Sharing energy storage distributed optimization scheduling method and system

Abstract

The invention discloses a shared energy storage distributed optimization scheduling method and system, and aims at privacy protection requirements of industrial users on energy consumption data, based on a differential privacy data disturbance mechanism, controllable noise is added to sensitive data, a privacy priority mechanism is introduced, optimization weights of different users are dynamically adjusted, and the privacy protection requirements of the industrial users on the energy consumption data are met. The method comprises the steps of realizing flexible balance between privacy protection strength and scheduling economy, constructing a shared energy storage distributed optimization target model considering privacy protection, designing an adaptive optimization algorithm, and gradually approaching an optimal solution through dynamic step length adjustment and weight coordination and iterative optimization to obtain an optimal scheduling optimization scheme. The technical problems that when an existing shared energy storage collaborative scheduling method is used for scheduling shared energy storage, collaborative optimization of industrial user privacy protection and scheduling efficiency is not fully considered due to the fact that the method focuses on improvement of an optimization algorithm or integration of an energy storage system, and the economical efficiency and expandability of the shared energy storage are difficult to improve are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system resource scheduling, and in particular to a method and system for distributed optimization scheduling of shared energy storage. Background Art

[0002] In the power system, the application of distributed optimized shared energy storage technology provides an important means to improve grid flexibility, promote renewable energy consumption, and resolve supply and demand contradictions. Shared energy storage achieves scale benefits by aggregating the energy storage resources of multiple industrial users, reduces individual investment costs, and participates in grid peak-shaving and valley-filling, ancillary services, etc. However, when industrial users participate in the collaborative scheduling of shared energy storage, they often need to share sensitive data such as their electricity load, energy storage status, and production plans. These data involve the core privacy of corporate operations, such as production processes, energy efficiency levels, and business strategies. Traditional collaborative scheduling methods are usually based on centralized optimization and require users to upload all data to a central controller. This not only poses the risk of privacy leakage, but may also limit user participation due to insufficient willingness to share data, affecting the collective benefits of shared energy storage.

[0003] Existing shared energy storage collaborative scheduling methods focus on improving optimization algorithms or integrating energy storage systems when scheduling shared energy storage, but fail to fully consider the collaborative optimization of industrial user privacy protection and scheduling efficiency, making it difficult to improve the economy and scalability of shared energy storage. Summary of the Invention

[0004] The present invention provides a distributed optimization scheduling method and system for shared energy storage, which is used to solve the technical problem that existing shared energy storage collaborative scheduling methods focus on improving optimization algorithms or integrating energy storage systems when scheduling shared energy storage, fail to fully consider the collaborative optimization of industrial user privacy protection and scheduling efficiency, and are difficult to improve the economy and scalability of shared energy storage.

[0005] In view of this, a first aspect of the present invention provides a method for distributed optimization scheduling of shared energy storage, comprising:

[0006] S1. Based on differential privacy, controllable noise is added to the sensitive energy storage data uploaded by industrial users. The privacy priority of industrial users is defined, and a distributed optimization target model for shared energy storage considering privacy protection is constructed. The distributed optimization model for shared energy storage considering privacy protection includes an objective function and constraints.

[0007] S2, configure the adaptive step size and coordination weight of iterative calculation;

[0008] S3: Based on the distributed architecture of shared energy storage and a distributed optimization target model for shared energy storage that takes privacy protection into consideration, the charging and discharging power of each industrial user in each time period is calculated. The charging and discharging power of each industrial user in each time period is encrypted and uploaded to the aggregation node, which then calculates a new global average.

[0009] S4. Determine whether the current number of iterations has reached the maximum number of iterations or whether the relative error of the industrial user has reached the preset accuracy. If so, output the current solution to generate a shared energy storage distributed optimization scheduling plan. Otherwise, add 1 to the number of iterations, and adjust the iteration step and coordination weight based on the adaptive step and coordination weight in the new iteration round, and return to step S3.

[0010] Optionally, the objective function is:

[0011]

[0012]

[0013]

[0014]

[0015] in, is the electricity price of the i-th industrial user in period t, T is the period, is the charging and discharging power of the i-th industrial user in period t, is the privacy priority of the i-th industrial user, is the data sensitivity index of the i-th industrial user, is the number of times the i-th industrial user has refused to share data in history, and are normalization factors, is the weight coefficient, is the privacy deviation penalty function, is the average charge and discharge power of all industrial users in period t, To control the privacy-economy trade-off of power, To control the privacy-economy trade-off of power consumption, is the electricity consumption of the i-th industrial user in period t, is the false power after adding noise, is the false charge and discharge power after adding noise, is the historical maximum change in electricity quantity, is the historical maximum change in power, Budget for privacy, Random noise generated for a Laplace distribution.

[0016] Optionally, the constraints include energy storage operation constraints, power balance constraints, and load transfer constraints;

[0017] The energy storage operation constraints are:

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] in, To improve the charging efficiency of shared energy storage power stations, To share the discharge efficiency of the energy storage power station, is the charging power between the i-th industrial user and the shared energy storage power station during period t, is the discharge power between the i-th industrial user and the shared energy storage power station in period t, For the charging power between industrial users and shared energy storage power stations, The discharge power between industrial users and shared energy storage power stations, is a 0-1 variable indicating the charge and discharge status of the shared energy storage power station. is the minimum capacity of the shared energy storage power station, is the maximum capacity of the shared energy storage power station, is the maximum charging power of the shared energy storage power station, is the maximum discharge power of the shared energy storage power station, N is the total number of industrial users, is the time variation;

[0027] The power balance constraint is:

[0028]

[0029]

[0030]

[0031] in, is the electricity purchase power of the i-th industrial user in period t, is the electricity sales power of the i-th industrial user in period t, is the original electricity load of the i-th industrial user in period t, is the photovoltaic power generation of the i-th industrial user in period t, is the load transfer amount of the i-th industrial user in period t;

[0032] The load transfer constraints are:

[0033]

[0034] in, is the maximum load transfer coefficient.

[0035] Optionally, the formula for solving the charging and discharging power of each industrial user in each time period is:

[0036]

[0037] in, is the charging and discharging power of the i-th industrial user in the k-th iteration period t, is the coordination weight at the kth iteration, is the average charge and discharge power in period t during the k-1th iteration.

[0038] Optionally, the adaptive step size is:

[0039]

[0040] in, is the initial step size, k is the current number of iterations, is the step size after k iterations, is the privacy priority of the i-th industrial user, and N is the total number of industrial users.

[0041] Optionally, the coordination weights are:

[0042]

[0043] in, is the coordination weight at the kth iteration, is the initial coordination weight, k is the current iteration number, is the indicator function, is the privacy priority of the i-th industrial user, and N is the total number of industrial users.

[0044] Optionally, the formula for judging whether the relative error of the industrial user reaches the preset accuracy is:

[0045]

[0046] in, is the charging and discharging power of the i-th industrial user in the k-th iteration period t, is the charging and discharging power of the i-th industrial user in the t period during the k-1th iteration, is the relative error threshold.

[0047] A second aspect of the present invention provides a shared energy storage distributed optimization scheduling system, comprising:

[0048] An optimization model construction module is used to add controllable noise to sensitive energy storage data uploaded by industrial users based on differential privacy, define the privacy priorities of industrial users, and build a shared energy storage distributed optimization target model that considers privacy protection. The shared energy storage distributed optimization model that considers privacy protection includes an objective function and constraints.

[0049] Iterative parameter configuration module, used to configure the adaptive step size and coordination weight of iterative calculation;

[0050] The solution module is used to solve the charging and discharging power of each industrial user in each time period based on the distributed architecture of shared energy storage and the distributed optimization target model of shared energy storage with privacy protection considerations. The charging and discharging power of each industrial user in each time period is encrypted and uploaded to the aggregation node for the aggregation node to calculate a new global average.

[0051] The judgment module is used to determine whether the current number of iterations has reached the maximum number of iterations or whether the relative error of the industrial user has reached the preset accuracy. If so, the current solution is output to generate a shared energy storage distributed optimization scheduling plan. Otherwise, the number of iterations is increased by 1, and the iteration step size and coordination weight are adjusted based on the adaptive step size and coordination weight in the new iteration round, and the solution module is returned to execute.

[0052] Optionally, the objective function is:

[0053]

[0054]

[0055]

[0056]

[0057] in, is the electricity price of the i-th industrial user in period t, T is the period, is the charging and discharging power of the i-th industrial user in period t, is the privacy priority of the i-th industrial user, is the data sensitivity index of the i-th industrial user, is the number of times the i-th industrial user has refused to share data in history, and are normalization factors, is the weight coefficient, is the privacy deviation penalty function, is the average charge and discharge power of all industrial users in period t, To control the privacy-economy trade-off of power, To control the privacy-economy trade-off of power consumption, is the electricity consumption of the i-th industrial user in period t, is the false power after adding noise, is the false charge and discharge power after adding noise, is the historical maximum change in electricity quantity, is the historical maximum change in power, Budget for privacy, Random noise generated for a Laplace distribution.

[0058] Optionally, the constraints include energy storage operation constraints, power balance constraints, and load transfer constraints;

[0059] The energy storage operation constraints are:

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] in, To improve the charging efficiency of shared energy storage power stations, To share the discharge efficiency of the energy storage power station, is the charging power between the i-th industrial user and the shared energy storage power station during period t, is the discharge power between the i-th industrial user and the shared energy storage power station in period t, For the charging power between industrial users and shared energy storage power stations, The discharge power between industrial users and shared energy storage power stations, is a 0-1 variable indicating the charge and discharge status of the shared energy storage power station. is the minimum capacity of the shared energy storage power station, is the maximum capacity of the shared energy storage power station, is the maximum charging power of the shared energy storage power station, is the maximum discharge power of the shared energy storage power station, N is the total number of industrial users, is the time variation;

[0069] The power balance constraint is:

[0070]

[0071]

[0072]

[0073] in, is the electricity purchase power of the i-th industrial user in period t, is the electricity sales power of the i-th industrial user in period t, is the original electricity load of the i-th industrial user in period t, is the photovoltaic power generation of the i-th industrial user in period t, is the load transfer amount of the i-th industrial user in period t;

[0074] The load transfer constraints are:

[0075]

[0076] in, is the maximum load transfer coefficient.

[0077] From the above technical solutions, it can be seen that the shared energy storage distributed optimization scheduling method provided by the present invention has the following advantages:

[0078] The distributed optimization scheduling method for shared energy storage provided by the present invention addresses the privacy protection needs of industrial users for energy consumption data. Based on the data perturbation mechanism of differential privacy, controllable noise is added to sensitive data, and a privacy priority mechanism is introduced to dynamically adjust the optimization weights of different users to achieve a flexible trade-off between privacy protection strength and scheduling economy. A distributed optimization target model for shared energy storage considering privacy protection is constructed. Taking into account the complexity and real-time requirements of industrial scenarios, an adaptive optimization algorithm is designed. Through dynamic step size adjustment and coordination weights, the optimal solution is gradually approached through iterative optimization to obtain the optimal scheduling optimization scheme. This significantly improves the convergence speed and computational efficiency when large-scale industrial users participate, effectively improves the economy and scalability of the shared energy storage system, and provides a solid technical foundation for the widespread application and sustainable development of distributed energy storage technology. This solves the technical problem that the existing shared energy storage collaborative scheduling method focuses on the improvement of the optimization algorithm or the integration of the energy storage system when scheduling shared energy storage, fails to fully consider the collaborative optimization of industrial user privacy protection and scheduling efficiency, and is difficult to improve the economy and scalability of shared energy storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0080] Figure 1 A schematic diagram of a process for distributed optimization scheduling of shared energy storage provided in the present invention;

[0081] Figure 2 A logic block diagram of a shared energy storage distributed optimization scheduling method provided in the present invention;

[0082] Figure 3 This is a structural diagram of a shared energy storage distributed optimization scheduling system provided in the present invention. DETAILED DESCRIPTION

[0083] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0084] For easier understanding, see Figure 1, the present invention provides an embodiment of a method for distributed optimization scheduling of shared energy storage, including:

[0085] Step S1: Based on differential privacy, controllable noise is added to the energy storage sensitive data uploaded by industrial users, and the privacy priority of industrial users is defined. A shared energy storage distributed optimization target model considering privacy protection is constructed. The shared energy storage distributed optimization model considering privacy protection includes an objective function and constraints.

[0086] It should be noted that in this embodiment of the present invention, in response to industrial users' demand for privacy protection of energy consumption data, a data perturbation mechanism based on differential privacy is designed to add controllable noise to sensitive data, ensuring that the data is "available but not visible." This step requires precise analysis of the data types and sensitivities of industrial users, including quantification of the privacy protection strength of key parameters such as energy consumption and power.

[0087] To protect the privacy of industrial users' energy usage data, differential privacy technology is used to add controllable noise to the sensitive energy storage data uploaded by industrial users. To accommodate the differentiated privacy protection needs of different users, user privacy priorities are defined. Ultimately, a distributed optimization target model for shared energy storage is constructed with privacy protection in mind, minimizing user energy costs, protecting user data privacy, and coordinating global system operations.

[0088] The objective function of the distributed optimization model of shared energy storage considering privacy protection is:

[0089]

[0090]

[0091]

[0092]

[0093] in, is the electricity price of the i-th industrial user in period t, T is the period, is the charging and discharging power of the i-th industrial user in period t, is the privacy priority of the i-th industrial user, is the data sensitivity index of the i-th industrial user, is the number of times the i-th industrial user has refused to share data in history, and are normalization factors, is the weight coefficient, is the privacy deviation penalty function, is the average charge and discharge power of all industrial users in period t, To control the privacy-economy trade-off of power, To control the privacy-economy trade-off of power consumption, is the electricity consumption of the i-th industrial user in period t, is the false power after adding noise, is the false charge and discharge power after adding noise, is the historical maximum change in electricity quantity, is the historical maximum change in power, and Used to quantify data sensitivity, Budget for privacy, Random noise generated for a Laplace distribution.

[0094] The industrial user alliance's scheduling decisions throughout the entire operation cycle of shared energy storage need to consider constraints such as energy storage operation constraints, power balance constraints, and load transfer restrictions. Therefore, the constraints of the distributed optimization target model for shared energy storage with privacy protection include energy storage operation constraints, power balance constraints, and load transfer constraints.

[0095] Energy storage needs to meet safe operating conditions and satisfy power constraints and state of charge constraints. Therefore, the energy storage operation constraints are:

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] in, To improve the charging efficiency of shared energy storage power stations, To share the discharge efficiency of the energy storage power station, is the charging power between the i-th industrial user and the shared energy storage power station during period t, is the discharge power between the i-th industrial user and the shared energy storage power station in period t, For the charging power between industrial users and shared energy storage power stations, The discharge power between industrial users and shared energy storage power stations, is a 0-1 variable indicating the charge and discharge status of the shared energy storage power station. is the minimum capacity of shared energy storage, is the maximum capacity of the shared energy storage power station, is the maximum charging power of the shared energy storage power station, is the maximum discharge power of the shared energy storage power station, N is the total number of industrial users, is the time variation;

[0105] The power balance constraint is:

[0106]

[0107]

[0108]

[0109] in, is the electricity purchase power of the i-th industrial user in period t, is the electricity sales power of the i-th industrial user in period t, is the original electricity load of the i-th industrial user in period t, is the photovoltaic power generation of the i-th industrial user in period t, is the load transfer amount of the i-th industrial user in period t;

[0110] When industrial users transfer load, they need to ensure that the total daily load remains unchanged, and the maximum load transfer amount at each moment is limited by the original power load. Therefore, the load transfer constraint is:

[0111]

[0112] in, is the maximum load transfer coefficient.

[0113] Step S2: Configure the adaptive step size and coordination weight of the iterative calculation.

[0114] It should be noted that in the embodiment of the present invention, in order to adapt to different privacy requirement scenarios, the algorithm adopts a dynamic balance strategy, including adaptive step size design and coordination weight (i.e., privacy-efficiency dynamic weight) adjustment.

[0115] The purpose of adaptive step size design is to face high privacy demand scenarios (privacy priority of the i-th industrial user The mean is large) the step size decays quickly to avoid optimization oscillations caused by noise disturbances; in the face of low privacy requirements (privacy priority of the i-th industrial user The mean is small) and a larger step size is maintained to accelerate convergence. In the embodiment of the present invention, the formula for the adaptive step size is:

[0116]

[0117] in, is the initial step size, k is the current number of iterations, is the step size after k iterations, is the privacy priority of the i-th industrial user, and N is the total number of industrial users. is the system average privacy priority, which is used to reflect the overall privacy sensitivity.

[0118] In each iteration, the objective function is dynamically adjusted based on the feedback from industrial users. The more privacy-sensitive users there are, the more emphasis is placed on global coordination. In this embodiment of the present invention, the coordination weight of the charging and discharging power of industrial users in the kth round is calculated as follows:

[0119]

[0120] in, is the coordination weight at the kth iteration, is the initial coordination weight, k is the current iteration number, is the indicator function, is the privacy priority of the i-th industrial user, and N is the total number of industrial users.

[0121] Step S3: Based on the distributed architecture of shared energy storage and the distributed optimization target model of shared energy storage considering privacy protection, the charging and discharging power of each industrial user in each time period is solved, and the charging and discharging power of each industrial user in each time period is encrypted and uploaded to the aggregation node for the aggregation node to calculate a new global average.

[0122] It should be noted that in the embodiment of the present invention, a flexible trade-off between privacy protection and scheduling efficiency is achieved through a privacy priority mechanism and a dynamic balance strategy, and the optimization scheduling problem is efficiently solved. The privacy priority mechanism achieves global coordinated optimization through limited information interaction, while protecting user privacy to the greatest extent. The algorithm adopts a distributed architecture, and each industrial user completes the optimization calculation locally and only needs to upload the necessary encrypted information to the coordination center, avoiding the direct exposure of the original sensitive data. When solving the charging and discharging power of each industrial user in each time period, the i-th industrial user solves the local optimization problem based on the current global average power and privacy priority, and obtains the charging and discharging power of the i-th industrial user in time period t. Specifically, the solution formula for solving the charging and discharging power of each industrial user in each time period is:

[0123]

[0124] in, is the charging and discharging power of the i-th industrial user in the k-th iteration period t, is the coordination weight at the kth iteration, is the average charge and discharge power in period t during the k-1th iteration.

[0125] The i-th industrial user will After encryption, it is uploaded to the aggregation node (aggregation node is blockchain or trusted third party), and the aggregation node calculates the new global average power :

[0126]

[0127] in, is the step size after k-1 iterations.

[0128] Step S4: Determine whether the current number of iterations has reached the maximum number of iterations or whether the relative error of the industrial user has reached the preset accuracy. If so, output the current solution to generate a shared energy storage distributed optimization scheduling plan. Otherwise, add 1 to the number of iterations, and adjust the iteration step and coordination weight based on the adaptive step and coordination weight in the new iteration round, and return to step S3.

[0129] It should be noted that when the iterative algorithm is initialized, the initial charge and discharge power of the i-th industrial user is randomly generated. , set as the privacy priority of the i-th industrial user , privacy budget and the initial global average charge and discharge power After solving the charging and discharging power of each industrial user in each period and calculating the new global average, it is determined whether the current number of iterations has reached the maximum number of iterations (i.e. ) or whether the relative error of industrial users reaches the preset accuracy. If so, the current solution is output to generate a shared energy storage distributed optimization scheduling plan. Otherwise, the number of iterations is increased by 1, and the iteration step size and coordination weight are adjusted based on the adaptive step size and coordination weight in the new iteration round, and the process returns to step S3.

[0130] The formula for judging whether the relative error of industrial users reaches the preset accuracy is:

[0131]

[0132] in, is the charging and discharging power of the i-th industrial user in the k-th iteration period t, is the charging and discharging power of the i-th industrial user in the t period during the k-1th iteration, is the relative error threshold, and its value is .

[0133] In this embodiment of the present invention, a privacy-preserving distributed optimization target model for shared energy storage is constructed. This model uses differential privacy technology to process key data (such as energy consumption and charge / discharge power) from user energy storage devices, effectively protecting user privacy. Furthermore, through distributed optimization and collaborative scheduling mechanisms, it leverages the power complementarity between users to achieve global optimization, improve scheduling efficiency, and enhance adaptability to complex scenarios. This model not only reduces users' initial investment costs but also increases the utilization rate of energy storage devices.

[0134] At the same time, the distributed optimization objective model for shared energy storage with privacy protection provided in the embodiments of the present invention fully considers the physical constraints of energy storage equipment, user production characteristics, and grid operation requirements, ensuring the feasibility and economy of the scheduling scheme. Through the power complementarity characteristics between industrial users, global optimization scheduling is achieved, ensuring the stable operation of the system. By dynamically adjusting the optimization weights of different industrial users, a flexible trade-off between privacy protection strength and scheduling economy is achieved. The adaptive optimization strategy significantly improves the convergence speed and computational efficiency when large-scale users participate.

[0135] The distributed optimization scheduling method for shared energy storage provided by the present invention addresses the privacy protection needs of industrial users for energy consumption data. Based on the data perturbation mechanism of differential privacy, controllable noise is added to sensitive data, and a privacy priority mechanism is introduced to dynamically adjust the optimization weights of different users to achieve a flexible trade-off between privacy protection strength and scheduling economy. A distributed optimization target model for shared energy storage considering privacy protection is constructed. Taking into account the complexity and real-time requirements of industrial scenarios, an adaptive optimization algorithm is designed. Through dynamic step size adjustment and coordination weights, the optimal solution is gradually approached through iterative optimization to obtain the optimal scheduling optimization scheme. This significantly improves the convergence speed and computational efficiency when large-scale industrial users participate, effectively improves the economy and scalability of the shared energy storage system, and provides a solid technical foundation for the widespread application and sustainable development of distributed energy storage technology. This solves the technical problem that the existing shared energy storage collaborative scheduling method focuses on the improvement of the optimization algorithm or the integration of the energy storage system when scheduling shared energy storage, fails to fully consider the collaborative optimization of industrial user privacy protection and scheduling efficiency, and is difficult to improve the economy and scalability of shared energy storage.

[0136] For easier understanding, see Figure 3 The present invention provides an embodiment of a shared energy storage distributed optimization scheduling system, including:

[0137] An optimization model construction module is used to add controllable noise to sensitive energy storage data uploaded by industrial users based on differential privacy, define the privacy priorities of industrial users, and build a shared energy storage distributed optimization target model that considers privacy protection. The shared energy storage distributed optimization model that considers privacy protection includes an objective function and constraints.

[0138] Iterative parameter configuration module, used to configure the adaptive step size and coordination weight of iterative calculation;

[0139] The solution module is used to solve the charging and discharging power of each industrial user in each time period based on the distributed architecture of shared energy storage and the distributed optimization target model of shared energy storage with privacy protection considerations. The charging and discharging power of each industrial user in each time period is encrypted and uploaded to the aggregation node for the aggregation node to calculate a new global average.

[0140] The judgment module is used to determine whether the current number of iterations has reached the maximum number of iterations or whether the relative error of the industrial user has reached the preset accuracy. If so, the current solution is output to generate a shared energy storage distributed optimization scheduling plan. Otherwise, the number of iterations is increased by 1, and the iteration step size and coordination weight are adjusted based on the adaptive step size and coordination weight in the new iteration round, and the solution module is returned to execute.

[0141] In one embodiment, the objective function is:

[0142]

[0143]

[0144]

[0145]

[0146] in, is the electricity price of the i-th industrial user in period t, T is the period, is the charging and discharging power of the i-th industrial user in period t, is the privacy priority of the i-th industrial user, is the data sensitivity index of the i-th industrial user, is the number of times the i-th industrial user has refused to share data in history, and are normalization factors, is the weight coefficient, is the privacy deviation penalty function, is the average charge and discharge power of all industrial users in period t, To control the privacy-economy trade-off of power, To control the privacy-economy trade-off of power consumption, is the electricity consumption of the i-th industrial user in period t, is the false power after adding noise, is the false charge and discharge power after adding noise, is the historical maximum change in electricity quantity, is the historical maximum change in power, Budget for privacy, Random noise generated for a Laplace distribution.

[0147] In one embodiment, the constraints include energy storage operation constraints, power balance constraints, and load transfer constraints;

[0148] The energy storage operation constraints are:

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157] in, To improve the charging efficiency of shared energy storage power stations, To share the discharge efficiency of the energy storage power station, is the charging power between the i-th industrial user and the shared energy storage power station during period t, is the discharge power between the i-th industrial user and the shared energy storage power station in period t, For the charging power between industrial users and shared energy storage power stations, The discharge power between industrial users and shared energy storage power stations, is a 0-1 variable indicating the charge and discharge status of the shared energy storage power station. is the minimum capacity of the shared energy storage power station, is the maximum capacity of the shared energy storage power station, is the maximum charging power of the shared energy storage power station, is the maximum discharge power of the shared energy storage power station, N is the total number of industrial users, is the time variation;

[0158] The power balance constraint is:

[0159]

[0160]

[0161]

[0162] in, is the electricity purchase power of the i-th industrial user in period t, is the electricity sales power of the i-th industrial user in period t, is the original electricity load of the i-th industrial user in period t, is the photovoltaic power generation of the i-th industrial user in period t, is the load transfer amount of the i-th industrial user in period t;

[0163] The load transfer constraints are:

[0164]

[0165] in, is the maximum load transfer coefficient.

[0166] In one embodiment, the formula for solving the charging and discharging power of each industrial user in each time period is:

[0167]

[0168] in, is the charging and discharging power of the i-th industrial user in the k-th iteration period t, is the coordination weight at the kth iteration, is the average charge and discharge power in period t during the k-1th iteration.

[0169] In one embodiment, the adaptive step size is:

[0170]

[0171] in, is the initial step size, k is the current number of iterations, is the step size after k iterations, is the privacy priority of the i-th industrial user, and N is the total number of industrial users.

[0172] In one embodiment, the coordination weights are:

[0173]

[0174] in, is the coordination weight at the kth iteration, is the initial coordination weight, k is the current iteration number, is the indicator function, is the privacy priority of the i-th industrial user, and N is the total number of industrial users.

[0175] In one embodiment, the formula for determining whether the relative error of an industrial user reaches a preset accuracy is:

[0176]

[0177] in, is the charging and discharging power of the i-th industrial user in the k-th iteration period t, is the charging and discharging power of the i-th industrial user in the t period during the k-1th iteration, is the relative error threshold.

[0178] The shared energy storage distributed optimization scheduling system provided by the present invention is used to execute the shared energy storage distributed optimization scheduling method provided by the present invention. Its principles and technical effects are the same as those of the shared energy storage distributed optimization scheduling method provided by the present invention, and will not be repeated here.

[0179] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for distributed optimization scheduling of shared energy storage, characterized in that: include: S1. Based on differential privacy, controllable noise is added to the sensitive energy storage data uploaded by industrial users. The privacy priority of industrial users is defined, and a distributed optimization target model for shared energy storage considering privacy protection is constructed. The distributed optimization model for shared energy storage considering privacy protection includes an objective function and constraints. S2, configure the adaptive step size and coordination weight of iterative calculation; S3: Based on the distributed architecture of shared energy storage and a distributed optimization target model for shared energy storage that takes privacy protection into consideration, the charging and discharging power of each industrial user in each time period is calculated. The charging and discharging power of each industrial user in each time period is encrypted and uploaded to the aggregation node, which then calculates a new global average. S4. Determine whether the current number of iterations has reached the maximum number of iterations or whether the relative error of the industrial user has reached the preset accuracy. If so, output the current solution to generate a shared energy storage distributed optimization scheduling plan. Otherwise, add 1 to the number of iterations, and adjust the iteration step and coordination weight based on the adaptive step and coordination weight in the new iteration round, and return to step S3.

2. The method for distributed optimization scheduling of shared energy storage according to claim 1, characterized in that: The objective function is: in, is the electricity price of the i-th industrial user in period t, T is the period, is the charging and discharging power of the i-th industrial user in period t, is the privacy priority of the i-th industrial user, is the data sensitivity index of the i-th industrial user, is the number of times the i-th industrial user has refused to share data in history, and are normalization factors, is the weight coefficient, is the privacy deviation penalty function, is the average charge and discharge power of all industrial users in period t, To control the privacy-economy trade-off of power, To control the privacy-economy trade-off of power consumption, is the electricity consumption of the i-th industrial user in period t, is the false power after adding noise, is the false charge and discharge power after adding noise, is the historical maximum change in electricity quantity, is the historical maximum change in power, Budget for privacy, Random noise generated for a Laplace distribution.

3. The distributed optimization scheduling method for shared energy storage according to claim 2, characterized in that: Constraints include energy storage operation constraints, power balance constraints, and load transfer constraints; The energy storage operation constraints are: in, To improve the charging efficiency of shared energy storage power stations, To share the discharge efficiency of the energy storage power station, is the charging power between the i-th industrial user and the shared energy storage power station during period t, is the discharge power between the i-th industrial user and the shared energy storage power station in period t, For the charging power between industrial users and shared energy storage power stations, The discharge power between industrial users and shared energy storage power stations, is a 0-1 variable indicating the charge and discharge status of the shared energy storage power station. is the minimum capacity of the shared energy storage power station, is the maximum capacity of the shared energy storage power station, is the maximum charging power of the shared energy storage power station, is the maximum discharge power of the shared energy storage power station, N is the total number of industrial users, is the time variation; The power balance constraint is: in, is the electricity purchase power of the i-th industrial user in period t, is the electricity sales power of the i-th industrial user in period t, is the original electricity load of the i-th industrial user in period t, is the photovoltaic power generation of the i-th industrial user in period t, is the load transfer amount of the i-th industrial user in period t; The load transfer constraints are: in, is the maximum load transfer coefficient.

4. The method for distributed optimization scheduling of shared energy storage according to claim 1, characterized in that: The formula for solving the charging and discharging power of each industrial user in each period is: in, is the charging and discharging power of the i-th industrial user in the k-th iteration period t, is the coordination weight at the kth iteration, is the average charge and discharge power in period t during the k-1th iteration.

5. The method for distributed optimization scheduling of shared energy storage according to claim 1, characterized in that: The adaptive step size is: in, is the initial step size, k is the current number of iterations, is the step size after k iterations, is the privacy priority of the i-th industrial user, and N is the total number of industrial users.

6. The method for distributed optimization scheduling of shared energy storage according to claim 1, characterized in that: The coordination weight is: in, is the coordination weight at the kth iteration, is the initial coordination weight, k is the current iteration number, is the indicator function, is the privacy priority of the i-th industrial user, and N is the total number of industrial users.

7. The method for distributed optimization scheduling of shared energy storage according to claim 1, characterized in that: The formula for judging whether the relative error of industrial users reaches the preset accuracy is: in, is the charging and discharging power of the i-th industrial user in the k-th iteration period t, is the charging and discharging power of the i-th industrial user in the t period during the k-1th iteration, is the relative error threshold.

8. A shared energy storage distributed optimization scheduling system, characterized in that: include: An optimization model construction module is used to add controllable noise to sensitive energy storage data uploaded by industrial users based on differential privacy, define the privacy priorities of industrial users, and build a shared energy storage distributed optimization target model that considers privacy protection. The shared energy storage distributed optimization model that considers privacy protection includes an objective function and constraints. Iterative parameter configuration module, used to configure the adaptive step size and coordination weight of iterative calculation; The solution module is used to solve the charging and discharging power of each industrial user in each time period based on the distributed architecture of shared energy storage and the distributed optimization target model of shared energy storage with privacy protection considerations. The charging and discharging power of each industrial user in each time period is encrypted and uploaded to the aggregation node for the aggregation node to calculate a new global average. The judgment module is used to determine whether the current number of iterations has reached the maximum number of iterations or whether the relative error of the industrial user has reached the preset accuracy. If so, the current solution is output to generate a shared energy storage distributed optimization scheduling plan. Otherwise, the number of iterations is increased by 1, and the iteration step size and coordination weight are adjusted based on the adaptive step size and coordination weight in the new iteration round, and the solution module is returned to execute.

9. The shared energy storage distributed optimization scheduling system according to claim 8, characterized in that: The objective function is: in, is the electricity price of the i-th industrial user in period t, T is the period, is the charging and discharging power of the i-th industrial user in period t, is the privacy priority of the i-th industrial user, is the data sensitivity index of the i-th industrial user, is the number of times the i-th industrial user has refused to share data in history, and are normalization factors, is the weight coefficient, is the privacy deviation penalty function, is the average charge and discharge power of all industrial users in period t, To control the privacy-economy trade-off of power, To control the privacy-economy trade-off of power consumption, is the electricity consumption of the i-th industrial user in period t, is the false power after adding noise, is the false charge and discharge power after adding noise, is the historical maximum change in electricity quantity, is the historical maximum change in power, Budget for privacy, Random noise generated for a Laplace distribution.

10. The shared energy storage distributed optimization scheduling system according to claim 9, characterized in that: Constraints include energy storage operation constraints, power balance constraints, and load transfer constraints; The energy storage operation constraints are: in, To improve the charging efficiency of shared energy storage power stations, To share the discharge efficiency of the energy storage power station, is the charging power between the i-th industrial user and the shared energy storage power station during period t, is the discharge power between the i-th industrial user and the shared energy storage power station in period t, For the charging power between industrial users and shared energy storage power stations, The discharge power between industrial users and shared energy storage power stations, is a 0-1 variable indicating the charge and discharge status of the shared energy storage power station. is the minimum capacity of the shared energy storage power station, is the maximum capacity of the shared energy storage power station, is the maximum charging power of the shared energy storage power station, is the maximum discharge power of the shared energy storage power station, N is the total number of industrial users, is the time variation; The power balance constraint is: in, is the electricity purchase power of the i-th industrial user in period t, is the electricity sales power of the i-th industrial user in period t, is the original electricity load of the i-th industrial user in period t, is the photovoltaic power generation of the i-th industrial user in period t, is the load transfer amount of the i-th industrial user in period t; The load transfer constraints are: in, is the maximum load transfer coefficient.

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