A power industry expansion optimization decision method and system
By establishing a decision-making model that minimizes the total cost of business expansion, and combining historical data and operational simulations, the capacity and billing methods for business expansion applications are optimized, solving the rigidity problem of the traditional business expansion process and achieving precise and economical optimization of business expansion applications.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- STATE GRID JIANGSU ELECTRIC POWER CO LTD MARKETING SERVICE CENT
- Filing Date
- 2023-12-14
- Publication Date
- 2026-07-31
AI Technical Summary
Traditional business expansion processes are relatively rigid and do not meet the high-quality development needs of the current water, electricity, gas, and heating industries. In particular, the demand-based electricity pricing mechanism linked to load factor poses new challenges to the optimization of decision-making for power business expansion applications.
A decision-making model with the goal of minimizing the total cost of business expansion is adopted. By deconstructing the influencing factors and mechanisms of the goal, and combining historical business expansion data and industry classification, the future electricity load is predicted, the business expansion application capacity and billing method are optimized, and the capacity value is adjusted by operation simulation to select the best billing method.
It has enabled the accurate determination of the installed capacity for business expansion applications and the optimization of billing methods, reduced investment and operating costs, improved the economic efficiency of electricity use, and met the needs of high-quality development.
Smart Images
Figure CN117829688B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power grid operation optimization technology, and relates to a power industry expansion application optimization decision-making method and system. Background Technology
[0002] For users expanding their power supply, reducing investment and optimizing operations are inherent needs. With the development of the water, electricity, gas, and heating industries, the investment and responsibility entities for supporting projects have changed. Electricity pricing, on the one hand, guides users to rationally apply for expansion capacity based on their own load behavior; on the other hand, it has also brought new changes to the expansion application model. In particular, the demand-based pricing mechanism is linked to the load factor. For two-part tariff users who choose the demand-based pricing method, if their monthly electricity consumption per kVA reaches 260 kWh or more, the monthly demand-based price will be charged at 90% of the approved standard. This change has a significant impact on the expansion model and raises new demands for optimized decision-making in power expansion applications. Traditional expansion processes are relatively rigid and do not meet the current high-quality development needs of the water, electricity, gas, and heating industries. Summary of the Invention
[0003] To address the shortcomings of existing technologies, this invention provides a method and system for optimizing power industry expansion application decisions, thereby achieving optimized decision-making for power industry expansion applications.
[0004] The present invention adopts the following technical solution.
[0005] A method for optimizing decision-making in power industry expansion applications includes the following steps:
[0006] Step 1: Establish a business expansion application decision model with the goal of minimizing the total business expansion cost, and deconstruct the decision objective and analyze the influencing factors and mechanisms to identify key influencing factors, including business expansion application capacity and billing method;
[0007] Step 2: Obtain historical business expansion data and classify it by industry to form a basic database;
[0008] Step 3: Based on the industry type of the business expansion user, obtain the load data of users in the same industry from the basic database, and predict the electricity load of the business expansion user;
[0009] Step 4: Determine the application capacity value for business expansion based on the predicted electricity load value, and adjust the application capacity value for business expansion through operation simulation to obtain the final application capacity for business expansion users;
[0010] Step 5: Compare different billing methods based on the maximum demand critical ratio under different voltage levels to determine the billing method for business expansion users. This method, together with the final business expansion application capacity, constitutes the business expansion application optimization decision plan.
[0011] Preferably, in step 3, power load data of users in the same industry are obtained from the basic database at a collection interval of 15 minutes, and multiple prediction models are combined to predict the maximum power load of the business expansion users in the near and medium to long term.
[0012] Preferably, in step 4, the required capacity of the business expansion user in the near and medium-to-long term is determined based on the predicted maximum electricity load values in the near and medium term, the power factor in the near and medium term, and the load rate, respectively, and the maximum value of the two is taken as the business expansion application capacity S;
[0013] S = max(S1, S2) (6)
[0014]
[0015]
[0016] Among them, S1 and S2 represent the required transformer capacity in the near and medium to long term;
[0017] P1 and P2 represent the maximum electricity load in the near and medium to long term, respectively.
[0018] For near-term and medium-to-long-term power factors;
[0019] k1 and k2 represent the near-term and medium-to-long-term load rates, respectively.
[0020] Preferably, in step 4, the principle for adjusting the application capacity value through simulation is as follows:
[0021] After simulating the normal operation of customers applying for new business expansion, the simulated power consumption and load curves are obtained and optimized to reduce the maximum power load, thereby reducing the capacity required for new business expansion. The details are as follows:
[0022] Based on the simulated electricity consumption and load curves, and considering peak-valley time-of-use pricing, the electricity consumption pattern is optimized and adjusted, thereby optimizing the load curve, reducing the peak-valley difference, and reducing the maximum loads P1 and P2, thus reducing the capacity required for business expansion.
[0023] Preferably, in step 4, for the determined business expansion application capacity value, if the business expansion application capacity value is greater than the user's maximum permitted business expansion application capacity, then capacity expansion construction is required, and the process enters the supporting power grid construction stage; otherwise, capacity expansion construction is not required. The formula for calculating the user's maximum permitted business expansion application capacity is:
[0024] P max =min(P c ,P u )-P l (7)
[0026] Where Pmax It is the user's maximum licensed installation capacity, P c It is the maximum remaining capacity of the substation or outgoing line bay to which the user's address belongs, P u P represents the maximum capacity that the line from the substation or bay to the user can carry. l Reduced power transmission to the line.
[0027] Preferably, in step 5, for users whose monthly electricity consumption per kVA is less than 260 kWh and users whose monthly electricity consumption per kVA is greater than or equal to 260 kWh, the maximum demand critical ratio is calculated and denoted as critical ratio 1 and critical ratio 2, respectively.
[0028] Preferably, the formula for calculating the critical ratio 1 is as follows:
[0029]
[0030] K i The critical ratio is 1 at voltage level i;
[0031] X i The price per unit capacity at voltage level i;
[0032] R i Price based on demand at voltage level i;
[0033] Preferably, the formula for calculating the critical ratio 1 is as follows:
[0034] V i =K i / 0.9(9)
[0035] Where K i The critical ratio is 1 at voltage level i;
[0036] V i The critical ratio is 2.
[0037] Preferably, in step 5, the voltage level during the simulation, the monthly electricity consumption per kilovolt-ampere, and the actual maximum monthly demand are obtained;
[0038] The corresponding critical value 1 and critical ratio 2 to be compared are calculated based on the capacity price and demand price of the voltage level.
[0039] Compare the actual maximum monthly demand with the critical ratios 1 and 2 to be compared:
[0040] When the monthly electricity consumption per kVA is less than 260 kW, if the ratio of the actual maximum demand to the operating capacity is lower than the critical ratio of 1, the demand-based electricity price is selected; otherwise, the capacity-based electricity price is selected. When the monthly electricity consumption per kVA is greater than or equal to 260 kW, if the ratio of the actual maximum demand to the operating capacity is lower than the critical ratio of 2, the demand-based electricity price is selected; otherwise, the capacity-based electricity price is selected.
[0041] A power industry expansion application optimization decision system includes:
[0042] The decision target analysis module is used to establish a business expansion application decision model with the goal of minimizing the total business expansion cost, and to deconstruct the decision target and analyze the target influencing factors and mechanisms to derive key influencing factors, including business expansion application capacity and billing method.
[0043] The data analysis module is used to acquire historical business data and classify it by industry to form a basic database;
[0044] The data prediction module is used to obtain load data of users in the same industry from the basic database based on the industry type of the business expansion user, and predict the electricity load of the business expansion user.
[0045] The business expansion application capacity decision module is used to determine the business expansion application capacity value based on the electricity load forecast value, and adjust the business expansion application capacity value through operation simulation to obtain the final business expansion application capacity of the business expansion user.
[0046] The billing method decision module is used to compare different billing methods based on the operation simulation data and the maximum demand threshold under different voltage levels, and determine the billing method for business expansion application users. Together with the final business expansion application capacity, it constitutes the business expansion application optimization decision scheme.
[0047] A terminal includes a processor and a storage medium; the storage medium is used to store instructions.
[0048] The processor is configured to operate according to the instructions to execute the steps of the method.
[0049] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method.
[0050] The beneficial effects of this invention are compared with those of the prior art:
[0051] This invention presents a business expansion application decision model with the goal of minimizing the total cost of business expansion. It deconstructs the decision objective and analyzes the influencing factors and mechanisms, thereby identifying the key influencing factors and determining the core aspects of business expansion: business expansion application capacity and billing method.
[0052] This invention categorizes historical business expansion data by industry, analyzes the characteristics of historical business expansion user behavior and energy consumption, and forms a basic database. This allows for a more detailed differentiation of the characteristics of business expansion in different industries, providing a solid data foundation for formulating the best business expansion plan.
[0053] This invention, from an industry expansion perspective, uses a variety of combined methods to predict future user electricity consumption, which helps to fully understand future electricity trends. Furthermore, by simulating operational data, it further improves the accuracy of capacity selection.
[0054] This invention compares different billing methods based on operational simulation data and the maximum demand threshold at different voltage levels, and provides the optimal billing method, thereby improving the economic efficiency of users' electricity consumption. Attached Figure Description
[0055] Figure 1 This is a flowchart of the power industry expansion application optimization decision-making method of the present invention;
[0056] Figure 2 This is a flowchart illustrating the principle of the power industry expansion application optimization decision-making method of the present invention. Detailed Implementation
[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.
[0058] like Figure 1-2 As shown, this invention provides an optimized decision-making method for power industry expansion applications. It adopts a goal-oriented approach, meticulously analyzing the factors influencing the decision objective, identifying the influencing mechanisms, proposing preliminary solutions, and iteratively simulating to find the optimal solution. Specifically, the method includes the following steps:
[0059] Step 1: Establish a business expansion application decision model with the goal of minimizing the total business expansion cost, and deconstruct the decision objective and analyze the influencing factors and mechanisms to identify key influencing factors, including business expansion application capacity and billing method;
[0060] (1) Deconstructing the goal: This part is mainly goal-oriented, deconstructing the goal, and identifying the factors influencing the decision-making goal.
[0061] In specific implementation, a business expansion application decision model is established with minimizing the total business expansion cost as the decision objective, and the decision objective is deconstructed and the influencing factors and mechanisms of the objective are analyzed; then the objective function is to achieve the minimum total business expansion cost, as shown in equation (1).
[0062] min f C =∑(C grid +C as (1)
[0063] in, fC For the total cost of business expansion, C grid and C as These are the investment costs and operating costs of the supporting power grid, respectively.
[0064] Supporting power grid investment C grid This includes extended investment and expansion investment, as shown in equation (2).
[0065] C grid =∑(P grid +α g (2)
[0066] Where α g and P grid These are the costs of extended investment and the costs of general expansion, respectively.
[0067] The aforementioned expansion investment P grid It is directly related to the expansion of the installed capacity of the business.
[0068] For users within the scope of the two-part system, the above operating costs include basic electricity charges and electricity charges, as shown in equation (3).
[0069] C as =∑(Q x +β*Q d (3)
[0070] Q x and Q d These are electricity charges based on the amount of electricity consumed and basic electricity charges.
[0071] β is the discount factor. The constraint for this factor is that the basic electricity charge for business expansion users is formed by demand-based electricity pricing. The value is generally 0.9 or 1. It can be interpreted as follows: if the monthly electricity consumption per kVA reaches 260 kWh or more, the monthly demand-based electricity price will be implemented at 90% of the approved standard, then β is 0.9; otherwise, it is 1.
[0072] From the perspective of business expansion costs, it is necessary to formulate a power supply plan based on the user's address, installed capacity, and other electricity demands, combined with distribution network planning and existing resources. Whether or not to initiate the construction of a supporting power grid for the business needs to be determined according to relevant policies at the investment interface. If the business expansion involves distribution network construction, the construction cost of the distribution network needs to be considered. The investment estimate for the distribution network is mainly based on the preliminary engineering design and is determined by multiple factors such as the investment scope, power supply distance, number of power supply circuits, and equipment selection.
[0073] From the perspective of user operating costs, the demand-based electricity pricing mechanism is linked to the load factor. Users can choose whether to implement a two-part tariff. If they choose to implement a two-part tariff, they can choose to implement transformer capacity billing or demand-based billing. For two-part tariff users who choose the demand-based electricity pricing method, if their monthly electricity consumption per kVA reaches 260 kWh or more, the monthly demand-based electricity price will be charged at 90% of the approved standard. If the business expansion capacity is determined and the billing method is chosen appropriately, then electricity costs can be reduced.
[0074] (2) Analysis of the influencing factors and mechanisms of the target
[0075] If the user's requested capacity is accurately determined and remains within a small range, and is within the current substation's power supply capacity (without needing to add outgoing lines from the low-voltage side of the power substation), then current investment and expansion costs can be reduced, and the load factor can be increased, reducing operating costs. If the determined capacity exceeds the substation's power supply capacity, accurately determining a reasonable requested capacity can reduce expansion investment costs.
[0076] In summary, accurately verifying users' electricity needs, determining appropriate capacity, and improving load factor can reduce supporting infrastructure construction, lower investment costs, and reduce electricity expenses. If electricity capacity is declared reasonably, electricity load is managed effectively, and capacity and demand changes and billing strategies are utilized efficiently, the total cost of business expansion can be further reduced. Key factors influencing the decision to minimize total business expansion costs include the applied-for capacity and the billing method.
[0077] Step 2: Obtain historical business expansion data and classify it by industry; analyze the characteristics of historical business expansion user behavior and energy consumption to form a basic database;
[0078] Establishment of basic database: Organize historical business expansion data (including supporting power grid construction, electricity price implementation policies, 96 points of electricity consumption data, etc.), and analyze user behavior characteristics and energy consumption characteristics according to industry classification.
[0079] The types of industries mentioned include: industrial, residential, and agricultural.
[0080] The behavioral characteristics include: industrial users expanding or reducing production capacity as production conditions change; residential users increasing or conserving energy as temperature, weather, and energy prices change; and agricultural users increasing or decreasing energy consumption according to planting and breeding seasons.
[0081] The energy consumption characteristics include: the power load curves of different times, different places, and different users vary greatly, forming a unique energy load curve for each user.
[0082] Step 3: Based on the industry type of the business expansion user, obtain the load data of users in the same industry from the basic database, and predict the electricity load of the business expansion user;
[0083] More preferably, low-frequency data such as monthly electricity consumption of users in the same industry can be obtained from the basic database, as well as high-frequency power load data collected every 15 minutes. Based on the high-low frequency mixed data, multiple prediction models are combined to predict the maximum power load and electricity consumption of the expanding business users in the near and medium to long term, thereby realizing the power consumption analysis of the expanding business users.
[0084] 1) Obtain behavioral and energy consumption characteristics data of users in the same industry from the basic database to predict the future electricity load or electricity consumption of business expansion users.
[0085] Based on the materials provided by the expanding customer, the nature of their electricity consumption is determined. Combined with historical electricity consumption trends of similar industry users, the electricity consumption and load development trends of this expanding customer are analyzed. Specific values can be obtained using a combination of predictive analysis methods. The regularity of electricity consumption released after the expansion of different industry users is also analyzed.
[0086] 2) Obtain behavioral and energy consumption characteristics data of users in the same industry from the basic database to predict the electricity load of business expansion users in the near and medium to long term.
[0087] Electricity consumption characteristic forecasting involves analyzing the energy consumption characteristics of an industry or process, combined with the daily electricity consumption characteristics, seasonal electricity consumption characteristics, maximum electricity load, and peak-valley difference of similar industry users, to predict future electricity load curves.
[0088] In practice, power load data from industry users is collected from a basic database at 15-minute intervals. Multiple prediction models are combined to predict the maximum power load of these users in the near and medium to long term. The near term typically covers 1-5 years, while the medium to long term covers 5-20 years. Existing general-purpose models can be selected based on actual needs.
[0089] Step 4: Determine the application capacity value for business expansion based on the predicted electricity load value, and adjust the application capacity value for business expansion through operation simulation to obtain the final application capacity for business expansion users;
[0090] 1) Preliminary determination of the capacity for business expansion applications
[0091] Under the premise of safe and reliable operation of the power grid, the power factor and load rate for the near term and medium term are determined according to the maximum power loads P1 and P2 in the near term and medium term and the local experience value, and the maximum value is taken as the value of the business expansion application capacity.
[0092]
[0093]
[0094] S = max(S1, S2) (6)
[0095] Among them, S1 and S2 are the required capacity in the near term and medium to long term, respectively, and the maximum value is taken as the capacity for business expansion application;
[0096] P1 and P2 represent the maximum electricity load in the near and medium to long term, respectively.
[0097] For near-term and medium-to-long-term power factors;
[0098] k1 and k2 represent the near-term and medium-to-long-term load rates, respectively.
[0099] 2) Run the simulation
[0100] The study analyzes the impact of industry development and other factors on the electricity consumption behavior of customers expanding their businesses, as well as the impact of peak-valley time-of-use pricing on customer load curves. By establishing various development scenarios and conducting operational simulations, the study rolls back to step 1) to redetermine the capacity for business expansion applications, ensuring that near- and medium-term expansion applications meet customer production and operational needs. Through operational simulations, it was found that optimizing the load curve and reducing maximum load can lower the overall capacity. The principle for adjusting the value of business expansion application capacity through operational simulations is as follows:
[0101] The electricity consumption and load curves generated after the simulated business expansion application user is operating normally;
[0102] By taking into account factors such as peak-valley time-of-use pricing, electricity consumption patterns can be optimized and adjusted, thereby optimizing the electricity load curve, reducing the peak-valley difference, and lowering the maximum loads P1 and P2, thus reducing the capacity required for business expansion applications.
[0103] 3) Determining the capacity scheme
[0104] The capacity is determined through operational simulation. If the determined expansion application capacity exceeds the user's maximum permitted application capacity, expansion construction is required, necessitating the development of the supporting power grid. The formula for calculating the user's maximum permitted expansion application capacity is as follows:
[0105] P max =min(P c ,P u )-P l (7)
[0107] Where P max It is the user's maximum licensed installation capacity, P c It is the maximum remaining capacity of the substation or outgoing line bay to which the user's address belongs, P u P represents the maximum capacity that the line from the substation or bay to the user can carry. l Reduced power transmission to the line.
[0108] Step 5: Based on the simulation data and the maximum demand threshold under different voltage levels, compare different billing methods to determine the billing method for business expansion users. This method, together with the final business expansion application capacity, constitutes the business expansion application optimization decision plan.
[0109] Billing method comparison:
[0110] According to regulations: electricity users with ratings between 100 kVA and 315 kVA can choose to apply either a single-rate or two-rate tariff; those with ratings of 315 kVA and above will be subject to a two-rate tariff. Electricity users with ratings of 315 kVA and above who were previously subject to general industrial and commercial tariffs and other rates can now choose to apply either a single-rate or two-rate tariff.
[0111] Industrial and commercial users subject to two-part tariffs can choose to pay capacity demand charges based on transformer capacity, contracted maximum demand, or actual maximum demand. Among them, users who choose to implement demand-based pricing (i.e., billed based on contracted maximum demand or actual maximum demand) and whose monthly electricity consumption per kVA reaches 260 kWh or more will have their monthly demand-based tariff charged at 90% of the approved standard.
[0112] Therefore, users can choose whether to implement a two-part tariff, and if so, choose whether to implement transformer capacity billing or demand-based billing.
[0113] Basic electricity fee is calculated based on capacity: Basic electricity fee = Transformer capacity × Capacity electricity price
[0114] Basic electricity charge is calculated based on actual maximum demand: Basic electricity charge = Actual maximum demand × Demand electricity price
[0115] When the monthly electricity consumption per kVA is ≥260 kWh, the demand price in the above formula can be multiplied by 0.9. Based on operational simulation data, different billing methods are calculated, compared, and the optimal billing method is determined. A critical ratio can be chosen as the dividing line. Therefore, for users with monthly electricity consumption less than 260 kWh per kVA and users with monthly electricity consumption greater than or equal to 260 kWh per kVA, the maximum demand critical ratio is calculated, denoted as critical ratio 1 and critical ratio 2, as follows:
[0116]
[0117] K i The maximum demand critical ratio for voltage level i is 1;
[0118] X i The price per unit capacity at voltage level i;
[0119] R i Price based on demand at voltage level i;
[0120] Vi =K i / 0.9 (9)
[0121] V i The critical ratio is 2.
[0122] There are two threshold ratios: one is the threshold ratio for users with monthly electricity consumption below 260 kWh per kVA (threshold ratio 1), and the other is the threshold ratio for users with monthly electricity consumption above 260 kWh per kVA (threshold ratio 2). The threshold ratios for billing method selection are shown in Table 1.
[0123] According to the Jiangsu Province power grid transmission and distribution price standards, as shown below:
[0124] The monthly demand price for 1-10 / 20 kV is 51.2 yuan / kW, and the capacity price is 32 yuan / kVA.
[0125] The demand price for 35 kV is 48 yuan / kW, and the capacity price is 30 yuan / kVA.
[0126] The demand price for 110 kV is 44.8 yuan / kW, and the capacity price is 28 yuan / kVA.
[0127] The demand-based electricity price for 220 kV and above is 41.6 yuan / kW, and the capacity-based price is 26 yuan / kVA.
[0128] Table 1 Critical Values for Billing Method Comparison
[0129]
[0130]
[0131] Business expansion plan finalization: Based on the finalized business expansion installation capacity and billing method, provide users with the optimal business expansion solution.
[0132] Embodiment 2 of the present invention provides a power industry expansion application optimization decision system, comprising:
[0133] The decision target analysis module is used to establish a business expansion application decision model with the goal of minimizing the total business expansion cost, and to deconstruct the decision target and analyze the target influencing factors and mechanisms to derive key influencing factors, including business expansion application capacity and billing method.
[0134] The data analysis module is used to acquire historical business data and classify it by industry to form a basic database;
[0135] The data prediction module is used to obtain energy consumption data of users in the same industry from the basic database based on the industry type of the business expansion user, and predict the electricity consumption and electricity load of the business expansion user.
[0136] The business expansion application capacity decision module is used to determine the business expansion application capacity value based on the electricity load forecast value, and adjust the business expansion application capacity value through operation simulation to obtain the final business expansion application capacity of the business expansion user.
[0137] The billing method decision module is used to compare different billing methods based on operation simulation data and the maximum demand threshold under different voltage levels, determine the billing method for business expansion users, and together with the final business expansion application capacity, constitute the business expansion application optimization decision scheme.
[0138] A terminal includes a processor and a storage medium; the storage medium is used to store instructions.
[0139] The processor is configured to operate according to the instructions to execute the steps of the method.
[0140] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method.
[0141] The beneficial effects of this invention are compared with those of the prior art:
[0142] This invention presents a business expansion application decision model with the goal of minimizing the total cost of business expansion. It deconstructs the decision objective and analyzes the influencing factors and mechanisms, thereby identifying the key influencing factors and determining the core aspects of business expansion: business expansion application capacity and billing method.
[0143] This invention categorizes historical business expansion data by industry, analyzes the characteristics of historical business expansion user behavior and energy consumption, and forms a basic database. This allows for a more detailed differentiation of the characteristics of business expansion in different industries, providing a solid data foundation for formulating the best business expansion plan.
[0144] This invention, from an industry expansion perspective, uses a variety of combined methods to predict future user electricity consumption, which helps to fully understand future electricity trends. Furthermore, by simulating operational data, it further improves the accuracy of capacity selection.
[0145] This invention compares different billing methods based on operational simulation data and the maximum demand threshold at different voltage levels, and provides the optimal billing method, thereby improving the economic efficiency of users' electricity consumption.
[0146] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.
[0147] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.
[0148] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.
[0149] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.
[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.
Claims
1. A method for optimizing decision-making in power industry expansion applications, characterized in that: The method includes the following steps: Step 1: Establish a business expansion application decision model with the goal of minimizing the total business expansion cost, and deconstruct the decision objective and analyze the influencing factors and mechanisms to identify key influencing factors, including business expansion application capacity and billing method; Step 2: Obtain historical business expansion data and classify it by industry to form a basic database; Step 3: Based on the industry type of the business expansion user, obtain the load data of users in the same industry from the basic database, and predict the electricity load of the business expansion user; Step 4: Determine the application capacity value for business expansion based on the electricity load forecast, and adjust the application capacity value for business expansion through operation simulation to obtain the final application capacity for business expansion users; Step 5: Compare different billing methods based on the maximum demand critical ratio under different voltage levels to determine the billing method for business expansion users. This method, together with the final business expansion application capacity, constitutes the business expansion application optimization decision plan.
2. The power industry expansion application optimization decision-making method according to claim 1, characterized in that: In step 3, power load data of users in the same industry are collected from the basic database at 15-minute intervals. Multiple prediction models are combined to predict the maximum power load of the business expansion users in the near and medium to long term.
3. The power industry expansion application optimization decision-making method according to claim 1, characterized in that: In step 4, based on the predicted maximum electricity load values in the near and medium-to-long term, the power factor in the near and medium-to-long term, and the load rate, the required capacity of the business expansion user in the near and medium-to-long term is determined respectively, and the maximum value of the two is taken as the business expansion application capacity S. S = max(S1, S2) (6) Among them, S1 and S2 represent the required transformer capacity in the near and medium to long term; P1 and P2 represent the maximum electricity load in the near and medium to long term, respectively. For near-term and medium-to-long-term power factors; k1 and k2 represent the near-term and medium-to-long-term load rates, respectively.
4. The power industry expansion application optimization decision-making method according to claim 1, characterized in that: In step 4, the principle for adjusting the application capacity value through simulation is as follows: After simulating the normal operation of customers applying for new business expansion, the simulated power consumption and load curves are obtained and optimized to reduce the maximum power load, thereby reducing the capacity required for new business expansion. The details are as follows: Based on the simulated electricity consumption and load curves, and considering peak-valley time-of-use pricing, the electricity consumption pattern is optimized and adjusted, thereby optimizing the load curve, reducing the peak-valley difference, and reducing the maximum loads P1 and P2, thus reducing the capacity required for business expansion.
5. The power industry expansion application optimization decision-making method according to claim 1, characterized in that: In step 4, for the determined business expansion application capacity, if the application capacity exceeds the user's maximum permitted business expansion application capacity, then capacity expansion construction is required, and the process proceeds to the supporting power grid construction stage. Otherwise, capacity expansion construction is not required. The formula for calculating the user's maximum permitted business expansion application capacity is as follows: P max =min(P c ,P u )-P l (7) Where P max It is the user's maximum licensed installation capacity, P c It is the maximum remaining capacity of the substation or outgoing line bay to which the user's address belongs, P u P represents the maximum capacity that the line from the substation or bay to the user can carry. l Reduced power transmission to the line.
6. The power industry expansion application optimization decision-making method according to claim 1, characterized in that: In step 5, for users whose monthly electricity consumption per kVA is less than 260 kWh and users whose monthly electricity consumption per kVA is greater than or equal to 260 kWh, the maximum demand critical ratio is calculated and denoted as critical ratio 1 and critical ratio 2, respectively.
7. The power industry expansion application optimization decision-making method according to claim 6, characterized in that: The formula for calculating the critical ratio 1 is as follows: K i The critical ratio is 1 at voltage level i; X i The price per unit capacity at voltage level i; R i The price is the quantity required at voltage level i.
8. The power industry expansion application optimization decision-making method according to claim 6, characterized in that: The formula for calculating the critical ratio 1 is as follows: V i =K i / 0.9 (9) Where K i The critical ratio is 1 at voltage level i; V i The critical ratio is 2.
9. A power industry expansion application optimization decision-making method according to any one of claims 6-8, characterized in that: In step 5, obtain the voltage level during the simulation, the monthly electricity consumption per kilovolt-ampere, and the actual maximum monthly demand; The corresponding critical value 1 and critical ratio 2 to be compared are calculated based on the capacity price and demand price of the voltage level. Compare the actual maximum monthly demand with the critical ratios 1 and 2 to be compared: When the monthly electricity consumption per kVA is less than 260 kW, if the ratio of the actual maximum demand to the operating capacity is lower than the critical ratio of 1, the demand-based electricity price is selected; otherwise, the capacity-based electricity price is selected. When the monthly electricity consumption per kVA is greater than or equal to 260 kW, if the ratio of the actual maximum demand to the operating capacity is lower than the critical ratio of 2, the demand-based electricity price is selected; otherwise, the capacity-based electricity price is selected.
10. A power industry expansion application optimization decision system, utilizing the method described in any one of claims 1-9, characterized in that: The system includes: The decision target analysis module is used to establish a business expansion application decision model with the goal of minimizing the total business expansion cost, and to deconstruct the decision target and analyze the target influencing factors and mechanisms to derive key influencing factors, including business expansion application capacity and billing method. The data analysis module is used to acquire historical business data and classify it by industry to form a basic database; The data prediction module is used to obtain load data of users in the same industry from the basic database based on the industry type of the business expansion user, and predict the electricity load of the business expansion user. The business expansion application capacity decision module is used to determine the business expansion application capacity value based on the electricity load forecast value, and adjust the business expansion application capacity value through operation simulation to obtain the final business expansion application capacity of the business expansion user. The billing method decision module is used to compare different billing methods based on the operation simulation data and the maximum demand threshold under different voltage levels, and determine the billing method for business expansion application users. Together with the final business expansion application capacity, it constitutes the business expansion application optimization decision scheme.
11. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-9.
12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-9.