Method and device for determining carbon saving potential of light storage direct flexible building and electronic equipment

Abstract

The application discloses a method and device for determining the carbon-saving potential of a light-storage-straight-flexible building and an electronic device. The method comprises: collecting historical operation data of a load included in the light-storage-straight-flexible building in a historical period, wherein the historical period is a period of a predetermined length before a current time node; predicting a predicted operation state of the load at a next time node of the current time node based on the historical operation data; determining an initial carbon-saving potential of the light-storage-straight-flexible building at the next time node based on the predicted operation state; and correcting the initial carbon-saving potential according to a carbon-saving response degree of a target account to determine a target carbon-saving potential of the light-storage-straight-flexible building at the next time node, wherein the carbon-saving response degree is used to indicate the degree to which the target account changes the power consumption behavior to reduce the carbon emission after receiving a carbon-saving incentive signal. The application solves the technical problem of low accuracy of the determination result of the carbon-saving potential of the light-storage-straight-flexible building in the related art.

Description

Technical Field

[0001] This application relates to the field of power systems, and more specifically, to a method, apparatus, and electronic equipment for determining the carbon-saving potential of photovoltaic-storage-DC-flexible buildings. Background Technology

[0002] With the integration of a high proportion of renewable energy, load-side adjustable resources have become an important means of various grid balancing and regulation operations. In photovoltaic-storage-direct-current-flexible (PV-SGC-Flex) systems, PV-SGC-Flex systems, as important flexibility resources, possess significant adjustable potential. Due to the diverse loads within PV-SGC-Flex systems and the limitations imposed by resource response during regulation, assessing the carbon-saving potential of adjustable resources within these systems becomes a crucial indicator for determining their overall carbon-saving potential. The accuracy of this assessment is affected by uncertainties such as meteorological conditions, operating status, response behavior, and model parameters. Furthermore, distribution network security must be considered during regulation. Current technologies for determining the carbon-saving potential of PV-SGC-Flex systems typically only consider the theoretical carbon-saving capabilities of various adjustable resources, without integrating them with real-time operating conditions or addressing the impact of user uncertainties on the carbon-saving potential. This leads to significant errors in the final carbon-saving potential determination results. Therefore, current technologies suffer from unsatisfactory accuracy in determining the carbon-saving potential of PV-SGC-Flex systems.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a method, apparatus, and electronic device for determining the carbon-saving potential of photovoltaic-storage-linear-flexible buildings, in order to at least solve the technical problem of low accuracy in determining the carbon-saving potential of photovoltaic-storage-linear-flexible buildings in related technologies.

[0005] According to one aspect of the embodiments of this application, a method for determining the carbon-saving potential of a photovoltaic-storage-direct-drive-flexible building is provided, comprising: collecting historical operating data of the loads included in the photovoltaic-storage-direct-drive-flexible building during a historical period, wherein the historical period is a period of predetermined duration prior to the current time node; predicting the predicted operating state of the loads at the next time node based on the historical operating data; determining the initial carbon-saving potential of the photovoltaic-storage-direct-drive-flexible building at the next time node based on the predicted operating state; and correcting the initial carbon-saving potential according to the carbon-saving response degree of the target account to determine the target carbon-saving potential of the photovoltaic-storage-direct-drive-flexible building at the next time node, wherein the target account is the electricity consumption account in the photovoltaic-storage-direct-drive-flexible building, and the carbon-saving response degree is used to indicate the extent to which the target account changes its electricity consumption behavior to reduce carbon emissions after receiving a carbon-saving incentive signal.

[0006] According to another aspect of the embodiments of this application, a device for determining the carbon-saving potential of a photovoltaic-storage-direct-drive-flexible building is provided, comprising: a data acquisition module for acquiring historical operating data of the loads included in the photovoltaic-storage-direct-drive-flexible building during a historical period, wherein the historical period is a period of predetermined duration prior to the current time node; a predicted operating status determination module for predicting the predicted operating status of the loads at the next time node based on the historical operating data; an initial carbon-saving potential determination module for determining the initial carbon-saving potential of the photovoltaic-storage-direct-drive-flexible building at the next time node based on the predicted operating status; and a target carbon-saving potential determination module for correcting the initial carbon-saving potential according to the carbon-saving response degree of a target account to determine the target carbon-saving potential of the photovoltaic-storage-direct-drive-flexible building at the next time node, wherein the target account is the electricity consumption account in the photovoltaic-storage-direct-drive-flexible building, and the carbon-saving response degree is used to indicate the degree to which the target account changes its electricity consumption behavior to reduce carbon emissions after receiving a carbon-saving incentive signal.

[0007] According to another aspect of the embodiments of this application, a non-volatile storage medium is provided, which stores multiple instructions, any one of which is adapted to be loaded by a processor and executed as a method for determining the carbon-saving potential of a photovoltaic-storage-flexible building.

[0008] According to another aspect of the embodiments of this application, an electronic device is provided, including: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any one of the following methods for determining the carbon-saving potential of a light-storage-direct-flexible building.

[0009] In this embodiment, historical operating data of the loads included in the photovoltaic-storage-direct-drive-flexible building (PV-SHU-CHP-Flex) is collected over a historical period, where the historical period is a predetermined duration prior to the current time node. Based on the historical operating data, the predicted operating state of the loads at the next time node is predicted. Based on the predicted operating state, the initial carbon-saving potential of the PV-SHU-CHP-Flex building at the next time node is determined. The initial carbon-saving potential is then corrected according to the carbon-saving response level of the target account to determine the target carbon-saving potential of the PV-SHU-CHP-Flex building at the next time node. The target account is the electricity consumption account within the PV-SHU-CHP-Flex building, and the carbon-saving response level indicates the degree to which the target account changes its electricity consumption behavior to reduce carbon emissions after receiving a carbon-saving incentive signal. This achieves the goal of determining the predicted operating state of the loads based on historical operating data of the PV-SHU-CHP-Flex building, and determining the target carbon-saving potential of the PV-SHU-CHP-Flex building based on the predicted operating state and the carbon emission responsibility factor. This improves the accuracy of the carbon-saving potential determination results for PV-SHU-CHP-Flex building, thus solving the technical problem of low accuracy in determining the carbon-saving potential of PV-SHU-CHP-Flex building in related technologies. Attached Figure Description

[0010] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0011] Figure 1 This is a flowchart of an optional method for determining the carbon-saving potential of a photovoltaic-storage-flexible building according to an embodiment of this application;

[0012] Figure 2 This is a first schematic diagram of an optional method for determining the carbon-saving potential of a photovoltaic-storage-flexible building according to an embodiment of this application;

[0013] Figure 3 This is a second schematic diagram of an optional method for determining the carbon-saving potential of a photovoltaic-storage-flexible building according to an embodiment of this application;

[0014] Figure 4 This is a third schematic diagram of an optional method for determining the carbon-saving potential of a photovoltaic-storage-flexible building according to an embodiment of this application;

[0015] Figure 5 This is a schematic diagram of an optional carbon-saving potential determination device for a photovoltaic-storage-flexible building according to an embodiment of this application. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] According to an embodiment of this application, a method embodiment for determining the carbon-saving potential of a photovoltaic-storage-flexible building is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0019] Figure 1 This is a flowchart of an optional method for determining the carbon-saving potential of a photovoltaic-storage-flexible building according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0020] Step S102: Collect historical operating data of the loads included in the photovoltaic-storage-direct-flexible building during historical time periods, wherein the historical time period is a predetermined duration before the current time node;

[0021] It is understandable that collecting historical operating data of loads included in a photovoltaic-storage-direct-source-flexible building (PV-SHU-CHP ...

[0022] Optionally, the terminal devices included in the photovoltaic-storage-direct-flexible building can be used to collect historical operating data of various loads.

[0023] Step S104: Based on historical operating data, predict the predicted operating status of the load at the next time node from the current time node;

[0024] It is understandable that, based on collected historical operational data, the predicted operational status of the load of a photovoltaic-storage-direct-drive-flexible building (PV-SHU-C ...

[0025] In one optional embodiment, determining the predicted operating state of the load at the next time node based on historical operating data includes: constructing a transition probability matrix based on historical operating data, wherein the transition probability matrix is ​​used to indicate the probability of the load switching between different operating states; determining the current operating state of the load at the current time node based on the current operating data of the load at the current time node; and determining the predicted operating state of the load based on the transition probability matrix and the current operating state.

[0026] It is understandable that a load transition probability matrix is ​​constructed based on historical operating data to determine the probability of load switching between different operating states. Based on the transition probability matrix and the current operating state of the load at the current time point, determined by the number of loads operating at that time point, the operating state of the load at the next time point is predicted, thus determining the predicted operating state of the load. The transition probability matrix constructed using historical operating data can accurately predict the future operating state of the load, reducing uncertainty in prediction and improving the accuracy of the carbon-saving potential determination results.

[0027] Optionally, to predict the operational status at the next time point, it is first necessary to collect historical operational data of each device or load in the photovoltaic-storage-direct-flex building under different conditions. This data can cover the operating duration, frequency, environmental conditions (such as temperature, humidity, and illumination), and other factors related to device operation under different states. Next, based on the operational characteristics of the device or load, the operational modes or conditions in the operational data are divided into multiple states. For example, for air conditioning, states can be divided into off, low-power cooling, medium-power cooling, high-power cooling, low-power heating, medium-power heating, and high-power heating. Based on historical operational data, the frequency of each state and the frequency of transition from one state to another are statistically analyzed, and a transition probability matrix is ​​constructed based on these frequencies. Then, based on the current operational data of the photovoltaic-storage-direct-flex building, the current operational status of the device or load is determined. Finally, based on the current operational status and the transition probability matrix, the possible operational status of the device or load at the next time interval (e.g., 15 minutes) is predicted, and the corresponding power is calculated.

[0028] Optionally, a day can be divided into 96 time nodes with 15-minute intervals, and the operational data collected by the photovoltaic-storage-direct-drive-flexible building can be divided according to these time nodes. For load data that is difficult to divide, average power is used as a typical representative. Based on the characteristics of different equipment, influencing factors are listed. For example, cooling and heating equipment is mainly related to the current temperature and humidity, lighting equipment and photovoltaics are mainly related to the sunrise time, and energy storage equipment is mainly related to the supply and demand of the distribution network. The operating status of each equipment is affected by multiple factors, and each factor is assigned a weight according to its influence on the equipment. Based on the above influencing factors, a transition probability matrix in the following form is constructed:

[0029]

[0030] Among them, P ij (t)=P{X t+1 =j,X t =i} indicates that the load is in state X at time node t. t =i, at time node t+1, is in state Xt+1 The probability of j is given by the state transition matrix, which includes p. ii (t) indicates that the load state in the next time period is still i, and n indicates that there are n possible states.

[0031] Optionally, based on the current operating data of the photovoltaic-storage-direct-drive-flexible building at the current time point, the predicted power of the load for the next 15 minutes can be predicted through a transition probability matrix; by predicting the power of the building group, a foundation can be laid for the dynamic assessment of the carbon-saving potential of the photovoltaic-storage-direct-drive-flexible building.

[0032] Step S106: Based on the predicted operating status, determine the initial carbon saving potential of the photovoltaic-storage-direct-flexible building at the next time node;

[0033] It is understandable that the initial carbon-saving potential of a photovoltaic-storage-direct-drive-flexible building (PV-SSDH-Flex) at the next time point can be determined based on the predicted operating status of its load at that time point. By predicting the predicted operating status of the load of PV-SSDH-Flex buildings at the next time point, the initial carbon-saving potential can be dynamically assessed, promoting the application of PV-SSDH-Flex technologies, achieving more efficient utilization of renewable energy, and reducing carbon emissions.

[0034] In one optional embodiment, determining the initial carbon-saving potential of the photovoltaic-storage-DC-flexible building at the next time node based on the predicted operating status includes: determining the carbon emission responsibility factor of the load at the next time node, and the constraints of the DC distribution network included in the photovoltaic-storage-DC-flexible building, wherein the DC distribution network refers to the power distribution system used for transmitting and distributing DC power, and the carbon emission responsibility factor represents the carbon-saving effect of the load; determining the predicted power of the load at the next time node based on the predicted operating status and the constraints; and determining the initial carbon-saving potential based on the predicted power and the carbon emission responsibility factor.

[0035] The process involves two main steps. First, determining the carbon emission responsibility factor for the load at the next time point. This factor reflects the load's contribution to environmental carbon emissions under a given operating condition, and its carbon-saving effect can be assessed. Second, defining the constraints of the DC distribution network included in the photovoltaic-storage-DC-flexible building (PV-SHU-DC-flexible building). This DC distribution network is the power transmission and distribution system within the PV-SHU-DC-flexible building, used for transmitting and distributing DC power. Based on the predicted operating condition of the load at the next time point and the constraints of the DC distribution network, the predicted power of the load at that time point is determined. Based on the predicted power and the carbon emission responsibility factor, the initial carbon-saving potential of the PV-SHU-DC-flexible building is determined. By combining the predicted operating condition of the load with the carbon emission responsibility factor, the carbon-saving potential of each load can be assessed more accurately, providing a reliable basis for formulating carbon-saving strategies. Furthermore, by considering the constraints of the DC distribution network, it is ensured that carbon-saving operations will not affect the safe operation of the distribution network, thus not only promoting the achievement of carbon-saving targets but also guaranteeing the reliability and stability of the power system.

[0036] Optionally, the different loads of a photovoltaic-storage-flexible building can be categorized into adjustable loads and transferable loads based on their regulation characteristics. Adjustable loads include air conditioning, ground source heat pumps, and smart lighting, while transferable loads include charging stations. The predicted power P of the air conditioning unit... AC It can be obtained in the following way:

[0037]

[0038] Where η represents the cooling energy efficiency ratio of the air conditioner, and T t in This represents the indoor temperature at time point t. T represents the indoor temperature at time point t+1. t out This represents the outdoor temperature at time point t. Let R represent the outdoor temperature at time node t+1, R represent the equivalent thermal resistance of the room, D represent the heat capacity, and Δt represent the time interval.

[0039] Optionally, the predicted power P of the geothermal heat pump dyrb It can be obtained in the following way:

[0040] P dyrb =ρ w *c w *A*ΔB

[0041] Where, ρ w c represents the density of water. w ΔB represents the specific heat capacity of water, A represents the supply and return water flow rates, and ΔB represents the temperature difference between the indoor and outdoor circulating water.

[0042] Optionally, the predicted power of the charging station It can be obtained in the following way:

[0043]

[0044] in, Q represents the predicted power of the charging station at time node t+1. t Q represents the electric vehicle's charge at time point t. t+1 η represents the electric vehicle's battery level at time point t+1. CP Δt represents the charging efficiency, and Δt represents the time interval.

[0045] Optionally, the predicted power P of the smart lighting l (t+1) can be obtained as follows:

[0046] P l (t+1)=η i *L(t)*P n

[0047] Among them, P l (t+1) represents the predicted power of the smart lighting at time node t+1, η i The indicator shows the lighting level, i = 1 to 4, corresponding to 0%, 10%, 40%, and 100% respectively; L(t) represents the lighting duration; P n This indicates the rated power of the smart lighting.

[0048] In one optional embodiment, the constraints include node voltage constraints and power flow constraints, wherein: the node voltage constraint is used to indicate that the voltage of each node in the DC distribution network is within a predetermined voltage range, wherein a node is a point in the DC distribution network used to connect two or more power devices or lines; the power flow constraint is used to indicate that by controlling the transmission of active power and reactive power, the voltage phase angle difference between nodes in the DC distribution network is maintained within a preset range, wherein active power is the active power flowing through each device and line in the DC distribution network, and reactive power is the reactive power flowing through each device and line in the DC distribution network.

[0049] It is understandable that the constraints of a DC distribution network include node voltage constraints and power flow constraints. A node refers to a point in the DC distribution network used to connect two or more power devices or lines. Node voltage constraints ensure that the voltage at each node in the DC distribution network is within a predetermined range. Power flow constraints control the active and reactive power flowing through each device and line in the DC distribution network to ensure that the voltage phase angle difference between nodes is within a preset range. By real-time detection and control of node voltage, active power, and reactive power, safety risks such as voltage fluctuations, power transmission overload, and phase angle differences exceeding allowable ranges can be effectively avoided, ensuring the stable operation of the distribution network. Furthermore, by incorporating distribution network constraints into the assessment of carbon-saving potential, energy management systems can be prompted to develop more rational dispatch strategies, balancing energy demand and carbon-saving targets, and avoiding efficiency losses and equipment damage caused by improper dispatching of the DC distribution network.

[0050] Optionally, the constraints of the DC distribution network include node voltage constraints and distribution network power flow constraints, with the node voltage constraints being:

[0051] U a,min ≤U a ≤U a,max

[0052] Among them, U a U represents the node voltage at node a. a,min U represents the minimum allowable node voltage deviation at node a. a,max This represents the maximum allowable node voltage deviation for node a.

[0053] Optionally, the power flow constraints of the distribution network are:

[0054]

[0055] Among them, U b P represents the node voltage at node b. a Q represents the active power of node a. a G represents the reactive power of node a, m represents the total number of nodes, and G represents the reactive power of node a. ab B represents the conductance of the line formed by nodes a and b. ab θ represents the susceptance of the line consisting of nodes a and b. ab U a and U b The voltage phase angle difference between them.

[0056] In one optional embodiment, determining the initial carbon-saving potential based on predicted power and a carbon emission responsibility factor includes: determining the maximum regulating power of the load based on predicted power, wherein the maximum regulating power is used to indicate the maximum amount of power regulation of the load; determining the load's response time and average response speed to the carbon-saving incentive signal, wherein the response time is used to indicate the time from receiving the regulation command to starting to execute power regulation, and the average response speed is used to indicate the amount of power adjustment by the load per unit time; determining a first weight value for the maximum regulating power, a second weight value for the response time, and a third weight value for the average response speed at the next time node; and determining the initial carbon-saving potential based on the maximum regulating power, response time, average response speed, first weight value, second weight value, third weight value, and carbon emission responsibility factor.

[0057] It is understandable that, based on the predicted power of the load at the next time node and the power at the current time node, the maximum regulating power of the load is determined, i.e., the maximum adjustment amount of the load's power, reflecting the maximum extent to which the load can change its power consumption in response to dispatch instructions while maintaining its basic functions. The load's reaction time and average response speed to carbon-saving incentive signals are determined, where the reaction time refers to the time from receiving the dispatch instruction to starting power adjustment, and the average response speed refers to the amount of power adjustment by the load per unit time. At the next time node, the first weight value of the maximum regulating power, the second weight value of the reaction time, and the third weight value of the average response speed are determined. Based on the above maximum regulating power, reaction time, average response speed, first weight value, second weight value, third weight value, and carbon emission responsibility factor, the initial carbon-saving potential is determined. By combining predicted power, maximum regulating power, reaction time, average response speed, and carbon emission responsibility factor, the carbon-saving potential of the load at a specific time node can be more accurately assessed, providing a scientific basis for formulating carbon-saving strategies.

[0058] Optionally, the maximum regulating power ΔP can be... max Response time ΔT and average response rate Δv are used as potential metrics to determine the carbon saving potential of photovoltaic-storage-flexible buildings. Maximum adjustable power ΔP maxThis refers to the difference between the total power consumption of an adjustable resource cluster, such as a load, when it receives a response signal and adjusts its load according to its own energy consumption characteristics (either reducing or increasing the load), and the initial power consumption. The response time ΔT represents the time from when the adjustable resource manager issues the response signal (i.e., the adjustment command) to when the load begins to adjust its power. Due to the inherent fluctuations in the load, it is difficult to accurately determine the specific moment when the adjustable resource cluster participates in the response; therefore, the difference between the moment when the adjustable resource cluster reaches half of its maximum adjustment power and the moment the response signal is issued is used as the response time indicator. The average response rate Δv represents the adjustment power per unit time of the adjustable resource cluster after receiving the response signal; the average response rate Δv is the ratio of the maximum adjustment rate to twice the response time.

[0059] Optionally, the initial carbon saving potential ΔC is determined based on the maximum regulating power, reaction time, average response speed, first weight value, second weight value, third weight value, and carbon emission responsibility factor, as follows: dmax :

[0060]

[0061] Where, ΔC dmax P represents the initial carbon-saving potential of load class d, c represents the carbon emission responsibility factor, and P represents the initial carbon-saving potential of load class d. d The power of the load is represented by d, α, β, and λ represent the first weight value, the second weight value, and the third weight value, respectively, which are totaled by 1. T represents the time when the load starts to perform power regulation.

[0062] Optionally, the initial carbon saving potential C of the photovoltaic-storage-flexible building can be obtained by summing the initial carbon saving potentials corresponding to various loads. p :

[0063] C p =ΔC ACmax +ΔC dyrbmax +ΔC CPmax +ΔC lmax

[0064] Where, ΔC ACmax Indicating the carbon-saving potential of air conditioners, ΔC dyrbmax ΔC represents the carbon-saving potential of ground source heat pumps. CPmax Indicates the carbon-saving potential of charging piles, ΔC lmax This indicates the carbon-saving potential of smart lighting.

[0065] In one optional embodiment, determining the carbon emission responsibility factor for the load at the next time point includes: determining the weather data for the area where the photovoltaic-storage-direct-drive-flexible building is located at the next time point; based on the weather data, determining the power supply of the power generation equipment included in the photovoltaic-storage-direct-drive-flexible building at the next time point; and based on the weather data and the power supply, determining the carbon emission responsibility factor.

[0066] It is understandable that weather monitoring is conducted in the area where the photovoltaic-storage-direct-drive-flexible (PV-SHU-DC-Flex) building is located to obtain weather data for the area at the next time point. Based on this weather data, the power supply capacity of the various power generation devices included in the PV-SHU-DC-Flex building is predicted at the next time point. Based on the weather data and power supply capacity, the carbon emission responsibility factor of the load is determined. By combining weather data and power supply forecasts from power generation devices, the carbon-saving potential of the load at the next time point can be more accurately assessed, avoiding assessment biases based on static data.

[0067] Step S108: The initial carbon saving potential is corrected based on the carbon saving response level of the target account, and the target carbon saving potential of the photovoltaic-storage-direct-drive-flexible building at the next time node is determined. The target account is the electricity consumption account of the photovoltaic-storage-direct-drive-flexible building, and the carbon saving response level is used to indicate the extent to which the target account changes its electricity consumption behavior to reduce carbon emissions after receiving the carbon saving incentive signal.

[0068] It is understandable that the electricity consumption account of a photovoltaic-storage-direct-drive-flexible building (PV-SHU-DC-Flexible), i.e., the target account, will change its electricity consumption behavior to reduce carbon emissions after receiving a carbon-saving incentive signal, based on the degree of carbon-saving response. Therefore, after determining the initial carbon-saving potential of the PV-SHU-DC-Flexible building, to reduce the error caused by the uncertainty of the target account, the initial carbon-saving potential is corrected based on the degree of carbon-saving response of the target account, thus obtaining the target carbon-saving potential of the PV-SHU-DC-Flexible building at the next time point. By introducing the degree of carbon-saving response of the target account, the initial carbon-saving potential can be corrected, reducing the error caused by the uncertainty of the target account, improving the accuracy of the determination results of the carbon-saving potential of the PV-SHU-DC-Flexible building, and providing more reliable data support for the formulation of energy conservation and emission reduction strategies.

[0069] In an optional embodiment, before correcting the initial carbon-saving potential based on the carbon-saving response level of the target account and determining the target carbon-saving potential of the photovoltaic-storage-flexible building at the next time node, the method further includes: determining the incentive parameters for the next time node; and determining the carbon-saving response level based on the incentive parameters.

[0070] It is understandable that the responsiveness of target accounts to carbon-saving incentive signals can be altered by setting incentive parameters, such as incentive electricity prices. By determining the incentive parameters for the next time point, and based on these parameters, the carbon-saving response level of the target accounts can be determined, thereby revising the initial carbon-saving potential and determining the target carbon-saving potential of photovoltaic-storage-direct-drive-flexible buildings. Combining incentive parameters with the carbon-saving response level of target accounts allows for the prediction of their response to carbon-saving strategies, improving the accuracy of carbon-saving potential predictions. Simultaneously, it enables incentive measures to more precisely stimulate target accounts to participate in carbon-saving behaviors, improving the efficiency and effectiveness of carbon-saving responses.

[0071] Optionally, the uncertainty in the carbon-saving response of photovoltaic-storage-flexible buildings is mainly related to user (i.e., target account) behavior. The initial carbon-saving potential can be adjusted by analyzing the user's carbon-saving response conditions. Electricity price incentives can be used as an incentive model for carbon-saving response. Figure 2 This is a first schematic diagram of an optional method for determining the carbon-saving potential of a photovoltaic-storage-flexible building according to an embodiment of this application, as shown below. Figure 2 The diagram illustrates a carbon-saving response model, demonstrating the relationship between the incentive electricity price and the degree of carbon-saving response of the target account. The horizontal axis represents the magnitude of the incentive electricity price, and the vertical axis represents the degree of carbon-saving response of the target account. Based on the carbon-saving response model and incorporating principles of consumer psychology, the incentive electricity price provides users with a minimum perceptible difference (difference threshold), which is the minimum amount of change that users can consistently perceive. When the change in the incentive electricity price is below this threshold, users typically cannot perceive the change; however, when it exceeds this threshold, the change becomes consistently perceptible. Figure 2 As shown, the transition point between the dead zone and the linear zone is the minimum perceptible difference (difference threshold). Figure 2 As shown, based on changes in the incentive electricity price, the carbon-saving response of the target account can be divided into three zones: the dead zone, the linear zone, and the saturation zone. In the dead zone, users have virtually no response or a very small response to changes in the incentive electricity price, i.e., the insensitive period. When this difference threshold is exceeded, users will enter the linear zone, begin to respond to carbon-saving behaviors, and their response will change linearly with the incentive electricity price. There is a saturation value for the incentive electricity price stimulus; beyond this saturation value, users will not have any further carbon-saving response, i.e., the response limit period has been reached.

[0072] Through the above steps S102 to S108, the predicted operating status of the load can be determined based on the historical operating data of the load of the photovoltaic-storage-direct-flexible building, and the target carbon-saving potential of the photovoltaic-storage-direct-flexible building can be determined based on the predicted operating status and the carbon emission responsibility factor. This achieves the technical effect of improving the accuracy of the carbon-saving potential determination results of the photovoltaic-storage-direct-flexible building, and thus solves the technical problem of low accuracy in determining the carbon-saving potential of the photovoltaic-storage-direct-flexible building in related technologies.

[0073] Based on the above embodiments and optional embodiments, this application proposes an optional implementation method for assessing and determining the carbon-saving potential of a photovoltaic-storage-flexible building complex.

[0074] As more flexible loads appear in photovoltaic-storage-direct-drive-flexible building complexes, assessing the carbon-saving potential of adjustable resources (such as flexible loads) can promote the integration of renewable energy. Renewable energy sources like wind and solar power are intermittent and fluctuating; adjustable resources can adjust and supplement renewable energy output when it is unstable, increasing the penetration rate of renewable energy in the energy system. Assessing the carbon-saving potential of adjustable resources can determine the scale of adjustable resources that can support large-scale integration of renewable energy, driving the energy system towards a low-carbon and clean direction. Adjustable resources can respond quickly to supply-demand imbalances, failures, or emergencies in the energy system, maintaining stable system operation. By assessing the carbon-saving potential of adjustable resources, it is possible to plan and deploy them in advance, enhancing the energy system's ability to cope with various risks.

[0075] By rationally analyzing the uncertainties in the carbon-saving response of the photovoltaic-storage-DC-flexible building complex, the safe operation of the distribution network, and the real-time operating status of the loads, the carbon-saving potential of the photovoltaic-storage-DC-flexible building complex can be assessed, thereby achieving carbon-saving effects and improving the utilization efficiency of photovoltaics and energy storage. This optional implementation method allows for a reasonable assessment of the adjustable potential of each load within the photovoltaic-storage-DC-flexible building complex without affecting user status, thus promoting the achievement of low-carbon goals for this complex. Figure 3 This is a second schematic diagram of an optional method for determining the carbon-saving potential of a photovoltaic-storage-flexible building according to an embodiment of this application, as shown below. Figure 3 The flowchart shown is for this optional implementation method. The following is based on… Figure 3 This optional implementation method is described below.

[0076] Step S1: Establish a prediction model. For example... Figure 3 As shown, historical operating data of the load of the photovoltaic storage flexible building group were collected, and a prediction model was established based on the load characteristics of the photovoltaic storage flexible building group. Figure 4 This is a third schematic diagram of an optional method for determining the carbon-saving potential of a photovoltaic-storage-flexible building according to an embodiment of this application. The process of establishing the prediction model is as follows: Figure 4 As shown, it specifically includes:

[0077] Step S11: Use the terminal device to collect historical operating data of various loads;

[0078] Step S12: Divide the day into 96 time nodes with 15-minute intervals, and divide the operation data collected by the photovoltaic-storage-direct-flexible building complex according to the time nodes. For load data that is difficult to divide, the average power is used as a typical representative.

[0079] Step S13: Based on the characteristics of different devices, list the influencing factors respectively. For example, cooling and heating devices are mainly related to the current temperature and humidity, lighting devices and photovoltaics are mainly related to the sunrise time, and energy storage devices are mainly related to the supply and demand of the power distribution network. In addition, the operating status of each device is affected by multiple factors. Assign weights to each factor according to the magnitude of its influence on the device.

[0080] Step S14: Construct the transition probability matrix according to the above influencing factors:

[0081]

[0082] Among them, P ij (t)=P{X t+1 =j,X t =i} indicates that the load is in state X at time node t. t =i, at time node t+1, is in state X t+1 The probability of j is given by the state transition matrix, which includes p. ii (t) indicates that the load state in the next time period is still i, and n indicates that there are n possible states.

[0083] Step S15: Based on the load operation data and operation status of the photovoltaic-storage-direct-flexible building complex at the t-th time node, predict the load operation status and predicted power for the next 15 minutes (i.e., the t+1-th time node) using the transition probability matrix.

[0084] Step S16, by predicting the power of the building complex, lays the groundwork for the subsequent dynamic assessment of the carbon-saving potential of the photovoltaic-storage-flexible building complex.

[0085] Step S2, initial carbon saving potential analysis of the building complex. For example... Figure 3 As shown, this paper analyzes the carbon-saving potential of the photovoltaic-storage-flexible building complex and its initial carbon-saving potential. The different loads of the photovoltaic-storage-flexible building complex are divided into adjustable loads and transferable loads according to their regulation characteristics. Adjustable loads include air conditioning, ground source heat pumps, and smart lighting, while transferable loads include charging piles.

[0086] Step S21: Determine the predicted power of the above four types of loads.

[0087] For the predicted power P of the air conditioner AC The formula is:

[0088]

[0089] Where η represents the cooling energy efficiency ratio of the air conditioner, and T t in This represents the indoor temperature at time point t. T represents the indoor temperature at time point t+1. t out This represents the outdoor temperature at time point t. Let R represent the outdoor temperature at time node t+1, R represent the equivalent thermal resistance of the room, D represent the heat capacity, and Δt represent the time interval.

[0090] Predicted power P of geothermal heat pump dyrb The formula is:

[0091] P dyrb =ρ w *c w *A*ΔB

[0092] Where, ρ w c represents the density of water. w ΔB represents the specific heat capacity of water, A represents the supply and return water flow rates, and ΔB represents the temperature difference between the indoor and outdoor circulating water.

[0093] Predicted power of charging piles The formula is:

[0094]

[0095] in, Q represents the predicted power of the charging station at time node t+1. t Q represents the electric vehicle's charge at time point t. t+1 η represents the electric vehicle's battery level at time point t+1. CP Δt represents the charging efficiency, and Δt represents the time interval.

[0096] Predicted power P of smart lighting l The formula for (t+1) is:

[0097] P l (t+1)=η i *L(t)*P n

[0098] Among them, P l (t+1) represents the predicted power of the smart lighting at time node t+1, η i The indicator shows the lighting level, i = 1 to 4, corresponding to 0%, 10%, 40%, and 100% respectively; L(t) represents the lighting duration; P nThis indicates the rated power of the smart lighting.

[0099] Step S22, adjust the maximum regulating power ΔP max Response time ΔT and average response rate Δv are used as potential metrics to determine the carbon-saving potential of a photovoltaic-storage-flexible building complex. Maximum adjustable power ΔP max This refers to the difference between the total power consumption of an adjustable resource cluster, such as a load, when it receives a response signal and adjusts its load according to its own energy consumption characteristics (either reducing or increasing the load), and the initial power consumption. The response time ΔT represents the time from when the adjustable resource manager issues the response signal (i.e., the adjustment command) to when the load begins to adjust its power. Due to the inherent fluctuations in the load, it is difficult to accurately determine the specific moment when the adjustable resource cluster participates in the response; therefore, the difference between the moment when the adjustable resource cluster reaches half of its maximum adjustment power and the moment the response signal is issued is used as the response time indicator. The average response rate Δv represents the adjustment power per unit time of the adjustable resource cluster after receiving the response signal; the average response rate Δv is the ratio of the maximum adjustment rate to twice the response time.

[0100] Step S23: Determine the initial carbon saving potential of the photovoltaic-storage-direct-drive-flexible building complex. Based on the maximum regulation power, response time, average response speed, first weight value, second weight value, third weight value, and carbon emission responsibility factor, determine the initial carbon saving potential ΔC. dmax :

[0101]

[0102] Where, ΔC dmax P represents the initial carbon-saving potential of load class d, c represents the carbon emission responsibility factor, and P represents the initial carbon-saving potential of load class d. d The power of the load is represented by d, α, β, and λ represent the first weight value, the second weight value, and the third weight value, respectively, which are totaled by 1. T represents the time when the load starts to perform power regulation.

[0103] The initial carbon saving potential C of the photovoltaic-storage-flexible building complex can be obtained by summing the initial carbon saving potentials corresponding to various loads. p :

[0104] C p =ΔC ACmax +ΔC dyrbmax +ΔC CPmax +ΔC lmax

[0105] Where, ΔC ACmax Indicating the carbon-saving potential of air conditioners, ΔC dyrbmax ΔC represents the carbon-saving potential of ground source heat pumps. CPmax Indicates the carbon-saving potential of charging piles, ΔC lmax This indicates the carbon-saving potential of smart lighting.

[0106] Step S3, Uncertainty Analysis. For example... Figure 3 As shown, this paper analyzes the impact of the uncertainty of the carbon-saving response degree of the photovoltaic value-added flexible building group on the carbon-saving potential of the photovoltaic value-added flexible building group, in view of the uncertainty of the carbon-saving response degree.

[0107] The uncertainty in the carbon-saving response of photovoltaic-storage-flexible building complexes is mainly related to user (i.e., target account) behavior. The initial carbon-saving potential can be adjusted by analyzing users' carbon-saving response conditions. Electricity price incentives can be used as the incentive model for carbon-saving response. For example... Figure 2 The diagram illustrates a carbon-saving response model, demonstrating the relationship between the incentive electricity price and the degree of carbon-saving response of the target account. The horizontal axis represents the magnitude of the incentive electricity price, and the vertical axis represents the degree of carbon-saving response of the target account. Based on the carbon-saving response model and incorporating principles of consumer psychology, the incentive electricity price provides users with a minimum perceptible difference (difference threshold), which is the minimum amount of change that users can consistently perceive. When the change in the incentive electricity price is below this threshold, users typically cannot perceive the change; however, when it exceeds this threshold, the change becomes consistently perceptible. Figure 2 As shown, the transition point between the dead zone and the linear zone is the minimum perceptible difference (difference threshold). Figure 2 As shown, based on changes in the incentive electricity price, the carbon-saving response of the target account can be divided into three zones: the dead zone, the linear zone, and the saturation zone. In the dead zone, users have virtually no response or a very small response to changes in the incentive electricity price, i.e., the insensitive period. When this difference threshold is exceeded, users will enter the linear zone, begin to respond to carbon-saving behaviors, and their response will change linearly with the incentive electricity price. There is a saturation value for the incentive electricity price stimulus; beyond this saturation value, users will not have any further carbon-saving response, i.e., the response limit period has been reached.

[0108] Step S4: Construct a dynamic assessment model for carbon-saving potential. For example... Figure 3 As shown, a dynamic assessment model for carbon-saving potential is established by combining the predicted operating conditions, the safe operating characteristics of the distribution network, and the real-time operating status.

[0109] Step S41: Collect the results of the predicted operating status of the load from the previous step S1;

[0110] Step S41: Construct real-time operation constraints for the DC distribution network of the photovoltaic-storage-DC-flexible building complex, including node voltage constraints and distribution network power flow constraints.

[0111] The node voltage constraint is:

[0112] U a,min ≤U a ≤U a,max

[0113] Among them, U a U represents the node voltage at node a. a,min U represents the minimum allowable node voltage deviation at node a. a,max This represents the maximum allowable node voltage deviation for node a.

[0114] The power flow constraints of the distribution network are:

[0115]

[0116] Among them, U b P represents the node voltage at node b. a Q represents the active power of node a. a G represents the reactive power of node a, m represents the total number of nodes, and G represents the reactive power of node a. ab B represents the conductance of the line formed by nodes a and b. ab θ represents the susceptance of the line consisting of nodes a and b. ab U a and U b The voltage phase angle difference between them.

[0117] Step S43: Combining steps S2 and S3, analyze the initial carbon saving potential of the photovoltaic-storage-direct-flexible building complex and the uncertainties of users, and determine the target carbon saving potential of the photovoltaic-storage-direct-flexible building complex.

[0118] Step S44: Update the target carbon saving potential assessment results every 15 minutes, continuously rolling out the assessment.

[0119] The aforementioned optional implementation methods, through real-time monitoring and analysis of the operational data of the photovoltaic-storage-DC-flexible system, predict the operational status of the photovoltaic-storage-DC-flexible building complex. By combining the load characteristics and energy demands of the building, and considering user uncertainties and the safe operation of the DC distribution network, the target carbon-saving potential of the photovoltaic-storage-DC-flexible building complex can be accurately calculated, which helps in formulating targeted energy-saving strategies. Simultaneously, it can improve the flexibility and reliability of the photovoltaic-storage-DC-flexible building complex's energy system, ensuring that under different energy demand and supply conditions, the building complex can flexibly adjust energy storage and release, guaranteeing stable power supply to the building and improving energy utilization efficiency.

[0120] The above-mentioned optional implementation methods achieve at least the following effects: When determining the target carbon-saving potential of the photovoltaic-storage-DC-flexible building complex, the carbon emission responsibility factor is introduced into the process of determining the carbon-saving potential of various loads, which can intuitively yield the carbon-saving potential of various loads; for the carbon-saving response of the photovoltaic-storage-DC-flexible building complex, the DC distribution network power flow constraints and node voltage constraints are included in the measurement scope to ensure the safe and stable operation of the distribution network; the target carbon-saving potential of the photovoltaic-storage-DC-flexible building complex is evaluated dynamically at 15-minute intervals, which will not consume too much computing power and can improve the accuracy of the carbon-saving potential.

[0121] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0122] The above embodiments and preferred embodiments, which have already been described, will not be repeated. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0123] According to an embodiment of this application, an apparatus embodiment for determining the carbon-saving potential of a photovoltaic-storage-flexible building is also provided. Figure 5 This is a schematic diagram of a device for determining the carbon-saving potential of a photovoltaic-storage-flexible building according to an embodiment of this application, as shown below. Figure 5 As shown, the carbon-saving potential determination device for the above-mentioned photovoltaic-storage-flexible building includes a data acquisition module 502, a predicted operating status determination module 504, an initial carbon-saving potential determination module 506, and a target carbon-saving potential determination module 508. The device will be described below.

[0124] Data acquisition module 502 is used to collect historical operating data of the loads included in the photovoltaic-storage-direct-flex building during historical periods, wherein the historical period is a period of predetermined duration prior to the current time point;

[0125] The predictive operating status determination module 504 is connected to the data acquisition module 502 and is used to predict the predicted operating status of the load at the next time node based on historical operating data.

[0126] The initial carbon saving potential determination module 506 is connected to the predicted operating status determination module 504 and is used to determine the initial carbon saving potential of the photovoltaic-storage-direct-flexible building at the next time node based on the predicted operating status.

[0127] The target carbon saving potential determination module 508 is connected to the initial carbon saving potential determination module 506. It is used to correct the initial carbon saving potential based on the carbon saving response level of the target account and determine the target carbon saving potential of the photovoltaic-storage-direct-drive-flexible building at the next time node. The target account is the electricity consumption account of the photovoltaic-storage-direct-drive-flexible building. The carbon saving response level is used to indicate the extent to which the target account changes its electricity consumption behavior to reduce carbon emissions after receiving the carbon saving incentive signal.

[0128] This application provides a device for determining the carbon-saving potential of a photovoltaic-storage-direct-drive-flexible building. A data acquisition module 502 is used to collect historical operating data of the loads included in the photovoltaic-storage-direct-drive-flexible building during historical time periods, where the historical time period is a predetermined duration prior to the current time node. A predicted operating status determination module 504, connected to the data acquisition module 502, is used to predict the predicted operating status of the loads at the next time node based on the historical operating data. An initial carbon-saving potential determination module 506, connected to the predicted operating status determination module 504, is used to determine the initial carbon-saving potential of the photovoltaic-storage-direct-drive-flexible building at the next time node based on the predicted operating status. A target carbon-saving potential determination module 508, connected to the initial carbon-saving potential determination module 506, is used to correct the initial carbon-saving potential according to the carbon-saving response level of the target account, and determine the target carbon-saving potential of the photovoltaic-storage-direct-drive-flexible building at the next time node. The target account is the electricity consumption account in the photovoltaic-storage-direct-drive-flexible building, and the carbon-saving response level indicates the degree to which the target account changes its electricity consumption behavior to reduce carbon emissions after receiving a carbon-saving incentive signal. The goal is to determine the predicted operating status of the load based on historical operating data of the photovoltaic-storage-direct-flexible building (PV-SHU-C ...

[0129] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0130] It should be noted that the data acquisition module 502, the predicted operating status determination module 504, the initial carbon-saving potential determination module 506, and the target carbon-saving potential determination module 508 correspond to steps S102 to S108 in the embodiments. The instances and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run on a computer terminal.

[0131] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0132] The aforementioned device for determining the carbon-saving potential of a photovoltaic-storage-flexible building may also include a processor and a memory. The data acquisition module 502, the predictive operating status determination module 504, the initial carbon-saving potential determination module 506, and the target carbon-saving potential determination module 508 are all stored as program units in the memory. The processor executes the aforementioned program units stored in the memory to achieve the corresponding functions.

[0133] The processor contains a core that retrieves the corresponding program unit from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0134] This application provides a non-volatile storage medium storing a program that, when executed by a processor, implements a method for determining the carbon-saving potential of a photovoltaic-storage-flexible building.

[0135] This application provides an electronic device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: collecting historical operating data of the loads included in a photovoltaic-storage-direct-drive-flexible building (PV-SHU-CHU-Flex) over a historical period, wherein the historical period is a predetermined duration prior to the current time point; based on the historical operating data, predicting the predicted operating state of the loads at the next time point; based on the predicted operating state, determining the initial carbon-saving potential of the PV-SHU-CHU-Flex building at the next time point; and correcting the initial carbon-saving potential according to the carbon-saving response level of the target account to determine the target carbon-saving potential of the PV-SHU-CHU-Flex building at the next time point, wherein the target account is the electricity consumption account in the PV-SHU-CHU-Flex building, and the carbon-saving response level indicates the degree to which the target account changes its electricity consumption behavior to reduce carbon emissions after receiving a carbon-saving incentive signal. The device described herein may be a server, PC, etc.

[0136] This application also provides a computer program product, which, when executed on a data processing device, is adapted to execute a program that initializes the method steps for determining the carbon-saving potential of a photovoltaic-storage-flexible building, which includes any of the above-described steps.

[0137] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0141] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0142] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0143] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0144] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0145] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining the carbon-saving potential of a photovoltaic-storage-flexible building, characterized in that, include: Collect historical operating data of the loads included in the photovoltaic-storage-direct-flexible building during historical time periods, wherein the historical time period is a period of predetermined duration prior to the current time point; Based on the historical operating data, predict the predicted operating status of the load at the next time node after the current time node; Based on the predicted operating status, the initial carbon-saving potential of the photovoltaic-storage-flexible building at the next time point is determined; The initial carbon-saving potential is corrected based on the carbon-saving response level of the target account to determine the target carbon-saving potential of the photovoltaic-storage-direct-drive-flexible building at the next time node. The target account is the electricity consumption account of the photovoltaic-storage-direct-drive-flexible building, and the carbon-saving response level is used to indicate the extent to which the target account changes its electricity consumption behavior to reduce carbon emissions after receiving a carbon-saving incentive signal. The determination of the initial carbon-saving potential of the photovoltaic-storage-DC-flexible building at the next time node, based on the predicted operating status, includes: determining the carbon emission responsibility factor of the load at the next time node, and the constraints of the DC distribution network included in the photovoltaic-storage-DC-flexible building, wherein the DC distribution network refers to a power distribution system used for transmitting and distributing DC power, and the carbon emission responsibility factor represents the carbon-saving effect of the load; determining the predicted power of the load at the next time node based on the predicted operating status and the constraints; and determining the maximum regulating power of the load based on the predicted power, wherein the maximum regulating power is used to indicate the power of the load. The maximum adjustment amount; determining the load's response time and average response speed to the carbon-saving incentive signal, wherein the response time indicates the time from when the load receives the adjustment command to when it begins to execute power adjustment, and the average response speed indicates the amount of power adjustment by the load per unit time; determining, at the next time node, a first weight value of the maximum adjustment power, a second weight value of the response time, and a third weight value of the average response speed; and determining the initial carbon-saving potential based on the maximum adjustment power, the response time, the average response speed, the first weight value, the second weight value, the third weight value, and the carbon emission responsibility factor.

2. The method according to claim 1, characterized in that, Determining the predicted operating status of the load at the next time node based on the historical operating data includes: Based on the historical operating data, a transition probability matrix is ​​constructed, wherein the transition probability matrix is ​​used to indicate the probability of the load switching between different operating states; Based on the current operating data of the load at the current time node, determine the current operating status of the load at the current time node; Based on the transition probability matrix and the current operating state, the predicted operating state of the load is determined.

3. The method according to claim 1, characterized in that, The constraints include node voltage constraints and distribution network power flow constraints, wherein: The node voltage constraint is used to indicate that the voltage of each node in the DC distribution network is within a predetermined voltage range, wherein the node is a point in the DC distribution network used to connect two or more power devices or lines; The power flow constraint of the distribution network is used to indicate that by controlling the transmission of active power and reactive power, the voltage phase angle difference between nodes in the DC distribution network is maintained within a preset range. The active power is the active power flowing through each device and line in the DC distribution network, and the reactive power is the reactive power flowing through each device and line in the DC distribution network.

4. The method according to claim 1, characterized in that, Determining the carbon emission responsibility factor for the load at the next time node includes: Determine the weather data for the area where the photovoltaic-storage-flexible building is located at the next time point; Based on the weather data, determine the power supply of the power generation equipment included in the photovoltaic-storage-flexible building at the next time node; The carbon emission responsibility factor is determined based on the weather data and the power supply.

5. The method according to any one of claims 1 to 4, characterized in that, Before revising the initial carbon-saving potential based on the carbon-saving response level of the target account to determine the target carbon-saving potential of the photovoltaic-storage-flexible building at the next time node, the method further includes: Determine the excitation parameters for the next time node; The degree of carbon-saving response is determined based on the excitation parameters.

6. A device for determining the carbon-saving potential of a photovoltaic-storage-flexible building, the device being used to perform the steps of the method according to any one of claims 1 to 5, characterized in that, include: The data acquisition module is used to collect historical operating data of the loads included in the photovoltaic-storage-direct-flex building during historical periods, wherein the historical period is a period of predetermined duration prior to the current time point; The predictive operating status determination module is used to predict the predicted operating status of the load at the next time node after the current time node based on the historical operating data. The initial carbon saving potential determination module is used to determine the initial carbon saving potential of the photovoltaic-storage-flexible building at the next time node based on the predicted operating status. The target carbon-saving potential determination module is used to correct the initial carbon-saving potential based on the carbon-saving response level of the target account, and determine the target carbon-saving potential of the photovoltaic-storage-direct-drive-flexible building at the next time node. The target account is the electricity consumption account of the photovoltaic-storage-direct-drive-flexible building, and the carbon-saving response level is used to indicate the extent to which the target account changes its electricity consumption behavior to reduce carbon emissions after receiving a carbon-saving incentive signal.

7. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions, which are adapted to be loaded by a processor and executed by the method for determining the carbon-saving potential of a photovoltaic-storage-flexible building according to any one of claims 1 to 5.

8. An electronic device, characterized in that, include: One or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method for determining the carbon-saving potential of a photovoltaic-storage-flexible building according to any one of claims 1 to 5.

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