Power grid resource optimization method and device based on multi-dimensional entropy theory, equipment and medium

Abstract

The application discloses a power grid resource optimization method and device based on multi-dimensional entropy theory, equipment and medium, based on the real-time operation data and historical load data of the distribution node, the load fluctuation rate, resource utilization rate and power loss rate of the distribution node are calculated, the entropy value of the load fluctuation rate, the entropy value of the resource utilization rate and the entropy value of the power loss rate are calculated respectively, the entropy value of the load fluctuation rate, the entropy value of the resource utilization rate and the entropy value of the power loss rate are weighted and summed to obtain multi-dimensional entropy, the load of the distribution node is predicted based on the historical load data, the load trend prediction function is generated, the resource allocation proportion of the distribution node is adaptively regulated based on the real-time operation data, the load trend prediction function and the multi-dimensional entropy, the uncertainty of the power grid system is quantitatively analyzed based on the multi-dimensional entropy theory, and the resource allocation proportion of the distribution node is adaptively regulated, realizing dynamic load balancing and optimal resource allocation of each distribution node.

Description

Technical Field

[0001] This invention relates to power grid resource optimization technology, and more particularly to a power grid resource optimization method, apparatus, equipment, and medium based on multidimensional entropy theory. Background Technology

[0002] With the continuous growth of electricity demand and the expansion of new energy access, the power distribution network faces more and more challenges in operation. The power system needs to rationally allocate resources among various distribution nodes to meet the load demand of different regions and times. Due to the existence of load fluctuation and uncertainty, the traditional power distribution system faces many limitations in resource allocation and regulation, making it difficult to achieve dynamic balance and efficient management.

[0003] Currently, most power distribution networks rely on fixed dispatching strategies or manual intervention for resource allocation. Fixed dispatching strategies are based on historical experience and set static load allocation rules. When the actual load fluctuates significantly, they are often difficult to respond in a timely manner, leading to overload of some nodes, uneven resource allocation, and even power outages. Manual intervention is not only inefficient but also unable to adapt to complex load changes in real time, increasing the cost of power grid operation and maintenance.

[0004] Some modern power distribution systems have attempted to adopt simple automated control technologies, such as time series analysis-based predictive algorithms, to optimize scheduling. However, these technologies can only handle single-dimensional load changes and fail to fully consider the uncertainties and complexities within the system. In complex power distribution network environments, uncertainties such as fluctuations in renewable energy generation, random changes in user loads, and line faults pose significant challenges to the stable operation of the power grid. Existing automated control technologies often struggle to cope with these uncertainties, resulting in low resource utilization efficiency, significant energy losses, and the potential for local nodes to be overloaded while other nodes remain idle.

[0005] In summary, existing distribution networks have significant shortcomings in load fluctuation management, resource utilization efficiency, and control response speed. Traditional fixed dispatch strategies and simple prediction algorithms cannot fully address the uncertainties in complex power grids, making it difficult to achieve efficient resource allocation and stable node operation. These problems lead to wasted power resources, increased overload risk, and decreased overall grid operating efficiency. Therefore, there is an urgent need for a control method based on more advanced intelligent algorithms to effectively address uncertainties in the power grid and achieve dynamic load balancing and optimal resource allocation at each distribution node. Summary of the Invention

[0006] This invention provides a method, device, equipment, and medium for optimizing power grid resources based on multidimensional entropy theory, so as to achieve dynamic load balancing and optimal resource allocation at each distribution node.

[0007] In a first aspect, the present invention provides a power grid resource optimization method based on multidimensional entropy theory, comprising:

[0008] Acquire real-time operating data and historical load data of each distribution node in the power distribution network;

[0009] The load fluctuation rate, resource utilization rate and power loss rate of the power distribution node are calculated based on the real-time operating data and historical load data of the power distribution node.

[0010] Calculate the entropy values ​​of the load volatility, resource utilization, and power loss rate respectively.

[0011] The multidimensional entropy is obtained by weighted summing of the entropy values ​​of the load volatility, resource utilization, and power loss rate.

[0012] Based on the historical load data, the load of the distribution node is predicted, and a load trend prediction function is generated;

[0013] Based on the real-time operating data, the load trend prediction function, and the multidimensional entropy, the resource allocation ratio of the power distribution node is adaptively adjusted.

[0014] Optionally, the formula for calculating the load fluctuation rate of the distribution node is:

[0015]

[0016] Among them, F var-combined (t,T) represents the load fluctuation rate of the distribution node, P(t) i ) for t i Real-time load value at time t, P(T) j () represents the time period T j Historical load values ​​within, To combine the load average of real-time and historical data, n and m are the total number of real-time data points and historical data points, respectively;

[0017] The formula for calculating the resource utilization rate is as follows:

[0018]

[0019] Where, η combined (t,T) represents the resource utilization rate of the distribution node, P out (t) and P in (t) represent the output power and input power in the real-time operating data, respectively, P out (T) and P in (T) represent the output power and input power in the historical load data, respectively;

[0020] The formula for calculating the power loss rate is:

[0021]

[0022] Among them, L loss-combined (t,T) represents the power loss rate of the distribution node.

[0023] Optionally, the entropy value of the load volatility is calculated using the following formula:

[0024] H1=-∑F var-combined (t,T)log(F var-combined (t,T))

[0025] Where H1 is the entropy value of the load volatility, and F var-combined (t,T) represents the load fluctuation rate of the distribution node;

[0026] The formula for calculating the entropy value of the resource utilization rate is:

[0027] H2=-∑η combined (t,T)log(η combined (t,T))

[0028] Where H2 is the entropy value of resource utilization, and η combined (t,T) represents the resource utilization rate of the power distribution node;

[0029] The formula for calculating the entropy value of the power loss rate is:

[0030] H3=-∑L loss-combined (t,T)log(L loss-combined (t,T))

[0031] Where H3 is the entropy value of the energy loss rate, and L loss-combined (t,T) represents the power loss rate of the distribution node.

[0032] Optionally, based on the historical load data, the load of the distribution node is predicted, and a load trend prediction function is generated, including:

[0033] The historical load data is decomposed into multiple time scales to obtain historical load data at multiple time scales;

[0034] Extract the trend and fluctuation components of the historical load data at different time scales;

[0035] Based on the trend and fluctuation terms of the historical load data at different time scales, predict the predicted values ​​of the trend and fluctuation terms of the historical load data at different time scales at future moments;

[0036] Calculate the multidimensional entropy of the historical load data at different time scales;

[0037] Based on the multidimensional entropy of the historical load data at different time scales and the predicted values ​​of the trend and fluctuation terms of the historical load data at different time scales in the future, a load trend prediction function is generated.

[0038] Optionally, the expression for the load trend prediction function is:

[0039]

[0040] Among them, C load-predict (t+Δt) is the predicted load value, S is the S-th time scale, and E s The multidimensional entropy of the historical load data at the S-th time scale. This represents the predicted value of the trend term at future times. This represents the predicted value of the fluctuation term at future moments.

[0041] Optionally, the mathematical expression for adaptively adjusting the resource allocation ratio of the distribution node based on the real-time operating data, the load trend prediction function, and the multidimensional entropy is as follows:

[0042]

[0043] Among them, M adaptive (t) represents the resource allocation ratio at time t, α i β i and γ i E is the adaptive coefficient. entropy (t) is the multidimensional entropy, D real-time (t) represents real-time running data, C load-predict (t+Δt) is the predicted value of the load.

[0044] Optionally, power grid resource optimization methods based on multidimensional entropy theory also include:

[0045] Determine whether the predicted load value has reached the peak load threshold;

[0046] If so, resource scheduling and control should be carried out in advance.

[0047] Secondly, the present invention also provides a power grid resource optimization device based on multidimensional entropy theory, comprising:

[0048] The data acquisition module is used to acquire real-time operating data and historical load data of each distribution node in the power distribution network;

[0049] The uncertainty index calculation module is used to calculate the load volatility, resource utilization and power loss rate of the power distribution node based on the real-time operating data and the historical load data of the power distribution node.

[0050] The entropy calculation module is used to calculate the entropy value of the load fluctuation rate, the entropy value of the resource utilization rate, and the entropy value of the power loss rate, respectively.

[0051] The weighted summation module is used to weight and sum the entropy values ​​of the load volatility, resource utilization, and power loss rate to obtain multidimensional entropy.

[0052] The load prediction function generation module is used to predict the load of the distribution node based on the historical load data and generate a load trend prediction function.

[0053] The resource allocation and control module adaptively controls the resource allocation ratio of the power distribution node based on the real-time operating data, the load trend prediction function, and the multidimensional entropy.

[0054] Thirdly, the present invention also provides an electronic device, comprising:

[0055] One or more processors;

[0056] Storage device for storing one or more programs;

[0057] When the one or more programs are executed by the one or more processors, the one or more processors implement the power grid resource optimization method based on multidimensional entropy theory as provided in the first aspect of the present invention.

[0058] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power grid resource optimization method based on multidimensional entropy theory as provided in the first aspect of the present invention.

[0059] This invention provides a power grid resource optimization method based on multidimensional entropy theory. It acquires real-time operating data and historical load data of each distribution node in the distribution network. Based on these data, it calculates the load volatility, resource utilization, and power loss rate of the distribution nodes. It then calculates the entropy values ​​of these values ​​separately, and finally weights and sums them to obtain the multidimensional entropy. Based on historical load data, it predicts the load of the distribution nodes, generating a load trend prediction function. Based on real-time operating data, the load trend prediction function, and the multidimensional entropy, it adaptively adjusts the resource allocation ratio of the distribution nodes. Finally, it quantifies the uncertainty of the power grid system based on multidimensional entropy theory and adaptively adjusts the resource allocation ratio of the distribution nodes to achieve dynamic load balancing and optimal resource allocation for each distribution node.

[0060] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 A flowchart illustrating a power grid resource optimization method based on multidimensional entropy theory provided by this invention;

[0063] Figure 2 A schematic diagram of the structure of a power grid resource optimization device based on multidimensional entropy theory provided by the present invention;

[0064] Figure 3 This is a schematic diagram of the structure of an electronic device provided by the present invention.

[0065] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0066] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0067] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention 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 the invention 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 a 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.

[0068] Figure 1 This is a flowchart illustrating a power grid resource optimization method based on multidimensional entropy theory provided by the present invention. This embodiment can perform real-time regulation of distribution node resources based on multidimensional entropy theory. This method can be executed by a power grid resource optimization device based on multidimensional entropy theory provided in this embodiment. This device can be implemented in software and / or hardware, and is typically configured in electronic equipment, such as... Figure 1 As shown, the power grid resource optimization method based on multidimensional entropy theory includes the following steps:

[0069] S101. Obtain real-time operating data and historical load data of each distribution node in the power distribution network.

[0070] In this embodiment of the invention, a sensor network is used to monitor each distribution node in the power distribution network, acquire real-time operating data of the distribution nodes, and construct a real-time operating dataset D for each distribution node. real-time :

[0071] D real-time (t)={P(t),W(t),V(t),I(t),L(t)}

[0072] Where P(t), W(t), V(t), I(t), and L(t) represent the real-time values ​​of the load, power consumption, voltage, current, and line loss of the distribution node at time t, respectively.

[0073] Collect load change data for each distribution node over different time periods to construct a historical load dataset D. historical The historical load data for each node is represented as follows:

[0074] D historical ={P(t)} i )∣t i ∈[t0,t n ]};

[0075] Wherein, P(t)i ) indicates that the distribution node is at time t i Load value at time [t0, t] n [] indicates the time range of historical data.

[0076] Based on time periods, the historical load dataset D historical The data is stored in segments, with each segment containing load changes over a predetermined time period:

[0077] D historical-segmented (T)={P(t i )∣t i ∈[T0,T n ]};

[0078] Among them, T0 and T n These represent the start and end times of each specific time period.

[0079] S102. Calculate the load fluctuation rate, resource utilization rate and power loss rate of the distribution node based on the real-time operation data and historical load data of the distribution node.

[0080] In this embodiment of the invention, real-time operating data and historical load data of the distribution node are comprehensively analyzed to quantify the uncertainty and complexity of the distribution node, thereby obtaining uncertainty indicators. Specifically, the uncertainty indicators include the load volatility, resource utilization rate, and power loss rate of the distribution node.

[0081] For example, in some embodiments of the present invention, the formula for calculating the load fluctuation rate of a distribution node is as follows:

[0082]

[0083] Among them, F var-combined (t,T) represents the load fluctuation rate of the distribution node, P(t) i ) for t i Real-time load value at time t, P(T) j () represents the time period T j Historical load values ​​within, To combine the load average of real-time and historical data, n and m are the total number of real-time data points and historical data points, respectively;

[0084] The formula for calculating resource utilization rate is:

[0085]

[0086] Where, η combined (t,T) represents the resource utilization rate of the distribution node, P out (t) and P in(t) represent the output power and input power in the real-time operating data, respectively, P out (T) and P in (T) represent the output power and input power in the historical load data, respectively;

[0087] The formula for calculating the power loss rate is:

[0088]

[0089] Among them, L loss-combined (t,T) represents the power loss rate of the distribution node.

[0090] S103. Calculate the entropy values ​​of load volatility, resource utilization, and power loss rate respectively.

[0091] In this embodiment of the invention, the entropy values ​​of load volatility, resource utilization, and power loss rate are calculated respectively.

[0092] For example, the formula for calculating the entropy value of load volatility is:

[0093] H1=-∑F var-combined (t,T)log(F var-combined (t,T))

[0094] Where H1 is the entropy value of the load volatility, and F var-combined (t,T) represents the load fluctuation rate of the distribution node;

[0095] The formula for calculating the entropy value of resource utilization rate is:

[0096] H2=-∑η combined (t,T)log(η combined (t,T))

[0097] Where H2 is the entropy value of resource utilization, and η combined (t,T) represents the resource utilization rate of the power distribution node;

[0098] The formula for calculating the entropy value of the power loss rate is:

[0099] H3=-∑L loss-combined (t,T)log(L loss-combined (t,T))

[0100] Where H3 is the entropy value of the energy loss rate, and L loss-combined (t,T) represents the power loss rate of the distribution node.

[0101] S104. The multidimensional entropy is obtained by weighted summing of the entropy values ​​of load volatility, resource utilization, and power loss rate.

[0102] In this embodiment of the invention, the entropy values ​​of load volatility, resource utilization, and energy loss rate are weighted and summed to obtain a multidimensional entropy. The specific weighted summation formula is as follows:

[0103] E entropy (t)=-w1·H1-w2·H2-w3·H3

[0104] Among them, E entropy (t) represents the multidimensional entropy, where w1, w2, and w3 are the weights of the entropy values ​​of load volatility, resource utilization, and power loss rate, respectively.

[0105] S105. Based on historical load data, predict the load of distribution nodes and generate a load trend prediction function.

[0106] In this embodiment of the invention, the load of a distribution node is predicted based on historical load data, and a load trend prediction function is generated. The load trend prediction function can predict the load of the distribution node over a future period of time.

[0107] In some embodiments of the present invention, the process of generating the load trend prediction function is as follows:

[0108] 1. Decompose the segmented historical load dataset into multiple time scales to obtain historical load data at multiple time scales.

[0109] For example, multi-timescale decomposition can be achieved using wavelet decomposition or empirical mode decomposition, and the embodiments of the present invention are not limited thereto.

[0110] 2. Extract the trend term T from historical load data at different time scales. s (t) and fluctuation term F s (t).

[0111] Trend Item T s (t) represents the long-term load variation trend, which is extracted using low-frequency components or principal components. For example, the low-frequency components (approximate coefficients) in wavelet decomposition reflect the overall load trend.

[0112] Fluctuation term F s (t) represents the short-term load fluctuation characteristics, typically described by high-frequency components or noise. Detail coefficients in wavelet decomposition can be used to capture the short-term characteristics of load fluctuations.

[0113] 3. Based on the trend and fluctuation terms of historical load data at different time scales, predict the predicted values ​​of the trend and fluctuation terms of historical load data at different time scales at future moments.

[0114] For example, for trend terms at the same time scale, multiple trend terms are fitted to obtain a fitting function, and the fitted function is used to predict the predicted value of the trend term of historical load data at that time scale at future times. Similarly, for fluctuation terms at the same time scale, multiple fluctuation terms are fitted to obtain a fitting function, and the fitted function is used to predict the predicted value of the fluctuation term of historical load data at that time scale at future times.

[0115] 4. Calculate the multidimensional entropy of historical load data at different time scales.

[0116] In this embodiment of the invention, the multidimensional entropy E of historical load data at different time scales is calculated. s Multidimensional entropy E s The calculation process can refer to the calculation process of multidimensional entropy in the foregoing embodiments, and will not be repeated here in the embodiments of the present invention.

[0117] 5. Generate a load trend prediction function based on the multidimensional entropy of historical load data at different time scales and the predicted values ​​of the trend and fluctuation terms of historical load data at different time scales at future moments.

[0118] In this embodiment of the invention, the uncertainty weights γ at each scale are calculated using multidimensional entropy theory. s The load trend forecast curve is generated by fusing trend and fluctuation terms at various scales. Uncertainty weight γ s The calculation formula is:

[0119]

[0120] Among them, E s Let S represent the multidimensional entropy corresponding to the S-th time scale.

[0121] The expression for the load trend prediction function is:

[0122]

[0123] Among them, C load-predict (t+Δt) is the predicted load value, S is the S-th time scale, and E s The multidimensional entropy of historical load data at the S-th time scale. This represents the predicted value of the trend term at future times. This represents the predicted value of the fluctuation term at future moments.

[0124] S106. Based on real-time operating data, load trend prediction function and multi-dimensional entropy adaptive adjustment of the resource allocation ratio of power distribution nodes.

[0125] In this embodiment of the invention, the resource allocation ratio of the power distribution node is adaptively adjusted based on real-time operating data, load trend prediction function, and multi-dimensional entropy. The specific data expression is as follows:

[0126]

[0127] Among them, M adaptive (t) represents the resource allocation ratio at time t, α i β i and γ i E is the adaptive coefficient. entropy (t) is the multidimensional entropy, D real-time (t) represents real-time running data, C load-predict (t+Δt) is the predicted value of the load.

[0128] In some embodiments of the present invention, based on the generated load trend prediction function C load-predict (t+Δt) is used to assess the potential load peaks and fluctuations at each distribution node in advance, and to identify the critical time points t that may lead to load anomalies. critical :

[0129] t critical ={t∣C load-predict (t)≥P max-threshold}

[0130] Among them, P max-threshold The preset peak load threshold is used when the predicted load C load-predict When (t) exceeds this threshold, it indicates that the power distribution node may face overload risk and resource scheduling and control need to be carried out in advance.

[0131] This invention provides a power grid resource optimization method based on multidimensional entropy theory. It acquires real-time operating data and historical load data of each distribution node in the distribution network. Based on these data, it calculates the load volatility, resource utilization, and power loss rate of the distribution nodes. It then calculates the entropy values ​​of these values ​​separately, and finally weights and sums them to obtain the multidimensional entropy. Based on historical load data, it predicts the load of the distribution nodes, generating a load trend prediction function. Based on real-time operating data, the load trend prediction function, and the multidimensional entropy, it adaptively adjusts the resource allocation ratio of the distribution nodes. Finally, it quantifies the uncertainty of the power grid system based on multidimensional entropy theory and adaptively adjusts the resource allocation ratio of the distribution nodes to achieve dynamic load balancing and optimal resource allocation for each distribution node.

[0132] Figure 2 A schematic diagram of a power grid resource optimization device based on multidimensional entropy theory provided by the present invention is shown below. Figure 2 As shown, the power grid resource optimization device based on multidimensional entropy theory includes:

[0133] The data acquisition module 201 is used to acquire real-time operating data and historical load data of each distribution node in the power distribution network;

[0134] Uncertainty index calculation module 202 is used to calculate the load fluctuation rate, resource utilization rate and power loss rate of the power distribution node based on the real-time operating data and the historical load data of the power distribution node;

[0135] Entropy calculation module 203 is used to calculate the entropy value of the load fluctuation rate, the entropy value of the resource utilization rate, and the entropy value of the power loss rate, respectively.

[0136] The weighted summation module 204 is used to weight and sum the entropy values ​​of the load volatility, resource utilization, and power loss rate to obtain multidimensional entropy.

[0137] The load prediction function generation module 205 is used to predict the load of the distribution node based on the historical load data and generate a load trend prediction function.

[0138] The resource allocation and control module 206 adaptively controls the resource allocation ratio of the power distribution node based on the real-time operating data, the load trend prediction function, and the multidimensional entropy.

[0139] In some embodiments of the present invention, the formula for calculating the load fluctuation rate of the distribution node is as follows:

[0140]

[0141] Among them, F var-combined (t,T) represents the load fluctuation rate of the distribution node, P(t) i ) for t i Real-time load value at time t, P(T) j () represents the time period T j Historical load values ​​within, To combine the load average of real-time and historical data, n and m are the total number of real-time data points and historical data points, respectively;

[0142] The formula for calculating the resource utilization rate is:

[0143]

[0144] Where, η combined (t,T) represents the resource utilization rate of the distribution node, P out (t) and P in (t) represent the output power and input power in the real-time operating data, respectively, Pout (T) and P in (T) represent the output power and input power in the historical load data, respectively;

[0145] The formula for calculating the power loss rate is:

[0146]

[0147] Among them, L loss-combined (t,T) represents the power loss rate of the distribution node.

[0148] In some embodiments of the present invention, the formula for calculating the entropy value of the load volatility is as follows:

[0149] H1=-∑F var-combined (t,T)log(F var-combined (t,T))

[0150] Where H1 is the entropy value of the load volatility, and F var-combined (t,T) represents the load fluctuation rate of the distribution node;

[0151] The formula for calculating the entropy value of the resource utilization rate is:

[0152] H2=-∑η combined (t,T)log(η combined (t,T))

[0153] Where H2 is the entropy value of resource utilization, and η combined (t,T) represents the resource utilization rate of the power distribution node;

[0154] The formula for calculating the entropy value of the energy loss rate is:

[0155] H3=-∑L loss-combined (t,T)log(L loss-combined (t,T))

[0156] Where H3 is the entropy value of the energy loss rate, and L loss-combined (t,T) represents the power loss rate of the distribution node.

[0157] In some embodiments of the present invention, the load prediction function generation module 205 includes:

[0158] The decomposition submodule is used to decompose the historical load data into multiple time scales to obtain historical load data at multiple time scales.

[0159] The extraction submodule is used to extract the trend and fluctuation items of the historical load data at different time scales.

[0160] The prediction submodule is used to predict the trend and fluctuation values ​​of the historical load data at different time scales in the future, based on the trend and fluctuation values ​​of the historical load data at different time scales.

[0161] The multidimensional entropy calculation submodule is used to calculate the multidimensional entropy of the historical load data at different time scales; the prediction function generation submodule is used to generate a load trend prediction function based on the multidimensional entropy of the historical load data at different time scales and the predicted values ​​of the trend and fluctuation terms of the historical load data at different time scales in the future.

[0162] In some embodiments of the present invention, the expression for the load trend prediction function is:

[0163]

[0164] Among them, C load-predict (t+Δt) is the predicted load value, S is the S-th time scale, and E s The multidimensional entropy of the historical load data at the S-th time scale. This represents the predicted value of the trend term at future times. This represents the predicted value of the fluctuation term at future moments.

[0165] In some embodiments of the present invention, the mathematical expression for adaptively adjusting the resource allocation ratio of the distribution node based on the real-time operating data, the load trend prediction function, and the multidimensional entropy is as follows:

[0166]

[0167] Among them, M adaptive (t) represents the resource allocation ratio at time t, α i β i and γ i E is the adaptive coefficient. entropy (t) is the multidimensional entropy, D real-time (t) represents real-time running data, C load-predict (t+Δt) is the predicted value of the load.

[0168] In some embodiments of the present invention, the power grid resource optimization device based on multidimensional entropy theory further includes:

[0169] The judgment module is used to determine whether the predicted value of the load has reached the peak load threshold.

[0170] The scheduling module is used to schedule and regulate resources in advance when the predicted load value reaches the peak load threshold.

[0171] The aforementioned power grid resource optimization device based on multidimensional entropy theory can execute the power grid resource optimization method based on multidimensional entropy theory provided in the foregoing embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the power grid resource optimization method based on multidimensional entropy theory.

[0172] Figure 3 This is a schematic diagram of an electronic device provided by the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0173] like Figure 3 As shown, the electronic device includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0174] Multiple components in the electronic device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0175] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as power grid resource optimization methods based on multidimensional entropy theory.

[0176] In some embodiments, the power grid resource optimization method based on multidimensional entropy theory can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the power grid resource optimization method based on multidimensional entropy theory described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the power grid resource optimization method based on multidimensional entropy theory by any other suitable means (e.g., by means of firmware).

[0177] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0178] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0179] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0180] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0181] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0182] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0183] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the power grid resource optimization method based on multidimensional entropy theory as provided in any embodiment of this application.

[0184] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0185] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and no limitation is imposed herein.

[0186] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A power grid resource optimization method based on multidimensional entropy theory, characterized in that, include: Acquire real-time operating data and historical load data of each distribution node in the power distribution network; The load fluctuation rate, resource utilization rate and power loss rate of the power distribution node are calculated based on the real-time operating data and historical load data of the power distribution node. Calculate the entropy values ​​of the load volatility, resource utilization, and power loss rate respectively. The multidimensional entropy is obtained by weighted summing of the entropy values ​​of the load volatility, resource utilization, and power loss rate. Based on the historical load data, the load of the distribution node is predicted, and a load trend prediction function is generated; Based on the real-time operating data, the load trend prediction function, and the multidimensional entropy, the resource allocation ratio of the power distribution node is adaptively adjusted. The expression for the load trend prediction function is: in, This is the predicted load value. For the S-th time scale, The multidimensional entropy of the historical load data at the S-th time scale. This represents the predicted value of the trend term at future times. This represents the predicted value of the fluctuation term at future moments; The mathematical expression for adaptively adjusting the resource allocation ratio of the distribution node based on the real-time operating data, the load trend prediction function, and the multidimensional entropy is as follows: ； ； in, Let represent the resource allocation ratio of the i-th distribution node at time t. , and For adaptive coefficients, For multidimensional entropy, To run data in real time, This is the predicted value of the load.

2. The power grid resource optimization method based on multidimensional entropy theory according to claim 1, characterized in that, The formula for calculating the load fluctuation rate of the distribution node is as follows: in, For the load fluctuation rate of the distribution node, for Real-time load value at any given moment. For time period Historical load values ​​within, To combine the load average of real-time and historical data, n and m are the total number of real-time data points and historical data points, respectively; The formula for calculating the resource utilization rate is as follows: in, For the resource utilization rate of power distribution nodes, and These represent the output power and input power in the real-time operating data, respectively. and These represent the output power and input power in the historical load data, respectively. The formula for calculating the power loss rate is: in, This represents the power loss rate of the power distribution node.

3. The power grid resource optimization method based on multidimensional entropy theory according to claim 1, characterized in that, The formula for calculating the entropy value of the load volatility is: in, The entropy value of load volatility. For the load fluctuation rate of the distribution node; The formula for calculating the entropy value of the resource utilization rate is: in, The entropy value is the resource utilization rate. Resource utilization rate of power distribution nodes; The formula for calculating the entropy value of the power loss rate is: in, The entropy value is the energy loss rate. This represents the power loss rate of the power distribution node.

4. The power grid resource optimization method based on multidimensional entropy theory according to claim 1, characterized in that, Based on the historical load data, the load of the distribution node is predicted, and a load trend prediction function is generated, including: The historical load data is decomposed into multiple time scales to obtain historical load data at multiple time scales; Extract the trend and fluctuation components of the historical load data at different time scales; Based on the trend and fluctuation terms of the historical load data at different time scales, predict the predicted values ​​of the trend and fluctuation terms of the historical load data at different time scales at future moments; Calculate the multidimensional entropy of the historical load data at different time scales; Based on the multidimensional entropy of the historical load data at different time scales and the predicted values ​​of the trend and fluctuation terms of the historical load data at different time scales in the future, a load trend prediction function is generated.

5. The power grid resource optimization method based on multidimensional entropy theory according to claim 1, characterized in that, Also includes: Determine whether the predicted load value has reached the peak load threshold; If so, resource scheduling and control should be carried out in advance.

6. A power grid resource optimization device based on multidimensional entropy theory, characterized in that, The apparatus for executing the power grid resource optimization method based on multidimensional entropy theory as described in any one of claims 1-5, the apparatus comprising: The data acquisition module is used to acquire real-time operating data and historical load data of each distribution node in the power distribution network; The uncertainty index calculation module is used to calculate the load volatility, resource utilization and power loss rate of the power distribution node based on the real-time operating data and the historical load data of the power distribution node. The entropy calculation module is used to calculate the entropy value of the load fluctuation rate, the entropy value of the resource utilization rate, and the entropy value of the power loss rate, respectively. The weighted summation module is used to weight and sum the entropy values ​​of the load volatility, resource utilization, and power loss rate to obtain multidimensional entropy. The load prediction function generation module is used to predict the load of the distribution node based on the historical load data and generate a load trend prediction function. The resource allocation and control module adaptively controls the resource allocation ratio of the power distribution node based on the real-time operating data, the load trend prediction function, and the multidimensional entropy.

7. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the power grid resource optimization method based on multidimensional entropy theory as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the power grid resource optimization method based on multidimensional entropy theory as described in any one of claims 1-5.

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