Power efficiency intelligent management method and system based on demand data analysis
By constructing the target electricity consumption spectrum and carbon emission characteristics, identifying electricity consumption characteristics, and implementing power supply strategies, the problems of low power efficiency management accuracy and efficiency are solved, and efficient and stable operation and load balance of the power system are achieved.
Patent Information
- Application Number
- CN202510288542.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-12
- Publication Date
- 2025-07-11
AI Technical Summary
The existing problems of poor accuracy and low efficiency in power efficiency management.
Through the method based on demand data analysis, a target electricity consumption graph is constructed, the target carbon emission characteristics are determined, the power consumption characteristics are identified, and the corresponding power supply strategies are implemented, including regulating electricity prices, regulating node status and power supply parameters to optimize power resource allocation.
It improves the accuracy and efficiency of power efficiency management, realizes stable operation and load balance of the power system, reduces the pressure during peak electricity consumption, and extends the service life of the hardware.
Smart Images
Figure CN120297610A_ABST
Abstract
Description
Technical Field
[0001] Embodiments of the present application relate to the technical field of power energy efficiency management, and in particular, to an intelligent power energy efficiency management method and system based on demand data analysis. Background Art
[0002] Power energy efficiency management is mainly used to improve the efficiency of power use, reduce energy waste, and optimize the allocation and use of power resources through a series of technologies, strategies, and measures.
[0003] In related technologies, there are problems such as poor accuracy and low efficiency in power energy efficiency management.
[0004] Therefore, there is an urgent need for a new technical solution to solve the above technical problems. Summary of the Invention
[0005] According to embodiments of the present application, an intelligent power energy efficiency management method and system based on demand data analysis are provided, and the present application can improve the accuracy and management efficiency of power energy efficiency management.
[0006] In the first aspect of the present application, an intelligent power energy efficiency management method based on demand data analysis is proposed, including:
[0007] Determine target carbon emission characteristics according to a target power consumption map, where the target power consumption map includes: target power consumption nodes and transmission lines corresponding to each target power consumption node;
[0008] Determine target power consumption characteristics according to the target carbon emission characteristics;
[0009] Execute a corresponding target power supply strategy according to the target power consumption characteristics.
[0010] In some feasible embodiments, the above target power consumption characteristics include: target peak-valley period ratio characteristics, and / or, target interruptible load capacity characteristics;
[0011] The above-mentioned executing a corresponding target power supply strategy according to the target power consumption characteristics includes:
[0012] Determine target power demand characteristics according to the target peak-valley period ratio characteristics;
[0013] Regulate the electricity price to a target electricity price according to the target power demand characteristics;
[0014] Wherein, the target electricity price is positively correlated with the target power demand characteristics, and / or, the target peak-valley period ratio characteristics.
[0015] In some feasible embodiments, the above-mentioned executing a corresponding target power supply strategy according to the target power consumption characteristics further includes:
[0016] Determine the target regulation node and the target regulation state corresponding to the target regulation node according to the carbon emission characteristics and the interruptible load capacity characteristics;
[0017] Regulate the target regulation node to the target regulation state.
[0018] In some feasible embodiments, the above method further includes:
[0019] Determine the target correlation coefficient according to the temperature characteristics and the power consumption characteristics;
[0020] Determine the target electricity price and / or the target regulation state according to the target correlation coefficient.
[0021] In some feasible embodiments, the method as described in any of the foregoing also includes:
[0022] Determine the first target cycle load characteristic and the second target cycle load characteristic according to the target carbon emission characteristic;
[0023] In the case where the difference between the first target cycle load characteristic and the second target cycle load characteristic is greater than a preset difference;
[0024] Regulate the power supply parameter to the corresponding target parameter to make the difference less than or equal to the preset difference.
[0025] In some feasible embodiments, the above target power consumption characteristics further include: target power consumption time characteristic, target power consumption load intensity characteristic, target power consumption fluctuation characteristic, target power consumption adjustability characteristic, and / or target power consumption correlation characteristic;
[0026] The above method further includes:
[0027] Determine the target parameter according to the target power consumption time characteristic, the target power consumption load intensity characteristic, the target power consumption fluctuation characteristic, the target power consumption adjustability characteristic, and / or the target power consumption correlation characteristic.
[0028] In some feasible embodiments, the above method further includes:
[0029] Determine the target parameter according to the target function;
[0030] Wherein, the target function includes:
[0031]
[0032] Wherein, t is the time index, T is the total number of time periods corresponding to the time index t, P grid,t is the grid power purchase volume corresponding to the time index t, C grid is the power purchase cost function, P DR,t is the demand response reduction amount corresponding to the time index t, C DRis the demand response cost function, CO 2排 is the carbon emission, and λ is the carbon emission cost coefficient.
[0033] In some feasible embodiments, the objective constraint conditions corresponding to the above objective function include:
[0034] The first objective constraint condition:
[0035] P load,t = P grid,t + P PV,t + P ESS,t
[0036] where P load,t is the total load demand corresponding to time index t, P grid,t is the electricity purchase quantity from the power grid corresponding to time index t, P PV,t is the electric power generated by the photovoltaic system corresponding to time index t, and P ESS,t is the electric power provided by the energy storage system corresponding to time index t.
[0037] In some feasible embodiments, the objective constraint conditions corresponding to the above objective function, and / or, further include:
[0038] The second objective constraint condition:
[0039] SOC min ≤ SOC t ≤ SOC max
[0040] where SOC min is the lowest state of charge allowed for the energy storage system, SOC t is the actual state of charge of the energy storage system corresponding to time index t, and SOC max is the maximum state of charge allowed for the energy storage system.
[0041] In the second aspect of the present application, a power energy efficiency intelligent management system based on demand data analysis is proposed, including:
[0042] A first determination unit, configured to determine target carbon emission characteristics according to a target power consumption map, where the target power consumption map includes: target power consumption nodes and transmission lines corresponding to each target power consumption node;
[0043] A second determination unit, configured to determine target power consumption characteristics according to the target carbon emission characteristics;
[0044] An execution unit, configured to execute a corresponding target power supply strategy according to the target power consumption characteristics.
[0045] The power energy efficiency intelligent management method and system based on demand data analysis provided by the embodiments of the present application, wherein the method includes: determining the target carbon emission characteristics according to the target power consumption map, where the target power consumption map includes: target power consumption nodes and transmission lines corresponding to each target power consumption node; determining the target power consumption characteristics according to the target carbon emission characteristics; and executing the corresponding target power supply strategy according to the target power consumption characteristics. The present application can improve the accuracy and efficiency of power energy efficiency management.
[0046] It should be understood that the content described in the Summary of the Invention section is not intended to limit the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0047] In combination with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present application will become more apparent. In the drawings, the same or similar reference numerals denote the same or similar elements, where:
[0048] Figure 1 is a flowchart of a power energy efficiency intelligent management method based on demand data analysis provided by the embodiments of the present application;
[0049] Figure 2 is a structural diagram of a power energy efficiency intelligent management system based on demand data analysis provided by the embodiments of the present application;
[0050] Figure 3 is a structural diagram of an electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.
[0052] In addition, the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.
[0053] In the first aspect of the embodiments of the present application, a power energy efficiency intelligent management method based on demand data analysis is proposed.Figure 1 It is a schematic flowchart of a power energy efficiency intelligent management method 100 provided by an embodiment of the present application. As Figure 1 shown, the method 100 includes:
[0054] Step S1; determining the target carbon emission characteristics according to the target power consumption map, where the target power consumption map includes: target power consumption nodes and transmission lines corresponding to each target power consumption node.
[0055] Exemplarily, the above-mentioned target power consumption nodes may include: multi-level power consumption nodes corresponding to industrial facility areas, multi-level power consumption nodes corresponding to commercial building areas, and / or multi-level power consumption nodes corresponding to residential areas, etc. Among them, the above-mentioned multi-level power consumption nodes may include: main road power consumption nodes and branch road power consumption nodes; main power consumption nodes and slave power consumption nodes; upper-level power consumption nodes and lower-level power consumption nodes, etc.
[0056] Exemplarily, the transmission lines corresponding to each target power consumption node may include: transmission lines for connecting each multi-level target power consumption node and transmission lines for connecting each multi-level target power consumption node to a power generation station or a substation.
[0057] Among them, intelligent electric meters or sensors may be arranged on each of the above-mentioned target power consumption nodes to collect data such as real-time power consumption, power factor, load type, line loss power, line current I, and line resistance R of each target power consumption node, for determining the target carbon emission characteristics.
[0058] It should be noted that the above-mentioned target carbon emission characteristics may include: carbon emission, carbon emission rate, carbon intensity peak, carbon sensitivity, and / or line carbon contribution ratio, etc. Among them, the above-mentioned carbon emission can be determined according to the following formula:
[0059]
[0060] Among them, C node is the carbon emission corresponding to the target power consumption node, P node,t is the power consumption of the target power consumption node at time period t, F source,t is the power source carbon emission factor corresponding to time period t, P loss,t is the line loss power corresponding to time period t, F loss,t is the line loss carbon emission factor.
[0061] It should be noted that the line loss power P loss,t corresponding to the above-mentioned time period t can be calculated and determined according to the line current I and resistance R. The above-mentioned line loss carbon emission factor F loss,t can be determined according to the power source type connected by the line.
[0062] Among them, the above carbon emission rate can be determined according to the following formula:
[0063] R 碳 (t) = P node (t)·F source (t) + P loss (t)·F loss (t)#(2)
[0064] Among them, R 碳 (t) is the carbon emission rate corresponding to the target power consumption node, P node (t) is the power consumption of the target power consumption node at time t, F source (t) is the power supply carbon emission factor corresponding to time t, P loss (t) is the line loss power corresponding to time t, F loss (t) is the line loss carbon emission factor.
[0065] Among them, the product of the power consumption P node (t) of the target power consumption node at time t and the power supply carbon emission factor F source (t) corresponding to time t can be used to represent the direct emission rate, and the product of the line loss power P loss (t) corresponding to time t and the above line loss carbon emission factor F loss (t) can be used to represent the indirect emission rate.
[0066] Among them, the line loss power P loss (t) corresponding to time t can be determined according to the following formula:
[0067] P loss (t) = I 2 (t)·R#(3)
[0068] Among them, I(t) is the line current and R is the resistance.
[0069] Among them, the power supply carbon emission factor F source (t) corresponding to time t can be determined according to the following formula:
[0070]
[0071] Among them, α i (t) is the power supply ratio of power supply i at time t, F source,i is the power supply carbon emission factor corresponding to power supply i.
[0072] Among them, the above carbon intensity peak corresponds to the carbon emission rate of the target power consumption node during the peak period, with the unit of kgCO2 / h.
[0073] Among them, the above carbon sensitivity corresponds to the influence coefficient of the change in power consumption of the target power consumption node on the total carbon emissions.
[0074] Among them, the line carbon contribution ratio corresponds to the proportion of the loss carbon emissions of the target transmission line in the total carbon emissions.
[0075] Step S2; Determine the target power consumption characteristics according to the target carbon emission characteristics.
[0076] In some feasible embodiments, the above target power consumption characteristics include: target peak-valley period ratio characteristics, target interruptible load capacity characteristics, target power consumption time characteristics, target power consumption load intensity characteristics, target power consumption fluctuation characteristics, target power consumption adjustability characteristics, and / or target power consumption correlation characteristics, etc.
[0077] Exemplarily, the above target peak-valley period ratio characteristics, target interruptible load capacity characteristics, target power consumption time characteristics, target power consumption load intensity characteristics, target power consumption fluctuation characteristics, target power consumption adjustability characteristics, and / or target power consumption correlation characteristics, etc. can be determined according to the above carbon emissions, carbon emission rate, carbon intensity peak, carbon sensitivity, and / or line carbon contribution ratio, etc.
[0078] Specifically, the high-carbon period and low-carbon period can be identified and determined according to the carbon intensity peak and carbon emission rate to determine the above target peak-valley period ratio characteristics.
[0079] Specifically, the high-carbon sensitive load capacity that can be quickly reduced can be determined according to the carbon sensitivity and line carbon contribution ratio to determine the above target interruptible load capacity characteristics.
[0080] Specifically, the above target power consumption time characteristics can be determined according to the time distribution of the carbon emission rate and the carbon intensity peak.
[0081] Specifically, the above target power consumption load intensity characteristics can be determined according to the carbon emissions and carbon sensitivity.
[0082] Specifically, the above target power consumption fluctuation characteristics can be determined according to the volatility of the carbon emission rate and the line carbon contribution ratio.
[0083] Specifically, the above target power consumption adjustability characteristics can be determined according to the carbon sensitivity and line carbon contribution ratio.
[0084] Specifically, the above target power consumption correlation characteristics can be determined according to the line carbon contribution ratio and carbon sensitivity.
[0085] Step S3; Execute the corresponding target power supply strategy according to the target power consumption characteristics.
[0086] Exemplarily, according to the above-mentioned target peak-valley period ratio feature, the peak electricity consumption period and the valley electricity consumption period can be identified, and the corresponding target power supply strategy can be executed to optimize the power supply time distribution.
[0087] Exemplarily, according to the above-mentioned target interruptible load capacity feature, the high-carbon sensitive load capacity that can be quickly reduced can be determined, and the corresponding target power supply strategy can be executed to urgently reduce the power supply amount or perform peak regulation operation on the power supply amount emergently.
[0088] Exemplarily, according to the above-mentioned target electricity consumption time feature, the start-stop times of the devices corresponding to the multi-level target electricity consumption nodes can be determined, and the corresponding target power supply strategy can be executed to avoid the peak electricity consumption period.
[0089] Exemplarily, according to the above-mentioned target electricity consumption load intensity feature, the corresponding target power supply strategy can be executed to limit the operating power of high-power-consuming devices.
[0090] Exemplarily, according to the above-mentioned target electricity consumption fluctuation feature, the corresponding target power supply strategy can be executed to smooth the load curve and avoid random fluctuations in power supply.
[0091] Exemplarily, according to the above-mentioned target electricity consumption adjustability feature, the adjustable range or response speed of the load can be determined to execute the corresponding power supply strategy and improve the adaptability to emergency power supply situations.
[0092] Exemplarily, according to the above-mentioned target electricity consumption correlation feature, the electricity consumption coupling relationship between devices can be identified and determined to execute the corresponding target power supply strategy, thereby improving the coordination of device operation.
[0093] Based on this, the power energy efficiency intelligent management method provided by this application, by constructing a target electricity consumption map according to the target electricity consumption nodes and the transmission lines corresponding to each target electricity consumption node, is beneficial to improving the refinement degree of the target electricity consumption map. Through the above-mentioned target electricity consumption map, the target carbon emission feature is determined, which can improve the automation determination accuracy and determination efficiency of the target carbon emission feature; according to the target carbon emission feature, the target electricity consumption feature is determined, which is beneficial to improving the determination accuracy and determination efficiency of the target electricity consumption feature; according to the target electricity consumption feature, the corresponding target power supply strategy is executed, which is beneficial to improving the matching degree between the target power supply strategy and the target electricity consumption feature, thereby improving the execution accuracy of the target power supply strategy, and further improving the power energy efficiency management accuracy and management efficiency.
[0094] In some feasible implementation manners, the above step S3; executing the corresponding target power supply strategy according to the target electricity consumption feature includes: determining the target power demand feature according to the target peak-valley period ratio feature; adjusting the electricity price to the target electricity price according to the target power demand feature; wherein, the target electricity price is positively correlated with the target power demand feature, and / or, the target peak-valley period ratio feature.
[0095] Exemplarily, in the case where, according to the above-mentioned target peak-valley period ratio feature, it is determined that the first time threshold corresponds to the peak electricity consumption period and the second time threshold corresponds to the valley electricity consumption period, it is determined that the electricity demand corresponding to the first time threshold is relatively high and the electricity demand corresponding to the second time threshold is relatively low. Then, the electricity price corresponding to the first time threshold is increased to the first target electricity price, and the electricity price corresponding to the second time threshold is decreased to the second target electricity price, where the second target electricity price is less than the first target electricity price.
[0096] It should be noted that the above-mentioned first target electricity price and the second target electricity price are positively correlated with the target power demand feature and / or the target peak-valley period ratio feature.
[0097] Among them, the above-mentioned first target electricity price and the second target electricity price can be determined according to the following formula:
[0098]
[0099] Among them, Price(t) is the target electricity price corresponding to the target time threshold, P base is the base electricity price, α is the first adjustment parameter, which can specifically take 0.5, β is the second adjustment parameter, which can specifically take 0.3, C peak (t) is the peak power supply cost corresponding to the target time threshold, C avg is the average power supply cost corresponding to the target time threshold, D(t) is the actual power supply volume corresponding to the target time threshold, D target (t) is the target power supply volume corresponding to the target time threshold.
[0100] Thus, the above method can accurately identify the target power demand feature according to the target peak-valley period ratio feature; dynamically adjust the electricity price to the target electricity price according to the target power demand feature, so as to guide users to adjust their electricity consumption behaviors through price signals, improve the balance between the power grid power supply behavior and the carbon emission behavior caused by the power grid, extend the service life of the target power system hardware, reduce the probability of the target power system collapsing due to excessive pressure during the peak electricity consumption period, and promote the stable operation of the target power system.
[0101] In some feasible implementation manners, the above step S3: execute the corresponding target power supply strategy according to the target electricity consumption feature further includes: determining the target regulation node and the target regulation state corresponding to the target regulation node according to the carbon emission feature and the interruptible load capacity feature; regulating the target regulation node to the target regulation state.
[0102] Exemplarily, the above carbon emission feature may include: carbon intensity peak, carbon sensitivity, etc.
[0103] Exemplarily, the above interruptible load capacity characteristics are used to describe a set of characteristics of the load capacity that can be temporarily cut off or regulated in the target power system. The above interruptible load capacity characteristics may include: adjustable power range, response speed, etc.
[0104] Specifically, according to the above carbon intensity peak, carbon sensitivity, adjustable power range, and / or response speed, etc., the target regulation nodes that can be preferentially temporarily cut off or used for regulating load capacity, and the target regulation status corresponding to the target regulation nodes can be identified and determined to reduce the total carbon emissions and improve the grid response efficiency. Among them, the above target regulation nodes may include: target power consumption nodes with relatively large carbon emissions, and / or target power consumption nodes with relatively high response efficiency. Among them, the above target regulation status may include: shutdown status, load reduction status, charge and discharge status, and / or discharge status, etc.
[0105] Thus, the above method can achieve accurate determination of the target regulation nodes and the target regulation status corresponding to the target regulation nodes based on the dual dimensions of carbon emissions and energy utilization efficiency, and accurately regulate the target regulation nodes to the target regulation status, so that the target power system can reduce carbon emissions while ensuring the basic power supply capacity, thereby realizing refined regulation of the target power system.
[0106] In some feasible implementation manners, the above method further includes: determining a target correlation coefficient according to temperature characteristics and power consumption characteristics; determining a target electricity price and / or a target regulation status according to the target correlation coefficient.
[0107] It should be noted that the above target correlation coefficient may include: temperature sensitivity coefficient.
[0108] Exemplarily, according to the above temperature characteristics and the above power consumption characteristics, the correlation coefficient between the electricity load and the temperature can be determined to determine the above temperature sensitivity coefficient, and according to the above temperature sensitivity coefficient, the target electricity price and / or the target regulation status can be determined.
[0109] It should be noted that the above temperature sensitivity coefficient may include: Pearson correlation coefficient. Among them, the value range of the above Pearson correlation coefficient can be greater than or equal to -1 and less than or equal to 1. Among them, when the above Pearson correlation coefficient is greater than 0, it is determined that the higher the temperature, the higher the power consumption; when the above Pearson correlation coefficient is less than 0, it is determined that the lower the temperature, the higher the power consumption. Among them, the greater the absolute value of the above Pearson correlation coefficient, the stronger the linear relationship between the power consumption of the target user's target power consumption node and the temperature change is determined.
[0110] Exemplarily, when the Pearson correlation coefficient corresponding to the air conditioner control node of the target user is greater than 0.7, it is determined that the target user is an air conditioner-dependent user, and the target control node corresponds to the air conditioner control node; when the target control node corresponds to the air conditioner control node and the Pearson correlation coefficient corresponding to the air conditioner control node of the target user is less than 0.7, it is determined that the target user is a non-air conditioner-dependent user.
[0111] Specifically, when it is determined that the above user is an air conditioner-dependent user, during peak electricity consumption periods, for the power consumption corresponding to the air conditioner control node, the corresponding electricity price can be controlled to increase by 20% to 50% to reach the target electricity price. And / or, the air conditioner control node is adjusted to the target control state so that the air conditioner temperature is greater than or equal to 22°C and less than or equal to 26°C; and / or, multiple levels of air conditioner control nodes are grouped and turned off alternately to achieve the alternate shutdown of non-critical air conditioning equipment and reduce the instantaneous load impact.
[0112] Thus, the above method can accurately and automatically determine the target correlation coefficient according to the temperature characteristics and electricity consumption characteristics; according to the target correlation coefficient, accurately and automatically determine the target electricity price, and / or accurately and automatically adjust the target control node to the target control state to achieve a higher smoothness of the corresponding load curve of the target power system in a situation where the load pressure of the target power system is relatively high, reduce the instantaneous load impact, and improve the stability of the target power system.
[0113] In some feasible implementation manners, the method as described in any of the above also includes: determining the first target cycle load characteristic and the second target cycle load characteristic according to the target carbon emission characteristic; when the difference between the first target cycle load characteristic and the second target cycle load characteristic is greater than a preset difference; adjusting the power supply parameter to the corresponding target parameter to make the above difference less than or equal to the preset difference.
[0114] Exemplarily, when it is determined that the difference between the first target cycle load characteristic and the second target cycle load characteristic is greater than the preset difference according to the target carbon emission characteristic, the power supply parameter corresponding to the first target cycle can be adjusted to the first target parameter; the power supply parameter corresponding to the second target cycle is adjusted to the second target parameter to make the above difference less than or equal to the preset difference. Among them, the above preset difference can be determined according to the actual situation.
[0115] Specifically, when the first target cycle corresponds to a weekday and the second target cycle corresponds to a weekend, and it is determined that the difference between the weekday load characteristic and the weekend load characteristic is greater than 30% according to the target carbon emission characteristic, then the power supply parameter corresponding to the weekday is adjusted to the first target parameter, and the power supply parameter corresponding to the weekend is adjusted to the second target parameter to narrow the above difference to less than or equal to 30%, thereby improving the load balance and the balance of carbon emissions.
[0116] Specifically, when the first target period corresponds to daytime and the second target period corresponds to nighttime, and it is determined that the difference between the daytime load characteristics and the nighttime load characteristics is greater than 30% according to the target carbon emission characteristics, the power supply parameters corresponding to daytime are adjusted to the first target parameters, and the power supply parameters corresponding to nighttime are adjusted to the second target parameters, so as to reduce the above difference to less than or equal to 30%, thereby improving the load balance and the carbon emission balance.
[0117] Thus, the above method can accurately and automatically determine the load characteristics of the first target period and the second target period according to the target carbon emission characteristics; when it is determined that the difference between the load characteristics of the first target period and the second target period is greater than the preset difference; accurately and automatically adjust the power supply parameters to the corresponding target parameters, so that the above difference is less than or equal to the preset difference, thereby improving the load balance of the target power system and the carbon emission balance of the target power system.
[0118] The above method further includes: determining the target parameters according to the target power consumption time characteristics, the target power consumption load intensity characteristics, the target power consumption fluctuation characteristics, the target power consumption adjustability characteristics, and / or the target power consumption correlation characteristics.
[0119] Exemplarily, the above target power consumption time characteristics are used to determine the power consumption mode of the target time period.
[0120] Exemplarily, the above target power consumption load intensity characteristics are used to determine the average load, peak load, and / or valley load of the target time period.
[0121] Exemplarily, the above target power consumption fluctuation characteristics are used to determine the degree of fluctuation of the power consumption over time.
[0122] Exemplarily, the above target power consumption adjustability characteristics are used to determine whether the target power consumption node can be used as a target control node.
[0123] Exemplarily, the above target power consumption correlation characteristics are used to determine whether there is a correlation in the power consumption behaviors of different target power consumption nodes and / or different target control nodes. Among them, the above different target power consumption nodes and / or different target control nodes can belong to different target users and / or be used to control different electrical devices.
[0124] Specifically, the above-mentioned target parameters can be determined according to the target power consumption time characteristics, target power consumption load intensity characteristics, target power consumption fluctuation characteristics, target power consumption adjustability characteristics, and / or target power consumption correlation characteristics, so that when it is determined that the difference between the first target cycle load characteristics and the second target cycle load characteristics is greater than a preset difference; the power supply parameters are adjusted to the corresponding target parameters, so that the above difference is less than or equal to the preset difference.
[0125] Thus, the above method can accurately determine the target parameters according to the target power consumption time characteristics, target power consumption load intensity characteristics, target power consumption fluctuation characteristics, target power consumption adjustability characteristics, and / or target power consumption correlation characteristics, so as to further accurately and automatically adjust the power supply parameters to the corresponding target parameters, making the above difference less than or equal to the preset difference, thereby further accurately improving the load balance of the target power system and the carbon emission balance of the target power system.
[0126] In some feasible implementation manners, the above method further includes: determining the target parameters according to the objective function; wherein, the objective function includes:
[0127]
[0128] wherein, t is the time index, T is the total number of time periods corresponding to the time index t, P grid,t is the grid power purchase quantity corresponding to the time index t, C grid is the power purchase cost function, P DR,t is the demand response curtailment quantity corresponding to the time index t, C DR is the demand response cost function, CO 2排 is the carbon emission, and λ is the carbon emission cost coefficient.
[0129] It should be noted that the above power purchase cost function C grid can depend on the above grid power purchase quantity P grid,t ; the above demand response curtailment quantity P DR,t can depend on the above power purchase cost function C grid ; the above carbon emission CO 2排 can be related to the above grid power purchase quantity P grid,t .
[0130] Thus, the above method can accurately determine the target parameters according to the objective function shown in formula (6), based on the power purchase cost, demand response cost, and carbon emission cost, so as to minimize the power supply cost of the target power system.
[0131] In some feasible implementation manners, the target constraint conditions corresponding to the above objective function include: the first target constraint condition:
[0132] Pload,t = P grid,t + P PV,t + P ESS,t #(7)
[0133] Wherein, P load,t is the total load demand corresponding to the time index t, P grid,t is the electricity purchase quantity from the power grid corresponding to the time index t, P PV,t is the electric energy generated by the photovoltaic system corresponding to the time index t, P ESS,t is the electric energy provided by the energy storage system corresponding to the time index t.
[0134] Thus, the above method can achieve precise regulation of the power supply parameters to the corresponding target parameters according to the first target constraint condition shown in formula (7), so that the power supply cost of the target power system is minimized under the condition of power supply-demand balance.
[0135] In some feasible embodiments, the target constraint condition corresponding to the above objective function, and / or, further includes: a second target constraint condition:
[0136] SOC min ≤ SOC t ≤ SOC max #(8)
[0137] Wherein, SOC min is the minimum state of charge allowed for the energy storage system, SOC t is the actual state of charge of the energy storage system corresponding to the time index t, SOC max is the maximum state of charge allowed for the energy storage system.
[0138] Thus, the above method can achieve precise regulation of the power supply parameters to the corresponding target parameters according to the second target constraint condition shown in formula (8), so that the power supply cost of the target power system is minimized under the condition that the state of charge of the target power system is within a safe and effective range.
[0139] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.
[0140] The above is the introduction of the method embodiments. The following further illustrates the solution of this application through device embodiments.
[0141] In the second aspect of the embodiments of the present application, an intelligent power energy efficiency management system based on demand data analysis is proposed. Figure 2 It is a structural schematic diagram of an intelligent power energy efficiency management system 200 provided by the embodiments of the present application. As Figure 2 shown, the system 200 includes: a first determination unit 210, a second determination unit 220, and an execution unit 230.
[0142] The first determination unit 210 is configured to determine a target carbon emission characteristic according to a target power consumption map, where the target power consumption map includes: target power consumption nodes and transmission lines corresponding to each target power consumption node;
[0143] The second determination unit 220 is configured to determine a target power consumption characteristic according to the target carbon emission characteristic;
[0144] The execution unit 230 is configured to execute a corresponding target power supply strategy according to the target power consumption characteristic.
[0145] Figure 3 It is a structural schematic diagram of an electronic device 300 provided by the embodiments of the present application. As Figure 3 shown, the electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage section 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the terminal device or server are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.
[0146] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read from it can be installed into the storage section 308 as needed.
[0147] In particular, according to the embodiments of the present application, the above method flow steps can be implemented as a computer software program. For example, the embodiments of the present application include a computer program product, which includes a computer program carried on a machine-readable medium, and the computer program contains program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, the above functions defined in the system of the present application are executed.
[0148] It should be noted that the computer-readable medium described in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device. And in the present application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.
[0149] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.
[0150] The units or modules involved in the embodiments described in the present application can be implemented in software or in hardware. The described units or modules can also be provided in a processor. Among them, the names of these units or modules do not, in some cases, constitute a limitation on the units or modules themselves.
[0151] The above description is only for the preferred embodiments of the present application and the explanation of the technical principles applied. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing application concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions applied in the present application.
Claims
1. An intelligent power energy efficiency management method based on demand data analysis, characterized in that including: determining a target carbon emission characteristic according to a target power consumption map, where the target power consumption map includes: target power consumption nodes and transmission lines corresponding to each of the target power consumption nodes; determining a target power consumption characteristic according to the target carbon emission characteristic; executing a corresponding target power supply strategy according to the target power consumption characteristic.
2. The power energy efficiency intelligent management method based on demand data analysis according to claim 1, characterized in that The target power consumption characteristic includes: a target peak-valley time ratio characteristic, and / or, a target interruptible load capacity characteristic; The executing a corresponding target power supply strategy according to the target power consumption characteristic includes: determining a target power demand characteristic according to the target peak-valley time ratio characteristic; regulating the electricity price to a target electricity price according to the target power demand characteristic; wherein, the target electricity price is positively correlated with the target power demand characteristic, and / or, the target peak-valley time ratio characteristic.
3. The intelligent power energy efficiency management method based on demand data analysis according to claim 2, characterized in that, The executing a corresponding target power supply strategy according to the target power consumption characteristic further includes: determining a target regulation node and a target regulation state corresponding to the target regulation node according to the carbon emission characteristic and the interruptible load capacity characteristic; regulating the target regulation node to the target regulation state.
4. The power energy efficiency intelligent management method based on demand data analysis according to claim 3, characterized in that further including: determining a target correlation coefficient according to a temperature characteristic and a power consumption characteristic; determining the target electricity price, and / or, the target regulation state according to the target correlation coefficient.
5. The intelligent power energy efficiency management method based on demand data analysis according to any one of claims 1 to 4, characterized in that, further including: determining a first target cycle load characteristic and a second target cycle load characteristic according to the target carbon emission characteristic; when the difference between the first target cycle load characteristic and the second target cycle load characteristic is greater than a preset difference; regulating power supply parameters to corresponding target parameters so that the difference is less than or equal to the preset difference.
6. The power energy efficiency intelligent management method based on demand data analysis according to claim 5, characterized in that The target power consumption characteristic further includes: a target power consumption time characteristic, a target power consumption load intensity characteristic, a target power consumption fluctuation characteristic, a target power consumption adjustability characteristic, and / or, a target power consumption correlation characteristic; further including: determining the target parameters according to the target power consumption time characteristic, the target power consumption load intensity characteristic, the target power consumption fluctuation characteristic, the target power consumption adjustability characteristic, and / or, the target power consumption correlation characteristic.
7. The intelligent power energy efficiency management method based on demand data analysis according to claim 6, characterized in that further including: determining the target parameters according to a target function; wherein, the target function includes: Among them, t is the time index, T is the total number of time periods corresponding to the time index t, and P grid,t is the electricity purchase volume of the power grid corresponding to the time index t, and C grid is the electricity purchase cost function, and P DR,t is the demand response reduction corresponding to the time index t, and C DR is the demand response cost function, and CO 2排 is the carbon emission, and λ is the carbon emission cost coefficient.
8. The intelligent power energy efficiency management method based on demand data analysis according to claim 7, characterized in that the target constraint condition corresponding to the target function includes: a first target constraint condition: P load,t = P grid,t + P PV,t + P ESS,t Among them, P load,t is the total load demand corresponding to the time index t, P grid,t is the electricity purchase quantity from the power grid corresponding to the time index t, P PV,t is the electric energy generated by the photovoltaic system corresponding to the time index t, P ESS,t is the electric energy provided by the energy storage system corresponding to the time index t.
9. The power energy efficiency intelligent management method based on demand data analysis according to claim 8, characterized in that, the target constraint condition corresponding to the target function, and / or, further includes: a second target constraint condition: SOC min ≤SOC t ≤SOC max where SOC min is the lowest state of charge allowed for the energy storage system, SOC t is the actual state of charge of the energy storage system corresponding to the time index t, SOC max is the maximum state of charge allowed for the energy storage system.
10. An intelligent power energy efficiency management system based on demand data analysis, characterized in that, including: a first determination unit, configured to determine a target carbon emission characteristic according to a target power consumption map, where the target power consumption map includes: target power consumption nodes and transmission lines corresponding to each of the target power consumption nodes; a second determination unit, configured to determine a target power consumption characteristic according to the target carbon emission characteristic; an execution unit, configured to execute a corresponding target power supply strategy according to the target power consumption characteristic.