Comprehensive energy multi-energy coordinated control method and system suitable for zero-carbon service area

Abstract

This invention discloses a comprehensive energy multi-energy coordinated control method and system applicable to zero-carbon service areas, belonging to the field of energy coordinated control technology. The method includes: acquiring the current service area load, calculating the power supply-demand difference, and acquiring an initial absorption scheme based on the power supply-demand difference; acquiring the adjustable load of the current service area and analyzing and acquiring control priorities; when the power supply-demand difference is greater than a power difference threshold, optimizing the initial absorption scheme with energy storage to acquire an optimized energy storage scheme and executing energy coordinated control; when the power supply-demand difference is less than the power difference threshold, acquiring a load control scheme based on the control priorities, and optimizing the initial absorption scheme with the goal of minimizing carbon emissions to acquire an optimized absorption scheme; and executing energy coordinated control using the load control scheme and the optimized absorption scheme. This invention solves the technical problem in the prior art where the inability to accurately coordinate comprehensive energy leads to poor low-carbon operation results.

Description

Technical Field

[0001] This invention relates to the field of energy coordination and control technology, specifically to a comprehensive energy multi-energy coordination and control method and system applicable to zero-carbon service areas. Background Technology

[0002] Zero-carbon service areas typically integrate various distributed renewable energy sources such as photovoltaics and wind power, and are equipped with energy storage systems, aiming to achieve net-zero emissions through multi-energy complementarity. However, in actual operation, due to the intermittent and fluctuating output of renewable energy and the highly random load demand within the service area, mismatches often occur between the energy supply and demand sides in terms of time and power. This makes it difficult to fully utilize the output characteristics of renewable energy to achieve precise energy coordination and control while ensuring the reliability of energy supply. Summary of the Invention

[0003] This application provides a comprehensive energy multi-energy coordinated control method and system applicable to zero-carbon service areas, which is used to address the technical problem that the inability to accurately coordinate comprehensive energy in the prior art leads to poor low-carbon operation results.

[0004] In view of the above problems, this application provides a comprehensive energy multi-energy coordinated control method and system applicable to zero-carbon service areas.

[0005] In a first aspect, this application provides a comprehensive energy multi-energy coordinated control method applicable to zero-carbon service areas, the method comprising:

[0006] Obtain the current service area load, calculate the power supply and demand difference, and based on the power supply and demand difference, obtain an initial absorption scheme, wherein the initial absorption scheme includes energy storage charging power and energy storage discharging power; Obtain the controllable load of the current service area and analyze it to obtain the control priority; When the power supply-demand difference is greater than the power difference threshold, the initial absorption scheme is optimized for energy storage to obtain an optimized energy storage scheme and energy coordination control is executed. When the power difference between supply and demand is less than the power difference threshold, a load control scheme is obtained based on the control priority, and the initial absorption scheme is optimized with the goal of minimizing carbon emissions to obtain an optimized absorption scheme. Energy coordination control is then performed using the load control scheme and the optimized absorption scheme.

[0007] Secondly, this application provides an integrated multi-energy coordinated control system applicable to zero-carbon service areas, including: The initial solution acquisition module is used to acquire the current service area load, calculate the supply-demand power difference, and acquire an initial absorption solution based on the supply-demand power difference. The initial absorption solution includes energy storage charging power and energy storage discharging power. The load acquisition module is used to acquire the controllable load of the current service area and analyze and acquire the control priority. The energy storage optimization module is used to optimize the initial absorption scheme when the power supply-demand difference is greater than the power difference threshold, obtain the optimized energy storage scheme, and perform energy coordination control. The coordination control module is used to obtain a load control scheme based on the control priority when the power supply and demand difference is less than the power difference threshold, and to optimize the initial absorption scheme with the goal of minimizing carbon emissions, thereby obtaining an optimized absorption scheme. The module then uses the load control scheme and the optimized absorption scheme to perform energy coordination control.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application proposes a comprehensive energy multi-energy coordinated control method and system applicable to zero-carbon service areas. By constructing a coordinated architecture for energy storage optimization, load regulation, and low-carbon optimization, it significantly improves the response accuracy and adaptability of energy coordination under different operating conditions. Specifically, firstly, by calculating the power difference between supply and demand in real time and introducing threshold judgment, the system's operating status is accurately identified: when the power deficit is large, the energy storage optimization mode is activated first, and the charging and discharging process of energy storage is finely time-shifted based on the peak and off-peak time sequence characteristics of renewable energy output, thereby effectively avoiding the extensive use of energy storage and improving the local absorption rate of renewable energy and the utilization efficiency of the energy storage system; when the power deficit is small, the system switches to the load-side regulation mode, and by conducting multi-dimensional priority evaluation of controllable loads, fine-grained and differentiated regulation of non-core loads is achieved, quickly smoothing power fluctuations while ensuring that the core functions of the service area are not affected. Compared with traditional methods, the technical solution provided in this application deeply integrates the optimization goal of minimizing carbon emissions while regulating load. It uses intelligent optimization algorithms to coordinate and optimize the timing of energy storage discharge and grid replenishment, ensuring that the system always operates in a low-carbon optimal state when dealing with small power fluctuations. This achieves the technical effect of improving the economy, reliability and low carbon emissions of the energy system in the zero-carbon service area. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart illustrating the integrated energy multi-energy coordinated control method for zero-carbon service areas provided in this application embodiment.

[0011] Figure 2 A schematic diagram of the structure of the integrated energy multi-energy coordinated control system for zero-carbon service areas provided in the embodiments of this application.

[0012] The components represented by each number in the attached diagram are explained below: Initial scheme acquisition module 100, load acquisition module 200, energy storage optimization module 300, and coordination control module 400. Detailed Implementation

[0013] This application provides a comprehensive energy multi-energy coordinated control method and system applicable to zero-carbon service areas, which is used to address the technical problem that the inability to accurately coordinate comprehensive energy in existing technologies leads to poor low-carbon operation results.

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0015] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or service that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or equipment.

[0016] Example 1, as Figure 1 As shown, this application provides a comprehensive energy multi-energy coordinated control method applicable to zero-carbon service areas, wherein the method includes: S10: Obtain the current service area load, calculate the power difference between supply and demand, and obtain an initial absorption scheme based on the power difference between supply and demand, wherein the initial absorption scheme includes energy storage charging power and energy storage discharging power.

[0017] In the existing zero-carbon service area energy management, due to the fluctuation of renewable energy output and random changes in load, the system has difficulty in quantifying the current supply and demand imbalance in real time and accurately, resulting in a lack of reliable data basis for subsequent control decisions.

[0018] Step S10 in the method provided in this application embodiment includes: Obtain the current load, wherein the current load is the total power consumption of all energy-consuming devices in the zero-carbon service area per unit time; Obtain total energy output, wherein the total energy output includes renewable energy output and grid energy output; The renewable energy output is obtained, wherein the renewable energy output is the actual electrical power output of distributed renewable energy in the service area per unit time, and the renewable energy output also includes obtaining the time-series change data of renewable energy output per unit time, performing feature analysis, and determining the peak and off-peak periods of renewable energy output. The power supply-demand difference is obtained by subtracting the current load from the total energy output; Based on the power supply and demand difference, an initial power consumption plan is obtained; The initial power consumption plan is obtained based on the power supply-demand difference, including: Collect energy storage charging efficiency and energy storage discharging efficiency; Determine the sign of the power supply-demand difference. If the power supply-demand difference is negative, it is determined that the total energy output is less than the current load. The absolute value of the power supply-demand difference is divided by the energy storage discharge efficiency to obtain the required discharge power. If the power supply-demand difference is positive, it is determined that the total energy output is greater than the current load. The required charging power is obtained by multiplying the absolute value of the power supply-demand difference by the energy storage charging efficiency. By integrating the required charging power and required discharging power, an initial consumption scheme is obtained.

[0019] In this embodiment of the application, the current service area load is obtained, the supply and demand power difference is calculated, and an initial absorption scheme is obtained based on the supply and demand power difference. The initial absorption scheme includes energy storage charging power and energy storage discharging power.

[0020] Specifically, firstly, the current load is obtained, whereby the current load is the total power consumption of all energy-consuming devices in the zero-carbon service area per unit time. For example, power consumption data of all energy-consuming devices in the service area are collected, and these data are summed to obtain the current service area load, which represents the total power consumption of all lighting, charging piles, air conditioning, commercial facilities, and other equipment in the service area per unit time.

[0021] Furthermore, the total energy output is obtained, which includes renewable energy output and grid energy output. For example, the actual output power of photovoltaic and wind turbines is collected to obtain renewable energy output. Simultaneously, the power injected into the service area from the grid is collected to obtain grid energy output. The renewable energy output and grid energy output are added together to obtain the total energy output.

[0022] Furthermore, the renewable energy output is obtained, wherein the renewable energy output is the actual electrical power output of distributed renewable energy within the service area per unit time. This renewable energy output also includes acquiring time-series variation data of renewable energy output per unit time, performing feature analysis, and determining peak and off-peak periods for renewable energy output. For example, the renewable energy output value for each unit time is recorded, and by statistically analyzing the time-series variation data over a past period, such as the past 30 days, the changing patterns of the output data are identified, thereby determining the peak and off-peak periods for renewable energy output within a day. For example, based on historical output curves, periods with average output exceeding the daily average output by 20% can be designated as peak periods.

[0023] Furthermore, the supply-demand power difference is obtained by subtracting the current load from the total energy output. Supply-demand power difference = Total energy output - Current load.

[0024] Furthermore, based on the power supply and demand difference, an initial power consumption plan is obtained.

[0025] Specifically, firstly, the energy storage charging efficiency and energy storage discharging efficiency are collected. For example, the energy storage charging efficiency and energy storage discharging efficiency are collected from the operating parameters of the energy storage device, which are inherent performance parameters of the energy storage device.

[0026] Furthermore, the sign of the power supply-demand difference is determined. If the power supply-demand difference is negative, it is determined that the total energy output is less than the current load. The required discharge power is obtained by dividing the absolute value of the power supply-demand difference by the energy storage discharge efficiency. For example, if the power supply-demand difference is negative, it indicates that the total energy output is less than the current load, and there is a power shortage that needs to be supplemented. In this case, the system calculates the required discharge power by dividing the absolute value of the power supply-demand difference by the energy storage discharge efficiency. For example, if the power supply-demand difference is -100 kW and the energy storage discharge efficiency is 95%, then the required discharge power = |-100| / 95% = 105.26 kW.

[0027] Furthermore, if the power supply-demand difference is positive, it is determined that the total energy output is greater than the current load. The required charging power is obtained by multiplying the absolute value of the power supply-demand difference by the energy storage charging efficiency. For example, if the power supply-demand difference is 80 kW and the energy storage charging efficiency is 92%, then the required charging power = |80| × 92% = 73.6 kW. Specifically, there are losses in AC-to-DC conversion and in the charging and discharging of the battery itself. The energy storage charging efficiency and energy storage discharging efficiency characterize the power that can be effectively charged and discharged during the process of AC power being converted into DC power and stored in the battery and output from the battery.

[0028] Furthermore, the required charging power and required discharging power are integrated to obtain an initial absorption scheme. The calculated required charging power and required discharging power are integrated to form a complete initial absorption scheme, which clarifies the charging or discharging power commands that the energy storage system needs to execute under the current supply and demand conditions.

[0029] By acquiring the load and total energy output of the service area in real time, the precise power supply and demand difference is calculated. Based on the sign and absolute value of this power difference, combined with energy storage efficiency parameters, an initial absorption scheme including the demand charging power and the demand discharging power is generated. The power imbalance is transformed into specific energy storage regulation commands, providing a decision-making basis for subsequent hierarchical coordinated control.

[0030] S20: Obtain the controllable load of the current service area and analyze and obtain the control priority.

[0031] Zero-carbon service areas exhibit diverse load types, with varying importance in ensuring normal operation and differing adjustment potential and carbon emission characteristics. Existing load control technologies often employ uniform adjustment standards, treating all adjustable loads indiscriminately. This crude approach may negatively impact the normal operation of the service area's core functions.

[0032] Step S20 in the method provided in this application embodiment includes: Obtain all loads in the current service area, and based on load type, obtain the adjustable load; The adjustable loads are prioritized to obtain a controllability priority score, and the adjustable loads are sorted from largest to smallest according to the controllability priority score to obtain the controllability priority. The process of prioritizing the adjustable load and obtaining a control priority score includes: Based on the types of adjustable loads, obtain the load importance coefficient and load carbon emission coefficient; Calculate the load ratio of the adjustable load in all adjustable loads to obtain the load elasticity coefficient; The load importance coefficient, the load carbon emission coefficient, and the load elasticity coefficient are weighted and calculated to obtain a control priority score.

[0033] In this embodiment of the application, the adjustable load of the current service area is obtained, and the adjustment priority is analyzed and obtained.

[0034] Specifically, firstly, all loads in the current service area are acquired, and based on load type, adjustable loads are identified. For example, the names, equipment types, and load categories of all electrical equipment within the service area are obtained. First, equipment information for all currently operating loads is collected, and the type label corresponding to each load is obtained. Further, the loads are divided into two main categories: non-adjustable loads and adjustable loads. For example, non-adjustable loads include lighting and fire-fighting facilities that ensure the basic safe operation of the service area, while adjustable loads include some air conditioning equipment and advertising light boxes.

[0035] Furthermore, the adjustable loads are prioritized to obtain a control priority score, and the adjustable loads are sorted from largest to smallest according to the control priority score to obtain the control priority.

[0036] Specifically, firstly, based on the types of adjustable loads, load importance coefficients and load carbon emission coefficients are obtained. Each adjustable load in the set is then prioritized. Based on the specific type of load, an importance coefficient is assigned. For example, charging piles, as core service facilities in the service area, have a high importance coefficient set to 0.8, while advertising light boxes, as non-essential facilities, have a low importance coefficient set to 0.2. Furthermore, different categories of loads are assigned different carbon emission baseline values ​​due to their functional positioning, service targets, and roles in achieving zero-carbon goals. For example, charging piles providing charging services for electric vehicles ultimately contribute to carbon reduction in the transportation sector, so their carbon emission coefficient can be set to a low value, such as 0.2; while conventional commercial facilities and lighting loads in the service area, whose energy consumption is auxiliary, have a medium carbon emission coefficient set to 0.5; and certain high-energy-consuming and non-essential advertising display equipment have a high carbon emission coefficient set to 0.7. The initial value of the carbon emission coefficient is derived from the decomposition of the overall carbon emission target of the service area and the analysis of historical energy consumption data of various loads, and is determined through expert experience or industry standards.

[0037] Further, the load proportion of the adjustable load in all adjustable loads is calculated to obtain the load resilience coefficient. Specifically, for each adjustable load, its actual operating power is divided by the total power of all adjustable loads to calculate the load proportion at the current moment, and this proportion is used as the load resilience coefficient for that load. Load resilience coefficient = Actual operating power / Total power of adjustable loads. The load resilience coefficient reflects the scale weight of the load in all adjustable resources; the larger the proportion, the greater its adjustability potential, and the higher the resilience coefficient.

[0038] Furthermore, the load importance coefficient, the load carbon emission coefficient, and the load resilience coefficient are weighted and calculated to obtain a control priority score. Control priority score = w1 × (1 - load importance coefficient) + w2 × load carbon emission coefficient + w3 × load resilience coefficient. Here, w1, w2, and w3 can be set based on the actual scenario. For example, if the service area is located in a relatively remote area with high requirements for comprehensive service coverage, the weight of the load importance coefficient is set higher, such as w1 set to 0.4, w2 set to 0.3, and w3 set to 0.3. In this case, the control priority of loads with lower load importance is higher.

[0039] Furthermore, the controllable loads are sorted from highest to lowest according to their control priority scores to obtain control priorities. The controllable loads with higher control priorities are those with lower load importance but a larger load proportion.

[0040] By identifying the types of all loads within the service area, controllable loads that can participate in regulation are selected. Furthermore, a comprehensive evaluation is conducted from multiple dimensions such as load importance, load proportion, and carbon emission coefficient to calculate the regulation priority score of each controllable load. This achieves a quantitative ranking of the value of load regulation, enabling a clear understanding of which loads can be prioritized for regulation when necessary and which loads must be guaranteed.

[0041] S30: When the power difference between supply and demand is greater than the power difference threshold, the initial absorption scheme is optimized for energy storage to obtain an optimized energy storage scheme and energy coordination control is executed.

[0042] When a service area experiences a significant power deficit or surplus, traditional energy storage dispatching methods often employ an immediate response strategy, directly controlling the charging and discharging of energy storage based solely on the current power supply-demand difference, without fully considering the operating characteristics of energy storage equipment and the temporal distribution patterns of renewable energy output.

[0043] Step S30 in the method provided in this application embodiment includes: Based on the energy storage charging efficiency, calculate the total energy required to charge the required charging power. Based on the total required charging energy, the rated charging power of the energy storage device, the peak period and the off-peak period, a charging time allocation scheme is obtained, the total required charging energy is allocated according to the time period, and the time-series power allocation result is obtained. If the total required charging energy is less than the maximum chargeable energy during the peak period, energy storage charging is only performed during the peak period. Using the time-series power allocation results, the required charging power in the initial consumption scheme is corrected to form an optimized energy storage scheme, and energy coordination control is executed.

[0044] In this embodiment of the application, when the power difference between supply and demand is greater than the power difference threshold, the initial absorption scheme is optimized for energy storage to obtain an optimized energy storage scheme and energy coordination control is performed.

[0045] Specifically, the first step is to obtain the power difference threshold. For example, load data and renewable energy output data for the service area over a past period (e.g., one year) are collected, and the fluctuation range and distribution pattern of the power difference between supply and demand are statistically analyzed to determine the power difference threshold. This threshold is typically set as a small positive value, such as tens of kilowatts, and its purpose is to distinguish the current operating condition of the system: when the power difference between supply and demand exceeds this threshold, it indicates a large power deficit or surplus, requiring priority adjustment through energy storage optimization; when the power difference between supply and demand is less than this threshold, it indicates smaller power fluctuations, allowing for fine-tuning through a combination of load-side regulation and low-carbon optimization, thereby avoiding frequent start-ups and shutdowns of energy storage devices and unnecessary deep charging and discharging.

[0046] Furthermore, based on the energy storage charging efficiency, the total required charging energy corresponding to the demanded charging power is calculated. For example, firstly, based on the energy storage charging efficiency and a preset optimization cycle duration, the total required charging energy corresponding to the demanded charging power in the initial consumption plan is calculated. For instance, if an optimization cycle is set to the next 24 hours, the demanded charging power is considered as the power value that needs to be continuously executed within this cycle. By multiplying the demanded charging power by the optimization cycle duration, the total required charging energy to be supplied to the energy storage device within this cycle is obtained. Total required charging energy = Demand charging power × Optimization cycle duration.

[0047] Further, based on the total required charging energy, the rated charging power of the energy storage device, the peak period, and the off-peak period, a charging time allocation scheme is obtained. The total required charging energy is allocated according to time periods, and the time-series power allocation result is obtained. If the total required charging energy is less than the maximum chargeable energy during the peak period, energy storage charging is only performed during the peak period. Specifically, the peak and off-peak sub-periods and their corresponding durations within the optimization cycle are extracted. For example, based on the analysis of renewable energy output characteristics, the peak period is determined to be from 10:00 to 14:00 daily, totaling 4 hours, and the off-peak period is the remaining 20 hours. The total required charging energy of 720 kWh is greater than the maximum chargeable energy of 200 kWh during the peak period. Therefore, the peak period needs to be fully charged at the rated power, i.e., the energy storage device charges for 4 hours at its maximum charging power of 50 kW during the peak period, resulting in an energy input of 50 × 4 = 200 kWh. The remaining required charging energy is 720 kWh minus 200 kWh, equal to 520 kWh, which needs to be allocated to the off-peak period. The off-peak period lasts for 20 hours, therefore the off-peak charging power is the remaining energy of 520 kWh divided by 20 hours, resulting in 26 kW. Since 26 kW is less than the rated charging power of 50 kW, this allocation scheme is feasible, yielding the following time-series power allocation: 50 kW charging power during peak hours (10:00-14:00) and 26 kW charging power during off-peak hours (the remaining 20 hours). Preferably, if the total required charging energy is less than the maximum chargeable energy during peak hours, no charging is needed during off-peak hours.

[0048] Furthermore, using the time-series power allocation results, the required charging power in the initial absorption scheme is modified to form an optimized energy storage scheme, and energy coordination control is executed. The time-series power allocation results are used to modify the single required charging power in the initial absorption scheme, forming an optimized energy storage scheme containing refined time-period instructions, and energy coordination control is executed according to this scheme.

[0049] The method provided in this application activates an energy storage optimization mode after determining that the power difference between supply and demand exceeds a preset threshold, and makes fine adjustments to the energy storage demand in the initial consumption plan. Specifically, by combining the peak and off-peak timing characteristics of renewable energy output and the rated power limit of energy storage devices, the total required charging energy is reasonably allocated by time period, guiding the charging task to be performed during peak periods when renewable energy output is sufficient as much as possible. This effectively avoids inefficient charging of energy storage during off-peak periods, and improves the real-time consumption capacity of renewable energy and the operating economy of the energy storage system.

[0050] S40: When the power difference between supply and demand is less than the power difference threshold, a load control scheme is obtained based on the control priority, and the initial absorption scheme is optimized with the goal of minimizing carbon emissions to obtain an optimized absorption scheme. Energy coordination control is then performed using the load control scheme and the optimized absorption scheme.

[0051] When small power fluctuations occur in the service area, existing technologies typically rely solely on energy storage systems for rapid mitigation, failing to optimize the combination of energy storage discharge and grid replenishment from a low-carbon perspective. Furthermore, when load-side adjustments are required, traditional methods may lead to a disconnect between load-side response and energy storage-side regulation.

[0052] Step S40 in the method provided in this application embodiment includes: Adjustable loads are selected from high to low based on control priority. The total controllable power is obtained by summing the adjustable power of the selected controllable loads until the total controllable power is greater than or equal to the absolute value of the power difference between supply and demand. The specific controllable power of the load to be controlled and each load is determined to form a load control scheme. The total energy to be released is calculated based on the required discharge power in the initial absorption scheme and the energy storage discharge efficiency. The energy storage device can release energy at present. If the total energy to be released is less than or equal to the current energy to be released, the required discharge power remains unchanged. If the total energy to be released is greater than the current energy to be released, the required discharge power is adjusted based on the total energy to be released, and the adjusted discharge power is obtained. With the goal of minimizing carbon emissions, a timing allocation strategy for energy storage discharge and grid replenishment is determined by combining the peak and off-peak periods. Among them, with the goal of minimizing carbon emissions, and combining the peak and off-peak periods, a timing allocation strategy for energy storage discharge and grid replenishment is determined, including: The optimization period is divided into multiple consecutive time periods. The energy storage discharge power and grid replenishment power in each time period are used as optimization variables to construct an objective function with the goal of minimizing total carbon emissions. The particle swarm optimization algorithm is used to solve the problem. The velocity and position of the particles are updated iteratively. The optimization is carried out with the goal of minimizing carbon emissions and ensuring that the energy required is greater than or equal to the total energy required, until the convergence condition is met. The optimal timing allocation strategy for energy storage discharge power and grid replenishment power is output. By integrating the revised demand discharge power, grid replenishment power, and timing allocation strategy, an optimized absorption scheme is obtained.

[0053] In this embodiment of the application, when the power difference between supply and demand is less than the power difference threshold, a load control scheme is obtained based on the control priority, and the initial absorption scheme is optimized with the goal of minimizing carbon emissions to obtain an optimized absorption scheme. Energy coordination control is then performed using the load control scheme and the optimized absorption scheme.

[0054] Specifically, firstly, adjustable loads are selected from high to low based on their control priority. For example, adjustable loads are selected sequentially from the control priority list in descending order of priority score. For each selected load, its current adjustable power range is collected; for example, the maximum adjustable power of a charging pile is currently 20 kilowatts.

[0055] Further, the adjustable power of the selected adjustable loads is accumulated to obtain the total controlled power, until the total controlled power is greater than or equal to the absolute value of the power difference between supply and demand. This determines the loads to be controlled and the specific adjustable power of each load, forming a load control scheme. For example, the adjustable power of these selected loads is accumulated to obtain the total controlled power, and the accumulated result is compared with the absolute value of the power difference between supply and demand. For instance, if there is a power deficit of 50 kW, which needs to be compensated by load reduction, accumulation begins with the highest priority load. The first load has an adjustable power of 20 kW, and the accumulated total controlled power is 20 kW, still less than 50 kW. The next load is then selected, with an adjustable power of 30 kW. After accumulation, the total controlled power reaches 50 kW, which is exactly equal to the absolute value of the power difference between supply and demand, at which point selection stops. The loads to be controlled are determined to be the two selected loads, and their adjustable powers are assigned to 20 kW and 30 kW respectively, forming a load control scheme.

[0056] Furthermore, based on the required discharge power in the initial consumption scheme, the total required energy is calculated in conjunction with the energy storage discharge efficiency. For example, if the required discharge power in the initial consumption scheme is 60 kW, and the optimization period is set to 24 hours, multiplying the required discharge power by 24 hours yields a total required energy of 1440 kWh.

[0057] Furthermore, the current dischargeable energy of the energy storage device is obtained. If the total required dischargeable energy is less than or equal to the current dischargeable energy, the required discharge power remains unchanged. If the total required dischargeable energy is greater than the current dischargeable energy, the required discharge power is adjusted based on the total required dischargeable energy, and the adjusted discharge power is obtained. For example, the remaining available capacity of the energy storage device, i.e., the current dischargeable energy, is read from the energy storage management system, assuming it is 1000 kWh. Since the total required dischargeable energy of 1440 kWh is greater than the current dischargeable energy of 1000 kWh, the required discharge power is adjusted. Dividing the current dischargeable energy of 1000 kWh by 24 hours yields an average dischargeable power of approximately 41.7 kW.

[0058] Furthermore, with the goal of minimizing carbon emissions, a timing allocation strategy for energy storage discharge and grid replenishment is determined by combining the peak and off-peak periods.

[0059] Specifically, firstly, the optimization period is divided into multiple consecutive time periods. The energy storage discharge power and grid replenishment power in each time period are used as optimization variables to construct an objective function that minimizes total carbon emissions. For example, the 24-hour optimization period is divided into multiple consecutive time periods, such as 24 time periods per hour. The energy storage discharge power and grid replenishment power in each time period are used as optimization variables to construct the objective function that minimizes total carbon emissions. Total carbon emissions = ∑(replenishment power in each time period × sum of carbon emission coefficients of all loads corresponding to that time period). Constraints include: the energy storage discharge power in each time period must not exceed the maximum discharge power of the energy storage device; the grid replenishment power must not exceed the grid connection capacity; and the total energy storage discharge during the entire period must be greater than or equal to the total required energy. Simultaneously, the impact of peak and off-peak periods on carbon emissions is considered: due to the large output of renewable energy during peak periods, the grid carbon emission coefficient may be lower, so energy storage discharge should be prioritized; during off-peak periods, the grid carbon emission coefficient is higher, so grid replenishment should be minimized.

[0060] Furthermore, a particle swarm optimization (PSO) algorithm is employed to solve the problem. The velocity and position of particles are iteratively updated, with the optimization objective of minimizing carbon emissions while ensuring that the energy required is greater than or equal to the total energy required. This iterative optimization continues until convergence is achieved, outputting the optimal time-series allocation strategy for energy storage discharge power and grid replenishment power. For example, the particle swarm size is set to 50 particles, each representing a candidate solution for energy storage discharge power and grid replenishment power across 24 time periods. The number of iterations is set to 200. Parameter settings are as follows: the inertia weight w is initially 0.9, decreasing linearly to 0.4 with each iteration; learning factors c1 and c2 are both set to 2. Individual learning factor c1 controls the step size of a particle learning towards its own historical best position, reflecting the particle's trust in its own experience; swarm learning factor c2 controls the step size of a particle learning towards the swarm's global best position, reflecting the particle's adoption of shared information from the swarm. Each particle randomly initializes its position and velocity within the solution space, calculates its total carbon emissions, and records its individual historical best position and the swarm's global best position. In each iteration, the particle updates its velocity and position according to the following formula: New velocity = Inertia weight × Current velocity + Individual learning factor × Random number × (Individual optimal position) Current position) + group learning factor × random number × (global optimal position) The current position is the new velocity, and the new position is the sum of the current position and the new velocity. During the iteration process, it is necessary to check whether each particle meets the constraints, such as the total discharge amount must be greater than or equal to the total energy required to be discharged. If not, corrections are made. When the maximum number of iterations is reached or the global optimal fitness remains unchanged for multiple consecutive iterations, the iteration stops, and the energy storage discharge power and grid replenishment power sequence corresponding to the global optimal particle for 24 time periods are output.

[0061] Furthermore, the revised demand discharge power, grid replenishment power, and timing allocation strategy are integrated to obtain an optimized absorption scheme. Specifically, the optimal timing allocation strategy obtained by the particle swarm optimization algorithm is combined with the revised demand discharge power constraint to form an optimized absorption scheme that includes energy storage discharge power and grid replenishment power for each time period. Simultaneously, the previously generated load regulation scheme is executed. Through the coordinated control of load-side reduction and optimized discharge on the energy storage side, overall carbon emissions are minimized while maintaining power balance.

[0062] For conditions with small power fluctuations, a collaborative control mechanism for load regulation and energy storage optimization was constructed. On the one hand, based on pre-acquired regulation priorities, controllable loads are selected sequentially from high to low, and the regulated power is accumulated to form a load regulation scheme that precisely matches the supply-demand gap, ensuring power balance with minimal load impact. On the other hand, with the goal of minimizing carbon emissions, the energy storage discharge power in the initial absorption scheme is optimized. Combining the energy storage discharge constraints and the timing characteristics of renewable energy, the allocation strategy for energy storage discharge and grid supplementation is collaboratively optimized. Through joint optimization on the load side and the energy storage side, the overall carbon emission level of the system is significantly reduced while ensuring energy supply reliability, achieving low-carbon operation under disturbance conditions.

[0063] Example 2, as Figure 2 As shown, based on the same inventive concept as the integrated energy multi-energy coordinated control method for zero-carbon service areas provided in Embodiment 1, this embodiment of the invention also provides an integrated energy multi-energy coordinated control system for zero-carbon service areas, including: The initial solution acquisition module 100 is used to acquire the current service area load, calculate the supply and demand power difference, and acquire an initial absorption scheme based on the supply and demand power difference. The initial absorption scheme includes energy storage charging power and energy storage discharging power. The load acquisition module 200 is used to acquire the controllable load of the current service area and analyze and acquire the control priority; The energy storage optimization module 300 is used to optimize the initial absorption scheme when the power supply and demand difference is greater than the power difference threshold, obtain the optimized energy storage scheme, and perform energy coordination control. The coordination control module 400 is used to obtain a load control scheme based on the control priority when the power supply and demand difference is less than the power difference threshold, and to optimize the initial absorption scheme with the goal of minimizing carbon emissions, thereby obtaining an optimized absorption scheme, and to perform energy coordination control using the load control scheme and the optimized absorption scheme.

[0064] In one embodiment, the initial scheme acquisition module 100 is further configured to: Obtain the current load, wherein the current load is the total power consumption of all energy-consuming devices in the zero-carbon service area per unit time; Obtain total energy output, wherein the total energy output includes renewable energy output and grid energy output; The renewable energy output is obtained, wherein the renewable energy output is the actual electrical power output of distributed renewable energy in the service area per unit time, and the renewable energy output also includes obtaining the time-series change data of renewable energy output per unit time, performing feature analysis, and determining the peak and off-peak periods of renewable energy output. The power supply-demand difference is obtained by subtracting the current load from the total energy output; Based on the power supply and demand difference, an initial power consumption plan is obtained; The initial power consumption plan is obtained based on the power supply-demand difference, including: Collect energy storage charging efficiency and energy storage discharging efficiency; Determine the sign of the power supply-demand difference. If the power supply-demand difference is negative, it is determined that the total energy output is less than the current load. The absolute value of the power supply-demand difference is divided by the energy storage discharge efficiency to obtain the required discharge power. If the power supply-demand difference is positive, it is determined that the total energy output is greater than the current load. The required charging power is obtained by multiplying the absolute value of the power supply-demand difference by the energy storage charging efficiency. By integrating the required charging power and required discharging power, an initial consumption scheme is obtained.

[0065] In one embodiment, the load acquisition module 200 is further configured to: Obtain all loads in the current service area, and based on load type, obtain the adjustable load; The adjustable loads are prioritized to obtain a controllability priority score, and the adjustable loads are sorted from largest to smallest according to the controllability priority score to obtain the controllability priority. The process of prioritizing the adjustable load and obtaining a control priority score includes: Based on the types of adjustable loads, obtain the load importance coefficient and load carbon emission coefficient; Calculate the load ratio of the adjustable load in all adjustable loads to obtain the load elasticity coefficient; The load importance coefficient, the load carbon emission coefficient, and the load elasticity coefficient are weighted and calculated to obtain a control priority score.

[0066] In one embodiment, the energy storage optimization module 300 is further configured to: Based on the energy storage charging efficiency, calculate the total energy required to charge the required charging power. Based on the total required charging energy, the rated charging power of the energy storage device, the peak period and the off-peak period, a charging time allocation scheme is obtained, the total required charging energy is allocated according to the time period, and the time-series power allocation result is obtained. If the total required charging energy is less than the maximum chargeable energy during the peak period, energy storage charging is only performed during the peak period. Using the time-series power allocation results, the required charging power in the initial consumption scheme is corrected to form an optimized energy storage scheme, and energy coordination control is executed.

[0067] In one embodiment, the coordination control module 400 is further configured to: Adjustable loads are selected from high to low based on control priority. The total controllable power is obtained by summing the adjustable power of the selected controllable loads until the total controllable power is greater than or equal to the absolute value of the power difference between supply and demand. The specific controllable power of the load to be controlled and each load is determined to form a load control scheme. The total energy to be released is calculated based on the required discharge power in the initial absorption scheme and the energy storage discharge efficiency. The energy storage device can release energy at present. If the total energy to be released is less than or equal to the current energy to be released, the required discharge power remains unchanged. If the total energy to be released is greater than the current energy to be released, the required discharge power is adjusted based on the total energy to be released, and the adjusted discharge power is obtained. With the goal of minimizing carbon emissions, a timing allocation strategy for energy storage discharge and grid replenishment is determined by combining the peak and off-peak periods. Among them, with the goal of minimizing carbon emissions, and combining the peak and off-peak periods, a timing allocation strategy for energy storage discharge and grid replenishment is determined, including: The optimization period is divided into multiple consecutive time periods. The energy storage discharge power and grid replenishment power in each time period are used as optimization variables to construct an objective function with the goal of minimizing total carbon emissions. The particle swarm optimization algorithm is used to solve the problem. The velocity and position of the particles are updated iteratively. The optimization is carried out with the goal of minimizing carbon emissions and ensuring that the energy required is greater than or equal to the total energy required, until the convergence condition is met. The optimal timing allocation strategy for energy storage discharge power and grid replenishment power is output. By integrating the revised demand discharge power, grid replenishment power, and timing allocation strategy, an optimized absorption scheme is obtained.

[0068] In summary, the embodiments of this application have at least the following technical effects: This application proposes a comprehensive energy multi-energy coordinated control method and system applicable to zero-carbon service areas. By constructing a coordinated architecture for energy storage optimization, load regulation, and low-carbon optimization, it significantly improves the response accuracy and adaptability of energy coordination under different operating conditions. Specifically, firstly, by calculating the power difference between supply and demand in real time and introducing threshold judgment, the system's operating status is accurately identified: when the power deficit is large, the energy storage optimization mode is activated first, and the charging and discharging process of energy storage is finely time-shifted based on the peak and off-peak time sequence characteristics of renewable energy output, thereby effectively avoiding the extensive use of energy storage and improving the local absorption rate of renewable energy and the utilization efficiency of the energy storage system; when the power deficit is small, the system switches to the load-side regulation mode, and by conducting multi-dimensional priority evaluation of controllable loads, fine-grained and differentiated regulation of non-core loads is achieved, quickly smoothing power fluctuations while ensuring that the core functions of the service area are not affected. Compared with traditional methods, the technical solution provided in this application deeply integrates the optimization goal of minimizing carbon emissions while regulating load. It uses intelligent optimization algorithms to coordinate and optimize the timing of energy storage discharge and grid replenishment, ensuring that the system always operates in a low-carbon optimal state when dealing with small power fluctuations. This achieves the technical effect of improving the economy, reliability and low carbon emissions of the energy system in the zero-carbon service area.

[0069] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0070] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0071] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A comprehensive energy multi-energy coordinated control method applicable to zero-carbon service areas, characterized in that, include: Obtain the current service area load, calculate the power supply and demand difference, and based on the power supply and demand difference, obtain an initial absorption scheme, wherein the initial absorption scheme includes energy storage charging power and energy storage discharging power; Obtain the controllable load of the current service area and analyze it to obtain the control priority; When the power supply-demand difference is greater than the power difference threshold, the initial absorption scheme is optimized for energy storage to obtain an optimized energy storage scheme and energy coordination control is executed. When the power difference between supply and demand is less than the power difference threshold, a load control scheme is obtained based on the control priority, and the initial absorption scheme is optimized with the goal of minimizing carbon emissions to obtain an optimized absorption scheme. Energy coordination control is then performed using the load control scheme and the optimized absorption scheme.

2. The integrated energy multi-energy coordinated control method applicable to zero-carbon service areas according to claim 1, characterized in that, Obtain the current service area load, calculate the supply-demand power difference, and based on the supply-demand power difference, obtain an initial absorption scheme, wherein the initial absorption scheme includes energy storage charging power and energy storage discharging power, including: Obtain the current load, wherein the current load is the total power consumption of all energy-consuming devices in the zero-carbon service area per unit time; Obtain total energy output, wherein the total energy output includes renewable energy output and grid energy output; The renewable energy output is obtained, wherein the renewable energy output is the actual electrical power output of distributed renewable energy in the service area per unit time. The renewable energy output also includes obtaining the time-series change data of renewable energy output per unit time, performing feature analysis, and determining the peak and off-peak periods of renewable energy output. The power supply-demand difference is obtained by subtracting the current load from the total energy output; Based on the power supply and demand difference, an initial power consumption plan is obtained.

3. The integrated energy multi-energy coordinated control method applicable to zero-carbon service areas according to claim 2, characterized in that, Based on the power supply and demand difference, an initial absorption scheme is obtained, including: Collect energy storage charging efficiency and energy storage discharging efficiency; Determine the sign of the power supply-demand difference. If the power supply-demand difference is negative, it is determined that the total energy output is less than the current load. The absolute value of the power supply-demand difference is divided by the energy storage discharge efficiency to obtain the required discharge power. If the power supply-demand difference is positive, it is determined that the total energy output is greater than the current load. The required charging power is obtained by multiplying the absolute value of the power supply-demand difference by the energy storage charging efficiency. By integrating the required charging power and required discharging power, an initial consumption scheme is obtained.

4. The integrated energy multi-energy coordinated control method applicable to zero-carbon service areas according to claim 1, characterized in that, Obtain the controllable load of the current service area and analyze and obtain the control priority, including: Obtain all loads in the current service area, and based on load type, obtain the adjustable load; The adjustable loads are prioritized to obtain a control priority score, and the adjustable loads are sorted from largest to smallest according to the control priority score to obtain the control priority.

5. The integrated energy multi-energy coordinated control method applicable to zero-carbon service areas according to claim 4, characterized in that, Prioritize the adjustable loads and obtain a controllability priority score, including: Based on the types of adjustable loads, obtain the load importance coefficient and load carbon emission coefficient; Calculate the load ratio of the adjustable load in all adjustable loads to obtain the load elasticity coefficient; The load importance coefficient, the load carbon emission coefficient, and the load elasticity coefficient are weighted and calculated to obtain a control priority score.

6. The integrated energy multi-energy coordinated control method applicable to zero-carbon service areas according to claim 2, characterized in that, When the power supply-demand difference exceeds a power difference threshold, the initial absorption scheme is optimized for energy storage to obtain an optimized energy storage scheme, and energy coordination control is executed, including: Based on the energy storage charging efficiency, calculate the total energy required to charge the required charging power. Based on the total required charging energy, the rated charging power of the energy storage device, the peak period and the off-peak period, a charging time allocation scheme is obtained, the total required charging energy is allocated according to the time period, and the time-series power allocation result is obtained. If the total required charging energy is less than the maximum chargeable energy during the peak period, energy storage charging is only performed during the peak period. Using the time-series power allocation results, the required charging power in the initial consumption scheme is corrected to form an optimized energy storage scheme and energy coordination control is executed.

7. The integrated energy multi-energy coordinated control method applicable to zero-carbon service areas according to claim 1, characterized in that, When the power supply-demand difference is less than a power difference threshold, a load control scheme is obtained based on the control priority, including: Adjustable loads are selected from high to low based on control priority. The total controllable power is obtained by summing the adjustable power of the selected controllable loads until the total controllable power is greater than or equal to the absolute value of the power difference between supply and demand. The specific controllable power of the load to be controlled and each load is determined to form a load control scheme.

8. The integrated energy multi-energy coordinated control method applicable to zero-carbon service areas according to claim 2, characterized in that, The initial absorption scheme is optimized with the goal of minimizing carbon emissions to obtain an optimized absorption scheme. Energy coordination control is then implemented using the load regulation scheme and the optimized absorption scheme, including: The total energy to be released is calculated based on the required discharge power in the initial absorption scheme and the energy storage discharge efficiency. The energy storage device can release energy at present. If the total energy to be released is less than or equal to the current energy to be released, the required discharge power remains unchanged. If the total energy to be released is greater than the current energy to be released, the required discharge power is adjusted based on the total energy to be released, and the adjusted discharge power is obtained. With the goal of minimizing carbon emissions, a timing allocation strategy for energy storage discharge and grid replenishment is determined by combining the peak and off-peak periods. By integrating the revised demand discharge power, grid replenishment power, and timing allocation strategy, an optimized absorption scheme is obtained.

9. The integrated energy multi-energy coordinated control method applicable to zero-carbon service areas according to claim 8, characterized in that, With the goal of minimizing carbon emissions, and considering both peak and off-peak periods, a time-series allocation strategy for energy storage discharge and grid replenishment is determined, including: The optimization period is divided into multiple consecutive time periods. The energy storage discharge power and grid replenishment power in each time period are used as optimization variables to construct an objective function with the goal of minimizing total carbon emissions. The particle swarm optimization algorithm is used to solve the problem. The velocity and position of the particles are updated iteratively. The optimization objective is to minimize carbon emissions and ensure that the energy required is greater than or equal to the total energy required. The optimization is carried out iteratively until the convergence condition is met, and the optimal timing allocation strategy for energy storage discharge power and grid replenishment power is output.

10. A comprehensive energy multi-energy coordinated control system applicable to zero-carbon service areas, characterized in that, For implementing the integrated energy multi-energy coordinated control method for zero-carbon service areas according to any one of claims 1-9, the system comprises: The initial solution acquisition module is used to acquire the current service area load, calculate the supply-demand power difference, and acquire an initial absorption solution based on the supply-demand power difference. The initial absorption solution includes energy storage charging power and energy storage discharging power. The load acquisition module is used to acquire the controllable load of the current service area and analyze and acquire the control priority. The energy storage optimization module is used to optimize the initial absorption scheme when the power supply-demand difference is greater than the power difference threshold, obtain the optimized energy storage scheme, and perform energy coordination control. The coordination control module is used to obtain a load control scheme based on the control priority when the power supply and demand difference is less than the power difference threshold, and to optimize the initial absorption scheme with the goal of minimizing carbon emissions, thereby obtaining an optimized absorption scheme. The module then uses the load control scheme and the optimized absorption scheme to perform energy coordination control.

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