Method, device and system for optimizing electric charge types of industrial and commercial users after distribution and storage

Abstract

The invention relates to the technical field of energy digitization, in particular to a method, a device and a system for optimizing electric charge types of industrial and commercial users after distribution and storage. The invention discloses an electric charge type optimization method for industrial and commercial users after distribution and storage. The method comprises the following steps: S1, data acquisition and processing; s2, modeling in peak and valley periods; s3, calculating monthly load characteristics; s4, calculating monthly capacity electricity price income; s5, calculating monthly demand electricity price income; s6, recommending electric charge types; s7, carrying out annual strategy optimization and policy adaptation; and S8, visually outputting a result. The invention further discloses a device and a system for optimizing the electric charge type after distribution and storage of the industrial and commercial users, which are used for implementing the method. According to the invention, the electricity cost of industrial and commercial users can be effectively reduced and the economic benefit of the energy storage system can be improved by intelligently optimizing the electricity charge type selection and the operation strategy of the energy storage system. Meanwhile, according to the device and the computer program product provided by the invention, scientific and convenient decision support can be provided for the user, and popularization and application of user-side energy storage are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of energy digitization, and particularly to a method, device, and system for optimizing electricity charge types after energy storage is configured for industrial and commercial users. Background Art

[0002] With the transformation of the global energy structure and the promotion of the "dual-carbon" goal, the proportion of renewable energy power generation represented by wind power and photovoltaic power has been continuously rising. However, the inherent intermittency and volatility of renewable energy pose severe challenges to the safe and stable operation of the power grid. In this context, energy storage technology, especially energy storage on the user side, as an important means to regulate the balance between power supply and demand and enhance the flexibility of the power grid, is attracting more and more attention.

[0003] As the main force of electricity consumption, industrial and commercial users often have characteristics such as large peak-valley differences in electricity load and high electricity consumption costs. Configuring an energy storage system, abbreviated as "energy storage configuration", can help industrial and commercial users achieve multiple values such as peak-valley arbitrage, demand management, and backup power supply, effectively reducing electricity consumption costs and improving electricity consumption reliability. However, the current selection and optimization of electricity charge types after energy storage configuration for industrial and commercial users still face many challenges:

[0004] 1. The electricity charge structure is complex, and it is difficult for users to make decisions: The current electricity charge system includes various types such as capacity-based electricity price, peak-valley time-of-use electricity price, and demand-based electricity price, and there are significant policy differences in different regions. Users lack professional knowledge and tools and are difficult to accurately evaluate the benefits of energy storage configuration under different electricity charge types, resulting in difficult decision-making.

[0005] 2. The operation strategy of the energy storage system does not match the electricity charge type: Different electricity charge types correspond to different charge and discharge strategies of the energy storage system. If the strategy is formulated improperly, it may lead to the benefits of energy storage configuration being lower than expected or even increasing electricity consumption costs.

[0006] 3. Lack of intelligent optimization tools: Existing technologies are mostly limited to the optimization of energy storage systems under a single electricity charge type and lack intelligent optimization tools that comprehensively consider various factors such as multiple electricity charge types, user load characteristics, and energy storage system parameters, making it difficult to meet the personalized needs of users. Summary of the Invention

[0007] In order to overcome the deficiencies of the prior art, the present invention provides a method, device, and system for optimizing electricity charge types after energy storage is configured for industrial and commercial users, which can solve the complexity and uncertainty faced by industrial and commercial users in selecting and optimizing electricity charge types after configuring an energy storage system. It can intelligently optimize the selection of electricity charge types according to the electricity consumption characteristics of users, the parameters of the energy storage system, and the policies of different electricity charge types, so as to maximize the economic benefits of the energy storage system. To solve the above technical problems, the present invention provides the following technical solutions:

[0008] An optimization method for electricity charge types after energy storage allocation for industrial and commercial users, comprising the following steps:

[0009] S1. Data collection and processing: cleaning, normalizing, and integrating the load curves of the collected original data;

[0010] S2. Peak-valley period modeling: modeling the peak-valley periods according to the peak-valley period division and peak-valley time-of-use electricity price data;

[0011] S3. Calculating monthly load characteristics, including the monthly maximum load and the theoretical minimum value of the maximum demand load;

[0012] S4. Calculating the monthly capacity electricity price revenue, and the calculation formula is: total monthly capacity electricity price revenue = charge-discharge revenue + current basic electricity charge - capacity basic electricity charge;

[0013] S5. Calculating the monthly demand electricity price revenue, and the calculation formula is: total monthly demand revenue = charge-discharge revenue + current basic electricity charge - demand basic electricity charge;

[0014] S6. Electricity charge type recommendation: if the revenue difference is greater than 0, recommend the capacity electricity price mode; if the revenue difference is less than 0, recommend the demand electricity price mode, and recommend the corresponding maximum monthly demand load;

[0015] S7. Annual strategy optimization and policy adaptation: when there is a conflict between the recommended electricity charge type plan and the current policy, automatically change the plan according to the revenue difference between different plans to meet the policy requirements, and give the revised recommended plan;

[0016] S8. Visual output of results: output the results obtained in step S7 in the form of a table.

[0017] On the basis of adopting the above technical solutions, the present invention can also adopt the following further technical solutions:

[0018] The data collection scope in step S1 at least includes:

[0019] User electricity consumption information, including transformer capacity, electricity price type, user type, maximum load rate limit, and minimum load rate limit, and the user type includes large industrial type and general industrial and commercial type;

[0020] Historical electricity consumption data, including complete annual time-of-use load values and new energy power generation data, and the sampling interval of the complete annual time-of-use load values is not greater than 15 minutes;

[0021] Energy storage system parameters, including energy storage device capacity, maximum charge-discharge power, charge-discharge efficiency, and maximum discharge depth;

[0022] Electricity charge policy information, including peak-valley period division, time-of-use electricity price, capacity electricity price, demand electricity price, and photovoltaic grid-connected electricity price.

[0023] In step S1, the data cleaning method is to remove outliers, and preprocessing the data can ensure the consistency and analyzability of the data.

[0024] The said step S2 includes the following steps:

[0025] S21. Period classification: Based on policy information, each day is divided into five types of periods: peak, high peak, flat period, low valley, and deep valley, and seasonal differences are also considered.

[0026] S22. Peak-valley electricity price rates: Different electricity price rates correspond to the periods divided in step S21. Generally, the electricity price during peak hours is the highest, the electricity price during valley hours is the lowest, and the electricity price during normal hours is between the two.

[0027] S23. Establishment of peak-valley period model: Combining the data in steps S21 and S22, define the period variable and the electricity price rate variable.

[0028] The specific method is as follows:

[0029] Suppose a day is divided into n consecutive periods, the duration of each period is Δt hours, and the period type is one of the above 5 types. Define the following variables:

[0030] t: Period index, t = 1, 2, ……, n

[0031] r t : Electricity price rate of period t (yuan / kWh)

[0032] According to the division of peak-valley periods, the electricity price rate r t can be expressed as:

[0033]

[0034] where, T 尖 、T 高 、T 平 、T 低 、T 深 respectively represent the sets of five types of periods: peak, high peak, flat period, low valley, and deep valley;

[0035] S24. Optimization of charge and discharge periods: Combining the model in step S23, merge consecutive charge and discharge periods to form a large-period cycle strategy, and improve the charge and discharge frequency and economy of energy storage devices.

[0036] In step S3, the monthly maximum load is the peak power consumption of the current month. The calculation method for the theoretical minimum value of the demand maximum load is as follows: Without considering the energy storage capacity and power limitations, calculate the charge-discharge load balance point for each day of the current month, and achieve the following formula: Charge amount × Charge-discharge efficiency = Discharge amount. Then, select the day with the largest load balance point, which is the theoretical minimum value of the monthly demand maximum load.

[0037] Step S4 includes the following steps:

[0038] S41. Assume that under the premise of adopting the capacity-based electricity price per month, set the operating boundaries of the energy storage device. The operating boundaries include the upper limit of the user load and the lower limit of the user load, where:

[0039] Upper limit of user load = User capacity × Maximum load rate limit;

[0040] Lower limit of user load = User capacity × Minimum load rate limit;

[0041] S42. According to the operating boundaries set in step S41, conduct charge-discharge simulation of the energy storage device. That is, in the capacity-based electricity price mode, the energy storage device only charges during the low and deep low periods and discharges during the peak and sharp peak periods, and does not operate during the flat period;

[0042] S43. After completing the charge-discharge simulation of the current month in step S42, calculate the charge-discharge income, where:

[0043] Charge-discharge income = Discharge electricity fee - Charge electricity fee;

[0044] Discharge electricity fee = Peak discharge amount × Peak electricity price + High peak discharge amount × High peak electricity price;

[0045] Charge electricity fee = Low valley charge amount × Low valley electricity price + Deep low valley charge amount × Deep low valley electricity price;

[0046] S44. Calculate the basic electricity fee. The calculation formula is:

[0047] Capacity basic electricity fee = Capacity × Capacity-based electricity price;

[0048] S45. Calculate the total monthly capacity income for the current month. The calculation formula is:

[0049] Total monthly capacity income = Charge-discharge income + Current basic electricity fee - Capacity basic electricity fee.

[0050] In step S5, assuming that the demand-based electricity price is adopted per month, the demand-based electricity price mode needs to first calculate the monthly demand maximum load with the highest income under the current energy storage capacity, and then conduct charge-discharge simulation to calculate the income;

[0051] Specifically, it includes the following steps:

[0052] S51. Set the upper and lower limits of the iteration interval, where:

[0053] The upper limit of the iteration interval = MIN(transformer capacity × demand price / capacity price, monthly maximum load);

[0054] The lower limit of the iteration interval = MAX(theoretical minimum value of maximum demand load, monthly maximum load - maximum power of energy storage);

[0055] S52. Within the interval obtained in step S51, through iterative calculation, find the monthly maximum demand load with the maximum total revenue, and set the operating boundaries of the energy storage device. The operating boundaries include the upper limit of user load and the lower limit of user load, where:

[0056] The upper limit of user load = monthly maximum demand load;

[0057] The lower limit of user load = user capacity × minimum load rate limit;

[0058] S53. According to the operating boundaries set in step S52, conduct charge and discharge simulation of the energy storage device. In the demand price mode, the energy storage device only charges during the low - valley and deep - valley periods, but can discharge at all times. During the low - valley, deep - valley, and flat periods, when the user load is higher than the maximum demand load, it is necessary to discharge to reduce the user load. During the peak and spike periods, the energy storage device discharges as much as possible;

[0059] S54. After completing the charge and discharge simulation of the current month in step S53, count the charge and discharge revenue, where:

[0060] Charge and discharge revenue = discharge electricity fee - charge electricity fee;

[0061] Discharge electricity fee = spike discharge volume × spike price + peak discharge volume × peak price + flat - period discharge volume × flat - period price + low - valley discharge volume × low - valley price + deep - valley discharge volume × deep - valley price;

[0062] Charge electricity fee = low - valley charge volume × low - valley price + deep - valley charge volume × deep - valley price;

[0063] S55. Calculate the basic electricity fee to obtain the revenue under the demand price mode for the current month, where:

[0064] Demand - side basic electricity fee = maximum demand load × demand price;

[0065] S56. Calculate the total monthly demand revenue. The calculation formula is:

[0066] Total monthly demand revenue = charge and discharge revenue + current situation basic electricity fee - demand - side basic electricity fee.

[0067] The revenue difference in step S6 is the revenue difference between the capacity-based electricity price and the demand-based electricity price calculated monthly, and the calculation formula is: Revenue difference = Revenue from capacity-based electricity price model - Revenue from demand-based electricity price model.

[0068] The results in step S8 include load characteristics, current basic electricity charges, calculation results of capacity-based electricity price schemes, calculation results of demand-based electricity price schemes, and recommended schemes.

[0069] On the other hand, the present invention also provides the following technical solutions:

[0070] An optimization device for electricity charge types after energy storage configuration for industrial and commercial users, used to implement the method as described above, includes:

[0071] A data acquisition module, configured to acquire the user's electricity load data, energy storage system parameters, and electricity charge policy information;

[0072] A data processing module, configured to perform preprocessing and normalization processing on the acquired data;

[0073] A peak-valley period modeling module, configured to establish a mathematical model for peak-valley period division;

[0074] An evaluation and selection module, configured to evaluate the revenues of different electricity charge types and select the optimal scheme;

[0075] A result output module, configured to output the optimization results in a visual form.

[0076] On the other hand, the present invention also provides the following technical solutions:

[0077] An optimization system for electricity charge types after energy storage configuration for industrial and commercial users, used to implement the method as described above, includes a computer-readable storage medium storing computer program instructions, and when the instructions are executed by a processor, the steps of the above-mentioned optimization method for electricity charge types after energy storage configuration for industrial and commercial users are implemented.

[0078] Compared with the prior art, the beneficial effects that the present invention can achieve are:

[0079] By intelligently optimizing the selection of electricity charge types and the operation strategy of the energy storage system, the present invention can effectively reduce the electricity consumption cost of industrial and commercial users and improve the economic benefits of the energy storage system. At the same time, the device and computer program product provided by the present invention can provide users with scientific and convenient decision-making support and promote the popularization and application of user-side energy storage. Description of the Drawings

[0080] Figure 1 It is a flowchart of an optimization method, device, and system for electricity charge types after energy storage configuration for industrial and commercial users of the present invention.

[0081] Figure 2This is the result output table of a method, device, and system for optimizing electricity charge types for industrial and commercial users after energy storage allocation in the present invention.

[0082] Figure 3 This is the schematic diagram of the device structure of a method, device, and system for optimizing electricity charge types for industrial and commercial users after energy storage allocation in the present invention.

[0083] Figure 4 This is the result output table of Embodiment 1 of a method, device, and system for optimizing electricity charge types for industrial and commercial users after energy storage allocation in the present invention. Detailed implementation manners

[0084] In combination with the accompanying drawings, a method, device, and system for optimizing electricity charge types for industrial and commercial users after energy storage allocation provided by the present invention will be further described.

[0085] Embodiment 1: Taking an enterprise in Zhejiang as an example, the full-process optimization calculation of electricity charge types is carried out as follows:

[0086] Step S1: Data collection and processing.

[0087] Collect user electricity consumption information, historical electricity load data and new energy power generation data of the user, energy storage system parameters, and local electricity charge policies.

[0088] The content of the user electricity consumption information is shown in Table 1. The user's distribution transformer capacity is 1600 kVA, and photovoltaic power generation has been installed, with a photovoltaic installed capacity of 796 kW.

[0089] Table 1:

[0090]

[0091] The user's historical electricity load data is shown in Table 2. The user has a total of three meters, one electricity consumption meter and two power generation meters, and the complete load for one year is obtained. In the example, the time-sharing load data from September 1, 2023 to August 31, 2024 is obtained, and the sampling interval is 15 minutes.

[0092] Table 2:

[0093] Time Electricity meter Power generation meter 1 Power generation meter 2 2023 / 9 / 10:00 264.6 0 -0.048 2023 / 9 / 10:15 218.1 0 -0.048 2023 / 9 / 10:30 206.7 0 -0.048 2023 / 9 / 10:45 197.7 0 -0.048 2023 / 9 / 11:00 159.3 0 -0.048 2023 / 9 / 11:15 156.9 0 -0.048 2023 / 9 / 11:30 155.1 0 -0.048 2023 / 9 / 11:45 144.6 0 -0.048 …… …… …… …… 2024 / 8 / 3122:00 309.9 0 0 2024 / 8 / 3122:15 337.2 0 0 2024 / 8 / 3122:30 315.6 0 0 2024 / 8 / 3122:45 323.1 0 0 2024 / 8 / 3123:00 308.4 0 0 2024 / 8 / 3123:15 328.8 0 0 2024 / 8 / 3123:30 264 0 0 2024 / 8 / 3123:45 273 0 0

[0094] The energy storage system parameters are shown in Table 3. Only the parameters required in this embodiment are listed here.

[0095] Table 3:

[0096]

[0097] The local electricity charge policy information includes peak-valley period division, peak-valley time-sharing electricity price, capacity electricity price, demand electricity price, and photovoltaic on-grid electricity price. Since the user is located in Zhejiang, the electricity charge policy information of Zhejiang Province is obtained.

[0098] In Zhejiang Province, the peak-valley periods for large industrial electricity users and general industrial and commercial electricity users are unified, divided into five types: off-peak, deep off-peak, flat period, peak, and super peak, as shown in Table 4.

[0099] Table 4:

[0100]

[0101] Deep off-peak electricity price during major holidays. From 10:00 to 14:00 during the Spring Festival, Labor Day, and National Day are set as deep off-peak periods. The specific times of the three holidays shall be subject to the annual announcement.

[0102] The floating ratio of the electricity fee policy in Zhejiang Province is based on the flat-period electricity price, as shown in Table 5. The flat-period electricity price includes the on-grid electricity price, line loss fees in the on-grid link, system operation fees, transmission and distribution electricity price (excluding capacity (demand) electricity price), government funds and surcharges.

[0103] Table 5:

[0104]

[0105]

[0106] Since the flat-period electricity price in Zhejiang Province is related to the monthly electricity purchase price on behalf of users, it is necessary to obtain the time-of-use electricity price for each month according to the electricity purchase price announcement for industrial and commercial users on behalf of relevant units issued each month, as shown in Table 6.

[0107] Table 6:

[0108] Period type January February March April May June July August September October November December Peak 1.1845 1.2277 1.2109 1.2733 1.2507 1.2401 1.2511 1.2538 1.2436 1.2043 1.6144 1.1763 High peak 0.8144 0.844 0.8325 0.8754 0.8599 0.9219 0.9294 0.9314 0.9238 0.8279 1.1099 0.8087 Low valley 0.35 0.3627 0.3578 0.3762 0.3695 0.3758 0.3789 0.3797 0.3766 0.3558 0.477 0.3475 Deep valley - 0.1395 - - 0.1421 - - - - 0.1368 - - Flat section 0.673 0.6975 0.688 0.7234 0.7106 0.7091 0.7149 0.7164 0.7106 0.6842 0.9173 0.6683

[0109] The capacity electricity price, demand electricity price, and photovoltaic on-grid electricity price are shown in Table 7.

[0110] Table 7:

[0111]

[0112] Preprocess the historical electricity load data and new energy power generation data, including data cleaning and normalization, to ensure the accuracy and consistency of the data, and merge the historical electricity load data and new energy power generation data into the final load curve data, and the results are shown in Table 8.

[0113] Table 8:

[0114]

[0115]

[0116] Step S2: Peak-valley period modeling.

[0117] Generate the peak-valley period division for January to December and the deep valley days according to the peak-valley period policy obtained in step S1, as shown in Table IX.

[0118] Table IX:

[0119]

[0120]

[0121] Analyze each peak-valley period division and generate a peak-valley period model respectively for subsequent analysis and calculation. Taking the peak-valley period division in January as an example, the generated model is shown in Table X.

[0122] Table X:

[0123] Large period serial number Small period serial number Period type Duration Start time 1 1 1 8 0:00～8:00 1 2 -1 1 8:00～9:00 1 3 -2 2 9:00～11:00 2 4 1 2 11:00～13:00 2 5 0 2 13:00～15:00 2 6 -2 2 15:00～17:00 2 7 -1 6 17:00～23:00 2 8 0 1 23:00～24:00

[0124] The electricity prices for each period are shown in Table XI.

[0125] Table XI:

[0126]

[0127]

[0128] Step S3: Calculate the load characteristics.

[0129] According to the load curve data obtained after data preprocessing in step S1, analyze the load characteristics and calculate the monthly maximum load and the lower limit of the demand maximum load respectively. The results are shown in Table XII.

[0130] Among them, in the present invention, steps S3 to S6 are all calculated only for the current month. For the convenience of display, the calculation results for 12 months are listed at one time.

[0131] Table XII:

[0132]

[0133] Step S4: Calculate the monthly capacity electricity price revenue.

[0134] Specifically:

[0135] Step S41: Assume that the capacity electricity price is adopted in the current month, and set the operation boundary of the energy storage device, including: the upper limit of the user load and the lower limit of the user load. Among them:

[0136] The upper limit of the user load = the user capacity × the maximum load rate limit = 1600 * 0.8 = 1280 kW;

[0137] The lower limit of the user load = the user capacity × the minimum load rate limit = 0 kW;

[0138] Since the calculation of the basic electricity charge revenue requires subtracting the current basic electricity charge from the capacity electricity price, it is necessary to calculate the current basic electricity charge according to the user's current electricity charge type. The calculation results are shown in Table XIII.

[0139] Table XIII:

[0140]

[0141] Step S42: According to the set operating boundary, perform charge and discharge simulation of the energy storage device. In the capacity electricity price mode, the energy storage device only charges during the low valley and deep valley periods, discharges during the peak and super peak periods, and does not work during the flat period.

[0142] After completing the charge and discharge simulation for the current month, calculate the charge and discharge revenue. The calculation formula is as follows:

[0143] 1. Charge and discharge revenue = Discharge electricity charge - Charge electricity charge;

[0144] 2. Discharge electricity charge = Super peak discharge amount × Super peak electricity price + Peak discharge amount × Peak electricity price;

[0145] 3. Charge electricity charge = Low valley charge amount × Low valley electricity price + Deep valley charge amount × Deep valley electricity price.

[0146] Step S44: Calculate the basic electricity charge to obtain the revenue under the capacity electricity price mode for the current month. The formula is as follows:

[0147] Capacity basic electricity charge = Capacity × Capacity electricity price.

[0148] Step S45: Calculate the total monthly capacity revenue for the current month. The calculation formula is as follows:

[0149] Total monthly capacity revenue = Charge and discharge revenue + Current basic electricity charge - Capacity basic electricity charge.

[0150] The calculation results are shown in Table XIV.

[0151] Table XIV:

[0152]

[0153] Step S5: Calculate the monthly demand electricity price revenue.

[0154] Assume that in the case of using the demand electricity price for the current month, the demand electricity price mode needs to first calculate the maximum monthly demand load with the highest revenue under the current energy storage capacity, and then perform charge and discharge simulation to calculate the revenue.

[0155] Specifically, it includes the analysis of the optimal monthly demand maximum load, that is, calculating the optimal monthly demand maximum load through an iterative method using tools, including:

[0156] Step S51: Set the upper and lower limits of the iteration interval. Taking January as an example.

[0157] The upper limit of the iteration interval = MIN (transformer capacity × demand price / capacity price, monthly maximum load) = 819 kW;

[0158] The lower limit of the iteration interval = MAX (theoretical minimum of demand maximum load, monthly maximum load - maximum power of energy storage) = 565 kW;

[0159] Within the interval obtained from the above calculations, through iterative calculations, find the monthly demand maximum load with the maximum total revenue. The calculation results are shown in "Demand Maximum Load (Recommended Value)" in Table XV.

[0160] Step S52: Set the operating boundaries of the energy storage device, including: upper limit of user load, lower limit of user load. The calculation formulas are as follows:

[0161] The upper limit of user load = monthly demand maximum load = 819 kW;

[0162] The lower limit of user load = user capacity × minimum load rate limit = 0 kW.

[0163] Step S53: According to the set operating boundaries, conduct charge and discharge simulations of the energy storage device. In the demand price mode, the energy storage device only charges during low and deep valley periods, but can discharge at all times. During low, deep valley, and flat periods, when the user load is higher than the demand maximum load, it is necessary to discharge to reduce the user load. During peak and spike periods, the energy storage device discharges as much as possible.

[0164] Step S54: After completing the charge and discharge simulation for the current month, calculate the charge and discharge revenue. The calculation formula is as follows:

[0165] Charge and discharge revenue = discharge electricity fee - charge electricity fee;

[0166] Discharge electricity fee = peak discharge amount × peak electricity price + high - peak discharge amount × high - peak electricity price + flat - period discharge amount × flat - period electricity price + low - valley discharge amount × low - valley electricity price + deep - valley discharge amount × deep - valley electricity price;

[0167] Charge electricity fee = low - valley charge amount × low - valley electricity price + deep - valley charge amount × deep - valley electricity price.

[0168] Step S55: Calculate the basic electricity fee to obtain the revenue under the demand price mode for the current month. The calculation formula is as follows:

[0169] Demand basic electricity fee = demand maximum load × demand electricity price.

[0170] Step S56: Calculate the total revenue of monthly demand for the current month. The calculation formula is as follows:

[0171] Total monthly demand revenue = charge and discharge revenue + current basic electricity charge - demand basic electricity charge.

[0172] The calculation results are shown in Table XV.

[0173] Table XV:

[0174]

[0175] Step S6: Select the recommended electricity charge type.

[0176] Compare the monthly revenue differences between the two schemes, and select the scheme with the larger revenue as the recommended electricity charge type configuration scheme for the current month. The results are shown in Table XVI.

[0177] Table XVI:

[0178]

[0179] Step S7: Optimize the combination of electricity charge types.

[0180] The recommended electricity charge type is calculated based on the user's electricity load characteristics, and there may be various combinations. However, the actual policy does not allow frequent changes in the electricity charge type. Then, there may be a conflict between the recommended scheme and the policy.

[0181] According to the two-part tariff policy in Zhejiang, the shortest change cycle of the electricity charge type is 3 months. Obviously, the above recommended electricity charge type conflicts with the current policy. Based on the monthly revenue differences, optimize the combination of the recommended electricity charge types to obtain the final recommended electricity charge type configuration scheme as shown in Table XVII.

[0182] Table XVII:

[0183]

[0184] Step S8: Output the results.

[0185] As Figure Four shown, output the results in the form of a table, including load characteristics, current basic electricity charge, calculation results of the capacity tariff scheme, calculation results of the demand tariff scheme, and the recommended scheme. Users can make further decisions based on the output results.

[0186] The present invention has been illustrated and described with reference to the preferred embodiments. However, those of ordinary skill in the art should understand that various changes in form and details can be made within the scope of the claims.

Claims

1. An optimization method for electricity charge types after energy storage configuration for industrial and commercial users, characterized in that It includes the following steps: S1. Data collection and processing, cleaning, normalizing the collected raw data, and integrating the load curve; S2. Modeling of peak and valley periods, modeling the peak and valley periods according to the division of peak and valley periods and the time-of-use electricity price data; S3. Calculating monthly load characteristics, including the monthly maximum load and the theoretical minimum value of the maximum demand load; S4. Calculating the monthly capacity electricity price revenue, and the calculation formula is: total monthly capacity electricity price revenue = charge and discharge revenue + current basic electricity charge - capacity basic electricity charge; S5. Calculating the monthly demand electricity price revenue, and the calculation formula is: total monthly demand revenue = charge and discharge revenue + current basic electricity charge - demand basic electricity charge; S6. Electricity price type recommendation, if the revenue difference is greater than 0, the capacity electricity price mode is recommended; if the revenue difference is less than 0, the demand electricity price mode is recommended, and the corresponding monthly maximum demand load is recommended; S7. Annual strategy optimization and policy adaptation, when there is a conflict between the recommended electricity price type plan and the current policy, automatically change the plan according to the revenue difference between different plans so as to meet the policy requirements, and give the revised recommended plan; S8. Visual output of results, outputting the results obtained in step S7 in the form of a table.

2. The optimized method for electricity charge types after energy storage allocation for industrial and commercial users according to claim 1, characterized in that The data collection scope in step S1 at least includes: User electricity consumption information, including distribution transformer capacity, electricity price type, user type, maximum load rate limit, and minimum load rate limit, and the user type includes large industrial type and general industrial and commercial type; Historical electricity consumption data, including the complete annual time-of-use load values and new energy power generation data, and the sampling interval of the complete annual time-of-use load values is not greater than 15 minutes; Energy storage system parameters, including energy storage device capacity, maximum charge and discharge power, charge and discharge efficiency, and maximum discharge depth; Electricity price policy information, including peak and valley period division, time-of-use electricity price, capacity electricity price, demand electricity price, and PV grid connection price.

3. The optimized method for electricity charge types after energy storage configuration for industrial and commercial users according to claim 1, characterized in that Step S2 includes the following steps: S21. Period classification, dividing each day into five types of periods: peak, high peak, flat period, low valley, and deep valley based on policy information, and considering seasonal differences; S22. Peak and valley electricity price rates, corresponding different electricity price rates to the periods divided in step S21; S23. Establishment of peak and valley period model, defining period variables and electricity price rate variables by combining the data in steps S21 and S22; S24. Optimization of charge and discharge periods, combining the model in step S23, merging continuous charge and discharge periods to form a large-period cyclic strategy.

4. The method for optimizing the electricity charge type after energy storage allocation for industrial and commercial users according to claim 1, wherein In step S3, the monthly maximum load is the peak power consumption in the current month, and the calculation method of the theoretical minimum value of the maximum demand load is: without considering the energy storage capacity and power limit, calculate the charge and discharge load balance point for each day of the current month, and realize the following formula: charge amount × charge and discharge efficiency = discharge amount, and take the day with the largest load balance point as the theoretical minimum value of the monthly maximum demand load.

5. The optimized method for electricity charge types after energy storage allocation for industrial and commercial users according to claim 1, characterized in that Step S4 includes the following steps: S41. Assuming that the capacity electricity price is adopted every month, setting the operation boundary of the energy storage device, and the operation boundary includes the user load upper limit and the user load lower limit, where: User load upper limit = user capacity × maximum load rate limit; Lower limit of user load = user capacity × minimum load rate limit; S42. According to the operating boundary set in step S41, perform charge and discharge simulation of the energy storage device, that is, in the capacity tariff mode, the energy storage device only charges during the low valley and deep valley periods, discharges during the peak and spike periods, and does not work during the flat period; S43. After completing the charge and discharge simulation of the current month in step S42, calculate the charge and discharge benefits, where: Charge and discharge benefits = discharge electricity fee - charge electricity fee; Discharge electricity fee = spike discharge volume × spike electricity price + peak discharge volume × peak electricity price; Charge electricity fee = low valley charge volume × low valley electricity price + deep valley charge volume × deep valley electricity price; S44. Calculate the basic electricity fee, and the calculation formula is: Capacity basic electricity fee = capacity × capacity electricity price; S45. Calculate the total monthly capacity revenue of the current month, and the calculation formula is: Total monthly capacity revenue = charge and discharge benefits + current basic electricity fee - capacity basic electricity fee.

6. The method for optimizing the electricity fee type after energy storage allocation for industrial and commercial users according to claim 1, wherein The step S5 includes the following steps: S51. Set the upper and lower limits of the iteration interval, where: Upper limit of the iteration interval = MIN(distribution transformer capacity × demand electricity price / capacity electricity price, monthly maximum load); Lower limit of the iteration interval = MAX(theoretical minimum value of the maximum demand load, monthly maximum load - maximum power of the energy storage device); S52. Within the interval obtained in step S51, through iterative calculation, find the monthly maximum demand load with the maximum total revenue, and set the operating boundary of the energy storage device. The operating boundary includes the upper limit of user load and the lower limit of user load, where: Upper limit of user load = monthly maximum demand load; Lower limit of user load = user capacity × minimum load rate limit; S53. According to the operating boundary set in step S52, perform charge and discharge simulation of the energy storage device. In the demand tariff mode, the energy storage device only charges during the low valley and deep valley periods, but can discharge at all times. During the low valley, deep valley and flat periods, when the user load is higher than the maximum demand load, it is necessary to discharge to reduce the user load. During the peak and spike periods, the energy storage device discharges as much as possible; S54. After completing the charge and discharge simulation of the current month in step S53, calculate the charge and discharge benefits, where: Charge and discharge benefits = discharge electricity fee - charge electricity fee; Discharge electricity fee = spike discharge volume × spike electricity price + peak discharge volume × peak electricity price + flat period discharge volume × flat period electricity price + low valley discharge volume × low valley electricity price + deep valley discharge volume × deep valley electricity price; Charge electricity fee = low valley charge volume × low valley electricity price + deep valley charge volume × deep valley electricity price; S55. Calculate the basic electricity fee to obtain the revenue under the demand tariff mode of the current month, where: Demand basic electricity fee = maximum demand load × demand electricity price; S56. Calculate the total monthly demand revenue of the current month, and the calculation formula is: Total monthly demand revenue = charge and discharge benefits + current basic electricity fee - demand basic electricity fee.

7. A method for optimizing the electricity charge type after energy storage allocation for industrial and commercial users according to claim 1, characterized in that The revenue difference in step S6 is the revenue difference between the capacity tariff and the demand tariff calculated monthly, and the calculation formula is: revenue difference = capacity tariff mode revenue - demand tariff mode revenue.

8. A method for optimizing the electricity charge type after energy storage allocation for industrial and commercial users, as claimed in claim 1, wherein The results in step S8 include load characteristics, current basic electricity fee, calculation results of the capacity tariff plan, calculation results of the demand tariff plan, and recommended plan.

9. An optimized device for electricity charge types after energy storage configuration for industrial and commercial users, characterized in that For implementing the method described in any one of claims 1-8, including: A data acquisition module, configured to acquire the user's electricity load data, energy storage system parameters, and electricity fee policy information; A data processing module, configured to perform preprocessing and normalization on the acquired data; A peak-valley period modeling module, configured to establish a mathematical model for peak-valley period division; An evaluation and selection module, configured to evaluate the benefits of different electricity fee types and select the optimal solution; A result output module, configured to output the optimization result in a visual form.

10. An optimized system for electricity charge types after energy storage allocation for industrial and commercial users, characterized in that A computer-readable storage medium for implementing the method according to any one of claims 1-8, including computer program instructions stored therein.

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