City-oriented source network load storage coordinated regulation and control system and method

Abstract

The invention discloses a city-oriented source-network load-storage coordinated regulation and control system and method, and relates to the technical field of power system source-network load-storage coordinated regulation and control, and the system comprises a data collection module which is in butt joint with monitoring equipment at each side of source-network load-storage to collect multiple types of data, and carries out the preprocessing and transmission of the data; the regulation / monitoring / management / scheduling module at each side of the source network load storage respectively executes output regulation, power grid monitoring, load management and energy storage scheduling; the central cooperative regulation and control module integrates the data and formulates a multi-objective optimization strategy; and the terminal interaction module provides a visual interface and an operation log record. According to the invention, source-network-load-storage multi-side data is integrated to guarantee accurate regulation and control, multi-side linkage is realized to improve operation efficiency, faults are quickly responded to reduce power failure influence, authority and log management standard operation and maintenance are refined, and reliability, economy and safety of city power regulation and control are comprehensively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system source network load storage collaborative regulation technology, and particularly relates to a source network load storage collaborative regulation system and method for a city. BACKGROUND

[0002] Current power regulation at the city level focuses on a single link, and the data of each side of the source network load storage is scattered and stored in different systems, lacking a unified collection and integration mechanism. The output data of new energy sources such as photovoltaic and wind power on the source side is managed by the local monitoring system of the power station, the operation data of the network side substation and line belongs to the power grid dispatching platform, the load data of industrial, commercial and residential users on the load side is scattered in the power marketing system and user-owned monitoring equipment, and the SOC and health state data of the storage side energy storage are managed by the energy storage operator. The data formats of each system are not unified, and the transmission protocols differ greatly, making it difficult for the city energy management department to obtain complete source network load storage operation status, and the regulation and control decision lacks comprehensive data support. At the same time, strict abnormal detection and time synchronization are not performed in the data collection process, and the load data uploaded by some monitoring equipment has abnormal values such as sudden drop to zero or far exceeding the historical peak value, and the time stamp deviation of different devices can reach several minutes, further affecting the reliability of the data and failing to provide a basis for accurate regulation and control.

[0003] In the existing regulation scheme, the regulation of each side of the source network load storage is isolated, and the coordination is insufficient. The source side relies only on its own unit adjustment to suppress the fluctuation of new energy output, without combining load transfer on the load side and charging and discharging on the storage side for joint optimization, resulting in frequent photovoltaic and wind power curtailment; the network side can only identify overload state, without linking the source side output distribution and load shedding on the load side, and the adjustment scheme needs to be manually developed, which may cause line overload trip with lagging response; the load side management mostly adopts administrative command load shedding, without considering the user electricity characteristics and response willingness, lacking demand response incentive mechanism, and the user participation in regulation and control is low, the rigid load and adjustable load are mixed together, affecting the user electricity experience; the storage side dispatching is mostly based on fixed period charging and discharging, without dynamic adjustment according to the real-time state of the source network load, and some energy storage devices have a rapid decline in health state due to overcharging and overdischarging, shortening the service life. In addition, the optimization target of the central regulation module is single, mostly focusing on power supply reliability, ignoring the balance between economy and environmental protection, and unable to meet the comprehensive regulation and control demand of reliability, economy and low carbon at the city level.

[0004] The existing system has obvious short boards in fault response and operation and maintenance. When a major power grid fault such as substation voltage loss or main line trip occurs, manual judgment of the fault range and emergency command are needed, and the delay of emergency measures such as energy storage compensation and load shedding may expand the power outage range. When a major source-side output interruption such as large-scale photovoltaic curtailment occurs, there is a lack of fast-response energy storage discharge and load adjustment linkage mechanism, which leads to the inability to timely make up for the power supply gap. At the same time, the terminal interaction module lacks fine-grained permission management, the power management, operation and maintenance, and user roles have fuzzy permission division, and there is a risk of unauthorized personnel modifying control parameters. The operation log record is incomplete, and the control command issuing, fault handling and other key operation processes cannot be traced, which does not meet the power system operation and maintenance audit requirements, and lacks intuitive operation state visualization display, making it difficult for operation and maintenance personnel to quickly locate abnormal equipment and fault causes, increasing the operation and maintenance difficulty. SUMMARY

[0005] The source-grid-load-storage collaborative control system and method for a city are proposed to solve the problems mentioned in the prior art.

[0006] In order to achieve the above purpose, the following technical scheme is adopted: a wind farm operation and maintenance system based on digital twinning, comprising the following modules:

[0007] A data acquisition module is deployed in a city administrative region, interfaces with source-side photovoltaic power stations, wind farms, thermal power plants, grid-side substations, transmission lines, distribution areas, load-side industrial users, commercial users, residential users, storage-side electrochemical energy storage power stations, pumped storage power stations, and user-side storage monitoring equipment, collects environmental data, pre-processes the data using edge computing nodes, and transmits the data to the central module through a 5G / optical fiber private network;

[0008] A source-side control module is connected with the data acquisition module, receives source-side output data, performs maximum power point tracking adjustment on photovoltaic power stations, performs variable pitch and yaw control on wind farms, and performs unit load distribution on thermal power plants. A power fluctuation suppression algorithm is built in, and when the source-side output fluctuation exceeds a preset threshold, the suppression measure is started;

[0009] A grid-side monitoring module is connected with the data acquisition module, and monitors the grid node voltage, line current, power factor and power flow distribution in real time, identifies abnormal states, and built-in fault location algorithm, when the power grid fails, quickly locate the fault section and generate isolation scheme, and calculate the transmission capacity margin of the power grid;

[0010] The load side management module is connected with the data acquisition module, classifies the load side users according to the electricity consumption characteristics as interruptible load, transferable load and rigid load, receives the electricity consumption plan and real-time load data of the users, formulates a load adjustment scheme, executes on-demand removal control on the interruptible load, executes period transfer scheduling on the transferable load, pushes the electricity consumption suggestions to the users, and guides the users to participate in demand response;

[0011] The storage side scheduling module is connected with the data acquisition module, receives the storage side SOC data, source network load operation data, formulates a storage energy charging and discharging strategy, controls the storage energy charging when the source side output is excessive, controls the storage energy discharging when the source side output is insufficient or the load side load is in peak, and monitors the health state parameters of the storage energy equipment;

[0012] The central collaborative regulation module is connected with the source side regulation module, the network side monitoring module, the load side management module and the storage side scheduling module, integrates the data of the modules to construct a city source network load storage operation state matrix, formulates a collaborative regulation strategy by using a multi-objective optimization algorithm, the optimization objectives include power supply reliability, economy and environmental protection, outputs the regulation instructions to the modules on each side, receives the feedback data of the modules, and dynamically adjusts the strategy;

[0013] The terminal interaction module is connected with the central collaborative regulation module, provides a visual interface, displays the source network load storage operation state, the regulation strategy execution situation and fault information to the city energy management department, displays the electricity consumption data and demand response income to the users, supports the operation and maintenance personnel to issue manual regulation instructions, and records all operation logs.

[0014] Further, the load side management module further includes a load prediction sub-module, the sub-module constructs a load prediction model based on historical load data, environmental data and holiday data, a multi-factor fusion formula is used in the prediction process to calculate a predicted load value, and the formula is

[0015]

[0016] wherein is a total load value of the load side in a prediction period; is a historical load weight coefficient, the value range is 0.4-0.6, the value is adjusted according to the similarity between the historical data and the prediction period, the higher the similarity is, the larger the value is; is a historical load weight coefficient, the value range is 0.4-0.6, the value is adjusted according to the similarity between the historical data and the prediction period, the higher the similarity is, the larger the value is; is an average load value of the historical same period corresponding to the prediction period; is an environmental influence weight coefficient, the value range is 0.2-0.3, the value is adjusted according to the influence degree of environmental factors on the load, the value increases in summer high temperature and winter low temperature; is an environmental influence weight coefficient, the value range is 0.2-0.3, the value is adjusted according to the influence degree of environmental factors on the load, the value increases in summer high temperature and winter low temperature; is an environmental influence weight coefficient, the value range is 0.2-0.3, the value is adjusted according to the influence degree of environmental factors on the load, the value increases in summer high temperature and winter low temperature; is a load deviation value after environmental factors correction, which is calculated by a regression algorithm from illumination, temperature and wind speed data; Holiday weight coefficient, value range 0.1-0.2, 0.1 for weekdays, 0.2 for weekends and statutory holidays; Holiday load correction value, positive value during holidays, negative value during weekdays; Trend weight coefficient, value range 0.05-0.15, adjusted according to long-term growth or decline trend of load, value increases during load growth stage Load trend correction value, based on load change data of 3-12 consecutive same periods before the prediction period, calculated by linear fitting or exponential smoothing algorithm.

[0017] Further, the grid-side monitoring module further includes a power flow calculation submodule, which constructs a power grid power flow calculation model based on the node voltage method, inputs the injection power of each node and the line impedance parameters, calculates the active power, reactive power and voltage drop of each transmission line, and sends a capacity warning to the central collaborative control module when the power flow of a certain line exceeds 80% of the rated capacity. The central module adjusts the source-side output distribution or load-side load transfer accordingly; at the same time, the power flow calculation submodule generates a power grid power flow distribution thermal map regularly to visually display the load condition of each line in the power grid by color gradient, with red indicating load exceeding 80%, yellow indicating load between 50%-80%, and green indicating load below 50%, providing data reference for power grid operation and expansion planning.

[0018] Further, the storage-side dispatching module further includes a storage health management submodule, which monitors the operating parameters of the storage device and calculates the health state value of the storage device. The SOH calculation is based on the number of charge and discharge cycles, capacity attenuation rate, and internal resistance change rate. When the SOH is below a preset threshold, the charge and discharge strategy of the storage is adjusted to reduce the charge and discharge depth and current, and to prolong the service life of the device. At the same time, the submodule records the fault information of the storage device, generates a fault maintenance work order and pushes it to the operation and maintenance terminal. The work order includes the fault device number, fault type, fault occurrence time, and recommended repair scheme. When a serious fault occurs, the connection between the storage device and the power grid is immediately cut off.

[0019] ​Further, the multi-objective optimization algorithm of the central collaborative regulation module uses the analytic hierarchy process to determine the weight of each optimization target, the weight of power supply reliability is set to 0.4, the weight of economy is set to 0.3, and the weight of environmental protection is set to 0.3; the power supply reliability is evaluated by the load outage rate, and the load outage rate is the ratio of the total amount of outage load to the total load demand; the economy is evaluated by the source network load storage regulation cost, and the regulation cost includes the source side generation cost, the storage side charging and discharging cost, and the load side load adjustment compensation cost; the environmental protection is evaluated by the carbon emission intensity, and the carbon emission intensity is the ratio of the total carbon emission amount to the total power generation amount in the regulation period; the algorithm inputs the source network load storage operation constraints, including the upper and lower limits of the output of each power generation equipment on the source side, the upper limit of the transmission capacity of each line on the network side, the SOC operation range on the storage side, and the rigid load guarantee demand on the load side, and uses the particle swarm optimization algorithm to solve the optimal regulation scheme, and sets the iteration termination condition in the solving process, and the iteration is terminated when the iteration number reaches the preset value or the change rate of the objective function value is less than 0.1% for 5 consecutive iterations.

[0020] Further, the edge computing node of the data acquisition module has data filtering and abnormality detection functions, and abnormal values obviously beyond the reasonable range are excluded during data filtering, for example, the case that the load data suddenly becomes zero or far exceeds the maximum value in the same period in history; the abnormality detection adopts the 3σ criterion, calculates the mean and standard deviation of the data sequence, and marks the abnormal data and records the abnormality occurrence time and value when the data deviates from the mean by more than 3 times the standard deviation; at the same time, the interpolation method is used to complete the missing data, the linear interpolation is used when the missing rate is less than 5%, the neighborhood mean interpolation is used when the missing rate is 5%-10%, and the data acquisition equipment fault alarm is triggered when the missing rate exceeds 10%; the edge computing node also performs time synchronization processing on the collected data, and unifies the data time of each monitoring device based on the GPS timestamp.

[0021] Further, the terminal interaction module supports multi-role permission management, and the roles are divided into energy management officers, operation and maintenance personnel, and users; the energy management officer has the permission to view the full amount of operation data, modify the regulation parameter threshold, and approve the collaborative regulation scheme; the operation and maintenance personnel have the permission to view the operation state of the equipment under their jurisdiction, receive fault maintenance work orders, and issue local manual regulation instructions for the equipment; the user only has the permission to view his own power consumption data, receive demand response invitations, and confirm load adjustment instructions; the permission management adopts a role-based access control model, which associates the permissions with the roles, and realizes the permission allocation by associating the users with the roles, and the permissions of each role can be added or deleted through the system background configuration; at the same time, the terminal interaction module records the operation logs of all roles, and the log content includes the operator's name, role, operation time, operation module, operation content, and operation result, and the log retention period is not less than 1 year.

[0022] Further, a source network load storage collaborative regulation method for a city, applicable to the source network load storage collaborative regulation system for a city, comprising:

[0023] Data collection step, through the deployment of monitoring equipment in the administrative region of the city, the output data of the source side photovoltaic power station, wind farm, thermal power plant, voltage and current and power flow data of the network side transformer substation, transmission line and distribution area, load data of industrial users, commercial users and residential users, SOC and health state data of the storage side electrochemical energy storage power station, pumped storage power station and user side energy storage, as well as environmental data, using edge computing node to preprocess data, executing data filtering, missing value completion and time synchronization operation, and transmitting to the central collaborative control module through 5G / optical fiber private network;

[0024] State analysis step, the central collaborative control module receives the preprocessed data, analyzes the source side output stability, calculates the output fluctuation amplitude and frequency, the fluctuation amplitude is the difference between the maximum and minimum values in unit time, and the fluctuation frequency is the number of times of exceeding the threshold value in unit time; analyze the network side operation safety, judge whether the voltage of each node is over the rated range, whether the line current is over the allowed value, and whether the power flow is over the transmission capacity; analyze the load characteristics of the load side, classify and count the proportion and time period distribution of interruptible load, transferable load and rigid load; analyze the storage side energy storage availability, evaluate whether the SOC is within the dispatching allowable range and whether the SOH meets the operation requirement; construct the city source network load storage operation state matrix, mark the abnormal state existing in each side, such as source side output fluctuation exceeding the standard, network side line overload and storage side SOH too low;

[0025] Strategy making step, the central collaborative control module based on the operation state matrix, adopts multi-objective optimization algorithm, determines the weight of power supply reliability, economy and environmental protection through analytic hierarchy process, solves the optimal solution through particle swarm optimization algorithm, and combines the operation constraints of source network load storage, including the upper and lower limits of the output of each power generation equipment on the source side, the upper limit of the transmission capacity of each line on the network side, the SOC operation range of the storage side, and the rigid load guarantee demand of the load side, to make the collaborative control strategy; the strategy content includes source side output adjustment instruction, including photovoltaic maximum power tracking parameter, wind farm variable pitch angle and thermal power unit load distribution ratio; network side operation and maintenance instruction, including fault isolation scheme and power flow optimization measure; load side load adjustment instruction, including interruptible load cut-off list and transferable load time period arrangement; storage side charging and discharging instruction, including charging power, discharging power and charging and discharging time length;

[0026] The central cooperative control module sends control instructions to the source side control module, the network side monitoring module, the load side management module and the storage side scheduling module. After receiving the instructions, each side module performs corresponding operations. The source side control module adjusts the operating parameters of the power generation equipment to make the actual output meet the requirements of the adjustment instructions. The network side monitoring module performs fault isolation or power flow optimization to eliminate abnormal conditions on the network side. The load side management module implements load adjustment, pushes adjustment notifications to users and feeds back user responses. The storage side scheduling module controls the charging and discharging of energy storage equipment and monitors the SOC changes in real time. Each side module feeds back the progress and results of instruction execution in real time, such as the actual output value after output adjustment, the actual load value after load adjustment and the SOC changes after energy storage charging and discharging.

[0027] The central cooperative control module receives feedback data from each side module and evaluates the execution effect of the control strategy. The power supply reliability after control is calculated, and the load outage rate before and after control is compared. The economy after control is calculated, and the total control cost of source, network, load and storage before and after control is compared. The environmental protection after control is calculated, and the carbon emission intensity before and after control is compared. The operating conditions of source, network, load and storage before and after control are compared, including source side output fluctuation amplitude, network side line power flow, load side load compliance rate and storage side SOC stability. When the evaluation result shows that a certain target does not meet the preset requirement, such as the economic improvement does not meet the preset proportion, the strategy formulation step is returned to adjust and optimize the target weight or operating constraint condition to formulate a control strategy again. When the evaluation result meets all preset requirements, the control process data of this time is recorded, including instruction content, execution result and evaluation index, and is stored in the system database.

[0028] Further, the strategy making step further comprises a demand response strategy making sub-step, which determines user demand response priority based on historical response willingness of the user and current electricity consumption characteristics of the user; the historical response willingness is evaluated by the number of times of participation of the user in demand response and response compliance rate, the more the number of times of participation and the higher the response compliance rate, the stronger the response willingness; the current electricity consumption characteristics are evaluated by user load type and electricity consumption period, the higher the proportion of adjustable load and the higher the overlap degree of electricity consumption period and load peak, the greater the regulation value; the user with high priority, i.e., the user with strong response willingness and great regulation value, is preferentially included in the demand response range; the demand response invitation is pushed to the user with high priority, the invitation content comprises adjustment period, adjustable load amount, and response benefit, i.e., electricity price subsidy amount; the user confirmation information is received, the adjustable load amount confirmed by the user within a preset time is included in the load side load adjustment instruction, and the user who does not feedback or feedbacks rejection is excluded from the demand response list; meanwhile, the total regulation capacity of the demand response, i.e., the sum of the adjustable load amounts confirmed by all the users, is calculated; when the total regulation capacity is lower than a preset value of the load side load adjustment demand, the response benefit, i.e., the electricity price subsidy amount, is increased, or the invitation user range is expanded to the secondary priority user, the invitation and confirmation process is repeated, and the total regulation capacity is satisfied until the load side load adjustment demand is satisfied.

[0029] Further, the effect evaluation step further comprises an emergency fault handling step; when the grid side monitoring module detects a major grid fault, the system skips the normal state analysis step and the strategy making step, and directly starts an emergency control plan; the central collaborative control module immediately issues an instruction to the storage side dispatching module to control the energy storage device to discharge at a rated power, the discharge duration is estimated according to the source side output gap, and the current output deficiency is preferentially made up; an instruction is issued to the load side management module to cut off all interruptible loads in a preset cutting order, industrial interruptible loads are cut off first, then commercial interruptible loads are cut off, and residential rigid loads are reserved to quickly reduce the load side load demand; an instruction is issued to the grid side monitoring module to remotely control the disconnection of the circuit breaker of the fault section to isolate the fault area; at the same time, an emergency fault alarm is sent to the municipal energy management department and the operation and maintenance personnel, the alarm information comprises fault type, fault location, fault influence range, and emergency measures started; the operation and maintenance personnel rush to the fault site after receiving the alarm to carry out fault troubleshooting and repair work; after the fault is eliminated, the central collaborative control module performs the state analysis step to evaluate the current recoverable operation capacity of the source grid load storage, and then performs the strategy making step to make a gradual recovery scheme, the source side stable output device is recovered first, then the transferable load and part of the interruptible load are gradually recovered, and finally the energy storage charging and discharging state is adjusted to the normal range to make the source grid load storage system return to the normal operation state.

[0030] Compared with the prior art, the present application has the following advantages:

[0031] The present application realizes comprehensive integration and high-quality management of multi-side data of source, network, load and storage at the data processing level, effectively solves the problems of data dispersion and low reliability in traditional systems. Through the unified deployment of the data acquisition module, the source, network, load and storage are connected to each other, covering all-dimensional data such as new energy output, power grid operation, user load and storage state. At the same time, with the help of edge computing nodes, abnormal detection, missing value completion and time synchronization are performed, invalid data is removed and time deviation is corrected, ensuring that the data transmitted to the central module has consistency and accuracy. The centralized integration of multi-side data provides a complete data basis for collaborative regulation, avoiding regulation decision errors caused by data fragmentation or deviation, and laying a solid data support for subsequent state analysis and strategy formulation.

[0032] At the collaborative regulation level, the present application breaks the isolated regulation pattern of source, network, load and storage, and realizes multi-side linkage optimization. The source side reduces the phenomenon of abandoned electricity and wind through new energy output fluctuation suppression algorithm combined with storage charging and discharging and load adjustment; the network side calculates the power flow and cooperates with the central regulation to give early warning of line overload and automatically trigger source and load adjustment, shortening the response time; the load side distinguishes interruptible, transferable and rigid loads through demand response strategy combined with user willingness and power consumption characteristics, uses income incentive to improve user participation, and balances regulation demand and user experience; the storage side dynamically adjusts the charging and discharging strategy based on the real-time state of source, network and load, avoids overcharging and overdischarging combined with the health management sub-module, and prolongs the service life of the equipment. The central collaborative regulation module balances the reliability, economy and environmental protection of power supply through multi-objective optimization algorithm, ensures that the regulation scheme takes into account the needs of all parties, and significantly improves the overall operation efficiency and comprehensive benefits of the municipal source, network, load and storage system.

[0033] At the fault response and operation management level, the present application greatly improves the emergency response capability and operation standardization of the system. When an emergency fault occurs, the system skips the conventional process and directly starts the preset plan, quickly issues instructions for full-power discharge of energy storage, interruption of interruptible load and isolation of fault section, maximizes the reduction of fault influence range and guarantees the power supply of core users; after the fault is eliminated, the system gradually restores operation through state analysis and strategy formulation, reducing the power outage time. The multi-role permission management of the terminal interaction module clearly divides the energy management, operation and user permissions, avoiding unauthorized operations; complete operation log records support full-process tracing, meeting the audit requirements; the visual interface intuitively displays the operation state and fault information, helping operation personnel quickly locate problems and improving operation efficiency. Overall, the present application improves the accuracy, collaboration and safety of municipal source, network, load and storage regulation, and provides strong support for reliable, economic and low-carbon operation of municipal power systems. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 A schematic diagram of a source, network, load and storage collaborative regulation system for municipalities is proposed for the present application;

[0035] Figure 2A schematic block diagram of a source network load storage collaborative regulation method for a city is proposed for the present application.

[0036] Figure 3 A load prediction accuracy comparison column chart for the load side;

[0037] Figure 4 A new energy consumption rate monthly trend line chart;

[0038] Figure 5 A different scene regulation response time horizontal bar chart;

[0039] Figure 6 A storage SOC daily change line chart;

[0040] Figure 7 A load peak valley difference rate quarterly grouping column chart. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0042] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship shown in the drawings, and are only used to facilitate the description of the present application and simplify the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0043] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0044] Reference Figures 1 to 7 A wind farm operation and maintenance management system based on digital twins includes the following modules:

[0045] The data acquisition module is deployed within the municipal administrative region and connects to monitoring equipment of photovoltaic power stations, wind farms, and thermal power plants on the source side; substations, transmission lines, and distribution substations on the grid side; industrial users, commercial users, and residential users on the load side; and electrochemical energy storage power stations, pumped storage power stations, and user-side energy storage on the storage side. It collects output data, voltage and current data, load data, energy storage SOC data, and environmental data such as light, wind speed, and temperature. The data is preprocessed using edge computing nodes and transmitted to the central module via a 5G / fiber optic private network, with transmission delay controlled within a preset range.

[0046] The source-side control module is connected to the data acquisition module, receives source-side power output data, performs maximum power point tracking regulation for photovoltaic power plants, performs pitch and yaw control for wind farms, and performs unit load distribution for thermal power plants. It has a built-in power output fluctuation suppression algorithm, which activates suppression measures when the source-side power output fluctuation exceeds the preset threshold to ensure stable power output.

[0047] The grid-side monitoring module connects to the data acquisition module to monitor grid node voltage, line current, power factor, and power flow distribution in real time. It identifies abnormal states such as overvoltage, overcurrent, and power exceeding limits. It has a built-in fault location algorithm that can quickly locate the fault section and generate an isolation scheme when a grid fault occurs. At the same time, it calculates the grid transmission capacity margin to provide grid-side constraints for control strategies.

[0048] The load-side management module, connected to the data acquisition module, classifies load-side users into interruptible loads, transferable loads, and rigid loads according to their electricity consumption characteristics. It receives user electricity consumption plans and real-time load data, formulates load adjustment schemes, performs on-demand disconnection control for interruptible loads, performs time-based transfer scheduling for transferable loads, pushes electricity consumption suggestions to users, and guides users to participate in demand response.

[0049] The energy storage-side scheduling module is connected to the data acquisition module. It receives energy storage SOC data and source-grid-load operation data, formulates energy storage charging and discharging strategies, controls energy storage charging when the source-side output is excessive, and controls energy storage discharging when the source-side output is insufficient or the load-side load is at its peak. At the same time, it monitors the health status parameters of the energy storage equipment, such as temperature and number of charging and discharging cycles, and controls the charging and discharging parameters to prevent overcharging and over-discharging.

[0050] The central coordination and control module connects with the source-side control module, grid-side monitoring module, load-side management module, and storage-side scheduling module. It integrates data from each module to construct a city-level source-grid-load-storage operation status matrix. It adopts a multi-objective optimization algorithm to formulate a coordination and control strategy. The optimization objectives include power supply reliability, economy, and environmental protection. It outputs control commands to each module and receives feedback data from each module to dynamically adjust the strategy.

[0051] The terminal interaction module connects to the central coordination and control module, providing a visual interface to display the operating status of power generation, grid, load and storage, the implementation of control strategies and fault information to municipal energy management departments, and to display electricity consumption data and demand response benefits to users. It also supports operation and maintenance personnel in issuing manual control commands and records all operation logs.

[0052] In this invention, the load-side management module also includes a load forecasting submodule. This submodule constructs a load forecasting model based on historical load data, environmental data, and holiday data. The forecasting process uses a multi-factor fusion formula to calculate the predicted load value. The formula is as follows:

[0053]

[0054] in This represents the total load value on the load side for the predicted period. This is the historical load weighting coefficient, ranging from 0.4 to 0.6. It is adjusted based on the similarity between historical data and the forecast period; the higher the similarity, the better. The larger the value; The forecast period is the average load value for the same historical period. This is the environmental impact weighting coefficient, ranging from 0.2 to 0.3, adjusted according to the degree of impact of environmental factors on the load, especially during high summer temperatures. The value increases, especially during low temperatures in winter. The value also increases; The load deviation value, corrected for environmental factors, is calculated from data on light, temperature, and wind speed using a regression algorithm. This is the weighting coefficient for holidays, with a value ranging from 0.1 to 0.2. 0.1 is used for weekdays, and 0.2 is used for weekends and statutory holidays. This is the load correction value for holidays; it is positive during holidays and negative during weekdays. is a trend weight coefficient, the value range is 0.05-0.15, is adjusted according to the long-term growth or decline trend of the load, and the load is in a growth stage is increased; is a load trend correction value, is calculated based on the load change data of 3-12 same periods (days / weeks / months) before a prediction period through a linear fitting or an exponential smoothing algorithm, and physically represents the influence of the long-term growth or decline characteristics of the load on the load in the prediction period. is a positive value, the load is in a decline stage is a negative value; by fusing the multi-dimensional influences of historical load, environmental factors, holiday characteristics and load trend, the precision of load prediction under different seasons, different date types and different environmental conditions is improved, and accurate data support is provided for load adjustment scheme making and source-grid-load-storage collaborative scheduling.

[0055] In the application, the grid side monitoring module further includes a power flow calculation submodule, the submodule constructs a power grid power flow calculation model based on a node voltage method, inputs each node injection power and line impedance parameters, calculates active power, reactive power and voltage drop of each power transmission line, sends a capacity warning to the central collaborative control module when the power flow of a certain line exceeds 80% of the rated capacity, the central module adjusts source side output distribution or load side load transfer accordingly to avoid line overload; at the same time, the power flow calculation submodule generates a power grid power flow distribution thermal map regularly to visually display the load conditions of each line of the power grid by color gradient, red represents load exceeding 80%, yellow represents load 50%-80%, and green represents load less than 50%, which provides data reference for power grid operation and expansion planning, and the calculation period of the submodule is synchronized with the data acquisition period to ensure that the power flow data is matched with the actual operation state of the power grid in real time.

[0056] In the application, the storage side scheduling module further includes an energy storage health management submodule, the submodule monitors the operating parameters such as charge-discharge current, voltage, temperature and SOC of the energy storage device, calculates the health state value (SOH) of the energy storage device, the SOH calculation is based on the number of charge-discharge cycles, capacity attenuation rate and internal resistance change rate, when the SOH is lower than a preset threshold, the energy storage charge-discharge strategy is adjusted to reduce the charge-discharge depth and current and prolong the service life of the device; at the same time, the submodule records fault information such as over-temperature fault and charge-discharge fault of the energy storage device, generates a fault maintenance work order and pushes it to an operation and maintenance terminal, the work order includes fault device number, fault type, fault occurrence time and recommended maintenance scheme; when a serious fault such as battery thermal runaway warning occurs, the connection between the energy storage device and the power grid is immediately cut off to ensure the safety of the power grid and the device.

[0057] In this invention, the multi-objective optimization algorithm of the central coordinated control module uses the analytic hierarchy process (AHP) to determine the weights of each optimization objective. The weight for power supply reliability is set to 0.4, the weight for economic efficiency is set to 0.3, and the weight for environmental protection is set to 0.3. Power supply reliability is assessed through load failure rate, which is the ratio of the total power failure load to the total load demand. Economic efficiency is assessed through the source-grid-load-storage control cost, which includes the generation cost on the source side, the charging and discharging cost on the storage side, and the load adjustment compensation cost on the load side. Environmental protection is assessed through carbon emission intensity, which is the ratio of the total carbon emissions to the total power generation during the control period. The algorithm is input with source-grid-load-storage operation constraints, including the upper and lower limits of the output of each generation device on the source side, the upper limit of the transmission capacity of each line on the grid side, the SOC operating range on the storage side, and the rigid load guarantee requirements on the load side. The particle swarm optimization algorithm is used to solve for the optimal control scheme. During the solution process, an iteration termination condition is set. The algorithm terminates when the number of iterations reaches a preset value or the rate of change of the objective function value is less than 0.1% for five consecutive iterations, ensuring that the algorithm outputs a feasible scheme within a preset time to meet the real-time control needs of the source-grid-load-storage system in the city.

[0058] In this invention, the edge computing nodes of the data acquisition module have data filtering and anomaly detection functions. During data filtering, outliers that are significantly outside the reasonable range are removed, such as load data suddenly dropping to zero or far exceeding the historical maximum value for the same period. Anomaly detection adopts the 3σ criterion, calculating the mean and standard deviation of the data sequence. When the data deviates from the mean by more than 3 times the standard deviation, it is marked as abnormal data and the time and value of the anomaly are recorded. At the same time, interpolation is used to complete missing data. When the missing rate is less than 5%, linear interpolation is used; when the missing rate is 5%-10%, neighborhood mean interpolation is used; when the missing rate exceeds 10%, a fault alarm for the data acquisition equipment is triggered. The edge computing nodes also perform time synchronization processing on the acquired data, unifying the data time of each monitoring device based on GPS timestamps, so that the timestamp deviation of data collected by different devices in the same period does not exceed 1 second, avoiding data matching errors caused by time deviations, and ensuring that the data transmitted to the central module has consistency and accuracy.

[0059] In this invention, the terminal interaction module supports multi-role permission management, with roles divided into energy management specialists, maintenance personnel, and users. Energy management specialists have the authority to view all operational data, modify control parameter thresholds, and approve collaborative control schemes. Maintenance personnel have the authority to view the operating status of their assigned equipment, receive fault repair work orders, and issue local manual control commands for equipment. Users only have the authority to view their own electricity consumption data, receive demand response invitations, and confirm load adjustment commands. The permission management adopts a role-based access control model, which associates permissions with roles, and assigns permissions to users based on roles. The permissions of each role can be added or deleted through system backend configuration. At the same time, the terminal interaction module records the operation logs of all roles. The log content includes the operator's name, operator's role, operation time, operation module, operation content, and operation result. The log retention period is no less than one year to facilitate subsequent auditing and operation traceability.

[0060] This invention also discloses a source-grid-load-storage coordinated regulation method for prefecture-level cities, applicable to the aforementioned source-grid-load-storage coordinated regulation system for prefecture-level cities, comprising:

[0061] The data acquisition process involves collecting output data from photovoltaic power plants, wind farms, and thermal power plants on the source side, voltage, current, and power flow data from substations, transmission lines, and distribution substations on the grid side, load data from industrial, commercial, and residential users on the load side, SOC and health status data from electrochemical energy storage power plants, pumped storage power plants, and user-side energy storage on the storage side, as well as environmental data such as sunlight, wind speed, and temperature. The data is preprocessed using edge computing nodes, and operations such as filtering outliers, filling in missing values, and time synchronization are performed. The data is then transmitted to the central collaborative control module via a 5G / fiber optic private network.

[0062] The status analysis steps involve the central coordinated control module receiving preprocessed data and analyzing the source-side power output stability, calculating the power output fluctuation amplitude and frequency. The fluctuation amplitude is the difference between the maximum and minimum power output per unit time, and the fluctuation frequency is the number of times the fluctuation exceeds the threshold per unit time. The grid-side operational safety is analyzed, determining whether the voltage of each node exceeds the rated range, whether the line current exceeds the allowable value, and whether the power flow exceeds the transmission capacity. The load-side load characteristics are analyzed, classifying and statistically analyzing the proportion and time distribution of interruptible loads, transferable loads, and rigid loads. The energy storage availability on the storage side is analyzed, assessing whether the State of Charge (SOC) is within the allowable dispatch range and whether the State of Balance (SOH) meets operational requirements. Finally, a city-level source-grid-load-storage operational status matrix is ​​constructed, marking abnormal states on each side, such as excessive source-side power output fluctuations, grid-side line overload, and low storage-side SOH.

[0063] The strategy formulation process involves the central coordinated control module using a multi-objective optimization algorithm based on the operating state matrix. The weights of power supply reliability, economy, and environmental protection are determined through the analytic hierarchy process (AHP), and the optimal solution is obtained through particle swarm optimization. This is combined with constraints on the source-grid-load-storage operation, including the upper and lower limits of output from each power generation unit on the source side, the upper limit of transmission capacity for each line on the grid side, the SOC operating range on the storage side, and the rigid load guarantee requirements on the load side. The coordinated control strategy is then formulated. The strategy content includes source-side output adjustment instructions, including photovoltaic maximum power point tracking parameters, wind farm pitch angle, and thermal power unit load allocation ratios; grid-side operation and maintenance instructions, including fault isolation schemes and power flow optimization measures; load-side load adjustment instructions, including a list of interruptible loads to be removed and the scheduling of transferable load periods; and storage-side charging and discharging instructions, including charging power, discharging power, and charging / discharging duration.

[0064] The strategy execution steps are as follows: the central coordination and control module sends control instructions to the source-side control module, grid-side monitoring module, load-side management module, and energy storage-side dispatch module. Upon receiving the instructions, each module performs corresponding operations: the source-side control module adjusts the operating parameters of the power generation equipment to ensure the actual output meets the control instructions; the grid-side monitoring module performs fault isolation or power flow optimization to eliminate abnormal grid conditions; the load-side management module implements load regulation, pushes regulation notifications to users, and provides feedback on user response; the energy storage-side dispatch module controls the charging and discharging of energy storage devices and monitors SOC changes in real time; each module provides real-time feedback on the progress and results of instruction execution, such as the actual output value after output adjustment, the actual load value after load adjustment, and the SOC changes after energy storage charging and discharging.

[0065] The effect evaluation steps involve the central coordinated control module receiving feedback data from various modules to assess the effectiveness of the control strategy; calculating the power supply reliability after control and comparing the change in load failure rate before and after control; calculating the economic efficiency after control and comparing the change in the total control cost of source-grid-load-storage before and after control; calculating the environmental impact after control and comparing the change in carbon emission intensity before and after control; comparing the operating status of source-grid-load-storage before and after control, including the fluctuation range of source-side output, grid-side line power flow, load-side load compliance rate, and storage-side SOC stability; if the evaluation results show that a certain target has not met the preset requirements, such as the economic improvement not reaching the preset proportion, the system returns to the strategy formulation step to adjust and optimize the target weights or operating constraints, and re-formulates the control strategy; if the evaluation results meet all preset requirements, the data of this control process is recorded, including the instruction content, execution results, and evaluation indicators, and stored in the system database.

[0066] In this invention, the strategy formulation step further includes a demand response strategy formulation sub-step. This sub-step determines the user demand response priority based on the historical response willingness and current electricity consumption characteristics of load-side users. Historical response willingness is assessed by the number of times users participate in demand response and the response compliance rate; the more times they participate and the higher the response compliance rate, the stronger their response willingness. Current electricity consumption characteristics are assessed by the user's load type and electricity consumption period; the higher the proportion of adjustable load and the higher the overlap between the electricity consumption period and the peak load, the greater the regulation value. Users with high priority, i.e., those with strong response willingness and high regulation value, are given priority in the demand response scope. Demand response invitations are pushed to priority users, and the invitation content includes the adjustment period, the amount of load to be adjusted, and the response benefit, i.e., the amount of electricity price subsidy. User confirmation information is received, and users provide feedback confirmation within a preset time. The confirmed adjustable load is then included in the load-side adjustment command. If the user does not respond or refuses the response, the user is removed from the demand response list. At the same time, the total adjustment capacity of the demand response is calculated, which is the sum of the adjustable load confirmed by all confirmed users. When the total adjustment capacity is lower than the preset value of the load-side adjustment demand, the response benefit is increased, i.e., the electricity price subsidy amount is increased, or the scope of invited users is expanded to include second-priority users. The invitation and confirmation process is repeated until the total adjustment capacity meets the load-side adjustment demand. This sub-step is executed synchronously with the formulation of the coordinated control strategy, and the adjustment capacity obtained from the demand response is incorporated into the overall load-side control resources. This makes the demand response an important supplement to the load-side adjustment, enhances the enthusiasm of load-side users to participate in the coordinated control of power generation, grid, load and storage, and reduces the impact on rigid loads, thus ensuring the user's electricity experience.

[0067] In this invention, the effect evaluation step is followed by an emergency fault handling step. When the grid-side monitoring module detects a major grid fault, such as a substation power outage, a main line trip, or a major power outage on the source side, such as a thermal power plant unit shutdown or large-scale photovoltaic curtailment, the system skips the conventional state analysis and strategy formulation steps and directly activates the emergency control plan. The central coordinated control module immediately issues instructions to the storage-side dispatch module to control the energy storage equipment to discharge at full power at its rated capacity. The discharge duration is estimated based on the power outage on the source side, prioritizing compensation for the current power shortage. Instructions are also issued to the load-side management module to cut off all interruptible loads according to a preset cut-off order, first cutting off industrial interruptible loads, then commercial interruptible loads, while retaining residential rigid loads to quickly reduce load demand on the load side. Finally, instructions are issued to the grid-side monitoring module to remotely disconnect the circuit breakers in the faulty section, isolating the faulty area and preventing further damage. To prevent the fault from spreading to non-faulty sections, an emergency fault alarm is sent to the municipal energy management department and operation and maintenance personnel. The alarm information includes the fault type, fault location, fault impact range, and emergency measures already activated. After receiving the alarm, the operation and maintenance personnel rush to the fault site to carry out fault investigation and repair work. After the fault is resolved, the central coordinated control module executes the status analysis step to assess the current recoverable operational capacity of the source-grid-load-storage system, and then executes the strategy formulation step to formulate a gradual recovery plan. First, the stable output equipment on the source side is restored, then the transferable load and some interruptible loads are gradually restored, and finally the energy storage charging and discharging status is adjusted to the normal range so that the source-grid-load-storage system returns to normal operation. This step shortens the response time after a major fault through pre-set plans and rapid command issuance, minimizes the impact of the fault on power supply reliability, and ensures the basic electricity needs of core users and residents in the municipality.

[0068] The following two examples further illustrate the specific implementation of this system:

[0069] Example 1

[0070] Northern industrial-led city-level source-grid-load-storage coordinated regulation and control application

[0071] This embodiment is applied to a northern industrial city with three industrial parks under its jurisdiction. Industrial users account for 60% of the total. The main new energy sources are two photovoltaic power stations and one wind farm, with one electrochemical energy storage power station and one pumped storage power station as supporting facilities. This system is needed to achieve coordinated regulation of source, grid, load and storage to improve the new energy absorption rate and power supply reliability. The system deployment and operation details are as follows.

[0072] I. System Module Deployment and Operation

[0073] 1. Data Acquisition Module Deployment: Install current and voltage sensors and output transmitters at various monitoring points in photovoltaic power plants, wind farms, and thermal power plants; deploy power flow monitoring devices in 220kV substations, 110kV transmission lines, and distribution substations; install smart meters and load monitoring terminals in industrial users, commercial complexes, and residential communities in industrial parks; and install SOC sensors and temperature monitoring equipment in electrochemical energy storage power stations and pumped storage power stations. Edge computing nodes are deployed near various data sources, collecting the following data types: real-time photovoltaic power output (450MW), wind power output (200MW), and thermal power output (150MW) from the source side; substation voltage (220kV±2%), line current (400A), and power flow (350MW) from the grid side; industrial load (500MW), commercial load (100MW), and residential load (150MW) from the load side; electrochemical energy storage SOC (80%) and pumped storage SOC (70%) from the storage side; and environmental data including irradiance (800W / m²), wind speed (6m / s), and temperature (25℃). The edge computing nodes perform preprocessing: filtering outliers where wind power output suddenly drops to 0; using linear interpolation to complete 10 minutes of missing photovoltaic power output data; synchronizing all data based on GPS timestamps with time deviation controlled within 1 second; and transmitting the data to the central collaborative control module via a 5G private network.

[0074] 2. Load-side load forecasting execution: The load forecasting submodule of the load-side management module calculates and forecasts the load based on historical data, environmental data, holiday data, and load trends. It uses the following formula... ,in =0.5, =100MW; =0.25, =10MW; =0.15, =5MW; =0.1, =8MW. Substituting into the calculation, we get... =0.5×100+0.25×10+0.15×5+0.1×8=50+2.5+0.75+0.8=54.05MW, with a prediction accuracy of 95%, providing a basis for the formulation of load regulation schemes.

[0075] 3. Centralized Coordinated Control Strategy Formulation: The central module integrates data to construct an operational status matrix, marking conditions such as wind power output fluctuations exceeding the threshold by ±10% on the source side, 110kV line power flow reaching 75% of rated capacity on the grid side, interruptible industrial loads accounting for 20% on the load side, and sufficient SOC (State of Charge) for electrochemical energy storage on the energy storage side. A multi-objective optimization algorithm is adopted, with power supply reliability weighted at 0.4, economic efficiency weighted at 0.3, and environmental protection weighted at 0.3. Input constraints include: photovoltaic output capped at 500MW, wind power output capped at 300MW, line transmission capacity capped at 450MW, energy storage SOC operating range at 20%-90%, and rigid load guarantee at 600MW. The particle swarm optimization algorithm converges after 80 iterations, and the output strategy is as follows: source-side photovoltaic power operates at maximum power tracking, wind power pitch is adjusted, and thermal power is maintained at 150MW; on the load side, 20MW of industrial loads are transferred to wind power peak output periods; and on the energy storage side, electrochemical energy storage is charged, and pumped storage is on standby.

[0076] 4. Strategy Execution and Effect Evaluation: Each module receives and executes instructions. The source-side control module adjusts the parameters of the photovoltaic inverter and the pitch angle of the wind turbine. The load-side management module notifies the industrial park to transfer 20MW of production load to 14:00-16:00. The storage-side scheduling module controls the charging of electrochemical energy storage. Feedback data shows: actual photovoltaic output 475MW, wind power 215MW, thermal power 150MW, total output 840MW; total load on the load side 840MW; energy storage SOC increased to 90%. Evaluation results: load failure rate 0%, control cost reduced by 8%, carbon emission intensity reduced by 12%. The terminal interaction module displays the operating status to energy management specialists, pushes normal equipment logs to maintenance personnel, and pushes load transfer confirmation information to industrial users. The operation log is complete.

[0077] II. Data Representation and Interpretation

[0078] Table 1. Comparison of key indicators of traditional regulation methods and the present invention in industrial cities in northern China

[0079] Comparison index Traditional regulation method Invention system New energy consumption rate 75% 92% Regulation response time 30 minutes 5 minutes Industrial user satisfaction 60% 88% Energy storage device life 8 years 12 years Line overload failure rate 8 times / year 1 time / year

[0080] Table 1 shows that traditional regulation suffers from isolated source, load, and storage systems, resulting in a low renewable energy absorption rate of only 75% and a 30-minute regulation time. Industrial users experience low satisfaction due to forced load shedding. Energy storage lifespan is shortened to 8 years due to disordered charging and discharging, leading to frequent line overload faults. This invention improves the absorption rate to 92% through multi-side collaboration and achieves a 5-minute rapid response. Demand response and load transfer enhance user satisfaction to 88%. Energy storage health management extends lifespan to 12 years. Power flow early warning and dynamic adjustment reduce overload faults to once per year. It fully adapts to the regulation needs of high-load, high renewable energy proportions in industrial cities, balancing reliability, economy, and environmental protection.

[0081] Example 2

[0082] Southern residential and commercial-driven city-level source-grid-load-storage coordinated regulation application

[0083] This embodiment is applied to a southern city dominated by residential and commercial users, where residents and commercial users account for 80% of the market. The main source of new energy is distributed photovoltaic power, which is supported by user-side energy storage and regional electrochemical energy storage power stations. This system is required to address the issues of large load peak-valley differences and distributed photovoltaic fluctuations. The system deployment and operation details are as follows.

[0084] I. System Module Deployment and Operation

[0085] 1. Data Acquisition Module Deployment: Output monitoring terminals are installed on the rooftops of distributed photovoltaic users; voltage and current monitoring devices are deployed in 110kV substations and 35kV distribution lines; smart meters are installed in residential communities, shopping malls, and supermarkets; and SOC and charge / discharge monitoring equipment is installed in user-side energy storage and regional electrochemical energy storage power stations. Edge computing nodes are deployed in each distribution substation area, collecting data including: real-time total output of distributed photovoltaic power generation on the source side (250MW); substation voltage on the grid side (110kV±3%), 35kV line current (300A), and power flow value (320MW); real-time residential load on the load side (300MW), real-time commercial load on the load side (200MW); average SOC of user-side energy storage on the storage side (60%), regional energy storage SOC (75%); and environmental data: irradiance (900W / m²), temperature (30℃), and wind speed (2m / s). Edge computing node preprocessing: Remove the outlier value of the mall load suddenly increasing to 500MW, use neighborhood mean interpolation to complete the missing 5-minute data of 3 distributed photovoltaics, synchronize the data timestamp, and transmit it to the central module through a dedicated fiber optic network.

[0086] 2. Grid-side power flow calculation and emergency fault handling: The power flow calculation submodule of the grid-side monitoring module takes into account the 35kV line impedance of 0.1Ω and the node injected power, and calculates the line's active power as 320MW, reactive power as 50Mvar, and voltage drop as 2kV. At 14:00 one day, the 35kV main line tripped due to a lightning strike. The grid-side module immediately sent an alarm to the central module, and the system activated its emergency plan: the central module issued instructions to the regional energy storage power station to control its full-power discharge of 150MW; it issued instructions to the load-side management module to disconnect 50MW of interruptible load from the shopping mall and 30MW of non-essential load from the supermarket; and it issued instructions to the grid-side module to disconnect the circuit breakers at both ends of the faulty line to isolate the fault. Simultaneously, an alarm was sent to the energy management department, and maintenance personnel arrived on-site within 30 minutes to repair the fault, which was resolved at 14:30. Central module execution status analysis: source-side photovoltaic 240MW, energy storage SOC dropped to 40% after discharge, load-side load 420MW; recovery strategy: regional energy storage converted to charging 50MW, gradually restore shopping mall load 30MW, system returned to normal at 15:00.

[0087] 3. Demand Response and Strategy Optimization: In the strategy formulation process, the demand response submodule evaluates users' historical responses: Residential user A participated 5 times with a 100% response rate, and shopping mall B participated 3 times with an 80% response rate; current electricity consumption characteristics: Residential user A can transfer 5kW of load, and shopping mall B can interrupt 50MW of load. An invitation is sent to A: adjustment period 20:00-22:00, 5kW transfer, revenue of 2 yuan / kWh, A confirms; an invitation is sent to B: cut-off period 19:00-20:00, 50MW cut-off, revenue of 0.5 yuan / kWh, B confirms. The total adjustment capacity is 55.005MW, meeting the load-side adjustment demand of 50MW. The central module integrates the demand response capacity and optimizes the strategy: distributed photovoltaic power generation on the source side operates according to actual output, energy storage and charging on the user side on the storage side is 30MW, and load-side transfer and cut-off are implemented, reducing the peak-valley difference by 30% after regulation.

[0088] 4. Energy Storage Health Management and Terminal Interaction: The health management submodule of the energy storage-side dispatch module monitors user-side energy storage equipment: charging / discharging current 50A, voltage 380V, temperature 35℃, SOC 60%, calculates SOH=90%, which is below the threshold of 80%, and maintains the current charging / discharging parameters. The terminal interaction module assigns permissions according to roles: energy management specialists can view all data and modify control parameters; maintenance personnel can view the status of energy storage equipment and receive fault work orders; residential users can view their own electricity consumption data and receive a demand response benefit of 10 yuan. Operation logs record all actions and are retained for 1 year.

[0089] II. Data Representation and Interpretation

[0090] Table 2 Comparison of key indicators of traditional regulation methods and the present invention in southern residential and commercial cities.

[0091] Comparison index Traditional regulation method Invention system Load peak-valley difference rate 45% 25% Fault outage duration 2 hours 30 minutes Distributed photovoltaic consumption rate 70% 95% Demand response participation rate 30% 75% Residential electricity cost 0.6 yuan / kWh 0.52 yuan / kWh

[0092] Table 2 shows that traditional regulation methods cannot cope with the peak-valley difference in residential and commercial loads, with a peak-valley difference rate of 45%, power outages lasting up to 2 hours, and a distributed photovoltaic (PV) absorption rate of only 70%. Demand response lacks incentives, resulting in a participation rate of only 30%, and high electricity costs for residents. This invention reduces the peak-valley difference to 25% through demand response and energy storage scheduling, shortens power outages to 30 minutes under emergency plans, and increases the distributed PV absorption rate to 95%. Incentives increase the participation rate to 75%, load shifting reduces residential electricity costs, and the invention is fully adapted to the load characteristics of residential and commercial cities, improving regulation flexibility and user experience.

[0093] Figure 3 Traditional single-data methods result in a peak load forecast accuracy of 68%, while this invention integrates multiple factors, achieving an accuracy of over 94% for each time period, thus supporting load-side regulation and adaptation.

[0094] Figure 4This indicates that traditional energy sources, loads, and storage are not linked, and the renewable energy consumption rate is less than 75%. The synergistic regulation of this invention enables the consumption rate to consistently exceed 90%, thereby improving the utilization rate of clean energy.

[0095] Figure 5 This indicates that traditional manual control takes more than 18 minutes to respond, while the automatic scheduling of this invention provides a response time of 6 minutes for faults and 4 minutes for overloads, ensuring reliable power supply.

[0096] Figure 6 This demonstrates that traditional fixed charging and discharging methods cause SOC fluctuations (risks of overcharging and over-discharging), while the present invention uses dynamic scheduling to stabilize the SOC and extend the lifespan of energy storage devices.

[0097] Figure 7 This indicates that the average annual peak-valley difference rate of traditional regulation is 44% (reaching 48% in Q3), while the load-storage synergy of this invention reduces it to 24%, which aligns with the economic low-carbon regulation target.

[0098] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A smart control system for coordinated dispatch of new energy sources in municipal control centers, characterized in that, Includes the following modules: The data acquisition module is deployed within the municipal administrative region and connects to monitoring equipment of photovoltaic power stations, wind farms, and thermal power plants on the source side; substations, transmission lines, and distribution substations on the grid side; industrial users, commercial users, and residential users on the load side; and electrochemical energy storage power stations, pumped storage power stations, and user-side energy storage on the storage side. It collects environmental data, preprocesses the data using edge computing nodes, and transmits it to the central module via a 5G / fiber optic private network. The source-side control module is connected to the data acquisition module, receives source-side power output data, performs maximum power point tracking regulation for photovoltaic power plants, performs pitch and yaw control for wind farms, and performs unit load distribution for thermal power plants. It has a built-in power output fluctuation smoothing algorithm, which activates smoothing measures when the source-side power output fluctuation exceeds the preset threshold. The grid-side monitoring module connects to the data acquisition module to monitor grid node voltage, line current, power factor, and power flow distribution in real time, identify abnormal states, and has a built-in fault location algorithm. When a fault occurs in the grid, it can quickly locate the fault section and generate an isolation scheme, while calculating the grid transmission capacity margin. The load-side management module, connected to the data acquisition module, classifies load-side users into interruptible loads, transferable loads, and rigid loads according to their electricity consumption characteristics. It receives user electricity consumption plans and real-time load data, formulates load adjustment schemes, performs on-demand disconnection control for interruptible loads, performs time-based transfer scheduling for transferable loads, pushes electricity consumption suggestions to users, and guides users to participate in demand response. The energy storage-side scheduling module is connected to the data acquisition module. It receives energy storage-side SOC data and source-grid-load operation data, formulates energy storage charging and discharging strategies, controls energy storage charging when the source-side output is excessive, controls energy storage discharging when the source-side output is insufficient or the load-side load is at its peak, and monitors the health status parameters of the energy storage equipment. The central coordination and control module connects with the source-side control module, grid-side monitoring module, load-side management module, and storage-side scheduling module. It integrates data from each module to construct a city-level source-grid-load-storage operation status matrix. It adopts a multi-objective optimization algorithm to formulate a coordination and control strategy. The optimization objectives include power supply reliability, economy, and environmental protection. It outputs control commands to each module and receives feedback data from each module to dynamically adjust the strategy. The terminal interaction module connects to the central coordination and control module, providing a visual interface to display the operating status of power generation, grid, load and storage, the implementation of control strategies and fault information to municipal energy management departments, and to display electricity consumption data and demand response benefits to users. It also supports operation and maintenance personnel in issuing manual control commands and records all operation logs.

2. The source-grid-load-storage coordinated control system for prefecture-level cities according to claim 1, characterized in that, The load management module also includes a load forecasting submodule. This submodule builds a load forecasting model based on historical load data, environmental data, and holiday data. The forecasting process uses a multi-factor fusion formula to calculate the predicted load value. The formula is as follows: in This represents the total load value on the load side for the predicted period. This is the historical load weighting coefficient, ranging from 0.4 to 0.

6. It is adjusted based on the similarity between historical data and the forecast period; the higher the similarity, the better. The larger the value; The forecast period is the average load value for the same historical period. This is the environmental impact weighting coefficient, ranging from 0.2 to 0.3, adjusted according to the degree of impact of environmental factors on the load, especially during high summer temperatures. The value increases, especially during low temperatures in winter. The value also increases; The load deviation value, corrected for environmental factors, is calculated from data on light, temperature, and wind speed using a regression algorithm. This is the weighting coefficient for holidays, with a value ranging from 0.1 to 0.

2. 0.1 is used for weekdays, and 0.2 is used for weekends and statutory holidays. This is the load correction value for holidays; it is positive during holidays and negative during weekdays. This is a trend weighting coefficient, ranging from 0.05 to 0.15, adjusted according to the long-term growth or decline trend of the load, specifically during the load growth phase. The value increases; This is a load trend correction value, calculated using linear fitting or exponential smoothing algorithms based on load change data from 3-12 consecutive periods prior to the forecast period.

3. The source-grid-load-storage coordinated control system for prefecture-level cities according to claim 1, characterized in that, The grid-side monitoring module also includes a power flow calculation submodule. This submodule constructs a power flow calculation model based on the nodal voltage method. It takes the injected power and line impedance parameters of each node as input and calculates the active power, reactive power, and voltage drop of each transmission line. When the calculated power flow of a certain line exceeds the rated capacity by 80%, it sends a capacity warning to the central coordination and control module. The central module adjusts the power output distribution on the source side or the load transfer on the load side accordingly. At the same time, the power flow calculation submodule periodically generates a heat map of the power flow distribution of the grid, which uses color gradients to intuitively display the load status of each line in the grid. Red indicates that the load exceeds 80%, yellow indicates that the load is 50%-80%, and green indicates that the load is below 50%, providing data reference for grid operation and maintenance and capacity expansion planning.

4. A source-grid-load-storage coordinated control system for prefecture-level cities according to claim 1, characterized in that, The energy storage-side dispatch module also includes an energy storage health management submodule. This submodule monitors the operating parameters of the energy storage equipment, calculates the health status value of the equipment, and calculates the SOH (State of Health) based on the number of charge-discharge cycles, capacity decay rate, and internal resistance change rate. When the SOH is lower than a preset threshold, the energy storage charge-discharge strategy is adjusted to reduce the depth of charge and discharge and the current. At the same time, this submodule records the fault information of the energy storage equipment, generates a fault repair work order, and pushes it to the operation and maintenance terminal. The work order includes the faulty equipment number, fault type, fault occurrence time, and suggested repair plan. When a serious fault occurs, the connection between the energy storage equipment and the grid is immediately disconnected.

5. A source-grid-load-storage coordinated control system for prefecture-level cities according to claim 1, characterized in that, The multi-objective optimization algorithm of the central coordinated control module uses the analytic hierarchy process (AHP) to determine the weights of each optimization objective. The weight for power supply reliability is set to 0.4, the weight for economic efficiency is set to 0.3, and the weight for environmental protection is set to 0.

3. Power supply reliability is assessed through load failure rate, which is the ratio of total power failure load to total load demand. Economic efficiency is assessed through source-grid-load-storage regulation costs, which include source-side generation costs, storage-side charging and discharging costs, and load-side load regulation compensation costs. Environmental friendliness is assessed through carbon emission intensity, which is the ratio of total carbon emissions to total generation during the regulation period. The algorithm is input with source-grid-load-storage operation constraints, including the upper and lower limits of output of each generation device on the source side, the upper limit of transmission capacity of each line on the grid side, the SOC operating range of the storage side, and the rigid load guarantee requirements on the load side. The optimal regulation scheme is solved using a particle swarm optimization algorithm. During the solution process, an iteration termination condition is set, and the process terminates when the number of iterations reaches a preset value or the rate of change of the objective function value is less than 0.1% for 5 consecutive iterations.

6. A source-grid-load-storage coordinated control system for prefecture-level cities according to claim 1, characterized in that, The edge computing nodes of the data acquisition module have data filtering and anomaly detection functions. During data filtering, outliers that are significantly outside the reasonable range are removed, such as load data suddenly dropping to zero or far exceeding the historical maximum value for the same period. Anomaly detection uses the 3σ criterion, calculating the mean and standard deviation of the data sequence. When data deviates from the mean by more than three times the standard deviation, it is marked as outlier data, and the time and value of the anomaly are recorded. At the same time, interpolation is used to complete missing data. When the missing rate is less than 5%, linear interpolation is used; when the missing rate is 5%-10%, neighborhood mean interpolation is used; when the missing rate exceeds 10%, a fault alarm for the data acquisition equipment is triggered. The edge computing nodes also perform time synchronization processing on the acquired data, unifying the data time of each monitoring device based on GPS timestamps.

7. A source-grid-load-storage coordinated control system for prefecture-level cities according to claim 1, characterized in that, The terminal interaction module supports multi-role permission management, with roles divided into energy management specialists, maintenance personnel, and users. Energy management specialists have the authority to view all operational data, modify control parameter thresholds, and approve collaborative control plans. Maintenance personnel have the authority to view the operating status of their assigned equipment, receive fault repair work orders, and issue local manual control commands for the equipment. Permission management adopts a role-based access control model, which assigns permissions by associating roles with users and vice versa. The permissions for each role can be added or deleted through system backend configuration. At the same time, the terminal interaction module records the operation logs of all roles, and the log retention period is no less than one year.

8. A source-grid-load-storage coordinated regulation method for prefecture-level cities, applicable to the source-grid-load-storage coordinated regulation system for prefecture-level cities as described in any one of claims 1-7, characterized in that, include: The data acquisition process involves collecting output data from photovoltaic power plants, wind farms, and thermal power plants on the source side, voltage, current, and power flow data from substations, transmission lines, and distribution substations on the grid side, load data from industrial, commercial, and residential users on the load side, SOC and health status data from electrochemical energy storage power plants, pumped storage power plants, and user-side energy storage on the storage side, as well as environmental data. The data is preprocessed using edge computing nodes, and operations such as filtering outliers, filling in missing values, and time synchronization are performed. The data is then transmitted to the central collaborative control module via a 5G / fiber optic private network. In the state analysis step, the central coordinated control module receives the preprocessed data, analyzes the stability of the power output on the source side, and calculates the power output fluctuation amplitude and fluctuation frequency. The fluctuation amplitude is the difference between the maximum and minimum power output per unit time, and the fluctuation frequency is the number of times the fluctuation exceeds the threshold per unit time. Analyze the safety of grid-side operation and determine whether the voltage of each node exceeds the rated range, the line current exceeds the allowable value, and the power flow exceeds the transmission capacity. Analyze the load characteristics on the load side, and classify and statistically analyze the proportion and time distribution of interruptible loads, transferable loads, and rigid loads; Analyze the availability of energy storage on the storage side, and assess whether the State of Charge (SOC) is within the dispatch allowable range and whether the State of Harm (SOH) meets the operational requirements; construct a city-level source-grid-load-storage operation status matrix to mark abnormal states on each side, such as excessive power output fluctuations on the source side, overloaded lines on the grid side, and excessively low SOH on the storage side; The strategy formulation process involves the central coordinated control module using a multi-objective optimization algorithm based on the operating state matrix. The weights of power supply reliability, economy, and environmental protection are determined through the analytic hierarchy process (AHP), and the optimal solution is obtained through particle swarm optimization. This is combined with constraints on the source-grid-load-storage operation, including the upper and lower limits of output from each power generation unit on the source side, the upper limit of transmission capacity for each line on the grid side, the SOC operating range on the storage side, and the rigid load guarantee requirements on the load side. The coordinated control strategy is then formulated. The strategy content includes source-side output adjustment instructions, including photovoltaic maximum power point tracking parameters, wind farm pitch angle, and thermal power unit load allocation ratios; grid-side operation and maintenance instructions, including fault isolation schemes and power flow optimization measures; load-side load adjustment instructions, including a list of interruptible loads to be removed and the scheduling of transferable load periods; and storage-side charging and discharging instructions, including charging power, discharging power, and charging / discharging duration. The strategy execution steps are as follows: the central coordination and control module sends control instructions to the source-side control module, grid-side monitoring module, load-side management module, and energy storage-side dispatch module. Upon receiving the instructions, each module performs corresponding operations: the source-side control module adjusts the operating parameters of the power generation equipment to ensure the actual output meets the control instructions; the grid-side monitoring module performs fault isolation or power flow optimization to eliminate abnormal grid conditions; the load-side management module implements load regulation, pushes regulation notifications to users, and provides feedback on user response; the energy storage-side dispatch module controls the charging and discharging of energy storage devices and monitors SOC changes in real time; each module provides real-time feedback on the progress and results of instruction execution, such as the actual output value after output adjustment, the actual load value after load adjustment, and the SOC changes after energy storage charging and discharging. The effect evaluation steps include: the central coordination and control module receiving feedback data from each side module to evaluate the implementation effect of the control strategy; calculating the power supply reliability after control and comparing the change in load power failure rate before and after control; calculating the economic efficiency after control and comparing the change in the total control cost of source, grid, load and storage before and after control. The system calculates the environmental impact of the regulation and compares the changes in carbon emission intensity before and after regulation. It also compares the operating status of the power generation, grid, load, and storage before and after regulation, including the fluctuation range of power output on the source side, power flow on the grid side, load compliance rate on the load side, and SOC stability on the storage side. If the assessment results show that a certain target does not meet the preset requirements, such as the economic improvement not meeting the preset ratio, the system returns to the strategy formulation steps to adjust and optimize the target weights or operating constraints, and re-formulates the regulation strategy. If the assessment results meet all preset requirements, the system records the data of this regulation process, including the instruction content, execution results, and assessment indicators, and stores them in the system database.

9. A source-grid-load-storage coordinated regulation method for prefecture-level cities according to claim 8, characterized in that, The strategy formulation process also includes a demand response strategy formulation sub-step, which determines the priority of user demand response based on the historical response willingness and current electricity consumption characteristics of load-side users. Historical response willingness is assessed by the number of times users participate in demand response and the response compliance rate; the more times they participate and the higher the response compliance rate, the stronger their response willingness. Current electricity consumption characteristics are assessed by user load type and electricity consumption period; the higher the proportion of adjustable load and the higher the overlap between electricity consumption period and peak load, the greater the regulation value. Users with high priority, i.e., those with strong response willingness and high regulation value, are given priority to be included in the demand response scope. Demand response invitations are pushed to priority users, and the invitation content includes the adjustment period, the amount of load to be adjusted, and the response benefit, i.e., the amount of electricity price subsidy. Upon receiving user confirmation, if the user responds with confirmation within a preset time, the confirmed adjustable load is included in the load adjustment command on the load side. If the user does not respond or refuses to respond, the user is removed from the demand response list. At the same time, the total adjustment capacity of the demand response is calculated, which is the sum of the adjustable load confirmed by all confirmed users. When the total adjustment capacity is lower than the preset value of the load adjustment demand on the load side, the response benefit is increased, i.e., the electricity price subsidy amount is increased, or the scope of invited users is expanded to include users with lower priority. The invitation and confirmation process is repeated until the total adjustment capacity meets the load adjustment demand on the load side.

10. A source-grid-load-storage coordinated regulation method for prefecture-level cities according to claim 8, characterized in that, The effectiveness evaluation step is followed by an emergency troubleshooting step. When the grid-side monitoring module detects a major power grid fault, the system skips the routine state analysis and strategy formulation steps and directly activates the emergency control plan. The central coordination and control module immediately issues instructions to the energy storage-side dispatch module, controlling the energy storage equipment to discharge at full power at its rated capacity. The discharge duration is estimated based on the power gap on the source side, prioritizing the compensation for the current power shortage. Instructions are also issued to the load-side management module to cut off all interruptible loads according to a preset cut-off sequence, first cutting off industrial interruptible loads, then commercial interruptible loads, while preserving residential rigid loads to quickly reduce load demand on the load side. Instructions are also issued to the grid-side monitoring module to remotely disconnect the circuit breakers in the faulty section, isolating the faulty area. Simultaneously, an emergency fault alarm is sent to the municipal energy management department and maintenance personnel, with alarm information including the fault type, fault location, fault impact range, and activated emergency measures. Upon receiving the alarm, maintenance personnel rush to the fault site to conduct fault investigation and repair work. After the fault is resolved, the central coordination and control module executes the status analysis step to assess the current recoverable operational capacity of the source, grid, load, and storage, and then executes the strategy formulation step to develop a gradual recovery plan, first restoring stable power output equipment on the source side, then gradually restoring transferable loads and some interruptible loads, and finally adjusting the energy storage charging and discharging status to the normal range.

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