New energy user side multi-energy efficiency collaborative optimization method and system

Abstract

The invention discloses a new energy user side multi-energy efficiency collaborative optimization method and system, and belongs to the technical field of power system dispatching, and the method comprises the steps: collecting the real-time operation parameters of photovoltaic equipment, energy storage equipment and electric equipment, carrying out the health degree evaluation of the equipment, generating an operation risk evaluation value, dividing the equipment into a quick response group and a slow compensation group, generating a hierarchical optimization instruction; and adjusting the hierarchical optimization instruction through a preset multi-objective optimization function, generating a control strategy, converting the control strategy into an equipment executable adjustment signal, and respectively sending the adjustment signal to the quick response group and the slow compensation group. According to the method, the real-time parameter acquisition and dynamic equipment grouping technology is adopted, the graded adjustment instruction is generated in combination with the multi-objective optimization function, collaborative optimization of economic cost, carbon emission and equipment loss can be achieved in a user side new energy scene, and the system regulation and control precision and comprehensive energy efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a method and system for collaborative optimization of multiple energy efficiency at the user side of new energy. Background Art

[0002] In modern power systems, the large-scale integration of user-side renewable energy devices (such as photovoltaic power generation, energy storage batteries, and smart loads) places higher demands on grid regulation, requiring real-time optimization and coordination of the operating status of various devices to achieve efficient energy utilization. Current technologies often employ a centralized control architecture based on fixed groups. These architectures issue unified regulation commands based on preset device control priorities and static optimization models. For example, energy storage devices are designated as fast-response units, while power-consuming devices are designated as slow-response units. Power allocation is then controlled in conjunction with electricity price signals.

[0003] Existing technologies usually rely on historical performance parameters or offline test results of equipment to divide control groups, and use a single economic or stability objective for optimization. For example, power regulation tasks are assigned after detecting the status of equipment at a fixed period, or the start and stop sequence of equipment is adjusted according to the peak-shaving demand of the power grid, but this does not fully incorporate the real-time health status and dynamic regulation potential of the equipment. In scenarios where equipment ages, load changes suddenly, or grid targets are frequently adjusted, such solutions are prone to regulation delays, equipment overload, or reduced energy efficiency due to rigid equipment grouping and a single optimization dimension. In addition, static control models are difficult to be compatible with the needs of multi-objective collaborative optimization, and cannot adaptively adjust the weights of economic and environmental goals, leading to problems such as increased equipment losses or excessive carbon emissions. Summary of the Invention

[0004] To solve the above problems, the present invention provides a multi-energy efficiency collaborative optimization method and system on the new energy user side. It adopts real-time parameter acquisition and dynamic equipment grouping technology, combined with multi-objective optimization functions to generate hierarchical adjustment instructions, which can achieve collaborative optimization of economic costs, carbon emissions and equipment losses in the user-side new energy scenario, and improve the system control accuracy and comprehensive energy efficiency.

[0005] The above objectives can be achieved through the following solutions:

[0006] The multi-energy efficiency collaborative optimization method and system for the new energy user side include: collecting real-time operating parameters of photovoltaic equipment, energy storage equipment and electrical equipment, including the voltage fluctuation value of photovoltaic output, aging data of energy storage batteries, and impedance characteristics of equipment power consumption; using the real-time operating parameters to evaluate the health of the equipment and generate an operating risk assessment value for each device; according to the operating risk assessment value, the equipment is divided into a fast response group and a slow compensation group; wherein the fast response group includes equipment that needs to be adjusted quickly, and the slow compensation group includes equipment that can be adjusted for a long time; obtaining the current power change data of the fast response group, the predicted adjustment capability data of the slow compensation group, and the power grid control target, and generating hierarchical optimization instructions; adjusting the hierarchical optimization instructions through a preset multi-objective optimization function to generate a control strategy including economic cost targets, carbon emission targets and equipment loss targets; converting the control strategy into an adjustment signal that can be executed by the equipment, and sending it to the fast response group and the slow compensation group respectively.

[0007] Optionally, the collection of real-time operating parameters of photovoltaic equipment, energy storage equipment and electrical equipment includes: obtaining the voltage fluctuation frequency and amplitude at the output end of the photovoltaic equipment as the voltage fluctuation value of the photovoltaic output; collecting the change data of the internal resistance of the energy storage battery with the use time, and extracting the aging degree data of the energy storage battery; measuring the change value of the impedance of the electrical equipment with the operating state, and generating the impedance characteristics of the power consumption of the equipment.

[0008] Optionally, dividing the equipment into a fast response group and a slow compensation group according to the operation risk assessment value includes: judging whether the operation risk assessment value is greater than a preset risk threshold; if not, dividing the corresponding equipment into a fast response group, and setting a priority identifier, wherein the priority identifier content includes the response speed and the adjustment allowed time; if so, dividing the corresponding equipment into a slow compensation group, and setting a compensation capability identifier, wherein the compensation capability identifier content includes the adjustable power range and the adjustment maintenance time.

[0009] Optionally, the obtaining of the current power change data of the fast response group, the predicted regulation capability data of the slow compensation group, and the grid regulation target, and the generation of hierarchical optimization instructions includes: obtaining the real-time power deviation data of the fast response group, and generating fast regulation instructions using a preset local computing node; obtaining the historical regulation success rate and grid regulation target of the slow compensation group, correcting the fast regulation instructions through a preset prediction algorithm, and generating long-term optimization instructions; wherein, the hierarchical optimization instructions include the fast regulation instructions and the long-term optimization instructions.

[0010] Optionally, the generation of a control strategy including economic efficiency, equipment life and environmental protection indicators includes: constructing a multi-objective optimization function including economic cost targets, carbon emission targets and equipment loss targets; inputting the real-time operating parameters into the multi-objective optimization function, and solving the multi-objective optimization function using a preset optimization algorithm to obtain the adjustment amount of the equipment; adjusting the hierarchical optimization instructions according to the matching relationship between the equipment corresponding to the adjustment amount and the priority identifier and the compensation capacity identifier, and generating a control strategy including a voltage set value, a power distribution scheme and an adjustment time.

[0011] Optionally, the method further includes: dynamically adjusting the weight coefficient of the economic cost target according to the operational risk assessment value; monitoring grid electricity price data in real time, and dynamically adjusting the weight coefficient of the carbon emission target according to the grid electricity price data.

[0012] Optionally, converting the control strategy into an adjustment signal executable by the device and sending it to the control terminals of the fast response group and the slow compensation group respectively includes: splitting the power allocation scheme into photovoltaic power generation limit values ​​and power-consuming equipment stop and start rules; encoding the adjustment time into a time instruction packet; and sending the voltage setting value, the photovoltaic power generation limit value, the power-consuming equipment stop and start rules and the time instruction packet to the control terminals of the fast response group and the slow compensation group.

[0013] Optionally, the method also includes: after executing the adjustment signal, monitoring the actual power change rate of the equipment in the fast response group; calculating the absolute value of the difference between the actual power change rate and the preset expected power change rate to obtain the change rate difference; when the change rate difference is greater than the preset deviation threshold, recalculating the operation risk assessment value; adjusting the priority identifier according to the updated operation risk assessment value, and generating a supplementary adjustment instruction in combination with the remaining adjustment capacity of the slow compensation group.

[0014] Optionally, generating a supplementary adjustment instruction includes: obtaining the remaining adjustment capacity of the slow compensation group; using the change rate difference to calculate the power deviation cumulative value of the fast response group; comparing the power deviation cumulative value with the remaining adjustment capacity to generate an adjustment sequence list; and selecting the slow compensation group equipment for segmented adjustment based on the adjustment sequence list and the pre-acquired grid peak regulation period.

[0015] Based on the same inventive concept, the present invention also provides a new energy user-side multi-energy efficiency collaborative optimization system, which includes:

[0016] The operating parameter acquisition module is used to collect real-time operating parameters of photovoltaic equipment, energy storage equipment, and power-consuming equipment, including the voltage fluctuation value of photovoltaic output, the aging data of energy storage batteries, and the impedance characteristics of equipment power consumption;

[0017] An operation risk assessment module is used to use the real-time operation parameters to perform equipment health assessment and generate an operation risk assessment value for each device;

[0018] an equipment grouping module, configured to divide the equipment into a quick response group and a slow compensation group according to the operational risk assessment value; wherein the quick response group includes equipment that requires quick adjustment, and the slow compensation group includes equipment that can be adjusted over a long period of time;

[0019] A hierarchical optimization module is used to obtain the current power change data of the fast response group, the predicted regulation capability data of the slow compensation group, and the power grid control target, and generate hierarchical optimization instructions;

[0020] A strategy generation module, configured to adjust the hierarchical optimization instructions through a preset multi-objective optimization function to generate a control strategy including an economic cost target, a carbon emission target, and an equipment loss target;

[0021] The instruction issuing module is used to convert the control strategy into an adjustment signal executable by the device and send it to the fast response group and the slow compensation group respectively.

[0022] Based on the same inventive concept, the present invention also provides:

[0023] Compared with the prior art, the present invention has the following advantages:

[0024] 1. This invention dynamically collects real-time operating parameters of photovoltaic, energy storage, and power-consuming equipment and evaluates their health. It then generates hierarchical optimization instructions based on grid control objectives. This allows for rapid identification of high-risk equipment and categorized control, significantly shortening response delays and reducing the impact of sudden failures on the system, ensuring accurate execution of control instructions and grid stability.

[0025] 2. Based on a preset multi-objective optimization function, it dynamically balances economic costs, carbon emissions, and equipment loss targets. It adjusts optimization weights based on equipment priority and compensation capacity identification. This solves the resource waste problem caused by the single optimization dimension of traditional solutions, reducing user electricity costs while also minimizing carbon footprint while ensuring equipment lifespan.

[0026] 3. By dynamically dividing the system into fast-response groups and slow-compensation groups, and updating the group priority identifiers and compensation capacity identifiers in real time, the system can flexibly adjust the regulation strategy according to changes in equipment health and grid demand, fully leveraging the energy storage potential and flexible regulation capabilities of slow-response equipment, effectively suppressing short-term power fluctuations and supporting long-term grid peak regulation needs.

[0027] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 It is a flow chart of the new energy user-side multi-energy efficiency collaborative optimization method according to an embodiment of the present invention.

[0030] Figure 2 Schematic diagram of photovoltaic output voltage fluctuation curve according to an embodiment of the present invention.

[0031] Figure 3 Schematic diagram of the internal resistance change curve of the energy storage battery according to an embodiment of the present invention.

[0032] Figure 4 2 is a schematic diagram of an impedance change curve of an electrical device according to an embodiment of the present invention.

[0033] Figure 5 It is a structural diagram of the new energy user-side multi-energy efficiency collaborative optimization system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0035] Reference Figure 1 An embodiment of the present invention proposes a multi-energy efficiency collaborative optimization method on the user side of new energy. It adopts real-time parameter collection and dynamic equipment grouping technology, combined with multi-objective optimization functions to generate hierarchical adjustment instructions, which can achieve collaborative optimization of economic costs, carbon emissions and equipment losses in the user-side new energy scenario, and improve the system control accuracy and comprehensive energy efficiency.

[0036] The method of this embodiment specifically includes:

[0037] Collect real-time operating parameters of photovoltaic equipment, energy storage equipment, and electrical equipment, including voltage fluctuations of photovoltaic output, aging data of energy storage batteries, and impedance characteristics of equipment power consumption;

[0038] Using the real-time operating parameters to evaluate the health of the equipment and generate an operating risk assessment value for each device;

[0039] According to the operational risk assessment value, the equipment is divided into a fast response group and a slow compensation group; wherein the fast response group includes equipment that needs to be adjusted quickly, and the slow compensation group includes equipment that can be adjusted over a long period of time;

[0040] Obtaining current power change data of the fast response group, predicted regulation capability data of the slow compensation group, and power grid control targets, and generating hierarchical optimization instructions;

[0041] Adjusting the hierarchical optimization instructions through a preset multi-objective optimization function to generate a control strategy including economic cost targets, carbon emission targets, and equipment loss targets;

[0042] The control strategy is converted into an adjustment signal executable by the device and sent to the fast response group and the slow compensation group respectively.

[0043] Specifically, by collecting voltage fluctuations of photovoltaic devices, aging data of energy storage devices, and impedance characteristics of power-consuming devices, the system comprehensively reflects the operational status of the devices. These parameters are used to assess device health and generate operational risk assessments, which provide a basis for subsequent device classification and optimized control. Based on the operational risk assessments, the devices are dynamically divided into a fast-response group and a slow-compensation group. The fast-response group includes devices with fast response and strong regulation capabilities, while the slow-compensation group includes devices with slower regulation capabilities but long-term operation. Priority and compensation capability indicators are assigned to achieve categorized management and differentiated control of devices. Based on the current power variation data of the fast-response group and the predicted regulation capability data of the slow-compensation group, combined with grid control objectives, hierarchical optimization instructions are generated. Fast regulation instructions are used to adjust the power output of the fast-response group devices in real time, while long-term optimization instructions guide the long-term regulation strategy of the slow-compensation group devices. A multi-objective optimization function is constructed, incorporating economic cost targets, carbon emission targets, and equipment loss targets. An optimization algorithm is used to find the optimal solution and generate a control strategy that includes voltage setpoints, power allocation schemes, and regulation time. Simultaneously, the weighting coefficients of various objectives are dynamically adjusted based on the device's operating status and grid electricity price data to ensure the optimality and flexibility of the control strategy. The generated control strategy is converted into an executable regulation signal for the device and sent to the control terminals of the fast response group and the slow compensation group, respectively. The signal includes voltage setpoints, power limit rules, and time instructions, ensuring that the device can efficiently and accurately execute the regulation task. After executing the regulation signal, the device's actual power change rate is monitored. Combined with the remaining regulation capacity of the slow compensation group, supplementary regulation instructions are dynamically generated to ensure stable and optimal system operation.

[0044] Optionally, the collecting of real-time operating parameters of photovoltaic equipment, energy storage equipment, and power-consuming equipment includes:

[0045] Obtain the voltage fluctuation frequency and amplitude at the output end of the photovoltaic device as the voltage fluctuation value of the photovoltaic output;

[0046] Specifically, such as Figure 2 As shown, the output voltage of photovoltaic equipment is often affected by factors such as weather changes and load fluctuations, resulting in voltage fluctuations. To obtain voltage fluctuation values, voltage sensors are used to collect the frequency and amplitude of voltage fluctuations at the photovoltaic output in real time. The voltage fluctuation frequency refers to the number of voltage changes per unit time, typically expressed in Hertz (Hz). The voltage fluctuation amplitude refers to the difference between the maximum and minimum voltage values ​​within a cycle, typically expressed in volts (V). Voltage fluctuation values ​​can be directly measured using voltage sensors, and the data is transmitted to a central control system for subsequent analysis.

[0047] Collect data on changes in the internal resistance of energy storage batteries over time and extract data on the aging degree of energy storage batteries;

[0048] Specifically, such as Figure 3 As shown in the figure, the aging of energy storage batteries can be measured by measuring the change in the battery's internal resistance over time. As the battery ages, its internal resistance gradually increases, resulting in a decrease in the battery's charge and discharge efficiency. By measuring the voltage drop and current during the charge and discharge process, the battery's internal resistance can be calculated using Ohm's law. The measured internal resistance data is recorded over time to form a battery aging curve, which is used for subsequent equipment health assessments.

[0049] Measure the impedance of electrical equipment as it changes with operating conditions to generate the impedance characteristics of the equipment's power consumption.

[0050] Specifically, such as Figure 4 As shown in Figure 1, the impedance signature of an electrical device reflects the device's electrical characteristics under different operating conditions. By measuring the device's impedance under different loads or operating conditions, we can generate an impedance signature of the device's power consumption. Impedance is calculated by measuring the device's voltage and current under different operating conditions. The measured impedance values ​​are recorded as they change over the device's operating state to form a device impedance signature curve.

[0051] By collecting real-time operating parameters of photovoltaic output, energy storage batteries, and power-consuming equipment, we can accurately assess the health of the equipment, providing a reliable basis for optimizing control strategies. This multi-dimensional parameter collection method not only improves the reliability of equipment operation but also provides data support for subsequent system optimization, helping to extend equipment life and improve system efficiency.

[0052] Optionally, dividing the equipment into a fast response group and a slow compensation group according to the operational risk assessment value includes:

[0053] Determining whether the operational risk assessment value is greater than a preset risk threshold;

[0054] Specifically, before making a judgment, it is necessary to use real-time operating parameters to evaluate the health of the equipment and generate an operating risk assessment value for each device. First, it is necessary to establish an equipment health assessment model to quantify the health status of the equipment, usually based on the collected real-time operating parameters and the performance indicators of the equipment. The input of the assessment model includes the voltage fluctuation value of the photovoltaic output, the aging data of the energy storage battery, and the impedance characteristics of the electrical equipment. The assessment model outputs the health assessment result of the equipment, that is, the operating risk assessment value. The quantitative indicators calculated through real-time operating parameters (such as the voltage fluctuation value of the photovoltaic output, the aging data of the energy storage battery, and the impedance characteristics of the equipment power consumption) reflect the health status and potential risks of the equipment. For the operating risk assessment value ,have:

[0055] ,

[0056] Where, Indicates the voltage fluctuation value of photovoltaic output, which is the difference between the maximum voltage and the minimum voltage; The internal resistance of the energy storage battery, the health score is negatively correlated with the internal resistance; Indicates the impedance characteristics of electrical equipment. The health score is related to the range of impedance value variation. 、 、 Represents the weight coefficient of each parameter, which is used to adjust the contribution of each factor to health.

[0057] The preset risk threshold is a predefined critical value, typically determined through historical data analysis and expert experience. It represents the critical level of equipment health and is used to distinguish whether the equipment requires rapid response or slow compensation. The equipment's operational risk assessment is compared with the preset risk threshold. If the risk assessment result is below the threshold, the equipment is considered healthy and can be classified as a rapid response group; otherwise, it is classified as a slow compensation group.

[0058] If not, the corresponding device is divided into a fast response group and a priority identifier is set, wherein the priority identifier includes the response speed and the adjustment allowable time;

[0059] Specifically, the fast-response group includes devices that require rapid adjustment. Priority indicators include response speed and maximum allowable adjustment time. Response speed is the amount of power adjustment a device can make per unit time, typically expressed as a percentage or power value. The maximum adjustment time, or maximum adjustment time, indicates the maximum time allowed from receiving an adjustment command to completing the adjustment, typically expressed in seconds or minutes.

[0060] If so, the corresponding device is divided into a slow compensation group, and a compensation capability identifier is set, wherein the compensation capability identifier includes an adjustable power range and an adjustment maintenance time.

[0061] Specifically, the slow compensation group includes devices capable of long-term adjustment. The compensation capability identifier includes the adjustable power range and the minimum adjustment duration. The adjustable power range is the amount of energy the device can adjust within a certain period of time, measured in kilowatt-hours. The minimum adjustment duration is the minimum time required for the device to complete an adjustment, typically expressed in hours.

[0062] Specifically, device classification is not fixed but dynamically adjusted based on real-time operational parameter changes. For example, if the health status of a device in the fast-response group deteriorates and its operational risk assessment exceeds a threshold, it will be reclassified to the slow-response group and its corresponding priority or compensation capability indicator will be updated.

[0063] By dynamically classifying devices and assigning corresponding priority and compensation capacity indicators, control tasks can be flexibly assigned based on the device's actual health status and regulation capabilities. This classification method not only improves the device's response speed and regulation accuracy, but also extends its service life, enabling efficient device management and collaborative optimization control. Furthermore, the dynamic adjustment mechanism can adapt to changes in device status, further improving system reliability and cost-effectiveness.

[0064] Optionally, acquiring the current power change data of the fast response group, the predicted regulation capability data of the slow compensation group, and the power grid regulation target, and generating hierarchical optimization instructions includes:

[0065] Acquire real-time power deviation data of the rapid response group and generate rapid adjustment instructions using a preset local computing node;

[0066] Specifically, the real-time power deviation data of the fast response group reflects the difference between the power output of the device in the current operating state and the expected value. The power deviation can be obtained by measuring the difference between the actual power and the set power. The calculation of the power deviation helps to understand the working status and adjustment requirements of the device in real time. The local computing node is a hardware device with processing capabilities that can quickly generate adjustment instructions based on real-time power deviation data. The generation of fast adjustment instructions is based on a preset control algorithm, such as a proportional integral derivative (PID) controller, which is used to adjust the output value. ,have:

[0067] ,

[0068] Where, is the control error, i.e., power deviation; is the proportional coefficient, used to adjust the response speed; is the integral coefficient, used to eliminate steady-state error; is the differential coefficient, which is used to reduce the dynamic deviation. Through the PID control algorithm, the local computing node can adjust the output value Generate fast regulation instructions to adjust the power output of the equipment in real time.

[0069] Obtaining the historical adjustment success rate of the slow compensation group and the power grid control target, and modifying the fast adjustment instruction through a preset prediction algorithm to generate a long-term optimization instruction;

[0070] Specifically, the historical regulation success rate of the slow compensation group reflects the success rate of the equipment during historical regulation processes and is typically measured by counting the percentage of regulation tasks completed by the equipment. Grid regulation objectives include peak regulation and frequency regulation, and these objectives are adjusted based on the actual needs of the grid. This data must be acquired through integration with information provided by the historical data recording system and the grid dispatching center. A preset prediction algorithm can modify the fast regulation instructions based on the historical regulation success rate of the slow compensation group and the grid regulation objectives to generate instructions suitable for long-term optimization. The prediction algorithm can utilize methods such as time series analysis and machine learning models. For example, a linear regression model can be used for prediction. By fitting historical data, regulation needs for a period of time in the future are predicted, thereby generating long-term optimization instructions.

[0071] The hierarchical optimization instructions include the quick adjustment instructions and the long-term optimization instructions.

[0072] Specifically, the hierarchical optimization instructions include rapid adjustment instructions and long-term optimization instructions. Rapid adjustment instructions are used by the fast-response group to adjust the power output of the equipment in real time; long-term optimization instructions are used by the slow-compensation group to optimize the equipment's operating strategy over the long term. This hierarchical optimization instruction system enables coordinated optimization control of equipment, improving the overall system's operating efficiency and stability.

[0073] For example, assume a new energy user's photovoltaic system consists of a photovoltaic array, a battery storage system, and several power consumers. The fast-response group includes the battery storage system and the photovoltaic inverter, while the slow-compensation group includes the power consumers. The actual power output of the photovoltaic inverter is 800W, the set power is 1000W, and the power deviation is -200W. Based on the power deviation, the local computing node uses a PID control algorithm to generate fast adjustment instructions to quickly adjust the output power of the photovoltaic inverter. The power consumers in the slow-compensation group have a historical adjustment success rate of 90%. The power grid requires additional peak-shaving capacity during peak load periods. Based on historical data and grid demand, it is predicted that the power consumption capacity of the power consumers will need to be increased during peak load periods, and corresponding long-term optimization instructions are generated. By dynamically generating hierarchical optimization instructions, adjustments can be made based on the different characteristics of the fast-response and slow-compensation groups. The fast-response group uses fast adjustment instructions to achieve immediate response, improving system stability; the slow-compensation group uses long-term optimization instructions to achieve collaborative optimization and improve resource utilization. This hierarchical control method not only improves the overall efficiency of the system, but also extends the service life of the equipment, adapts to the regulation needs of the power grid, and achieves coordinated optimization of multiple energy efficiencies.

[0074] Optionally, generating a control strategy including economic efficiency, equipment life, and environmental protection indicators includes:

[0075] Construct a multi-objective optimization function including economic cost objectives, carbon emission objectives, and equipment loss objectives;

[0076] Inputting the real-time operating parameters into the multi-objective optimization function, solving the multi-objective optimization function using a preset optimization algorithm, and obtaining the adjustment amount of the equipment;

[0077] According to the matching relationship between the device corresponding to the adjustment amount and the priority identifier and the compensation capability identifier, the hierarchical optimization instruction is adjusted to generate a control strategy including a voltage setting value, a power allocation scheme and an adjustment time.

[0078] Specifically, the multi-objective optimization function combines factors such as economic cost, carbon emissions, and equipment loss to solve the optimal equipment adjustment amount. Constructing a multi-objective optimization function requires quantifying these objectives and assigning corresponding weight coefficients. For the multi-objective optimization function, there are:

[0079] ,

[0080] Where, is the adjustment amount of the equipment, is the total objective value of the optimization function, The economic cost target reflects the impact of equipment adjustment on economic cost. For carbon emission targets, reflect the impact of equipment adjustment on carbon emissions, It is the equipment loss target, reflecting the impact of equipment adjustment on equipment life. is the initial weight coefficient of the economic cost target, is the initial weight coefficient of the carbon emission target, It is the initial weight coefficient of the equipment loss target, which is used to adjust the relative importance of each target.

[0081] Specifically, real-time operating parameters are input as constraints into a multi-objective optimization function. Pre-set algorithms, such as genetic algorithms, particle swarm optimization (PSO), or other optimization algorithms, are then used to solve for the equipment's regulation parameters, finding the optimal balance point for the multi-objective optimization function—that is, the optimal balance between economic efficiency, environmental friendliness, and equipment lifespan. The solution requires the input of real-time operating parameters (such as equipment regulation, grid electricity prices, and carbon emission coefficients) and outputs the optimal solution as the basis for equipment regulation. The priority identifiers for the fast-response group, including response speed and maximum regulation time, determine the equipment's regulation priority. The compensation capacity identifiers for the slow-response group, including the adjustable power range and minimum regulation duration, determine the equipment's regulation capacity. Based on the output of the optimization algorithm, specific control strategies, such as voltage setpoints, power allocation schemes, and regulation times, are generated for different equipment and regulation requirements.

[0082] By constructing a multi-objective optimization function and finding the optimal solution, we can comprehensively consider economic efficiency, environmental protection, and equipment lifespan to achieve optimal system control. This approach not only improves economic efficiency and environmental performance, but also extends the lifespan of equipment, providing a scientific basis for the coordinated optimization of multiple energy efficiency levels for new energy users. Furthermore, the ability to dynamically adjust control strategies enables the system to better adapt to grid regulation needs, improving overall operational flexibility and reliability.

[0083] Optionally, the method comprises:

[0084] Dynamically adjust the weight coefficient of the economic cost target according to the operational risk assessment value;

[0085] Specifically, the initial weight coefficient of the economic cost target will be dynamically adjusted according to the aging degree of the equipment. As the equipment ages, especially the energy storage equipment and the power equipment, the health status of the equipment will decrease and the operation risk assessment value will increase. In order to extend the life of the equipment and reduce the damage to the old equipment caused by high-load operation, it is necessary to dynamically reduce the weight of the economic cost target. For the initial weight coefficient of the adjusted economic cost target, ,have:

[0086] ,

[0087] Where, It is the attenuation coefficient of the aging degree on the economic target weight, which is used to control the speed of weight reduction.

[0088] Real-time monitoring of grid electricity price data, and dynamic adjustment of the weight coefficient of the carbon emission target based on the grid electricity price data.

[0089] Specifically, during peak hours of grid electricity prices, in order to reduce the carbon emissions of the entire system, it is necessary to dynamically increase the constraint intensity of carbon emission targets. This method can limit the use of high-carbon emission equipment and reduce carbon emissions. By monitoring grid electricity price data in real time, peak hours can be identified. For the initial weight coefficient of the adjusted carbon emission target ,have

[0090] ,

[0091] Where, It is the enhancement coefficient of carbon emission target during peak period, which is used to control the increase of constraint intensity. This identifies peak hours for grid electricity prices, typically represented as a binary variable (0 for off-peak hours, 1 for peak hours). By dynamically strengthening the constraints of carbon emission targets, clean power sources (such as photovoltaics and energy storage) can be prioritized during peak hours and the operation of high-carbon emission equipment can be restricted. By monitoring the operating status of equipment and the grid's regulatory needs in real time, the weights and constraints of various objectives can be dynamically adjusted to achieve an optimal balance between economic efficiency, equipment lifespan, and environmental protection indicators. For example, if equipment is aging and operating during peak hours, the model will prioritize reducing economic efficiency and strengthening environmental constraints to ensure sustainable operation of the equipment.

[0092] Optionally, converting the control strategy into an adjustment signal executable by the device and sending the signal to the control terminals of the fast response group and the slow compensation group respectively includes:

[0093] The power allocation plan is divided into photovoltaic power generation limit values ​​and power equipment shutdown and startup rules;

[0094] Encoding the adjustment time into a time instruction packet;

[0095] The voltage setting value, the photovoltaic power generation limit value, the power consumption equipment stop and start rule and the time instruction packet are sent to the control terminals of the fast response group and the slow compensation group.

[0096] Specifically, the power allocation plan is first broken down into photovoltaic (PV) power generation limits and consumer device shutdown and start-up rules. PV power generation limits are determined based on real-time operating parameters (such as PV output voltage fluctuations and the health of energy storage batteries) and device health assessment results to ensure PV equipment output power remains within a safe range. Consumer device shutdown and start-up rules are set based on the device's impedance characteristics and grid regulation targets, specifying when to start and stop devices to meet grid peak load requirements. Second, the regulation time needs to be encoded into a time instruction packet. For example, the time point of each regulation action is converted into a timestamp or time period recognizable by the device to ensure that the device executes the regulation action at the specified time. This step may require incorporating the device's regulation capability identifier (such as the adjustable power range and the minimum regulation duration). Finally, the voltage setpoint, PV power generation limit, consumer device shutdown and start-up rules, and time instruction packet are transmitted to the device's control terminal. This transmission process involves transmitting signals to the device controller via the network and ensuring accurate signal reception. Based on the received signals, the control terminal performs the corresponding regulation action, such as adjusting PV output or starting and stopping the consumer.

[0097] Optionally, the method further includes:

[0098] After executing the adjustment signal, monitoring the actual power change rate of the devices in the fast response group;

[0099] Calculating the absolute value of the difference between the actual power change rate and the preset expected power change rate to obtain a change rate difference;

[0100] When the change rate difference is greater than a preset deviation threshold, recalculating the operation risk assessment value;

[0101] The priority identifier is adjusted according to the updated operational risk assessment value, and a supplementary adjustment instruction is generated in combination with the remaining adjustment capacity of the slow compensation group.

[0102] Specifically, after receiving an adjustment command, the fast-response group device measures the deviation of its actual power rate of change from the expected rate of change, obtaining a rate difference to determine whether the actual adjustment speed deviates from the expected one. A preset deviation threshold is used to determine whether the actual adjustment speed exceeds an acceptable range. When the absolute value of the rate difference exceeds the deviation threshold, it indicates that the actual adjustment speed has deviated from expectations, and the operational risk assessment value needs to be recalculated. The operational risk assessment value reflects the health status and potential risks of the device. When the actual adjustment speed deviates from expectations, the device may be at risk of failure or increased wear, requiring a risk reassessment. This reassessment more accurately reflects the current risk status of the device, providing a basis for subsequent adjustments. Based on the updated risk assessment results, the priority identifiers of the fast-response group devices are dynamically adjusted to optimize the allocation of adjustment tasks. Supplemental adjustment instructions are generated based on the remaining adjustment capacity of the slow-compensation group devices to eliminate accumulated deviations. Supplemental adjustment instructions are generated based on the device's compensation capacity identifier to ensure the feasibility and safety of the adjustment process. The supplemental adjustment instructions are sent to the control terminal of the slow-compensation group device via the communication network. The device gradually adjusts its operating status according to the instructions to gradually eliminate accumulated deviations. The regulation process follows the preset regulation sequence list and grid peak regulation period to ensure that the regulation time matches the grid demand.

[0103] Optionally, generating the supplementary adjustment instruction includes:

[0104] Obtaining the remaining adjustment capacity of the slow compensation group;

[0105] Calculating the power deviation cumulative value of the fast response group using the change rate difference;

[0106] Specifically, for each slow compensation group device, calculate its remaining regulation capacity, that is, the maximum regulation capacity minus the used part. For example, if the maximum regulation capacity of energy storage device A is 100kWh and 80kWh has been used, the remaining regulation capacity is 20kWh. Calculate the difference between the actual power change rate and the expected power change rate to obtain the change rate difference. For the power deviation accumulation value ,have:

[0107] ,

[0108] Where, is the rate of change difference, It is a time interval. For example, if the deviation between the actual power and the expected power of the equipment in the quick response group is 5% per minute, it will be 1.5 after 30 minutes of accumulation.

[0109] Comparing the power deviation accumulated value with the remaining regulation capacity to generate a regulation sequence list;

[0110] According to the adjustment sequence list and the pre-acquired power grid peak regulation period, the slow compensation group equipment is selected for segmented adjustment.

[0111] Specifically, the remaining capacity of each slow-compensation group device is compared with the cumulative deviation to generate a ranking. The ranking is from highest to lowest remaining capacity. For example, if device A has 15 kWh remaining and device B has 20 kWh remaining, the resulting order would be: device B, then device A. Based on the peak load period (e.g., off-peak hours), devices in the sequential list are selected for adjustment based on demand. For example, device A is first used to adjust 10 kWh, followed by device B, which adjusts 5 kWh, to meet demand in stages.

[0112] For example, after the peak period, user-side energy storage equipment is predicted to require an additional 25kWh of regulation. Device A in the slow-compensation group has 20kWh remaining, and device B has 15kWh remaining. According to the order list, device A is prioritized for regulating 20kWh, followed by device B for regulating 5kWh, ensuring effective execution of the supplementary regulation instructions. Dynamically assessing device regulation capabilities optimizes task allocation, reduces overload risks, improves system stability and device lifespan, and simultaneously meets the grid's peak regulation needs.

[0113] Based on the same inventive concept, Figure 5 As shown, the present invention also provides a new energy user-side multi-energy efficiency collaborative optimization system, the system comprising:

[0114] The operating parameter acquisition module is used to collect real-time operating parameters of photovoltaic equipment, energy storage equipment, and power-consuming equipment, including the voltage fluctuation value of photovoltaic output, the aging data of energy storage batteries, and the impedance characteristics of equipment power consumption;

[0115] An operation risk assessment module is used to use the real-time operation parameters to perform equipment health assessment and generate an operation risk assessment value for each device;

[0116] an equipment grouping module, configured to divide the equipment into a quick response group and a slow compensation group according to the operational risk assessment value; wherein the quick response group includes equipment that requires quick adjustment, and the slow compensation group includes equipment that can be adjusted over a long period of time;

[0117] A hierarchical optimization module is used to obtain the current power change data of the fast response group, the predicted regulation capability data of the slow compensation group, and the power grid control target, and generate hierarchical optimization instructions;

[0118] A strategy generation module, configured to adjust the hierarchical optimization instructions through a preset multi-objective optimization function to generate a control strategy including an economic cost target, a carbon emission target, and an equipment loss target;

[0119] The instruction issuing module is used to convert the control strategy into an adjustment signal executable by the device and send it to the fast response group and the slow compensation group respectively.

[0120] It should be noted that the formulas appearing above can translate physical quantities of different properties into unitless standard values ​​or superimposable parameters of the same dimension through the principle of dimensional consistency and mathematical standardization means (such as normalization, dimensionless parameter conversion or unit system unification), thereby eliminating the interference of different dimensions on the operation logic, so that the formulas have mathematical operation rationality and objective law adaptability while retaining the distribution characteristics of the original data. It is a conventional technical means and will not be elaborated here. The electrical connection between the above-mentioned units does not necessarily mean a direct connection of the circuit. The indirect connection method can be applied to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above is only an exemplary embodiment of the present invention and the scope of the present invention cannot be limited thereto.

[0121] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. Those skilled in the art will readily conceive of other embodiments of the present invention after considering the disclosure of the specification and practical truths. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary technical means in the art not described herein.

Claims

1. A new energy user-side multi-energy efficiency collaborative optimization method, characterized in that: The method comprises: Collect real-time operating parameters of photovoltaic equipment, energy storage equipment, and electrical equipment, including voltage fluctuations of photovoltaic output, aging data of energy storage batteries, and impedance characteristics of equipment power consumption; Using the real-time operating parameters to evaluate the health of the equipment and generate an operating risk assessment value for each device; According to the operational risk assessment value, the equipment is divided into a fast response group and a slow compensation group; wherein the fast response group includes equipment that needs to be adjusted quickly, and the slow compensation group includes equipment that can be adjusted over a long period of time; Obtaining current power change data of the fast response group, predicted regulation capability data of the slow compensation group, and power grid control targets, and generating hierarchical optimization instructions; Adjusting the hierarchical optimization instructions through a preset multi-objective optimization function to generate a control strategy including economic cost targets, carbon emission targets, and equipment loss targets; The control strategy is converted into an adjustment signal executable by the device and sent to the fast response group and the slow compensation group respectively.

2. The new energy user-side multi-energy efficiency collaborative optimization method according to claim 1 is characterized in that: The real-time operating parameters of photovoltaic equipment, energy storage equipment and power equipment are collected, including: Obtain the voltage fluctuation frequency and amplitude at the output end of the photovoltaic device as the voltage fluctuation value of the photovoltaic output; Collect data on changes in the internal resistance of energy storage batteries over time and extract data on the aging degree of energy storage batteries; Measure the impedance of electrical equipment as it changes with operating conditions to generate the impedance characteristics of the equipment's power consumption.

3. The new energy user-side multi-energy efficiency collaborative optimization method according to claim 1 is characterized in that: The dividing the equipment into a fast response group and a slow compensation group according to the operation risk assessment value includes: Determining whether the operational risk assessment value is greater than a preset risk threshold; If not, the corresponding device is divided into a fast response group and a priority identifier is set, wherein the priority identifier includes the response speed and the adjustment allowable time; If so, the corresponding device is divided into a slow compensation group, and a compensation capability identifier is set, wherein the compensation capability identifier includes an adjustable power range and an adjustment maintenance time.

4. The new energy user-side multi-energy efficiency collaborative optimization method according to claim 3 is characterized in that: The obtaining of the current power change data of the fast response group, the predicted regulation capability data of the slow compensation group, and the grid control target, and generating the hierarchical optimization instructions includes: Acquire real-time power deviation data of the rapid response group and generate rapid adjustment instructions using a preset local computing node; Obtaining the historical adjustment success rate of the slow compensation group and the power grid control target, and modifying the fast adjustment instruction through a preset prediction algorithm to generate a long-term optimization instruction; The hierarchical optimization instructions include the quick adjustment instructions and the long-term optimization instructions.

5. The new energy user-side multi-energy efficiency collaborative optimization method according to claim 4 is characterized in that: The control strategy for generating economic, equipment life and environmental indicators includes: Construct a multi-objective optimization function including economic cost objectives, carbon emission objectives, and equipment loss objectives; Inputting the real-time operating parameters into the multi-objective optimization function, solving the multi-objective optimization function using a preset optimization algorithm, and obtaining the adjustment amount of the equipment; According to the matching relationship between the device corresponding to the adjustment amount and the priority identifier and the compensation capability identifier, the hierarchical optimization instruction is adjusted to generate a control strategy including a voltage setting value, a power allocation scheme and an adjustment time.

6. The new energy user-side multi-energy efficiency collaborative optimization method according to claim 5 is characterized in that: The method further comprises: Dynamically adjust the weight coefficient of the economic cost target according to the operational risk assessment value; Real-time monitoring of grid electricity price data, and dynamic adjustment of the weight coefficient of the carbon emission target based on the grid electricity price data.

7. The new energy user-side multi-energy efficiency collaborative optimization method according to claim 5 is characterized in that: The step of converting the control strategy into an adjustment signal executable by the device and sending the signal to the control terminals of the fast response group and the slow compensation group respectively includes: The power allocation plan is divided into photovoltaic power generation limit values ​​and power equipment shutdown and startup rules; Encoding the adjustment time into a time instruction packet; The voltage setting value, the photovoltaic power generation limit value, the power consumption equipment stop and start rule and the time instruction packet are sent to the control terminals of the fast response group and the slow compensation group.

8. The new energy user-side multi-energy efficiency collaborative optimization method according to claim 7 is characterized in that: The method further comprises: After executing the adjustment signal, monitoring the actual power change rate of the devices in the fast response group; Calculating the absolute value of the difference between the actual power change rate and the preset expected power change rate to obtain a change rate difference; When the change rate difference is greater than a preset deviation threshold, recalculating the operation risk assessment value; The priority identifier is adjusted according to the updated operational risk assessment value, and a supplementary adjustment instruction is generated in combination with the remaining adjustment capacity of the slow compensation group.

9. The new energy user-side multi-energy efficiency collaborative optimization method according to claim 8 is characterized in that: Generating the supplementary adjustment instruction includes: Obtaining the remaining adjustment capacity of the slow compensation group; Calculating the power deviation cumulative value of the fast response group using the change rate difference; Comparing the power deviation accumulated value with the remaining regulation capacity to generate a regulation sequence list; According to the adjustment sequence list and the pre-acquired power grid peak regulation period, the slow compensation group equipment is selected for segmented adjustment.

10. A new energy user-side multi-energy efficiency collaborative optimization system, applied to the new energy user-side multi-energy efficiency collaborative optimization method according to any one of claims 1 to 9, characterized in that: The system comprises: The operating parameter acquisition module is used to collect real-time operating parameters of photovoltaic equipment, energy storage equipment, and power-consuming equipment, including the voltage fluctuation value of photovoltaic output, the aging data of energy storage batteries, and the impedance characteristics of equipment power consumption; An operation risk assessment module is used to use the real-time operation parameters to perform equipment health assessment and generate an operation risk assessment value for each device; an equipment grouping module, configured to divide the equipment into a quick response group and a slow compensation group according to the operational risk assessment value; wherein the quick response group includes equipment that requires quick adjustment, and the slow compensation group includes equipment that can be adjusted over a long period of time; A hierarchical optimization module is used to obtain the current power change data of the fast response group, the predicted regulation capability data of the slow compensation group, and the power grid control target, and generate hierarchical optimization instructions; A strategy generation module, configured to adjust the hierarchical optimization instructions through a preset multi-objective optimization function to generate a control strategy including an economic cost target, a carbon emission target, and an equipment loss target; The instruction issuing module is used to convert the control strategy into an adjustment signal executable by the device and send it to the fast response group and the slow compensation group respectively.

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