A regional power consumption regulation method based on mathematical programming and related device

Abstract

The application belongs to the technical field of power utilization regulation, and discloses a regional power utilization regulation method based on mathematical programming and related devices, which comprises the following steps: obtaining photovoltaic power generation data, commercial power price data and energy storage system data in a region to be regulated; obtaining photovoltaic power generation prediction data in the region to be regulated based on the photovoltaic power generation data in the region to be regulated and a pre-constructed photovoltaic prediction model; generating a regulation strategy for a future preset time period in the region to be regulated by using a pre-constructed regulation strategy optimization model; the pre-constructed regulation strategy optimization model adopts a multi-objective MILP optimization model; in the multi-objective MILP optimization model, the minimization of power utilization cost and the minimization of energy storage loss are taken as double objectives, an energy storage loss cost coefficient is introduced to balance the multi-objectives, and load balancing constraints, energy storage physical constraints and power grid power change constraints are introduced; the application can effectively reduce the power utilization cost of industrial and commercial users and ensure the stability and reliability of the regulation strategy.

Description

Technical Field

[0001] This invention belongs to the field of power consumption control technology, and specifically relates to a regional power consumption control method and related device based on mathematical programming. Background Technology

[0002] With the transformation of the energy structure and the continuous increase in the penetration rate of distributed energy, users are facing the dual pressure of fluctuating electricity prices and rising energy costs. At the same time, due to the volatility and randomness of the output of distributed energy (such as photovoltaic power generation), the problem of regional energy supply and demand imbalance is becoming increasingly prominent. In the current power sector, regional power management needs to take into account cost control, clean energy consumption and safe operation of equipment. The core requirement is to achieve the dual goals of cost reduction and efficiency improvement and emission reduction through scientific regulation and control strategies.

[0003] Currently, regional power consumption regulation typically includes integrated grid regulation operation and management mode and source-storage-load coordinated microgrid regulation technology based on direct power control. However, the existing regulation technologies mentioned above suffer from insufficient multi-objective coordination, weak uncertainty response, and poor scenario adaptability, making it difficult to adapt to the diverse needs of complex industrial and commercial scenarios.

[0004] Specifically, the core of the integrated operation and management model for power grid control is to organically integrate power grid dispatching and operation management. By monitoring the power grid's operating status in real time, it achieves precise power dispatching based on regional electricity demand. Its technical characteristics rely on automated monitoring systems to collect power grid data and adopt traditional rule-based dispatching strategies, focusing on the overall security and stability of the power grid. However, it is only designed for the overall dispatching of the large power grid and does not consider the distributed energy characteristics of industrial and commercial users, making it unable to adapt to the integration of multi-source data and personalized electricity demand on the user side. Secondly, with the core objective of safe and stable power grid operation, it does not incorporate the reduction of user electricity costs and the improvement of clean energy consumption rate into the optimization system, thus being out of touch with the core demands of industrial and commercial users. In addition, the rule-based dispatching strategy lacks multi-constraint processing capabilities and cannot dynamically respond to complex operating conditions such as photovoltaic power fluctuations and electricity price changes.

[0005] For microgrid regulation technology based on power direct control of source-storage-load system, the technical approach is to build a DC power supply and consumption system for user-side source-storage-grid-load, analyze the flexibility characteristics of equipment such as air conditioning, energy storage, and photovoltaic, and propose a power direct control flexible regulation algorithm. By adjusting the power of the equipment in real time, local photovoltaic consumption and grid quota reduction can be achieved. Although this technology achieves source-storage-load synergy, due to the simplified algorithm of power direct control, it cannot simultaneously take into account complex requirements such as cost minimization, energy storage lifetime protection, and multi-time period constraints. Moreover, it has a weak ability to handle uncertainties such as photovoltaic prediction errors and electricity price fluctuations. Summary of the Invention

[0006] To address the technical problems existing in the prior art, this invention provides a regional power consumption regulation method and related device based on mathematical programming, in order to solve the problems of insufficient multi-objective coordination, weak uncertainty response, and poor scenario adaptability of existing regulation technologies, which makes it difficult to adapt to the diverse needs of complex industrial and commercial scenarios.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: This invention provides a regional power consumption regulation method based on mathematical programming, comprising: Obtain photovoltaic power generation data, grid electricity price data, and energy storage system data within the area to be regulated; Based on the photovoltaic power generation data in the area to be regulated, and combined with the pre-constructed photovoltaic prediction model, the photovoltaic power generation prediction data in the area to be regulated is obtained. Based on photovoltaic power generation forecast data, grid electricity price data, and energy storage system data within the area to be regulated, a pre-built regulation strategy optimization model is used to generate a regulation strategy for the future preset time period within the area to be regulated. Among them, the pre-constructed regulation strategy optimization model adopts a multi-objective MILP optimization model. In the multi-objective MILP optimization model, the dual objectives are minimizing electricity cost and minimizing energy storage loss. The energy storage loss cost coefficient is introduced to balance multiple objectives, and load balance constraints, energy storage physical constraints and grid power change constraints are also introduced.

[0008] Furthermore, the photovoltaic power generation data within the area to be regulated is the photovoltaic power generation data for a preset historical period within the area to be regulated; the grid electricity price data within the area to be regulated is the time-of-use electricity price data for the day within the area to be regulated; and the energy storage system data within the area to be regulated is the maximum charging and discharging power data of the energy storage system within the area to be regulated. Among these, the data formats of the photovoltaic power generation data, grid electricity price data, and energy storage system data within the area to be regulated are all CSV format.

[0009] Furthermore, the pre-constructed photovoltaic prediction model adopts a stochastic fluctuation model, a pre-trained long short-term memory network model, or a pre-trained autoregressive integral moving average model.

[0010] Furthermore, the objective function of the multi-objective MILP optimization model is as follows:

[0011] in, for Time-of-use electricity pricing; for Electricity purchased from the power grid during specific time periods; This is the energy storage loss cost coefficient; for Energy storage charging and discharging power during specific time periods.

[0012] Furthermore, the load balancing constraints are as follows:

[0013] in, for User load demand during specific time periods; for Actual or predicted photovoltaic power output during the time period; for Electricity purchased from the power grid during specific time periods; for Time-of-use energy storage charging and discharging power; Energy storage physical constraints include energy storage state of charge constraints, energy storage charge and discharge power constraints, and energy storage state transition constraints; The energy storage state of charge constraints are as follows:

[0014] in, This represents the minimum state of charge of the energy storage system. This represents the maximum state of charge of the energy storage system. for State of charge of the time-phase energy storage system; The energy storage charging and discharging power constraints are as follows:

[0015] in, for The charging and discharging power of the time-limited energy storage system; This represents the maximum charging and discharging power of the energy storage system. Energy storage state transition constraints are as follows:

[0016] in, for State of charge of the time-phase energy storage system; The charging efficiency of the energy storage system; for Charging power of time-limited energy storage systems; The discharge efficiency of the energy storage system; for Discharge power of the time-limited energy storage system; The constraints on power grid variation are as follows:

[0017] in, for Time period and Difference in electricity purchased from the power grid during different time periods This represents the maximum power change rate.

[0018] Furthermore, the regulation strategy for the future preset time period within the area to be regulated includes the photovoltaic power, grid power purchase, energy storage system charging and discharging status, and real-time cost for each time period within the future preset time period within the area to be regulated.

[0019] This invention also provides a regional power control system based on mathematical programming, comprising: The data management layer is used to acquire photovoltaic power generation data, grid electricity price data, and energy storage system data within the area to be regulated; The photovoltaic prediction layer is used to obtain photovoltaic power generation prediction data for the area to be regulated, based on photovoltaic power generation data in the area to be regulated and combined with a pre-built photovoltaic prediction model. The optimization calculation layer is used to generate a control strategy for a future preset period in the area to be controlled, based on photovoltaic power generation forecast data, grid electricity price data, and energy storage system data in the area to be controlled, using a pre-built control strategy optimization model. Among them, the pre-constructed regulation strategy optimization model adopts a multi-objective MILP optimization model. In the multi-objective MILP optimization model, the dual objectives are minimizing electricity cost and minimizing energy storage loss. The energy storage loss cost coefficient is introduced to balance multiple objectives, and load balance constraints, energy storage physical constraints and grid power change constraints are also introduced.

[0020] The present invention also provides an electronic device, comprising: A processor is used to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, performs the aforementioned regional power regulation method based on mathematical programming.

[0021] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned regional power regulation method based on mathematical programming.

[0022] The present invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the aforementioned regional power consumption control method based on mathematical programming.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows: The regional electricity regulation method based on mathematical programming provided by this invention uses a multi-objective MILP optimization model as a pre-constructed regulation strategy optimization model. By comprehensively considering various factors such as photovoltaic power generation forecast data, grid electricity price data, and energy storage system data, it generates a regulation strategy for a future preset period in the area to be regulated. This can effectively reduce the electricity costs of industrial and commercial users, save operating costs for enterprises, and ensure the stability and reliability of the regulation strategy. Specifically, in the multi-objective MILP optimization model, the objectives are to minimize electricity costs and minimize energy storage losses. Under the premise of meeting users' electricity demand, it can simultaneously achieve the goals of minimizing electricity costs, maximizing clean energy consumption rate, and minimizing energy storage lifespan loss, avoiding the overall benefit decline caused by single-objective optimization. In addition, by introducing an energy storage loss cost coefficient to balance multiple objectives, and simultaneously introducing load balance constraints, energy storage physical constraints, and grid power change constraints, it can effectively cope with the physical constraints and time coupling constraints of energy storage devices, as well as the uncertainties caused by photovoltaic power prediction errors and electricity price fluctuations, thereby improving the stability and reliability of the regulation strategy.

[0024] The regional power consumption control system, electronic device, computer-readable storage medium, and computer program product based on mathematical programming provided by this invention possess all the advantages of the aforementioned regional power consumption control method based on mathematical programming. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart of the regional power consumption control method based on mathematical programming provided in Example 1; Figure 2 This is a comparison chart of the predicted and actual photovoltaic power generation values ​​for the next 24 hours in the example area generated using the basic model in Example 1. Figure 3 This is a schematic diagram of the control strategy for the example area in Example 1 over the next 24 hours; Figure 4 This is a comparison chart of hourly electricity costs in the example area over 24 hours in Example 1; Figure 5 This is a comparison chart of the total electricity cost in the example area over 24 hours in Example 1; Figure 6 This is a structural block diagram of a regional power control system based on mathematical programming provided in Example 2; Figure 7 This is a structural block diagram of the electronic device provided in Example 3. Detailed Implementation

[0027] To make the technical problems solved by the present invention, the technical solutions, and the beneficial effects clearer, the following specific embodiments provide a further detailed description of the present invention. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of the invention.

[0028] Before describing the specific embodiments of this application, some of the technical terms involved in the embodiments of this application are explained as follows: MILP: Mixed Integer Linear Programming, a mathematical optimization method for solving extrema problems of objective functions involving integer variables and linear constraints. It can handle complex optimization scenarios that combine discrete decisions with continuous variables.

[0029] PV: Photovoltaic, refers to the technology of directly converting solar energy into electrical energy using the photovoltaic effect of semiconductor materials.

[0030] SOC: State of Charge, is the percentage of the energy storage system's current remaining power relative to its total capacity. It is a core indicator for measuring the charging and discharging capabilities of an energy storage system.

[0031] LSTM: Long Short-Term Memory, a special type of recurrent neural network (RNN) that can effectively capture long-term dependencies in time series data and is often used for prediction tasks.

[0032] ARIMA: Auto Regressive Integrated Moving Average, is a time series forecasting method that combines autoregressive, differencing, and moving average components, and is suitable for forecasting linear time series data.

[0033] ROOF_PV: Roof Photovoltaic, a photovoltaic power generation system installed on the roof of a building, is a common form of distributed photovoltaics.

[0034] SHED_PV: Shed Photovoltaic, a photovoltaic power generation system installed on the roof of various sheds (such as carports and warehouses).

[0035] WALL_PV: Wall Photovoltaic, a photovoltaic power generation system integrated into the curtain wall structure of a building, combining power generation and architectural decoration functions.

[0036] ETA_INV: Inverter Efficiency, refers to the energy conversion efficiency of the inverter in converting the direct current generated by the photovoltaic modules into alternating current.

[0037] Annual Attenuation Rate: The annual attenuation rate of photovoltaic modules refers to the percentage decrease in power generation caused by factors such as material aging and environmental impact each year.

[0038] This invention provides a regional power consumption regulation method based on mathematical programming, comprising the following steps: Step 100: Obtain photovoltaic power generation data, grid electricity price data, and energy storage system data within the area to be regulated.

[0039] Step 200: Based on the photovoltaic power generation data in the area to be regulated, and combined with the pre-constructed photovoltaic prediction model, obtain the photovoltaic power generation prediction data in the area to be regulated.

[0040] Step 300: Based on the photovoltaic power generation forecast data, grid electricity price data, and energy storage system data within the area to be regulated, a pre-constructed regulation strategy optimization model is used to generate a regulation strategy for a future preset time period within the area to be regulated. The pre-constructed regulation strategy optimization model employs a multi-objective MILP optimization model. In this model, the dual objectives are minimizing electricity costs and minimizing energy storage losses. An energy storage loss cost coefficient is introduced to balance these multiple objectives, while also incorporating load balance constraints, energy storage physical constraints, and grid power change constraints.

[0041] In the above implementation, to address the problems of insufficient multi-objective coordination, weak uncertainty response, and poor scenario adaptability of existing regional power regulation technologies, the following approach is adopted: Photovoltaic power generation, grid electricity prices, and energy storage system data for the region to be regulated are acquired. Photovoltaic power generation forecast data is obtained by combining this with a pre-constructed photovoltaic forecasting model. Then, a multi-objective MILP optimization model is used to generate a regulation strategy, with the dual objectives of minimizing electricity costs and minimizing energy storage losses. This model incorporates an energy storage loss cost coefficient to balance multiple objectives and introduces various constraints. Starting with data acquisition and forecasting, a foundation for precise regulation is provided. The multi-objective MILP optimization model is based on mathematical programming, comprehensively considering costs and losses. By introducing a cost coefficient to balance the relationship between the two, and with multiple constraints ensuring that the regulation strategy conforms to actual operating conditions, it can dynamically respond to complex operating conditions such as photovoltaic power fluctuations and electricity price changes. This better adapts to the diverse needs of complex industrial and commercial scenarios, achieving cost reduction, efficiency improvement, and emission reduction goals.

[0042] The following specific embodiments further explain the regional power consumption regulation method based on mathematical programming provided by the present invention: As attached Figure 1 As shown, this embodiment 1 provides a regional power consumption regulation method based on mathematical programming, including the following steps: Step 1: Obtain photovoltaic power generation data, grid electricity price data, and energy storage system data within the area to be regulated. The obtained photovoltaic power generation data, grid electricity price data, and energy storage system data within the area to be regulated are stored in a pre-defined MySQL database.

[0043] Specifically, the photovoltaic power generation data in the area to be regulated is the photovoltaic power generation data for a preset historical period in the area to be regulated; the grid electricity price data in the area to be regulated is the time-of-use electricity price data for the day in the area to be regulated; and the energy storage system data in the area to be regulated is the maximum charging and discharging power data of the energy storage system in the area to be regulated.

[0044] The photovoltaic power acquisition device obtains the actual photovoltaic power data of the area to be regulated over the past 24 hours, thus obtaining the photovoltaic power generation data of the area to be regulated. The photovoltaic power generation data of the area to be regulated is in CSV format, which includes a "time-power" field to record the actual photovoltaic power of the area to be regulated over the past 24 hours.

[0045] Obtain the time-of-use electricity price data for the day in the area to be regulated from the power grid company to obtain the grid electricity price data for the area to be regulated. The grid electricity price data for the area to be regulated is in CSV format and contains a "time-price" field to record the time-of-use electricity price for the area to be regulated within 24 hours.

[0046] Read the maximum charge and discharge power data of the energy storage system in the area to be regulated to obtain the energy storage system data in the area to be regulated. The data format of the energy storage system data in the area to be regulated is CSV format, which includes the field "capacity - maximum charge and discharge power" to define the physical parameters of the energy storage device.

[0047] Step 2: Based on the photovoltaic power generation data within the area to be regulated, and combined with the pre-built photovoltaic prediction model, obtain the photovoltaic power generation prediction data within the area to be regulated. The pre-built photovoltaic prediction model employs a stochastic fluctuation model, a pre-trained long short-term memory network model, or a pre-trained autoregressive integral moving average model.

[0048] It should be noted that the pre-built photovoltaic prediction models include two categories: basic models and extended models. The stochastic fluctuation model serves as the basic model, used to generate photovoltaic power generation prediction data for the region under control by adding preset noise to the actual photovoltaic power data for a preset historical period. The pre-trained Long Short-Term Memory (LSTM) network model and the pre-trained Autoregressive Integral Moving Average (AMI) model serve as extended models. These models are trained using the actual photovoltaic power data and the irradiance and temperature data for the preset historical period in the region under control. The pre-trained LSTM network model and AMI model are then used to predict the photovoltaic power generation prediction data for the region under control. Specifically, the photovoltaic power generation prediction data for the region under control refers to the predicted photovoltaic power generation for the next 24 hours and serves as the input to the pre-built control strategy optimization model.

[0049] Step 3: Based on the photovoltaic power generation forecast data, grid electricity price data, and energy storage system data within the area to be regulated, a pre-built regulation strategy optimization model is used to generate a regulation strategy for a future preset period within the area to be regulated. The pre-built regulation strategy optimization model employs a multi-objective MILP optimization model. This model has two objectives: minimizing electricity costs and minimizing energy storage losses. It incorporates an energy storage loss cost coefficient to balance these multiple objectives, and also includes load balance constraints, energy storage physical constraints, and grid power change constraints.

[0050] In this embodiment 1, a regulation strategy optimization model is constructed based on mixed-integer linear programming (MILP) to obtain a pre-constructed regulation strategy optimization model. The pre-constructed regulation strategy optimization model includes a multi-objective MILP optimization model and constraints. The multi-objective MILP optimization model has two objectives: minimizing electricity cost and minimizing energy storage loss. Minimizing electricity cost is the core objective, and an energy storage loss cost coefficient is introduced. Balancing energy storage lifetime loss; constraints include load balancing, energy storage physical constraints, and grid power variation constraints.

[0051] Specifically, the objective function of the multi-objective MILP optimization model is as follows:

[0052] in, for Time-of-use electricity pricing; for Electricity purchased from the power grid during specific time periods; Let be the energy storage loss cost coefficient, taken as . ; for Energy storage charging and discharging power during specific time periods.

[0053] The load balance constraints are as follows:

[0054] in, for User load demand during specific time periods; for Actual or predicted photovoltaic power output during the time period; for Electricity purchased from the power grid during specific time periods; for Energy storage charging and discharging power during specific time periods.

[0055] Energy storage physical constraints include energy storage state of charge constraints, energy storage charge and discharge power constraints, and energy storage state transition constraints.

[0056] Specifically, the energy storage state of charge constraints are as follows:

[0057] in, This represents the minimum state of charge of the energy storage system. This represents the maximum state of charge of the energy storage system. for State of charge of the time-limited energy storage system.

[0058] The energy storage charging and discharging power constraints are as follows:

[0059] in, for The charging and discharging power of the time-limited energy storage system; This represents the maximum charging and discharging power of the energy storage system.

[0060] Energy storage state transition constraints are as follows:

[0061] in, for State of charge of the time-phase energy storage system; The charging efficiency of the energy storage system; for Charging power of time-limited energy storage systems; The discharge efficiency of the energy storage system; for Discharge power of the time-limited energy storage system.

[0062] The constraints on power grid variation are as follows:

[0063] in, for Time period and Difference in electricity purchased from the power grid during different time periods This represents the maximum power change rate.

[0064] It should be noted that the control strategy for the future preset time period in the area to be controlled includes the photovoltaic power, grid power purchase, and the charging and discharging status and real-time cost of the energy storage system for each time period within the future preset time period in the area to be controlled. Among them, the control strategy for the future preset time period in the area to be controlled serves as a refined power consumption control plan for the next 24 hours in the area to be controlled. It can be used to display the comparison between actual and predicted photovoltaic values, details of the control plan, and benefit analysis through tables and charts. The control strategy for the future preset time period in the area to be controlled is distributed to the energy storage control system and photovoltaic inverter through the Modbus protocol to control the operation of the equipment in real time.

[0065] Example explanation: Taking the power consumption regulation process of a region that includes photovoltaic power generation and energy storage systems as an example, the regional power consumption regulation method based on mathematical programming in Embodiment 1 is illustrated as follows: (1) Data preparation The system uses a photovoltaic power acquisition device to obtain the actual photovoltaic power data for the past 24 hours in the region. This data is then formatted as a CSV file with a "time-power" field to obtain the photovoltaic power generation data for the example area. The system also obtains the daily time-of-use electricity price data for the example area from the power grid company, formatting it as a "time-price" field to obtain the grid electricity price data for the example area. Finally, the system reads the maximum charge / discharge power data of the energy storage system in the example area, formatting it as a "capacity-max_power" field to obtain the energy storage system data for the example area.

[0066] (2) Photovoltaic power prediction Read photovoltaic (PV) power generation data within the example area, and use either a basic model or an extended model to obtain PV power generation forecast data for the example area. Specifically, under the basic model, a stochastic fluctuation model is used, based on the actual PV power data for a preset historical period in the example area, with preset noise added, to generate a predicted PV power output for the next 24 hours within the example area. This yields the PV power generation forecast data for the example area, as shown in the attached figure. Figure 2 As shown, attached Figure 2The paper presents a comparison chart of the predicted and actual photovoltaic power generation in the example area for the next 24 hours, generated using the basic model. Under the extended model, the pre-trained long short-term memory network model or the pre-trained autoregressive integral moving average model predicts the photovoltaic power generation in the example area for the next 24 hours, thus obtaining the photovoltaic power generation prediction data for the example area.

[0067] (3) Optimization model solution and regulation strategy generation Read photovoltaic power generation forecast data, grid electricity price data, and energy storage system data from the example area and input them into the pre-built regulation strategy optimization model; set the model parameters; among them, set the energy storage loss cost coefficient in the multi-objective MILP optimization model to... The minimum state of charge of the energy storage system is set to The maximum state of charge of the energy storage system is set to The charging efficiency of the energy storage system is set to... The discharge efficiency of the energy storage system is set to... Maximum power change rate .

[0068] It should be noted that in this embodiment 1, scenario customization is achieved through parameter configuration to obtain multi-scenario adaptation strategies; for example, in the scenario of high energy-consuming enterprises, the weight of "electricity cost" is reduced first, and the constraints on energy storage loss are appropriately relaxed; in the scenario of industrial park microgrids, the weight of "photovoltaic absorption rate" is increased first, and the power purchase from the grid is strictly controlled; in the scenario of virtual power plants, the target of "grid ancillary service revenue" is added to expand the dimension of the objective function.

[0069] Next, the PuLP solver is used to solve the pre-built regulation strategy optimization model, generating the regulation strategy for the next 24 hours in the example area, as shown in the attached figure. Figure 3 As shown; from the appendix Figure 3 As can be seen, during the period from 0:00 to 5:00 (low electricity price, <0.5 yuan / kWh): photovoltaic power is low, energy storage discharge (30kW) meets the load, and at the same time, grid-purchased electricity (e.g., 18.47kW) supplements and charges the energy storage; during the period from 10:00 to 14:00 (peak photovoltaic period, high electricity price, >0.9 yuan / kWh): photovoltaic power (e.g., 75.3kW) is used first to meet the load, and excess power is used to charge the energy storage, reducing grid-purchased electricity; during the period from 18:00 to 21:00 (peak electricity consumption period, high electricity price): energy storage discharge (30kW) is the main method, and grid-purchased electricity is a supplement to reduce costs.

[0070] (4) Strategy implementation and effect feedback Using the Modbus protocol, the generated control strategy for the next 24 hours within the sample area is distributed to the energy storage control system of the energy storage system and the photovoltaic inverter of the photovoltaic power generation system for real-time equipment control. Actual operating data is collected hourly and compared with predicted and strategy values ​​to calculate deviations. Details of the control scheme and benefit analysis are displayed on a visual interface, and users can download strategy reports. (See attached...) Figure 4-5 As shown, attached Figure 4 The document provides a comparison chart of hourly electricity costs for a sample area over 24 hours, with appendices. Figure 5 The document provides a comparison chart of total electricity costs for a sample area over 24 hours; from the attached... Figure 4-5 As can be seen from the data, after using the regional electricity consumption control method based on mathematical programming in Example 1 to control the electricity consumption of the example area, the hourly electricity cost of the example area decreased significantly during peak hours and the cost fluctuated slightly within a reasonable range during off-peak hours. The total electricity cost of the example area decreased significantly compared with the uncontrolled state, which fully verified the economy and feasibility of the control scheme.

[0071] Optionally, in this embodiment 1, an anomaly handling mechanism is also introduced, specifically including a data missing handling mechanism, a solution failure handling mechanism, and a device failure handling mechanism. Specifically, the data missing handling mechanism: if a certain type of data is not uploaded or is missing, it is filled with the historical average, and a message "Data missing, filled with the average" is displayed. The solution failure handling mechanism: if the pre-built photovoltaic prediction model fails to solve due to constraint conflicts, the pre-determined non-core constraints are automatically relaxed, the solution is re-solved, and a message "Constraints have been adjusted; it is recommended to check energy storage parameters" is displayed. The device failure handling mechanism: if the energy storage device fails, it automatically switches to "emergency mode," the strategy is adjusted to "prioritize photovoltaic + grid power purchase," and an alarm is triggered to prompt the user for repair.

[0072] The regional electricity regulation method based on mathematical programming described in Embodiment 1 uses a multi-objective MILP optimization model as a pre-constructed regulation strategy optimization model. By comprehensively considering various factors such as photovoltaic power generation forecast data, grid electricity price data, and energy storage system data, it generates a regulation strategy for a future preset period within the region to be regulated. The method described in Embodiment 1 can simultaneously achieve three major objectives—minimizing electricity costs, maximizing clean energy (photovoltaic) absorption rate, and minimizing energy storage lifespan loss—while meeting user electricity demand, thus avoiding the overall benefit decline caused by single-objective optimization. By introducing load balance constraints, energy storage physical constraints, and grid power change constraints, it can effectively address the physical constraints and time coupling constraints of energy storage devices, as well as the uncertainties caused by photovoltaic power prediction errors and electricity price fluctuations, thereby improving the stability and reliability of the regulation strategy. The method described in Embodiment 1 can be adapted to different scenarios such as industrial and commercial parks, high-energy-consuming enterprises, and virtual power plants, and supports flexible access of multiple energy types such as photovoltaic, energy storage, and grid electricity, meeting the personalized regulation needs of different users.

[0073] In this embodiment 1, a multi-objective MILP optimization model is used as a pre-built control strategy optimization model. Through precise optimization of the multi-objective MILP optimization model, electricity costs for industrial and commercial users can be reduced. Simultaneously, the number of energy storage charge / discharge cycles is reduced, extending energy storage lifespan by 3-5 years and lowering equipment replacement costs. Secondly, it can significantly improve the absorption rate of clean energy such as photovoltaics and reduce fossil fuel consumption. For example, based on a 10MW photovoltaic installed capacity, annual carbon emissions can be reduced by approximately 5000 tons, contributing to the achievement of emission reduction targets. Furthermore, by employing robust optimization and rolling time-domain optimization strategies, the strategy deviation rate caused by photovoltaic prediction errors is less than or equal to 10%. The response time to price fluctuations is less than or equal to 1 minute, ensuring the stable operation of the power system and avoiding grid impact. The method described in this embodiment 1 supports multiple scenarios such as industrial and commercial parks, virtual power plants, and microgrids. It can be quickly adapted to different user needs through parameter configuration and has strong scalability. In addition, it supports the introduction of a visual interface to realize one-stop operation of data uploading, strategy viewing, and benefit analysis. No professional algorithm knowledge is required, and ordinary operation and maintenance personnel can use it, reducing the technical threshold. In this embodiment 1, through the closed loop of prediction-optimization-execution-feedback, the actual data is updated and the strategy is adjusted every hour. At the same time, an anomaly handling mechanism is introduced to improve the reliability of the method.

[0074] Example 2 As attached Figure 6 As shown, this embodiment 2 provides a regional power control system based on mathematical programming, including a data management layer, a photovoltaic prediction layer, and an optimization calculation layer.

[0075] The data management layer is used to acquire photovoltaic power generation data, grid electricity price data, and energy storage system data within the area to be regulated; the photovoltaic prediction layer is used to obtain photovoltaic power generation prediction data within the area to be regulated based on the photovoltaic power generation data within the area to be regulated, combined with a pre-built photovoltaic prediction model; the optimization calculation layer is used to generate a control strategy for a future preset period within the area to be regulated based on the photovoltaic power generation prediction data, grid electricity price data, and energy storage system data within the area to be regulated, using a pre-built control strategy optimization model.

[0076] In this embodiment 2, the pre-constructed regulation strategy optimization model adopts a multi-objective MILP optimization model. In the multi-objective MILP optimization model, the dual objectives are minimizing electricity cost and minimizing energy storage loss. The energy storage loss cost coefficient is introduced to balance multiple objectives, and load balance constraints, energy storage physical constraints, and grid power change constraints are also introduced.

[0077] In this embodiment 2, the data management layer, photovoltaic prediction layer and optimization calculation layer are separated into independent modules. The photovoltaic prediction layer reserves LSTM and ARIMA extension interfaces, and the optimization calculation layer supports flexible configuration of constraints and objective functions to adapt to the needs of multiple scenarios.

[0078] It should be noted that the hardware and software environment for implementing the regional power consumption control method based on mathematical programming described in Example 1 and the regional power consumption control system based on mathematical programming described in Example 2 is as follows: The hardware environment includes development / running hardware and external devices; the development / running hardware has a processor clock speed of ≥2GHz, memory of ≥2GB, and supports x86 architecture; the external devices include: photovoltaic power acquisition devices, energy storage control systems, and data storage servers; the photovoltaic power acquisition devices include current and voltage sensors, the energy storage control system supports RS485 / Modbus communication, and the data storage server has a hard drive storage capacity of ≥500GB.

[0079] The software environment includes the operating system, development tools, and dependency libraries. The operating system is a Windows 10 64-bit system for development, and supports Linux and UnionTech operating systems for runtime. Development tools include Python 3.6 or later, PyCharm 2023.1 (IDE), and MySQL 5.7 or later (database). Dependency libraries include Pandas (data processing), NumPy (numerical computation), PuLP (MILP solver), PyQt5 (visual interface), Statsmodels (ARIMA prediction), and PyTorch (LSTM model training).

[0080] Example 3 As attached Figure 7As shown, this embodiment 3 provides an electronic device, including: a memory for storing a computer program; a processor for executing the computer program to implement the steps of the regional power consumption control method based on mathematical programming; or, the processor executing the computer program to implement the functions of each module in the aforementioned regional power consumption control system based on mathematical programming.

[0081] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a preset function, the instruction segments describing the execution process of the computer program in the electronic device.

[0082] The electronic device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above are examples of electronic devices and do not constitute a limitation on the electronic device. It may include more components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0083] The processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor, or any conventional processor, etc. The processor is the control center of the electronic device, connecting various parts of the entire electronic device through various interfaces and lines.

[0084] The memory can be used to store the computer program and / or module. The processor implements various functions of the electronic device by running or executing the computer program and / or module stored in the memory and by calling the data stored in the memory.

[0085] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function (such as sound playback, image playback, etc.). The data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart memory cards, secure digital cards, flash memory cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0086] Example 4 This embodiment 4 also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the regional power consumption control method based on mathematical programming.

[0087] If the modules / units integrated in the regional power control system based on mathematical programming are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0088] Based on this understanding, the present invention can implement all or part of the processes in the above-mentioned regional power consumption control method based on mathematical programming, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above-mentioned regional power consumption control method based on mathematical programming. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or a preset intermediate form, etc.

[0089] The computer-readable storage medium may include any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0090] Example 5 This embodiment 5 provides a computer product, which includes a computer program stored in a computer-readable storage medium. The processor of the electronic device reads the computer program from the computer-readable storage medium and executes the computer program, so that the electronic device can execute the regional power consumption control method based on mathematical programming described in embodiment 1, which will not be repeated here.

[0091] It should be noted that those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods.

[0092] The regional power regulation method based on mathematical programming described in this invention uses a multi-objective MILP optimization model as a pre-constructed regulation strategy optimization model. In this multi-objective MILP optimization model, minimizing electricity costs and minimizing energy storage losses are the dual objectives. An energy storage loss cost coefficient is introduced to balance the multiple objectives, while simultaneously integrating load balancing, energy storage physical constraints, and time coupling constraints to form a complete mathematical programming model. This effectively overcomes the limitations of single-objective optimization in existing technologies. Secondly, a robust optimization method is employed to address photovoltaic prediction errors and electricity price fluctuations. An uncertainty parameter range is introduced into the optimization model to ensure that the regulation strategy can still meet the constraints and approach the optimal solution within the parameter fluctuation range. This ensures the stability of the regulation strategy under parameter fluctuations and dynamic operating conditions, which is superior to the simplified power control methods of existing technologies. Specifically, the robust optimization strategy addresses photovoltaic prediction errors and electricity price fluctuations; the rolling time-domain optimization strategy handles 24-hour cycle scheduling. Specifically, the rolling time-domain optimization strategy divides the 24-hour scheduling cycle into multiple short periods. For each period, the model parameters are updated based on the latest actual data, dynamically adjusting the regulation strategy for subsequent periods to improve real-time performance and accuracy.

[0093] In this invention, a robust optimization model is constructed by introducing an uncertainty parameter range to address photovoltaic prediction errors and electricity price fluctuations. Specifically, a robust optimization method is employed to introduce an uncertainty parameter range into the optimization model to ensure that the control strategy can still meet the constraints and approach the optimal solution within the parameter fluctuation range. Simultaneously, the 24-hour scheduling cycle is divided into a rolling time domain of 1 hour / segment, with model parameters updated based on real-time data for each segment. Specifically, a rolling time domain optimization strategy is used to divide the 24-hour scheduling cycle into multiple short segments, updating model parameters based on the latest actual data for each segment, dynamically adjusting the control strategy for subsequent segments to improve real-time performance and accuracy. Furthermore, scenario customization is achieved through parameter configuration, enabling rapid adaptation to industrial and commercial, virtual power plant, and microgrid scenarios.

[0094] The above embodiments are merely one of the implementation methods for achieving the technical solution of the present invention. The scope of protection claimed by the present invention is not limited to this embodiment, but also includes any variations, substitutions and other implementation methods that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention.

Claims

1. A regional power consumption control method based on mathematical programming, characterized in that, include: Obtain photovoltaic power generation data, grid electricity price data, and energy storage system data within the area to be regulated; Based on the photovoltaic power generation data in the area to be regulated, and combined with the pre-constructed photovoltaic prediction model, the photovoltaic power generation prediction data in the area to be regulated is obtained. Based on photovoltaic power generation forecast data, grid electricity price data, and energy storage system data within the area to be regulated, a pre-built regulation strategy optimization model is used to generate a regulation strategy for the future preset time period within the area to be regulated. Among them, the pre-constructed regulation strategy optimization model adopts a multi-objective MILP optimization model. In the multi-objective MILP optimization model, the dual objectives are minimizing electricity cost and minimizing energy storage loss. The energy storage loss cost coefficient is introduced to balance multiple objectives, and load balance constraints, energy storage physical constraints and grid power change constraints are also introduced.

2. The regional power consumption regulation method based on mathematical programming according to claim 1, characterized in that, The photovoltaic power generation data within the area to be regulated is the photovoltaic power generation data for a preset historical period within the area to be regulated; the grid electricity price data within the area to be regulated is the time-of-use electricity price data for the day within the area to be regulated; and the energy storage system data within the area to be regulated is the maximum charging and discharging power data of the energy storage system within the area to be regulated. Among these, the data formats of the photovoltaic power generation data, grid electricity price data, and energy storage system data within the area to be regulated are all CSV format.

3. The regional power consumption regulation method based on mathematical programming according to claim 1, characterized in that, The pre-built photovoltaic prediction model adopts a stochastic fluctuation model, a pre-trained long short-term memory network model, or a pre-trained autoregressive integral moving average model.

4. The regional power consumption regulation method based on mathematical programming according to claim 1, characterized in that, The objective function of the multi-objective MILP optimization model is as follows: in, for Time-of-use electricity pricing; for Electricity purchased from the power grid during specific time periods; This is the energy storage loss cost coefficient; for Energy storage charging and discharging power during specific time periods.

5. The regional power consumption regulation method based on mathematical programming according to claim 1, characterized in that, The load balance constraints are as follows: in, for User load demand during specific time periods; for Actual or predicted photovoltaic power output during the time period; for Electricity purchased from the power grid during specific time periods; for Time-limited energy storage charging and discharging power; Energy storage physical constraints include energy storage state of charge constraints, energy storage charge and discharge power constraints, and energy storage state transition constraints; The energy storage state of charge constraints are as follows: in, This represents the minimum state of charge of the energy storage system. This represents the maximum state of charge of the energy storage system. for State of charge of the time-phase energy storage system; The energy storage charging and discharging power constraints are as follows: in, for The charging and discharging power of the time-limited energy storage system; This represents the maximum charging and discharging power of the energy storage system. Energy storage state transition constraints are as follows: in, for State of charge of the time-phase energy storage system; The charging efficiency of the energy storage system; for Charging power of time-limited energy storage systems; The discharge efficiency of the energy storage system; for Discharge power of the time-limited energy storage system; The constraints on power grid variation are as follows: in, for Time period and Difference in electricity purchased from the power grid during different time periods This represents the maximum power change rate.

6. The regional power consumption regulation method based on mathematical programming according to claim 1, characterized in that, The regulation strategy for the future preset time period in the area to be regulated includes the photovoltaic power, grid power purchase, and the charging and discharging status and real-time cost of the energy storage system in each time period within the future preset time period in the area to be regulated.

7. A regional power control system based on mathematical programming, characterized in that, include: The data management layer is used to acquire photovoltaic power generation data, grid electricity price data, and energy storage system data within the area to be regulated; The photovoltaic prediction layer is used to obtain photovoltaic power generation prediction data for the area to be regulated, based on photovoltaic power generation data in the area to be regulated and combined with a pre-built photovoltaic prediction model. The optimization calculation layer is used to generate a control strategy for a future preset period in the area to be controlled, based on photovoltaic power generation forecast data, grid electricity price data, and energy storage system data in the area to be controlled, using a pre-built control strategy optimization model. Among them, the pre-constructed regulation strategy optimization model adopts a multi-objective MILP optimization model. In the multi-objective MILP optimization model, the dual objectives are minimizing electricity cost and minimizing energy storage loss. The energy storage loss cost coefficient is introduced to balance multiple objectives, and load balance constraints, energy storage physical constraints and grid power change constraints are also introduced.

8. An electronic device, characterized in that, include: A processor is used to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, performs the regional power consumption control method based on mathematical programming as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the regional power consumption control method based on mathematical programming as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the regional power consumption control method based on mathematical programming as described in any one of claims 1-6.

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