricity, and it varies from day to day. The daily valley filling / leveling space is calculated using the following two formulas.

[0031] (8) (9) In the formula, E vall.i To fill the valley space on a certain day E flat.i To fill the space for a certain day P 0 represents the monthly demand defense value. p L This represents the load power.

[0032] Generally, large industrial users' side-by-side energy storage operates in a "one charge, one discharge," "two charge, two discharge," or a mode in between. To maximize energy storage revenue or shorten the project payback period, the "two charge, two discharge" mode is more commonly used. For provinces where midday is a valley period, two peak-valley price difference revenues are obtained. For provinces where midday is a normal period, one peak-valley price difference and one peak-flat price difference are obtained. In the case of obtaining two peak-valley price differences, two daily average valley filling space should be calculated, rather than daily valley filling / flat filling space. The daily average valley filling / flat filling space is the expected value of the valley filling / flat filling space for each day within the month. ⑦ Calculate the daily average peak reduction space The daily peak shaving capacity is the time integral of the load power below the monthly demand defense value during peak hours on a given day. In other words, it represents the electrical energy required to reduce the entire load to zero. Load power exceeding the monthly demand defense value is considered to have been mitigated by the basic electricity tariff-regulated energy storage configuration. It should be noted that the daily peak shaving capacity is the maximum amount of electricity used for peak shaving, not the actual amount of electricity shaving, and it is impossible to completely reduce the peak load to zero. Generally, peak hours consist of two periods: the morning peak period and the evening peak period. The daily peak shaving capacity is the sum of the peak shaving capacities for both periods. If peak electricity prices exist in certain months, their impact should be considered. The daily average peak shaving capacity is the expected value of the daily peak shaving capacity within the month. (10) In the formula, is E peak.i For peak reduction space on a certain day; ⑧ Calculate monthly electricity demand adjustment The smaller of the monthly average daily valley filling / flattening space and the daily average peak shaving space is taken as the upper limit of energy storage configuration for large industrial users. Several energy storage configurations are selected from zero to the upper limit of energy storage configuration, distinguishing the number of energy storage hours (such as 1h, 2h, 4h and other commonly used energy storage configurations). The monthly power regulation revenue of each energy storage configuration is obtained by using a daily optimization method. The equivalent static payback period and internal rate of return are calculated based on the monthly data. Thus, each energy storage configuration can obtain a set of monthly power regulation revenue, equivalent static payback period and equivalent internal rate of return indicators. According to the project's requirements for the above indicators, a certain energy storage configuration is selected as the monthly power regulation demand. Table 1. Economic indicators of different energy storage configurations

[0033] In the above table, E max Set the upper limit for monthly energy storage configuration.

[0034] It should be noted that, within a certain range, the larger the scale of the energy storage system, the more fully it can utilize the average daily valley filling / flattening space and the average daily peak shaving space, thus generating greater revenue. However, in actual operation, due to limitations in regulation capacity, it is difficult for energy storage systems to fully utilize valley filling / flattening and peak shaving space. An excessively large configuration can lead to resource waste. Furthermore, as the scale of energy storage increases, the internal rate of return (IRR) will decrease, and the project payback period will be extended. For large industrial user-side energy storage projects, owners not only focus on revenue indicators but also often pay close attention to the rate of return and payback period. Therefore, a comprehensive consideration should be given when adjusting monthly electricity demand. ⑨ Calculate annual electricity regulation demand Considering the varying electricity demand across different months, configuring the system based on months with lower demand would fail to fully utilize the peak-shaving / valley-filling capacity of each month. Conversely, configuring it based on months with higher demand would result in underutilization of the energy storage system, impacting its profitability. After obtaining the monthly energy storage configuration demand from step ⑧, the calculated power value for the annual energy storage configuration demand is derived. P cal.qua and energy calculation value E cal.qua The expected value can be obtained from the adjustment demand for each month. If the historical data has a large time scale, such as multi-year historical data, then the expected value of the adjustment demand for each month should be obtained from the multi-year historical data. ⑩ Determine the final energy storage configuration for large industrial users. Combining the annual basic electricity price-regulated energy storage demand obtained from steps ①-⑤ with the annual electricity price-regulated energy storage demand obtained from steps ⑥-⑨, the calculated value of the energy storage scale on the large industrial user side is obtained. The final energy storage configuration values ​​are determined based on the following principles: First, the larger value of the annual basic electricity price-regulated energy storage demand and the electricity price-regulated energy storage demand is taken; second, the superposition value of the annual basic electricity price-regulated energy storage demand and the electricity price-regulated energy storage demand is taken, as shown in the following formula. (11) The above-mentioned value selection principles are based on the following considerations: For the power portion of the energy storage configuration, a larger value is chosen to simultaneously meet both basic electricity tariff adjustment needs and electricity consumption tariff adjustment needs. For the energy portion of the energy storage configuration, storage capacity needs to be reserved for demand mitigation or mitigation of impact loads; therefore, the sum of the two energy values ​​is required. Ultimately, the energy storage configuration for large users will be a commonly used energy storage power capacity and storage duration that is not lower than the calculated energy storage scale value.

[0035] It should be noted that this technical solution configures basic electricity fee adjustment demand and electricity fee adjustment demand separately, and weakens the mutual influence between the two. It can be used as an engineering application method to obtain a more accurate energy storage configuration for large industrial users.

[0036] The above description is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for configuring large user-side energy storage for demand and peak-valley regulation, characterized in that, It consists of two parts: basic electricity fee adjustment configuration and electricity consumption fee adjustment configuration. (1) Basic electricity fee regulation energy storage configuration This includes generating monthly power distribution curves, generating monthly energy distribution curves, determining monthly demand adjustment needs, and determining annual adjustment needs; ① Generate monthly power distribution curves Obtain annual metering / measurement power data from key metering points for large users to be configured with energy storage, with a time resolution of 30s, 1min, or 5min time intervals, and monthly demand power calculated as 15min average power. Take monthly power data calculated over 30 days, starting from 0MW, and calculate the number of data points greater than each power value at 30s, 1min, or 5min intervals until the number of data points for that power value is 0. Plot power as the x-axis and the number of data points as the y-axis to form a power distribution curve, as shown in the following formula. （1） In the formula, n ( p () indicates that the power is greater than the power value p The number of data points P max This represents the maximum monthly load power. ② Form a monthly energy distribution curve The energy value within the sampling interval is calculated by each power point and the sampling time interval. Therefore, the monthly energy distribution curve is formed based on the monthly power distribution curve, as shown in equation (2). Combining the monthly power distribution curve and the monthly energy distribution curve, it can be seen that the power value is greater than the power value. p If the number of data points and the load energy exceeding that tier value are considered, then from a monthly perspective, if the energy storage configuration is based on the maximum load power and the maximum power value... p If the difference between the power and the energy is greater than this power value, then it can be considered that the energy storage can control the monthly power of the load below this step value, that is, control the maximum demand power. （2） In the formula, e ( p () indicates that the power is greater than the power value p The load energy value; ③ Form a sequence for calculating normalized variance. The monthly power distribution curve and monthly energy distribution curve yield the power value above the load. p The data point number and the energy required to reduce the load power, wherein the data point number represents the time when the load point occurred within the month; above the load power value p The concentration of data points is directly related to the energy required to mitigate loads. If the concentration of data points is high, it means that these data points occur within a concentrated period of time, which requires high energy storage configuration to mitigate these loads and continuously mitigate these loads within a certain period of time. If the concentration of data points is low, that is, the dispersion is high, it means that these data points occur within a dispersed time range, and these loads are short-term impact loads, which requires low energy storage configuration to mitigate these loads. Daily power values ​​exceeding the target within the month are statistically analyzed. p The load energy of 0, assuming a month is calculated as 30 days, forms the energy sequence for the whole month used for normalized variance calculation, as shown in the following formula; （3） ④ Determine monthly demand adjustment needs The above-mentioned monthly above-load power values ​​were measured using normalized variance. p The concentration of energy value 0 is used as an energy selection coefficient to calculate the energy storage capacity used for demand adjustment in that month, which is higher than the load power value. p The difference between the maximum value of the data point with a value of 0 and the load power is used as the calculated value of the energy storage power for demand adjustment in that month. The normalized standard deviation is calculated as follows: （4） In the formula, R mon To normalize the standard deviation, Var ( E mon ) represents the variance of the energy series within the month. Var max The maximum variance within the month is the variance of the energy series calculated by concentrating the maximum power of the energy series within the month on a certain day or several consecutive days. This is used to limit demand to power values. p At time 0, the calculated energy storage required for demand regulation is... （5） Used to limit demand to power value p At time 0, the calculated energy storage power required for demand regulation is... （6） In the formula, p max This represents the maximum load power within the month. ⑤ Determine the annual demand adjustment needs Demand regulation needs to consider economic efficiency. As described in the above scheme, when a certain load power is selected, there is a set of energy storage power calculation values ​​and energy storage energy calculation values ​​used for demand regulation. At the same time, this load power is the demand defense value for that month. Energy storage power and energy storage energy generate investment costs, while the demand defense value generates regulation revenue. Therefore, a series of load power points should correspond to a series of regulation revenues and energy storage investments. The maximum value of the difference between monthly regulation revenues and monthly energy storage investments is the energy storage power and energy corresponding to the defensive value for demand regulation. （7） In the formula, REV ( p 0) To limit demand to power values p The gains obtained at time 0, P D For demand-based electricity pricing, C E The unit energy cost of energy storage C P Cost per unit power of energy storage; Take the value that makes equation (7) reach its maximum value, that is, take it as Max( REV ( p 0)) p 0 is the optimal demand limit value, and then substitute it into equation (5) and equation (6) to calculate the monthly energy storage configuration for adjusting demand; Electricity demand charges are calculated and collected monthly, with a total of 12 months in a year. Following steps ①-④, the optimal energy storage configuration for demand regulation is calculated each month, resulting in 12 energy storage configurations. Therefore, the expected value of the calculated monthly energy storage power configuration is used as the calculated energy storage power. P cal.bas The expected value of energy storage configuration for each month is calculated as the energy storage calculation energy. E cal.bas ; (2) Energy storage configuration for power consumption and electricity price adjustment The process for configuring energy storage to regulate electricity consumption and electricity costs includes calculating the average daily valley filling / flattening space, calculating the average daily peak shaving space, calculating the monthly electricity regulation demand, and calculating the annual electricity regulation demand. ⑥ Calculate the daily average filling / leveling space. The monthly demand defense values ​​determined in steps ①-⑤ will vary each month after selecting the basic electricity tariff adjustment energy storage configuration, depending on the monthly load. Based on this defense value, the 15-minute average monthly electricity load cannot exceed this load value. At the same time, considering that user-side energy storage usually determines its operation curve daily according to peak-valley time-of-use electricity price, the average daily valley filling / flattening space within the month is calculated. The daily valley filling space is the time integral of load power below the monthly demand protection value during the valley period on a given day, i.e., the load electrical energy. The load electrical energy above the monthly demand protection value is considered to have been smoothed by the basic electricity tariff regulation energy storage configuration and controlled below the demand protection value. This load electrical energy is the monthly energy value used for valley filling. It should be noted that the daily valley filling space is the maximum amount of electricity available for valley filling, not the actual valley filling electricity, and it varies from day to day. The leveling space is the time integral of load power below the monthly demand protection value during the normal period on a given day, i.e., the load electrical energy. This load electrical energy is the monthly energy value used for valley filling, and it is also the maximum amount of electricity used for leveling, not the actual leveling electricity, and it varies from day to day. The daily valley filling / leveling space is calculated using the following two formulas.

2. （8） （9） In the formula, E vall.i To fill the valley space on a certain day E flat.i To fill the space for a certain day P 0 represents the monthly demand defense value. p L For load power; ⑦ Calculate the daily average peak reduction space The daily peak reduction space is the time integral of the load power below the monthly demand defense value during the peak period of a certain day, that is, the electrical energy required to reduce the entire load to 0. The load power above the monthly demand defense value is considered to have been reduced by the basic electricity price regulation energy storage configuration. The daily peak shaving capacity is the maximum amount of electricity used for peak shaving, not the actual amount of electricity shaving, and it is impossible to completely reduce the peak load to 0; a peak period includes two periods, namely the morning peak period and the evening peak period, and the daily peak shaving capacity is the sum of the peak shaving capacity of the two peak periods; if there are peak electricity prices in several months, the impact of peak electricity prices should be considered. The daily average peak reduction space is the expected value of the peak reduction space for each day within the month; （10） In the formula, is E peak.i For peak reduction space on a certain day; ⑧ Calculate monthly electricity demand adjustment The smaller of the monthly average daily valley filling / flattening space and the daily average peak shaving space is taken as the upper limit of energy storage configuration on the large industrial user side. The energy storage hours are distinguished, and several energy storage configurations are taken from zero to the upper limit of energy storage configuration. The monthly power regulation revenue of each energy storage configuration is obtained by using a daily optimization method. The equivalent static payback period and internal rate of return are calculated based on the monthly data. Then, each energy storage configuration can obtain a set of monthly power regulation revenue, equivalent static payback period and equivalent internal rate of return indicators. According to the project's requirements for the above indicators, a certain energy storage configuration is selected as the monthly power regulation demand. ⑨ Calculate annual electricity regulation demand After obtaining the monthly energy storage configuration demand from step ⑧, the calculated power value of the annual energy storage configuration demand is obtained. P cal.qua and energy calculation value E cal.qua The expected value can be obtained from the adjustment demand of each month; if the historical data has a large time scale, such as multi-year historical data, then the expected value of the adjustment demand of each month obtained from multi-year historical data should be used. ⑩ Determine the final energy storage configuration for large industrial users. Combining the annual basic electricity price-regulated energy storage demand obtained from steps ①-⑤ with the annual electricity price-regulated energy storage demand obtained from steps ⑥-⑨, the calculated value of the energy storage scale on the large industrial user side is obtained. The final energy storage configuration values ​​are determined based on the following principles: First, the larger value of the annual basic electricity price-regulated energy storage demand and the electricity price-regulated energy storage demand is taken; second, the superposition value of the annual basic electricity price-regulated energy storage demand and the electricity price-regulated energy storage demand is taken, as shown in the following formula. （11）。

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