Power distribution optimization system based on homomorphic encryption

Abstract

The invention discloses a homomorphic encryption-based power distribution optimization system, which comprises a client, a distribution optimization server and a power distribution execution end, the client comprises a key generation module, a power demand acquisition module, a power supply electric quantity acquisition module, a homomorphic encryption module and a homomorphic decryption module; wherein the key generation module is used for generating a pair of homomorphic encryption public and private keys; the power demand acquisition module and the power supply capacity acquisition module are respectively used for acquiring a charging pile power load demand and a power generation end power supply capacity; the homomorphic encryption module is used for encrypting the acquired data through a public key; the distribution optimization server is used for analyzing the encrypted data to obtain an encrypted power distribution instruction; the homomorphic decryption module is used for decrypting the power distribution instruction through the private key; and the power distribution execution end is used for performing power distribution on the charging piles according to the decrypted power distribution instruction. According to the system, a homomorphic encryption technology is introduced, and safe and efficient distribution of electric power resources is realized on the premise of ensuring user privacy.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution, and more specifically to an optimized power distribution system based on homomorphic encryption. Background Art

[0002] With the popularization of electric vehicles, charging piles, as key facilities for energy replenishment of electric vehicles, the optimization problem of their power distribution has become increasingly prominent. In the existing power distribution system, a centralized data processing method is usually adopted, that is, collecting the power demand information of all charging piles and the power supply information of the power generation end, and then processing and optimizing on the central server. However, this centralized processing method has the following problems:

[0003] 1. Risk of privacy leakage: The power demand information of charging piles may contain users' privacy data, and the centralized data processing method is prone to privacy leakage.

[0004] 2. Security issues: The central server may become the target of attacks. Once the server is breached, the entire power distribution system will face security risks.

[0005] 3. Real-time issues: The central control system needs to process a large amount of data, with low computing efficiency and difficulty in meeting real-time requirements, resulting in low power distribution efficiency.

[0006] Therefore, how to achieve the safe and efficient distribution of power resources on the premise of ensuring user privacy is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0007] In view of the above problems, the present invention provides an optimized power distribution system based on homomorphic encryption to at least solve some of the technical problems mentioned in the above background art.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] The present invention provides an optimized power distribution system based on homomorphic encryption, including: a client, a distribution optimization server, and a power distribution execution end;

[0010] The client includes: a key generation module, a power demand acquisition module, a power supply acquisition module, a homomorphic encryption module, and a homomorphic decryption module; where:

[0011] The key generation module is used to generate a homomorphic encryption public key and a homomorphic encryption private key;

[0012] The power demand acquisition module is used to acquire the power load demand of each charging pile in the target area;

[0013] The power supply quantity acquisition module is used to acquire the power supply quantity of the power generation end in the target area;

[0014] The homomorphic encryption module is used to encrypt the power load demand and the power supply quantity of the power generation end through the homomorphic encryption public key;

[0015] The allocation optimization server is used to perform power allocation optimization analysis on the encrypted power load demand and power supply quantity of the power generation end to obtain an encrypted power allocation instruction;

[0016] The homomorphic decryption module is used to decrypt the encrypted power allocation instruction through the homomorphic encryption private key;

[0017] The power distribution execution end is used to perform power distribution on each charging pile in the target area according to the decrypted power allocation instruction.

[0018] Further, the key generation module adopts a fully homomorphic encryption algorithm.

[0019] Further, the power load demand includes the current charging power, the remaining time required to complete the current charging task, the valley and peak values of the current time period, the reserved charging time, and the reserved charging quantity.

[0020] Further, the power supply quantity acquisition module includes power supply quantity acquisition sub-modules of at least one power generation system;

[0021] The power generation system includes a thermal power generation system, a hydropower generation system, a wind power generation system, and / or a solar power generation system.

[0022] Further, the power distribution instruction includes the power distribution priority and the charging power to be allocated for each charging pile.

[0023] Further, the allocation optimization server includes an allocation priority calculation sub-module, an allocation power quantity calculation sub-module, and a condition constraint sub-module;

[0024] The allocation priority calculation sub-module is used to obtain the encrypted power distribution priority for each charging pile according to the encrypted power load demand and the power supply quantity of the power generation end;

[0025] The allocation power quantity calculation sub-module is used to obtain the encrypted charging power to be allocated for each charging pile according to the encrypted power supply quantity of the power generation end and the power distribution priority;

[0026] The condition constraint sub-module is used to provide constraint conditions for the calculations of the allocation priority calculation sub-module and the allocation power quantity calculation sub-module.

[0027] Further, the power distribution priority is expressed as:

[0028]

[0029] where priority i represents the power distribution priority of the i-th charging pile; P i represents the current charging power of the i-th charging pile; C represents the valley-peak value in the current period; C = 1 indicates that the current period is a peak electricity consumption period; C = 0 indicates that the current period is a low electricity consumption period; E i represents the reserved charging amount of the i-th charging pile; cp(T′ resv,i ) represents the result of approximating T′ resv,i using Chebyshev polynomials; T′ resv,i represents converting T resv,i to a value between [0, 10]; T resv,i represents the time difference between the reserved charging time of the i-th charging pile and the current time; cp(T′ req,i ) represents the result of approximating T′ req,i using Chebyshev polynomials; T′ req,i represents converting T req,i to a value between [0, 10]; T req,i represents the remaining time required for the i-th charging pile to complete the current charging task; n represents the total number of n charging piles; P j represents the current charging power of the j-th charging pile; cp(G') represents the result of approximating G' using Chebyshev polynomials; G' represents converting G to a value between [0, 10]; G represents the power supply of the power generation end; A represents a scaling factor.

[0030] Furthermore, the to-be-allocated charging power is expressed as:

[0031]

[0032] where P alloc,i represents the to-be-allocated charging power of the i-th charging pile; T n (·) represents Chebyshev polynomials; represents the total sum of the power distribution priorities of all charging piles, and the total sum of the power distribution priorities is within the interval [a, b]; α represents a calculation parameter.

[0033] Furthermore, the constraint conditions include:

[0034] (1) The to-be-allocated charging power of each charging pile is greater than or equal to the minimum charging power and less than or equal to the maximum charging power; expressed as:

[0035] P min,i ≤P alloc,i ≤P max,i

[0036] Among them, P alloc,i represents the actual charging power allocated to the i-th charging pile; P min,i represents the minimum charging power; P max,i represents the minimum charging power;

[0037] (2) The change range of the charging rate of each charging pile is less than the maximum power change rate; expressed as:

[0038] |P alloc,i -P i |≤ΔP max

[0039] Among them, P i represents the current charging power of the i-th charging pile; ΔP max represents the maximum power change rate;

[0040] (3) For the case of reserved charging, during the period from the current moment to the reserved charging moment, the reserved charging amount is allocated to the corresponding charging pile; expressed as:

[0041] E i ≤P alloc,i ×T resv,i

[0042] Among them, E i represents the reserved charging amount of the i-th charging pile; T resv,i represents the time difference between the reserved charging moment of the i-th charging pile and the current moment.

[0043] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a power distribution optimization system based on homomorphic encryption, which has the following beneficial effects:

[0044] 1. By introducing homomorphic encryption technology, the present invention enables the power demand information of the charging piles to be transmitted and processed in an encrypted state, effectively protecting the privacy data of users. And the homomorphic encryption technology allows calculations to be performed on encrypted data, and the power distribution optimization analysis can be completed without decryption, thereby reducing the risk of system attacks.

[0045] 2. By performing power distribution optimization analysis locally, the present invention can respond to the dynamic power demands of the charging piles in real time, improving the efficiency of power distribution.

[0046] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on the provided drawings.

[0048] Figure 1 Schematic diagram of the power distribution optimization system framework based on homomorphic encryption provided by the embodiment of the present invention.

[0049] Figure 2 Schematic diagram of the distribution optimization server framework provided by the embodiment of the present invention. Detailed implementation manners

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0051] The embodiment of the present invention discloses a power distribution optimization system based on homomorphic encryption. Refer to Figure 1 As shown, it includes a client, a distribution optimization server, and a power distribution execution end;

[0052] The client includes: a key generation module, a power demand acquisition module, a power supply amount acquisition module, a homomorphic encryption module, and a homomorphic decryption module; where:

[0053] The key generation module is used to generate a homomorphic encryption public key and a homomorphic encryption private key;

[0054] The power demand acquisition module is used to acquire the power load demands of each charging pile in the target area;

[0055] The power supply amount acquisition module is used to acquire the power supply amount of the power generation end in the target area;

[0056] The homomorphic encryption module is used to encrypt the power load demand and the power supply amount of the power generation end through the homomorphic encryption public key;

[0057] The distribution optimization server is used to perform power distribution optimization analysis on the encrypted power load demand and the power supply amount of the power generation end to obtain an encrypted power distribution instruction;

[0058] The homomorphic decryption module is used to decrypt the encrypted power distribution instruction through the homomorphic encryption private key;

[0059] The power distribution execution end is used to perform power distribution for each charging pile in the target area according to the decrypted power distribution instruction.

[0060] By introducing the homomorphic encryption technology, the system enables the power demand information of the charging piles to be transmitted and processed in an encrypted state, effectively protecting the user's privacy data. And the homomorphic encryption technology allows calculations to be performed on encrypted data, and the power distribution optimization analysis can be completed without decryption, thus reducing the risk of system attacks. In addition, through local power distribution optimization analysis, the present invention can respond in real time to the dynamic power demands of the charging piles, improving the efficiency of power distribution.

[0061] Next, each of the above modules will be described in detail.

[0062] 1. Key generation module

[0063] The key generation module is used to generate a homomorphic encryption public key and a homomorphic encryption private key by using the fully homomorphic encryption algorithm; specifically, the CKKS fully homomorphic encryption algorithm can be used; CKKS (Cheon-Kim-Kim-Song) is an approximate homomorphic encryption (AHE) scheme for real numbers and complex numbers. It allows addition and multiplication operations to be performed on ciphertexts while maintaining data privacy. And the CKKS fully homomorphic encryption algorithm can encrypt multiple messages at one time to form a ciphertext, which is called the batch processing technology. This feature enables it to effectively process a large number of data points. In the embodiments of the present invention, it can effectively handle the access of multiple charging piles and multiple power generation systems to the network.

[0064] 2. Power demand acquisition module

[0065] The power demand acquisition module is used to acquire the power load demands of each charging pile in the target area, specifically including the current charging power, the remaining time required to complete the current charging task, the valley and peak values of the current time period, the reserved charging time, and the reserved charging amount, providing basic data for subsequent power distribution optimization analysis.

[0066] 3. Power supply quantity acquisition module

[0067] The power supply quantity acquisition module is used to acquire the power supply quantity of the power generation end in the target area. Specifically:

[0068] The power supply quantity acquisition module includes at least one power supply quantity acquisition sub-module for a power generation system; that is, for a target area with multiple power generation systems, a power supply quantity acquisition sub-module is configured for each power generation system to obtain the power supply quantity of the power generation system; the power supply quantities of all power generation systems are aggregated into the power supply quantity at the power generation end of the charging pile. Among them, the power generation system includes a thermal power generation system, a hydropower generation system, a wind power generation system, and / or a solar power generation system. The embodiment of the present invention supports the acquisition of the power supply quantity of multiple power generation systems, can adapt to the characteristics of different power generation methods, and thus improves the adaptability of power distribution.

[0069] 4. Homomorphic encryption module

[0070] The homomorphic encryption module is used to encrypt the power load demand and the power supply quantity at the power generation end through the homomorphic encryption public key. The encrypted data can directly participate in the subsequent power distribution optimization calculation without decryption, which helps to protect the privacy of users and also reduces the risk of system attacks.

[0071] 5. Distribution optimization server

[0072] The distribution optimization server is used to perform power distribution optimization analysis on the encrypted power load demand and the power supply quantity at the power generation end to obtain an encrypted power distribution instruction; among them, the power distribution instruction includes the power distribution priority and the charging power to be distributed for each charging pile.

[0073] See Figure 2 As shown, the distribution optimization server includes a distribution priority calculation sub-module, a distribution power quantity calculation sub-module, and a conditional constraint sub-module; specifically:

[0074] (1) Distribution priority calculation sub-module

[0075] The distribution priority calculation sub-module is used to obtain the encrypted power distribution priority for each charging pile according to the encrypted power load demand and the power supply quantity at the power generation end; ensuring the fairness and efficiency of power distribution. Among them, the power distribution priority in plain text is expressed as:

[0076]

[0077] Among them, priority i represents the power distribution priority of the i-th charging pile; P i represents the current charging power of the i-th charging pile; C represents the valley-peak value of the current period; C = 1 indicates that the current period is a peak electricity consumption period; C = 0 indicates that the current period is a low electricity consumption period; E i represents the reserved charging quantity of the i-th charging pile; T resv,i represents the time difference between the reserved charging time of the i-th charging pile and the current time; T req,irepresents the remaining time required for the i-th charging pile to complete the current charging task; n represents the total number of n charging piles; P j represents the current charging power of the j-th charging pile; G represents the power supply from the power generation end;

[0078] In this calculation of power distribution priority:

[0079] The first part considers the balance between the current charging demand and the future reserved charging demand; among them, P i ×(1 - C) represents giving a higher weight during off-peak hours; is the evaluation of the future reserved charging demand, indicating how much energy is needed per hour on average to meet the reservation; among them, T resv,i and T req,i are not zero. In the actual operation process, if the actual values of these two data are 0, for the convenience of calculation, their values can be regarded as 1.

[0080] The second part reflects the impact of the overall system load on a single request. When the total demand of all charging piles approaches or exceeds the supply from the power generation end, this item will reduce the priority of each request, prompting the system to allocate limited resources more carefully.

[0081] Since homomorphic ciphertext cannot perform division operations, therefore, the power distribution priority under the above plaintext is transformed as follows:

[0082] ① Processing

[0083] Convert T resv,i table to a value between [0, 10] (scaled down by A times), denoted as T' resv,i ; then use the Chebyshev polynomial F(x) to approximate to obtain a value of

[0084] ② Processing

[0085] Convert T req,i to a value between [0, 10] (scaled down by A times), denoted as T' req,i ; then use the Chebyshev polynomial F(x) to approximate to obtain a value of

[0086] ③ Processing :

[0087] Convert G to a value between [0, 10] (scaled down by A times), denoted as G'; then use the Chebyshev polynomial F(x) to approximate to obtain a value of

[0088] Obtain the power distribution priority that can be calculated in the ciphertext state, expressed as:

[0089]

[0090] (2) Allocation power calculation sub-module

[0091] This allocation power calculation sub-module is used to obtain the encrypted charging power to be allocated for each charging pile according to the encrypted power supply of the power generation end and the power distribution priority; it optimizes the power resource allocation and improves the power usage efficiency.

[0092] Among them, the charging power to be allocated in plaintext is expressed as:

[0093]

[0094] Among them, P alloc,i represents the charging power to be allocated for the i-th charging pile; represents the total sum of the power distribution priorities of all charging piles.

[0095] Since homomorphic ciphertext cannot perform division operations, therefore, the above charging power to be allocated in plaintext is transformed as follows:

[0096] ① Select the interval: Assume is within the interval [a, b];

[0097] ② Transform the variable: Transform to the interval [-1, 1], that is:

[0098]

[0099] Among them, α is a calculation parameter, representing the constant after preprocessing, that is

[0100] ③ Construct the Chebyshev polynomial: Use the Chebyshev polynomial T n (y) to approximate

[0101] ④ Inverse transformation: Transform y back to the original interval [a, b] of, that is:

[0102]

[0103] Finally, obtain the charging power to be allocated that can be calculated in the ciphertext state, expressed as:

[0104]

[0105] (3) Conditional constraint sub-module

[0106] The conditional constraint sub-module is used to provide constraint conditions for the calculations of the above-mentioned priority assignment calculation sub-module and power allocation calculation sub-module, ensuring the rationality and safety of power distribution, and preventing system overload and resource waste. Among them, the constraint conditions specifically include:

[0107] (1) The charging power to be allocated to each charging pile is greater than or equal to the minimum charging power and less than or equal to the maximum charging power, expressed as:

[0108] P min,i ≤P alloc,i ≤P max,i

[0109] Among them, P alloc,i represents the actual charging power allocated to the i-th charging pile; P min,i represents the minimum charging power; P max,i represents the maximum charging power;

[0110] (2) The change range of the charging rate of each charging pile is less than the maximum power change rate, expressed as:

[0111] |P alloc,i -P i |≤ΔP max

[0112] Among them, P i represents the current charging power of the i-th charging pile; ΔP max represents the maximum power change rate;

[0113] (3) For the case of reserved charging, from the current moment to the reserved charging moment, the reserved charging amount is allocated to the corresponding charging pile, expressed as:

[0114] E i ≤P alloc,i ×T resv,i

[0115] Among them, E i represents the reserved charging amount of the i-th charging pile; T resv,i represents the time difference between the reserved charging moment of the i-th charging pile and the current moment;

[0116] (4) The total power allocated to all charging piles is less than or equal to the power supply of the power generation end.

[0117] 6. Homomorphic decryption module

[0118] The homomorphic decryption module is used to decrypt the encrypted power distribution instruction with the homomorphic encryption private key, restore the real power distribution instruction, so that the instruction can be understood and executed by the charging pile, and at the same time ensure the security of the decryption process.

[0119] 7. Power distribution execution end

[0120] This power distribution execution end is used to distribute power to each charging pile in the target area according to the decrypted power distribution instruction, which helps to achieve the effective management of power resources, improve the working efficiency of the charging pile and user satisfaction.

[0121] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other.

[0122] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A power distribution optimization system based on homomorphic encryption, characterized in that, It includes a client, an allocation optimization server, and a power distribution execution terminal; The client includes: a key generation module, a power demand acquisition module, a power supply quantity acquisition module, a homomorphic encryption module, and a homomorphic decryption module; where: The key generation module is used to generate a homomorphic encryption public key and a homomorphic encryption private key; The power demand acquisition module is used to acquire the power load demands of each charging pile in the target area; The power supply quantity acquisition module is used to acquire the power supply quantity of the power generation end in the target area; The homomorphic encryption module is used to encrypt the power load demand and the power supply quantity of the power generation end through the homomorphic encryption public key; The allocation optimization server is used to perform power distribution optimization analysis on the encrypted power load demand and the power supply quantity of the power generation end to obtain an encrypted power distribution instruction; The homomorphic decryption module is used to decrypt the encrypted power distribution instruction through the homomorphic encryption private key; The power distribution execution terminal is used to perform power distribution on each charging pile in the target area according to the decrypted power distribution instruction.

2. The power distribution optimization system based on homomorphic encryption according to claim 1, characterized in that The key generation module adopts a fully homomorphic encryption algorithm.

3. The power distribution optimization system based on homomorphic encryption according to claim 1, characterized in that, The power load demand includes the current charging power, the remaining time required to complete the current charging task, the valley and peak values at the current time period, the reserved charging time, and the reserved charging quantity.

4. The power distribution optimization system based on homomorphic encryption according to claim 1, characterized in that The power supply quantity acquisition module includes a power supply quantity acquisition sub-module for at least one power generation system; The power generation system includes a thermal power generation system, a hydropower generation system, a wind power generation system, and / or a solar power generation system.

5. The power distribution optimization system based on homomorphic encryption according to claim 1, characterized in that, The power distribution instruction includes the power distribution priority and the charging power to be allocated for each charging pile.

6. The power distribution optimization system based on homomorphic encryption according to claim 5, wherein, The allocation optimization server includes an allocation priority calculation sub-module, an allocation power quantity calculation sub-module, and a condition constraint sub-module; The allocation priority calculation sub-module is used to obtain the encrypted power distribution priority for each charging pile according to the encrypted power load demand and the power supply quantity of the power generation end; The allocation power quantity calculation sub-module is used to obtain the encrypted charging power to be allocated for each charging pile according to the encrypted power supply quantity of the power generation end and the power distribution priority; The condition constraint sub-module is used to provide constraint conditions for the calculations of the allocation priority calculation sub-module and the allocation power quantity calculation sub-module.

7. The power distribution optimization system based on homomorphic encryption according to claim 6, characterized in that, The power distribution priority is expressed as: Among them, priority i represents the power distribution priority of the i-th charging pile; P i represents the current charging power of the i-th charging pile; C represents the valley-peak value in the current period; C = 1 indicates that the current period is a peak electricity consumption period; C = 0 indicates that the current period is a low electricity consumption period; E i represents the reserved charging amount of the i-th charging pile; cp(T′ resv,i ) represents the result of approximating T′ resv,i using Chebyshev polynomials; T′ resv,i represents converting T resv,i to a value between [0, 10]; T resv,i represents the time difference between the reserved charging time of the i-th charging pile and the current time; cp(T′ req,i ) represents the result of approximating T′ req,i using Chebyshev polynomials; T′ req,i represents converting T req,i to a value between [0, 10]; T req,i represents the remaining time required for the i-th charging pile to complete the current charging task; n represents that there are n charging piles in total; Pj represents the current charging power of the j-th charging pile; cp(G') represents the result of approximating G' using Chebyshev polynomials; G' represents converting G to a value between [0, 10]; G represents the power supply of the power generation end; A represents the scaling factor.

8. The power distribution optimization system based on homomorphic encryption according to claim 7, characterized in that, The charging power to be allocated is expressed as: Among them, P alloc,i represents the charging power to be allocated for the i-th charging pile; T n (·) represents the Chebyshev polynomial; represents the total power distribution priority of all charging piles, and the total power distribution priority is within the interval [a, b]; α represents the calculation parameter.

9. The power distribution optimization system based on homomorphic encryption according to claim 8, wherein The constraint conditions include: (1) The charging power to be allocated for each charging pile is greater than or equal to the minimum charging power and less than or equal to the maximum charging power; expressed as: P min,i ≤P alloc,i ≤P max,i Among them, P alloc,i represents the actual charging power allocated to the i-th charging pile; P min,i represents the minimum charging power; P max,i represents the minimum charging power; (2) The change range of the charging rate of each charging pile is less than the maximum power change rate; expressed as: |P alloc,i -P i | ≤ ΔP max Among them, P i represents the current charging power of the i-th charging pile; ΔP max represents the maximum power change rate; (3) For the case of reserved charging, during the period from the current time to the reserved charging time, the reserved charging quantity is allocated to the corresponding charging pile; expressed as: E i ≤P alloc,i ×T resv,i Among them, E i represents the reserved charging amount of the i-th charging pile; T resv,i represents the time difference between the reserved charging time of the i-th charging pile and the current time.

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