A distributed smart grid hierarchical optimization scheduling method and system

Abstract

The application provides a distributed smart grid hierarchical optimization scheduling method and system, which obtains system input data and distribution network input data, and iteratively optimizes and solves a pre-constructed upper system optimization model and a lower distribution network optimization model to obtain a target optimization scheduling result by taking the system input data and the distribution network input data as inputs. Compared with a traditional distribution network optimization operation model, the upper system optimization model and the lower distribution network optimization model considering operation risks are pre-constructed multi-target double optimization models, which comprehensively consider multiple benefit subjects of distribution system levels and distribution network levels, fully consider the schedulable resources of the system, and ensure the safe and economic operation of the system.

Description

Technical Field

[0001] This invention relates to the field of power grid optimization operation, and specifically to a distributed smart grid hierarchical optimization scheduling method and system. Background Technology

[0002] With the rapid development of smart distribution networks in recent years, a new type of intelligent distribution system characterized by multi-party coordination and interaction, participation of multiple stakeholders, and high integration of physical and information systems has gradually taken shape. Due to the large-scale integration of distributed renewable energy sources, the distribution network is gradually transitioning to an active state, and the random fluctuations and intermittency of renewable energy output bring a series of safety and stability issues to the system.

[0003] Most existing power grid optimization operation models only consider the economic indicators of the power grid or simply convert operational risks into risk costs for economic research, and rarely consider the multiple types of operational risk indicators of the power grid.

[0004] Therefore, it is of great significance to comprehensively consider the various types of operational risks of the power grid and ensure the stable operation of the power grid system. Summary of the Invention

[0005] To address the issue that most existing technologies focus on economic dispatch without comprehensively considering the power grid's security indicators and the requirements of multiple stakeholders in the system, this invention proposes a distributed smart grid hierarchical optimization dispatch method and system.

[0006] Firstly, a hierarchical optimization scheduling method for distributed smart grids is provided, including:

[0007] Acquire system input data and distribution network input data, wherein the system input data includes grid operation cost data and grid over-limit risk data, and the distribution network input data includes load compensation cost data and energy storage cost data of each distribution network;

[0008] Using the system input data and the distribution network input data as input, the pre-constructed upper-level system optimization model and lower-level distribution network optimization model are iteratively optimized and solved to obtain the target optimization scheduling result;

[0009] The upper-level system optimization model is constructed with the goal of minimizing the operating cost and grid over-limit risk within the power grid system scheduling cycle, and the output is the coordinated scheduling scheme of the distribution network.

[0010] The lower-level distribution network optimization model is constructed with the goal of minimizing the operating cost of each distribution network and outputs the distribution network scheduling scheme of each distribution network.

[0011] Preferably, the process of constructing the upper-level system optimization model includes:

[0012] A distributed smart grid is constructed based on the smart grid to be optimized, wherein the distributed smart grid includes multiple distribution networks containing new energy sources, energy storage devices and flexible loads;

[0013] The first system objective function of the upper-level system optimization model is constructed based on the system input data to minimize the operating cost within the dispatch cycle of the distributed smart grid.

[0014] The second system objective function of the upper-level system optimization model is constructed based on the lowest risk of exceeding the limit during the dispatch cycle of the distributed smart grid, and system constraints are constructed for the first system objective function and the second system objective function.

[0015] The upper-level system optimization model outputs the coordinated scheduling scheme of the distribution network and iteratively optimizes it based on the distribution network scheduling scheme of the lower-level distribution network optimization model.

[0016] Preferably, the objective function of the first system is calculated as follows:

[0017] minF1=C1+C2+C3

[0018] Among them, F1 is the operating cost within the system scheduling cycle; C1 is the net cost of purchasing electricity from the upper-level power grid; C2 is the cost of purchasing electricity from distributed power operators; and C3 is the network loss cost, which is calculated based on the distribution network scheduling scheme of each distribution network.

[0019] Preferably, the objective function of the second system is calculated as follows:

[0020] minF2=K SVR

[0021] Where F2 represents the risk of exceeding operational limits during the system scheduling cycle, and K SVR This refers to the system's risk index value for exceeding operational limits.

[0022] Preferably, the construction process of the lower-level distribution network optimization model includes:

[0023] Based on the coordinated scheduling scheme of the distribution network output by the upper-level system optimization model and the distribution network input data of each distribution network, the distribution network objective function of the lower-level distribution network optimization model is constructed with the lowest cost of the distribution network in the distributed smart grid, and distribution network constraints are constructed for the distribution network objective function.

[0024] The lower-level distribution network optimization model is constructed based on the distribution network and takes the input data of the distribution network as input.

[0025] Preferably, the objective function of the power distribution network is calculated as follows:

[0026] minF3=C ESS +C cut +C sl

[0027] Where F3 is the objective function of each distribution network; C ESS The charging and discharging cost of energy storage is calculated based on the coordinated scheduling scheme; C cut To compensate for the reduced load; C sl This is the cost of compensating for transferable loads.

[0028] Preferably, the system constraints and distribution network constraints include at least one or more of the following: line power flow constraints, controllable load constraints, distributed power source operation constraints, energy storage operation constraints, and system security constraints.

[0029] Preferably, the power grid purchase cost data includes the net power purchase cost from the upstream power grid, the power purchase cost from distributed power operators, and network loss costs;

[0030] The power grid over-limit risk data includes system operation over-limit risk index values;

[0031] The load compensation cost data for each distribution network includes compensation costs for load reduction and compensation costs for load transfer.

[0032] The energy storage cost data for each distribution network includes the charging and discharging cost data of the energy storage devices.

[0033] Preferably, the step of iteratively optimizing and solving the pre-constructed upper-level system optimization model and lower-level distribution network optimization model using the system input data and distribution network input data as input to obtain the target optimization scheduling result includes:

[0034] Based on a preset distributed optimization scheduling strategy, the system input data is input into the pre-built upper-level system optimization model for coordinated optimization to generate a coordinated scheduling scheme for the power distribution network;

[0035] Based on the coordination and scheduling scheme of the distribution network, optimize the objective function of each distribution network, and optimize and solve the pre-constructed lower-level distribution network optimization model based on the optimized objective function and the input data of the distribution network, so as to generate the distribution network scheduling scheme of each distribution network.

[0036] The distribution network scheduling schemes of each distribution network are fed back to the upper-level system optimization model for further coordination and optimization, generating the next coordination and scheduling scheme for the distribution network. This process continues until a preset number of iterations is met, at which point the distributed distribution network scheduling schemes corresponding to each distribution network are obtained.

[0037] The distributed distribution network scheduling schemes corresponding to each of the aforementioned distribution networks are used as the target optimization scheduling results.

[0038] Preferably, the optimization of the pre-constructed lower-level distribution network optimization model based on the optimized distribution network objective function and the input data of the distribution network, to generate a distribution network scheduling scheme for each distribution network, includes:

[0039] According to the preset distributed optimization scheduling strategy, the distribution networks in the distributed smart grid are decoupled based on the augmented Lagrange function;

[0040] Given that each decoupled distribution network satisfies the preset boundary constraints, the pre-constructed lower-level distribution network optimization model is optimized and solved using the alternating direction multiplier algorithm and the optimized distribution network objective function based on the input data of the distribution network, thereby generating a distribution network scheduling scheme for each distribution network.

[0041] Preferably, the step of feeding back the distribution network scheduling scheme of each distribution network to the upper-level system optimization model for further coordination and optimization, generating the next coordination and scheduling scheme for the distribution network, until a preset number of iterations is met, and then obtaining the distributed distribution network scheduling scheme corresponding to each distribution network, includes:

[0042] The power distribution network scheduling scheme of each power distribution network is fed back to the upper-level system optimization model;

[0043] Based on the distribution network scheduling schemes of each distribution network, the first system objective function and the second system objective function are optimized, and the next coordination and scheduling scheme of the distribution network is generated based on the optimized first system objective function and the optimized second system objective function.

[0044] The next coordination and scheduling scheme is fed back to the lower-level distribution network optimization model for iterative optimization. This process is repeated alternately until a preset number of iterations is met, and then the distributed distribution network scheduling scheme corresponding to each distribution network is obtained.

[0045] Preferably, after generating the target optimized scheduling result, the method further includes:

[0046] Determine the current operating status of the distributed smart grid, and when the current operating status of the distributed smart grid is determined to be a normal operating status, acquire the real-time operating data of the distributed smart grid;

[0047] Risk assessment is performed based on the real-time operation data of the distributed smart grid, and risk decision-making is carried out based on the risk assessment results.

[0048] When the current operating state of the distributed smart grid is determined to be an abnormal operating state, abnormal operating information is obtained, and an abnormal control strategy is determined based on the abnormal operating information to eliminate the abnormal operation.

[0049] Preferably, the risk decision-making process based on the risk assessment results includes:

[0050] When the risk assessment result indicates that the distributed smart grid is in a risky state, a risk defense control decision is initiated, and the risky state is lifted based on the risk defense control decision.

[0051] When the risk assessment result indicates that the distributed smart grid is not in a risky state, an emergency plan is formulated based on the risk coefficient of the equipment in the distributed smart grid, and the risk defense database is updated based on the emergency plan.

[0052] Preferably, the step of determining anomaly control strategies based on the abnormal operation information to eliminate abnormal operation includes:

[0053] Based on the abnormal operation information, identify the abnormal component and disconnect the switch corresponding to the abnormal component;

[0054] Determine whether there is an emergency plan in the risk defense database that matches the abnormal operation information;

[0055] If so, retrieve the emergency response plan and perform anomaly elimination based on the emergency response plan;

[0056] If not, then extract abnormal features based on the abnormal operation information, generate an abnormality elimination decision based on the abnormal features, and perform abnormality elimination based on the abnormality elimination decision.

[0057] Secondly, this application provides a distributed smart grid hierarchical optimization scheduling system, comprising:

[0058] The acquisition module is used to acquire system input data and distribution network input data. The system input data includes grid operation cost data and grid over-limit risk data. The distribution network input data includes load compensation cost data and energy storage cost data for each distribution network.

[0059] The optimization module is used to iteratively optimize and solve the pre-constructed upper-level system optimization model and lower-level distribution network optimization model using the system input data and distribution network input data as inputs, so as to obtain the target optimization scheduling result;

[0060] The upper-level system optimization model is constructed with the goal of minimizing the operating cost and grid over-limit risk within the power grid system scheduling cycle, and the output is the coordinated scheduling scheme of the distribution network.

[0061] The lower-level distribution network optimization model is constructed with the goal of minimizing the operating cost of each distribution network and outputs the distribution network scheduling scheme of each distribution network.

[0062] Preferably, the process of constructing the upper-level system optimization model in the optimization module includes:

[0063] A distributed smart grid is constructed based on the smart grid to be optimized, wherein the distributed smart grid includes multiple distribution networks containing new energy sources, energy storage devices and flexible loads;

[0064] The first system objective function of the upper-level system optimization model is constructed based on the system input data to minimize the operating cost within the dispatch cycle of the distributed smart grid.

[0065] The second system objective function of the upper-level system optimization model is constructed based on the lowest risk of exceeding the limit during the dispatch cycle of the distributed smart grid, and system constraints are constructed for the first system objective function and the second system objective function.

[0066] The upper-level system optimization model outputs the coordinated scheduling scheme of the distribution network and iteratively optimizes it based on the distribution network scheduling scheme of the lower-level distribution network optimization model.

[0067] Preferably, the objective function of the first system is calculated as follows:

[0068] minF1=C1+C2+C3

[0069] Among them, F1 is the operating cost within the system scheduling cycle; C1 is the net cost of purchasing electricity from the upper-level power grid; C2 is the cost of purchasing electricity from distributed power operators; and C3 is the network loss cost, which is calculated based on the distribution network scheduling scheme of each distribution network.

[0070] Preferably, the objective function of the second system is calculated as follows:

[0071] minF2=K SVR

[0072] Where F2 represents the risk of exceeding operational limits during the system scheduling cycle, and K SVR This refers to the system's risk index value for exceeding operational limits.

[0073] Preferably, the construction process of the lower-level distribution network optimization model in the optimization module includes:

[0074] Based on the coordinated scheduling scheme of the distribution network output by the upper-level system optimization model and the distribution network input data of each distribution network, the distribution network objective function of the lower-level distribution network optimization model is constructed with the lowest cost of the distribution network in the distributed smart grid, and distribution network constraints are constructed for the distribution network objective function.

[0075] The lower-level distribution network optimization model is constructed based on the distribution network and takes the input data of the distribution network as input.

[0076] Preferably, the objective function of the power distribution network is calculated as follows:

[0077] minF3=C ESS +C cut +C sl

[0078] Where F3 is the objective function of each distribution network; C ESS The charging and discharging cost of energy storage is calculated based on the coordinated scheduling scheme; C cut To compensate for the reduced load; C sl This is the cost of compensating for transferable loads.

[0079] Preferably, the system constraints and distribution network constraints include at least one or more of the following: line power flow constraints, controllable load constraints, distributed power source operation constraints, energy storage operation constraints, and system security constraints.

[0080] Preferably, the power grid purchase cost data in the acquisition module includes the net power purchase cost from the upper-level power grid, the power purchase cost from distributed power operators, and the network loss cost;

[0081] The power grid over-limit risk data includes system operation over-limit risk index values;

[0082] The load compensation cost data for each distribution network includes compensation costs for load reduction and compensation costs for load transfer.

[0083] The energy storage cost data for each distribution network includes the charging and discharging cost data of the energy storage devices.

[0084] Preferably, the optimization module is further configured to:

[0085] Based on a preset distributed optimization scheduling strategy, the system input data is input into the pre-built upper-level system optimization model for coordinated optimization to generate a coordinated scheduling scheme for the power distribution network;

[0086] Based on the coordination and scheduling scheme of the distribution network, optimize the objective function of each distribution network, and optimize and solve the pre-constructed lower-level distribution network optimization model based on the optimized objective function and the input data of the distribution network, so as to generate the distribution network scheduling scheme of each distribution network.

[0087] The distribution network scheduling schemes of each distribution network are fed back to the upper-level system optimization model for further coordination and optimization, generating the next coordination and scheduling scheme for the distribution network. This process continues until a preset number of iterations is met, at which point the distributed distribution network scheduling schemes corresponding to each distribution network are obtained.

[0088] The distributed distribution network scheduling schemes corresponding to each of the aforementioned distribution networks are used as the target optimization scheduling results.

[0089] Preferably, the optimization module is further configured to:

[0090] According to the preset distributed optimization scheduling strategy, the distribution networks in the distributed smart grid are decoupled based on the augmented Lagrange function;

[0091] Given that each decoupled distribution network satisfies the preset boundary constraints, the pre-constructed lower-level distribution network optimization model is optimized and solved using the alternating direction multiplier algorithm and the optimized distribution network objective function based on the input data of the distribution network, thereby generating a distribution network scheduling scheme for each distribution network.

[0092] Preferably, the optimization module is further configured to:

[0093] The power distribution network scheduling scheme of each power distribution network is fed back to the upper-level system optimization model;

[0094] Based on the distribution network scheduling schemes of each distribution network, the first system objective function and the second system objective function are optimized, and the next coordination and scheduling scheme of the distribution network is generated based on the optimized first system objective function and the optimized second system objective function.

[0095] The next coordination and scheduling scheme is fed back to the lower-level distribution network optimization model for iterative optimization. This process is repeated alternately until a preset number of iterations is met, and then the distributed distribution network scheduling scheme corresponding to each distribution network is obtained.

[0096] Preferably, the system further includes:

[0097] Prevention module, used for:

[0098] Determine the current operating status of the distributed smart grid, and when the current operating status of the distributed smart grid is determined to be a normal operating status, acquire the real-time operating data of the distributed smart grid;

[0099] Risk assessment is performed based on the real-time operation data of the distributed smart grid, and risk decision-making is carried out based on the risk assessment results.

[0100] When the current operating state of the distributed smart grid is determined to be an abnormal operating state, abnormal operating information is obtained, and an abnormal control strategy is determined based on the abnormal operating information to eliminate the abnormal operation.

[0101] Preferably, the prevention module is further used for:

[0102] When the risk assessment result indicates that the distributed smart grid is in a risky state, a risk defense control decision is initiated, and the risky state is lifted based on the risk defense control decision.

[0103] When the risk assessment result indicates that the distributed smart grid is not in a risky state, an emergency plan is formulated based on the risk coefficient of the equipment in the distributed smart grid, and the risk defense database is updated based on the emergency plan.

[0104] Preferably, the prevention module is further used for:

[0105] Based on the abnormal operation information, identify the abnormal component and disconnect the switch corresponding to the abnormal component;

[0106] Determine whether there is an emergency plan in the risk defense database that matches the abnormal operation information;

[0107] If so, retrieve the emergency response plan and perform anomaly elimination based on the emergency response plan;

[0108] If not, then extract abnormal features based on the abnormal operation information, generate an abnormality elimination decision based on the abnormal features, and perform abnormality elimination based on the abnormality elimination decision.

[0109] In another aspect, this application also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0110] The memory is used to store one or more programs;

[0111] When the one or more programs are executed by the at least one processor, a distributed smart grid hierarchical optimization scheduling method as described above is implemented.

[0112] In another aspect, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements a distributed smart grid hierarchical optimization scheduling method as described above.

[0113] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0114] This invention provides a hierarchical optimization scheduling method and system for distributed smart grids. The method acquires system input data and distribution network input data, and uses these as inputs to iteratively optimize and solve pre-constructed upper-level system optimization models and lower-level distribution network optimization models to obtain the target optimization scheduling result. The upper-level system optimization model is constructed with the objective of minimizing the operating cost and grid over-limit risk within the grid system scheduling cycle, and outputs a coordinated scheduling scheme for the distribution network. The lower-level distribution network optimization model is constructed with the objective of minimizing the operating cost of each distribution network, and outputs a distribution network scheduling scheme for each distribution network. Compared to traditional distribution network optimization operation models, the upper-level system optimization model and lower-level distribution network optimization model proposed in this invention, which consider operational risks, comprehensively consider the multiple stakeholders at both the distribution system level and each distribution network level through a pre-constructed multi-objective dual-optimization model, fully considering the system's schedulable resources and ensuring the safe and economical operation of the system. Attached Figure Description

[0115] Figure 1 This is a flowchart of the distributed smart grid hierarchical optimization scheduling method of the present invention;

[0116] Figure 2 This is a schematic diagram of the flexible interaction mechanism of the distributed smart grid source-grid-load-storage in the distributed smart grid hierarchical optimization scheduling method of the present invention.

[0117] Figure 3 This is a schematic diagram illustrating the partitioning decoupling of the distributed optimization strategy implementation of the hierarchical optimization scheduling method for distributed smart grids of the present invention.

[0118] Figure 4 This is a schematic diagram of the distributed optimization solution results of the distributed smart grid hierarchical optimization scheduling method of the present invention;

[0119] Figure 5 This diagram shows the optimized scheduling results of each distribution network in the hierarchical optimized scheduling method of the distributed smart grid of the present invention.

[0120] Figure 6 This is a flowchart illustrating the operational risk prevention method of the distributed smart grid hierarchical optimization scheduling method of the present invention.

[0121] Figure 7 This is a schematic diagram of the distributed smart grid hierarchical optimization scheduling system of the present invention;

[0122] Figure 8 This is a schematic diagram of an electronic device structure according to the present invention. Detailed Implementation

[0123] This invention proposes a hierarchical optimization scheduling method and system for distributed smart grids. This method acquires system input data and distribution network input data, and uses these as inputs to iteratively optimize and solve pre-constructed upper-level system optimization models and lower-level distribution network optimization models to obtain the target optimization scheduling result. This addresses the shortcomings of existing distribution network optimization operation models, which mostly consider only the economic indicators of the power grid or simply convert operational risks into risk costs for economic research, rarely considering the multiple types of operational risk indicators of the power grid. Furthermore, existing distribution network optimization operation methods mostly consider only the interests of a single entity to formulate optimization strategies, rarely considering the different stakeholders and their interactions in different distribution zones or equipment. This invention achieves distributed optimization operation of distribution zones and further protects the privacy information of distribution zones and different stakeholders.

[0124] To better understand the present invention, the following description, in conjunction with the accompanying drawings and embodiments, will further illustrate the content of the present invention.

[0125] Example 1:

[0126] A hierarchical optimization scheduling method for distributed smart grids, such as Figure 1 As shown, it includes:

[0127] Step 1: Obtain system input data and distribution network input data. The system input data includes grid operation cost data and grid over-limit risk data. The distribution network input data includes load compensation cost data and energy storage cost data for each distribution network.

[0128] Step 2: Using the system input data and the distribution network input data as input, iteratively optimize and solve the pre-constructed upper-level system optimization model and lower-level distribution network optimization model to obtain the target optimization scheduling result.

[0129] Among them, the upper-level system optimization model is constructed with the goal of minimizing the operating cost and grid over-limit risk within the power grid system scheduling cycle, and the output is the coordinated scheduling scheme of the distribution network;

[0130] The lower-level distribution network optimization model is constructed with the goal of minimizing the operating cost of each distribution network and outputs the distribution network scheduling scheme of each distribution network.

[0131] In this embodiment, the process of constructing the upper-level system optimization model in step 2 includes:

[0132] A distributed smart grid is constructed based on the smart grid to be optimized, wherein the distributed smart grid includes multiple distribution networks containing new energy sources, energy storage devices and flexible loads;

[0133] The first system objective function of the upper-level system optimization model is constructed based on the system input data to minimize the operating cost within the dispatch cycle of the distributed smart grid.

[0134] The second system objective function of the upper-level system optimization model is constructed based on minimizing the risk of exceeding limits during the dispatch cycle of the distributed smart grid, and system constraints are constructed for the first system objective function and the second system objective function.

[0135] The upper-level system optimization model outputs the coordinated scheduling scheme of the distribution network and iteratively optimizes it based on the distribution network scheduling scheme of the lower-level distribution network optimization model.

[0136] In this embodiment, the calculation formula for the first system objective function of the upper-level system optimization model is as follows:

[0137] minF1=C1+C2+C3

[0138] Among them, F1 is the operating cost within the system scheduling cycle; C1 is the net cost of purchasing electricity from the upper-level power grid; C2 is the cost of purchasing electricity from distributed power operators; and C3 is the network loss cost, which is calculated based on the distribution network scheduling scheme of each distribution network.

[0139] In this embodiment, the calculation formula for the second system objective function of the upper-level system optimization model is as follows:

[0140] minF2=K SVR

[0141] Where F2 represents the risk of exceeding operational limits during the system scheduling cycle, and K SVR This refers to the system's risk index value for exceeding operational limits.

[0142] In this embodiment, the construction process of the lower-level distribution network optimization model in step 2 includes:

[0143] Based on the coordinated scheduling scheme of the distribution network output by the upper-level system optimization model and the distribution network input data of each distribution network, the distribution network objective function of the lower-level distribution network optimization model is constructed with the lowest distribution network cost in the distributed smart grid, and distribution network constraints are constructed for the distribution network objective function.

[0144] The lower-level distribution network optimization model is built based on the distribution network and takes the input data of the distribution network as input.

[0145] In this embodiment, the objective function of the distribution network optimization model is calculated as follows:

[0146] minF3=C ESS +C cut +Csl

[0147] Where F3 is the objective function of each distribution network; C ESS The charging and discharging cost of energy storage is calculated based on the coordinated scheduling scheme; C cut To compensate for the reduced load; C sl This is the cost of compensating for transferable loads.

[0148] In one embodiment, the system constraints and distribution network constraints include at least one or more of the following: line power flow constraints, controllable load constraints, distributed power source operation constraints, energy storage operation constraints, and system security constraints.

[0149] In one embodiment, the power grid purchase cost data includes the net purchase cost from the upstream power grid, the purchase cost from distributed power operators, and network loss costs; the power grid over-limit risk data includes system operation over-limit risk index values; the load compensation cost data of each distribution network includes compensation costs for load reduction and compensation costs for load transfer; and the energy storage cost data of each distribution network includes charging and discharging cost data of energy storage devices.

[0150] In this embodiment, the process of iteratively optimizing the pre-constructed upper-level system optimization model and lower-level distribution network optimization model using system input data and distribution network input data as input to obtain the target optimization scheduling result includes:

[0151] Based on a preset distributed optimization scheduling strategy, the system input data is input into the pre-built upper-level system optimization model for coordinated optimization to generate a coordinated scheduling scheme for the power distribution network;

[0152] Based on the coordination and scheduling scheme of the distribution network, optimize the objective function of each distribution network, and optimize and solve the pre-constructed lower-level distribution network optimization model based on the optimized objective function and the input data of the distribution network, so as to generate the distribution network scheduling scheme of each distribution network.

[0153] The distribution network scheduling schemes of each distribution network are fed back to the upper-level system optimization model for further coordination and optimization, generating the next coordination and scheduling scheme for the distribution network. This process continues until a preset number of iterations is met, at which point the distributed distribution network scheduling schemes corresponding to each distribution network are obtained.

[0154] The distributed distribution network scheduling schemes corresponding to each of the aforementioned distribution networks are used as the target optimization scheduling results.

[0155] In this embodiment, the process of optimizing and solving the pre-constructed lower-level distribution network optimization model based on the optimized distribution network objective function and the input data of the distribution network to generate the distribution network scheduling scheme for each distribution network includes:

[0156] According to the preset distributed optimization scheduling strategy, the distribution networks in the distributed smart grid are decoupled based on the augmented Lagrange function;

[0157] Given that each decoupled distribution network satisfies the preset boundary constraints, the pre-constructed lower-level distribution network optimization model is optimized and solved using the alternating direction multiplier algorithm and the optimized distribution network objective function based on the input data of the distribution network, thereby generating a distribution network scheduling scheme for each distribution network.

[0158] In this embodiment, the process of feeding back the distribution network scheduling schemes of each distribution network to the upper-level system optimization model for further coordination and optimization, generating the next coordination and scheduling scheme for the distribution network, and obtaining the distributed distribution network scheduling scheme corresponding to each distribution network after a preset number of iterations is met, includes:

[0159] The power distribution network scheduling scheme of each power distribution network is fed back to the upper-level system optimization model;

[0160] Based on the distribution network scheduling schemes of each distribution network, the first system objective function and the second system objective function are optimized, and the next coordination and scheduling scheme of the distribution network is generated based on the optimized first system objective function and the optimized second system objective function.

[0161] The next coordination and scheduling scheme is fed back to the lower-level distribution network optimization model for iterative optimization. This process is repeated alternately until a preset number of iterations is met, and then the distributed distribution network scheduling scheme corresponding to each distribution network is obtained.

[0162] Compared to traditional distribution network optimization and scheduling strategies, the smart grid distributed optimization and scheduling strategy proposed in this invention, which considers the risk of privacy information leakage, can achieve distributed iterative solutions between different distribution networks, reduce unnecessary information interaction between different areas of the distributed smart grid, and play a role in protecting critical information.

[0163] In this embodiment, after generating the target optimized scheduling result, the following steps are also included:

[0164] Determine the current operating status of the distributed smart grid, and when the current operating status of the distributed smart grid is determined to be a normal operating status, acquire the real-time operating data of the distributed smart grid;

[0165] Risk assessment is performed based on the real-time operation data of the distributed smart grid, and risk decision-making is carried out based on the risk assessment results.

[0166] When the current operating state of the distributed smart grid is determined to be an abnormal operating state, abnormal operating information is obtained, and an abnormal control strategy is determined based on the abnormal operating information to eliminate the abnormal operation.

[0167] In this embodiment, the process of making risk decisions based on risk assessment results also includes:

[0168] When the risk assessment result indicates that the distributed smart grid is in a risky state, a risk defense control decision is initiated, and the risky state is lifted based on the risk defense control decision.

[0169] When the risk assessment result indicates that the distributed smart grid is not in a risky state, an emergency plan is formulated based on the risk coefficient of the equipment in the distributed smart grid, and the risk defense database is updated based on the emergency plan.

[0170] In this embodiment, the process of determining anomaly control strategies based on abnormal operation information and eliminating abnormal operations includes:

[0171] Based on the abnormal operation information, identify the abnormal component and disconnect the switch corresponding to the abnormal component;

[0172] Determine whether there is an emergency plan in the risk defense database that matches the abnormal operation information;

[0173] If so, retrieve the emergency response plan and perform anomaly elimination based on the emergency response plan;

[0174] If not, then extract abnormal features based on the abnormal operation information, generate an abnormality elimination decision based on the abnormal features, and perform abnormality elimination based on the abnormality elimination decision.

[0175] Compared with the traditional distribution network optimization operation architecture, the distributed smart grid operation risk prevention method proposed in this invention constructs a full-process defense system from risk assessment to risk identification and risk decision-making, which can effectively improve the system's scientific prevention and rapid response capabilities for operation risks.

[0176] Example 2:

[0177] The following section provides a further explanation of the hierarchical optimization scheduling of distributed smart grids in Example 1, using specific calculation formulas, diagrams, and examples. See the description below for details:

[0178] Step 101: Construct a distributed smart grid model that includes new energy sources, energy storage devices, and flexible loads;

[0179] Preferably, the distributed smart grid model containing new energy sources, energy storage devices, and flexible loads proposed in this invention mainly includes three distribution networks containing distributed new energy sources, energy storage devices, and flexible loads. Each distribution network includes one distributed new energy device (two wind turbines and one photovoltaic unit), and each distribution network contains flexible and controllable loads such as loads that can be reduced or transferred. Loads that can be reduced can withstand certain interruptions or power reductions, and their operating time can be shortened; they can be partially or completely reduced according to supply and demand. The electricity consumption of transferable loads can be flexibly adjusted in different time periods, but the total load after the transfer must remain unchanged from before the transfer throughout the entire scheduling cycle.

[0180] Step 102: Propose a flexible interaction mechanism between power generation, grid, load, and storage in a distributed smart grid;

[0181] The distributed smart grid proposed in this invention, which includes new energy sources, energy storage devices, and flexible loads, features flexible interaction among stakeholders such as grid operators, distributed power generation operators, and energy storage operators, and also establishes transactional relationships with electricity users. The relationships within the flexible interaction mechanism of source-grid-load-storage in the distributed smart grid proposed in this invention are as follows: Figure 2 As shown.

[0182] In one embodiment, to ensure full absorption of distributed renewable energy, energy storage operators will prioritize bilateral partnerships with distributed power generation operators. Remaining energy storage capacity will be used to achieve high-volume, low-storage profits based on time-of-use pricing. Distributed power generation operators prioritize reducing construction and maintenance costs of their equipment and transaction expenses with energy storage operators, as well as increasing electricity sales revenue. Distributed power generation capacity allocation affects transaction revenue between energy storage and distributed power generation operators, and the electricity purchase and sale revenue of distribution network operators. Grid operators prioritize ensuring the safe and economical operation of the grid, reducing grid losses and operating costs. Grid expansion affects the grid connection scale of distributed power generation, thus impacting the revenue of energy storage operators. Energy storage operators prioritize increasing transaction revenue with distributed power generation operators and arbitrage income from high-volume, low-storage profits, while reducing investment and maintenance costs for energy storage devices. The capacity allocation of energy storage devices affects the absorption rate of distributed power generation and the grid expansion and operating costs. Electricity users participating in demand response aim to reduce electricity costs and increase outage compensation. Based solely on fixed electricity price signals and incentive mechanisms, electricity users unilaterally transmit their electricity consumption plans to grid operators with the goal of maximizing their own profits.

[0183] Step 103: Establish a hierarchical optimization scheduling model for distributed smart grids that takes into account operational risks;

[0184] Taking into account multiple stakeholders and system operation security, the upper layer is set as the system optimization layer and the lower layer as the distribution network optimization layer. The optimization objective functions and constraints of the system optimization layer and the distribution network optimization layer are determined respectively, and a multi-objective hierarchical optimization scheduling model for distributed smart grid is established.

[0185] (1) At the system optimization layer (i.e., the aforementioned upper-level system optimization model), with grid operators and distributed power generation operators as the main stakeholders, the objective function can be expressed as:

[0186] min F1=C1+C2+C3

[0187] minF2=K SVR

[0188] Where F1 is the operating cost within the system scheduling cycle, reflecting economic efficiency; F2 is the operational over-limit risk within the scheduling cycle, reflecting safety. C1 is the net cost of purchasing electricity from the upper-level grid; C2 is the cost of purchasing electricity from distributed power generation operators; C3 is the network loss cost; K SVR This refers to the system's risk index value for exceeding operational limits.

[0189] The specific calculation methods for net electricity purchase cost from the upstream power grid, electricity purchase cost from distributed power operators, and network loss cost are as follows:

[0190]

[0191] Where T is the number of time periods in the scheduling cycle; α purc,t and α sell,t These are the electricity purchase and sales identifiers between the distributed smart grid and the upper-level grid during time period t, where α represents the electricity purchase and sales information. purc,t =1, α sell,t =0, α when selling electricity purc,t =0, α sell,t =1; C grid,t P is the transaction price of electricity with the upper-level power grid during time period t; grid,t P represents the active power exchanged between the distributed smart grid and the upper-level grid during time period t. grid,t >0 indicates inflow from the main network, P grid,t >0 indicates flow to the main network; L is the time interval of the scheduling period; N DG C represents the total number of distributed power sources in the system. DG,i P represents the feed-in tariff for the i-th distributed power source; DG,t,i C represents the actual active power output of the i-th distributed power source during time period t; loss,t P represents the network loss cost electricity price during time period t; loss,t Let t be the total active network loss of the system during time period t.

[0192] The calculation method for the system's operation limit exceedance risk index is as follows:

[0193] K SVR =K SVVR +K SVPR

[0194] Among them, K SVVR K SVPR These are the system voltage over-limit risk indicators and the power over-limit risk indicators, respectively.

[0195] The calculation method for the system voltage over-limit risk index is as follows:

[0196]

[0197]

[0198] Where, f(V) t,i Let be the voltage probability density function of the i-th node in time period t; the upper and lower limits of the per-unit value of the node voltage are 1.05 and 0.95, respectively; V up,t,i,max and V down,t,i,max These are the maximum upper limit voltage and the maximum lower limit voltage of the i-th node in time period t, respectively; V up,t,i,av and V down,t,i,av Let V be the average voltage exceeding the upper limit and the average voltage exceeding the lower limit of the i-th node in time period t, respectively, used to characterize the average degree to which the node voltage exceeds the upper and lower limits. t,i This represents the voltage of the i-th node during time period t.

[0199] Calculate the voltage over-limit probability, maximum and average over-limit levels of the nodes, and thus determine the voltage over-limit risk of the nodes:

[0200]

[0201] S VVR,t,i,max =(V up,t,i,max -1.05)+(0.95-V down,t,i,max )

[0202] S VVR,t,i,av =(V up,t,i,av -1.05)+(0.95-V down,t,i,av )

[0203] K VVR,t,i =P VVR,t,i (S VVR,t,i,max +S VVR,t,i,av )

[0204] Among them, P VVR,t,i S represents the voltage exceedance probability of the i-th node during time period t; VVR,t,i,max S VVR,t,i,av These represent the maximum and average voltage exceedance levels of the i-th node during time period t; KVVR,t,i This represents the voltage over-limit risk of the i-th node during time period t.

[0205] By combining the voltage exceedance risk of each node within the same time period from both the average and maximum values, the system voltage exceedance risk for time period t can be obtained. Furthermore, by combining the system voltage exceedance risks of each time period, the overall system voltage exceedance risk index can be obtained.

[0206]

[0207] Among them, K SVVR,t The risk of system voltage exceeding the limit during time period t; N n K represents the number of system nodes. SVVR This is a risk indicator for system voltage exceeding limits.

[0208] The calculation method for the system power over-limit risk index is similar to that for voltage over-limit risk, and will not be repeated here.

[0209] (2) In the distribution network optimization layer (i.e., the aforementioned lower-level distribution network optimization model), with energy storage operators and power users as the main stakeholders, and considering load demand response, its objective function can be expressed as:

[0210] minF3=C ESS +C cut +C sl

[0211] Where F3 is the optimization objective function for each distribution network; C ESS The cost of charging and discharging energy storage; C cut To compensate for the reduced load; C sl This is the cost of compensating for transferable loads.

[0212] The calculation methods for the charging and discharging costs of energy storage, the compensation costs for load reduction, and the compensation costs for load transfer are as follows:

[0213]

[0214] Where, N ESS P represents the total number of energy storage devices in the distribution network; α and β are the charging and discharging cost coefficients of the i-th energy storage device; ch,t,i and P dis,t,i C represents the charging and discharging power of the i-th energy storage device during time period t; cut,t P is the compensation price for load reduction during period t; cut,t The active power that can be reduced during time period t; C sl,t P is the compensation electricity price for load transferable during time period t; sl,t Let t be the active power of the load that can be transferred during time period t.

[0215] The constraints considered in optimized scheduling mainly include: line power flow constraints, controllable load constraints, distributed power source operation constraints, energy storage operation constraints, and system security constraints.

[0216] The system's line power flow constraints are expressed as follows:

[0217]

[0218]

[0219]

[0220] Where k(e,:) represents the branch k with node e as the first node, and k(:,e) represents the branch k with node e as the last node; P k,t Q k,t I k,t U represents the active power, reactive power, and current of branch k; i,t U represents the starting voltage value of node i during time period t; j,t R represents the final voltage value of node i during time period t. k and X k Let be the resistance and reactance of branch k; i and j represent the start and end points of the branch, respectively; P e,t,Ing Q e,t,Ing The active and reactive power injected into the node, Ω AL This represents the set of branches.

[0221] The controllable load constraints of each distribution network are expressed as follows:

[0222] -P cut,max ≤P cut,t ≤0

[0223] P sl,min ≤P sl,t ≤P sl,max

[0224]

[0225] Among them, P cut,max The power reduction limit for load reduction in the distribution network; P sl,max and P sl,min These represent the upper and lower limits of transferable load power in the distribution network within the same time period.

[0226] The operating constraints of distributed power sources are expressed as follows:

[0227] 0≤P DG,t,i ≤P DG,i,max

[0228] Among them, P DG,i,maxThis represents the upper limit of active power output for distributed photovoltaic and wind turbines.

[0229] Energy storage operation constraints are expressed as follows:

[0230] y ch,t,i +y dis,t,i ≤1

[0231]

[0232] E t+1,i =E t,i +αP ch,t,i -βP dis,t,i

[0233] SOC min E max,i ≤E t,i ≤SOC max E max,i

[0234] Among them, y ch,t,i and y dis,t,i P is a 0-1 variable characterizing the charge / discharge state; ESS,max This refers to the upper limit of charge / discharge power; E t,i Let E be the energy storage capacity of the ESS during time period t. t+1,i E represents the energy storage capacity of the ESS during the t+1 time period. max,i Maximum energy storage capacity; SOC max and SOC min These are the upper and lower limits of the energy storage state of charge.

[0235] System security constraints are represented as follows:

[0236]

[0237] Among them, U e,max and U e,min I represents the upper and lower limits of the operating voltage at node e. k,max This represents the maximum value of the current flowing through branch k.

[0238] Step 104: Develop a distributed optimization scheduling strategy for smart grids that takes into account the risk of privacy information leakage;

[0239] The distributed optimization solution method for smart grids that considers privacy information protection proposed in this invention, such as... Figure 3 As shown, the decoupling between the various distribution networks is first completed. Under the premise of satisfying the boundary constraints, the proposed distributed smart grid hierarchical optimization scheduling model is solved in a distributed manner using the alternating direction multiplier method. This method relaxes the coupling constraints between distribution networks through the augmented Lagrangian function. After relaxation, it can be decomposed into multiple local subproblems, which are solved independently by each data center.

[0240] The following boundary constraints must be satisfied between the decoupled subnets:

[0241]

[0242] The original optimization problem is transformed into multiple sub-problems in multiple sub-regions. Using the augmented Lagrangian method to transform the boundary constraints, the optimization problem for each distribution network can be expressed as:

[0243]

[0244] Among them, f i The objective function for the partitioned sub-regions; LA i Let ρ be the augmented Lagrangian function of the partitioned subregion, γ and η be the augmented Lagrangian multipliers, ρ be the penalty factor, and e be the current iteration number. Let i be the augmented Lagrange multiplier of branch i in the e-th iteration. Let i be the augmented Lagrange multiplier of branch i in the e-th iteration. Let i be the augmented Lagrange multiplier of branch i in the e-th iteration. Let x represent the penalty factor for branch i in the e-th iteration. P and x U It is a global variable; Coupled branch power; Coupled branch node voltage amplitude, U i P represents the voltage amplitude of branch i. i x represents the branch power of branch i. e This represents the value of the global variable in the e-th iteration.

[0245] The interactive iterative process of the two-layer optimal scheduling model for distributed smart grids is as follows:

[0246] The upper-level system optimization layer is responsible for globally optimizing the entire distributed smart grid at the system level, ensuring the system operates economically, efficiently, safely, and stably under various conditions, and maximizing the satisfaction of electricity demand. Simultaneously, the system optimization layer fully considers the balance of interests among multiple stakeholders and the flexible interaction mechanism among power generation, grid, load, and storage, ensuring a fair and reasonable distribution of benefits for all parties through reasonable electricity pricing mechanisms and energy trading strategies.

[0247] The lower-level distribution network optimization layer is responsible for the local optimization of the distribution network. Based on the coordination optimization results given by the upper-level model and combined with the actual situation of each distribution network, the distribution network optimization layer can ensure the stability and reliability of power supply, while reducing operating costs and improving the utilization efficiency of distributed energy.

[0248] The distributed optimization solution results for each distribution network are as follows: Figure 4 and Figure 5 As shown, by using distributed iterative solutions among different distribution networks, information interaction between different areas of the distributed smart grid is reduced, playing a crucial role in protecting the key privacy information of each distribution network and lowering the risk of user privacy information leakage. Furthermore, due to its distributed operation characteristics, it has strong embeddability, which is beneficial for topology expansion and system modification. Here, x represents a global variable, P represents power, U represents voltage, l is the branch number, and i is the node number.

[0249] Step 105: Propose methods for preventing operational risks in distributed smart grids.

[0250] The distributed smart grid operation risk prevention method proposed in this invention mainly involves key technologies such as risk identification, risk assessment, and risk defense decision-making. The architecture of the distributed smart grid operation risk prevention method is as follows: Figure 6 As shown, this includes two key tasks: risk prevention during normal operation of the distributed smart grid and power restoration during fault conditions.

[0251] When the distributed smart grid is operating normally, the system performs risk assessments based on real-time operational data and quantitatively calculates the risk operation index of the distribution network. Based on the risk assessment results, risk identification technology is used to determine the risk status of the distributed smart grid. If the distribution network is in a risky state, risk defense control decisions are initiated, and effective control strategies are adopted to eliminate the risk. If the distribution network is not in a risky state, contingency plans are formulated sequentially based on the risk ranking of the equipment, and the risk defense database is updated.

[0252] When a fault occurs in a distributed smart grid, the system identifies the faulty component based on the fault information and controls the corresponding smart switch to trip and clear the fault. After the fault is cleared, the system checks if there are similar contingency plans in the risk defense database that match the actual fault. If a match is found, the contingency plan is retrieved and a secondary response is executed. The contingency plan is then quickly adjusted according to the actual fault situation to rapidly restore power supply to the unloaded area. If a match fails, the initial response is executed, extracting fault characteristics such as the power supply unit where the fault occurred, network connectivity, and the degree of fault impact. Power supply restoration decisions are then used to generate control strategies to restore power supply.

[0253] Distributed smart grid risk assessment quantitatively diagnoses the operational risk level of distribution equipment and networks based on multi-source information provided by the power grid's physical information system. The operational risk of a distributed smart grid is comprehensively characterized by fault probability and fault consequences, including equipment fault probability diagnosis and system outage loss analysis. On the one hand, the equipment and system risk operation indices provided by the risk assessment serve as the basis for risk identification; on the other hand, distributed smart grid risk defense and control primarily aim to reduce the operational risk of the distribution network, and the risk assessment indicators can evaluate the effectiveness of risk defense strategies.

[0254] The risk assessment system for distributed smart grids comprises two parts: equipment failure probability diagnosis and system outage loss analysis. The equipment failure probability diagnosis process includes constructing a diagnostic model and analyzing the impact of internal equipment factors and external factors such as weather conditions and construction on the equipment failure probability. The system outage loss analysis process includes assessing the risk of load outage losses and, based on the outage loss risks of each load, evaluating the overall system outage loss risk.

[0255] The task of risk identification is to determine the normal and risk states of the distributed smart grid based on risk assessment results, and to promptly initiate the risk defense decision-making process when the system is determined to be in a risk state. The risk identification mechanism of the distributed smart grid is as follows: First, it diagnoses the failure probability of grid equipment and determines whether the failure probability exceeds a set threshold. If it exceeds the threshold, a risk warning signal is issued, and contingency plans are developed for high-risk equipment, and the risk defense database is updated. Second, it assesses the risk operation index at the system level and determines whether the operational risk index of the distributed smart grid exceeds a set threshold. If it exceeds the threshold, a risk defense activation signal is issued, initiating the risk defense decision-making process and quickly developing targeted risk defense and control measures.

[0256] Example 3:

[0257] Based on the same inventive concept, this invention also provides a distributed smart grid hierarchical optimization scheduling system, such as... Figure 7 As shown, it includes:

[0258] The acquisition module is used to acquire system input data and distribution network input data. The system input data includes grid operation cost data and grid over-limit risk data. The distribution network input data includes load compensation cost data and energy storage cost data for each distribution network.

[0259] The optimization module is used to iteratively optimize and solve the pre-constructed upper-level system optimization model and lower-level distribution network optimization model using the system input data and distribution network input data as inputs, so as to obtain the target optimization scheduling result;

[0260] The upper-level system optimization model is constructed with the goal of minimizing the operating cost and grid over-limit risk within the power grid system scheduling cycle, and the output is the coordinated scheduling scheme of the distribution network.

[0261] The lower-level distribution network optimization model is constructed with the goal of minimizing the operating cost of each distribution network and outputs the distribution network scheduling scheme of each distribution network.

[0262] Preferably, the process of constructing the upper-level system optimization model in the optimization module includes:

[0263] A distributed smart grid is constructed based on the smart grid to be optimized, wherein the distributed smart grid includes multiple distribution networks containing new energy sources, energy storage devices and flexible loads;

[0264] The first system objective function of the upper-level system optimization model is constructed based on the system input data to minimize the operating cost within the dispatch cycle of the distributed smart grid.

[0265] The second system objective function of the upper-level system optimization model is constructed based on the lowest risk of exceeding the limit during the dispatch cycle of the distributed smart grid, and system constraints are constructed for the first system objective function and the second system objective function.

[0266] The upper-level system optimization model outputs the coordinated scheduling scheme of the distribution network and iteratively optimizes it based on the distribution network scheduling scheme of the lower-level distribution network optimization model.

[0267] Preferably, the objective function of the first system is calculated as follows:

[0268] minF1=C1+C2+C3

[0269] Among them, F1 is the operating cost within the system scheduling cycle; C1 is the net cost of purchasing electricity from the upper-level power grid; C2 is the cost of purchasing electricity from distributed power operators; and C3 is the network loss cost, which is calculated based on the distribution network scheduling scheme of each distribution network.

[0270] Preferably, the objective function of the second system is calculated as follows:

[0271] minF2=K SVR

[0272] Where F2 represents the risk of exceeding operational limits during the system scheduling cycle, and K SVR This refers to the system's risk index value for exceeding operational limits.

[0273] Preferably, the construction process of the lower-level distribution network optimization model in the optimization module includes:

[0274] Based on the coordinated scheduling scheme of the distribution network output by the upper-level system optimization model and the distribution network input data of each distribution network, the distribution network objective function of the lower-level distribution network optimization model is constructed with the lowest cost of the distribution network in the distributed smart grid, and distribution network constraints are constructed for the distribution network objective function.

[0275] The lower-level distribution network optimization model is constructed based on the distribution network and takes the input data of the distribution network as input.

[0276] Preferably, the objective function of the power distribution network is calculated as follows:

[0277] minF3=C ESS +C cut +C sl

[0278] Where F3 is the objective function of each distribution network; C ESS The charging and discharging cost of energy storage is calculated based on the coordinated scheduling scheme; C cut To compensate for the reduced load; C sl This is the cost of compensating for transferable loads.

[0279] Preferably, the system constraints and distribution network constraints include at least one or more of the following: line power flow constraints, controllable load constraints, distributed power source operation constraints, energy storage operation constraints, and system security constraints.

[0280] Preferably, the power grid purchase cost data in the acquisition module includes the net power purchase cost from the upper-level power grid, the power purchase cost from distributed power operators, and the network loss cost;

[0281] The power grid over-limit risk data includes system operation over-limit risk index values;

[0282] The load compensation cost data for each distribution network includes compensation costs for load reduction and compensation costs for load transfer.

[0283] The energy storage cost data for each distribution network includes the charging and discharging cost data of the energy storage devices.

[0284] Preferably, the optimization module is further configured to:

[0285] Based on a preset distributed optimization scheduling strategy, the system input data is input into the pre-built upper-level system optimization model for coordinated optimization to generate a coordinated scheduling scheme for the power distribution network;

[0286] Based on the coordination and scheduling scheme of the distribution network, optimize the objective function of each distribution network, and optimize and solve the pre-constructed lower-level distribution network optimization model based on the optimized objective function and the input data of the distribution network, so as to generate the distribution network scheduling scheme of each distribution network.

[0287] The distribution network scheduling schemes of each distribution network are fed back to the upper-level system optimization model for further coordination and optimization, generating the next coordination and scheduling scheme for the distribution network. This process continues until a preset number of iterations is met, at which point the distributed distribution network scheduling schemes corresponding to each distribution network are obtained.

[0288] The distributed distribution network scheduling schemes corresponding to each of the aforementioned distribution networks are used as the target optimization scheduling results.

[0289] Preferably, the optimization module is further configured to:

[0290] According to the preset distributed optimization scheduling strategy, the distribution networks in the distributed smart grid are decoupled based on the augmented Lagrange function;

[0291] Given that each decoupled distribution network satisfies the preset boundary constraints, the pre-constructed lower-level distribution network optimization model is optimized and solved using the alternating direction multiplier algorithm and the optimized distribution network objective function based on the input data of the distribution network, thereby generating a distribution network scheduling scheme for each distribution network.

[0292] Preferably, the optimization module is further configured to:

[0293] The power distribution network scheduling scheme of each power distribution network is fed back to the upper-level system optimization model;

[0294] Based on the distribution network scheduling schemes of each distribution network, the first system objective function and the second system objective function are optimized, and the next coordination and scheduling scheme of the distribution network is generated based on the optimized first system objective function and the optimized second system objective function.

[0295] The next coordination and scheduling scheme is fed back to the lower-level distribution network optimization model for iterative optimization. This process is repeated alternately until a preset number of iterations is met, and then the distributed distribution network scheduling scheme corresponding to each distribution network is obtained.

[0296] Preferably, the system further includes:

[0297] Prevention module, used for:

[0298] Determine the current operating status of the distributed smart grid, and when the current operating status of the distributed smart grid is determined to be a normal operating status, acquire the real-time operating data of the distributed smart grid;

[0299] Risk assessment is performed based on the real-time operation data of the distributed smart grid, and risk decision-making is carried out based on the risk assessment results.

[0300] When the current operating state of the distributed smart grid is determined to be an abnormal operating state, abnormal operating information is obtained, and an abnormal control strategy is determined based on the abnormal operating information to eliminate the abnormal operation.

[0301] Preferably, the prevention module is further used for:

[0302] When the risk assessment result indicates that the distributed smart grid is in a risky state, a risk defense control decision is initiated, and the risky state is lifted based on the risk defense control decision.

[0303] When the risk assessment result indicates that the distributed smart grid is not in a risky state, an emergency plan is formulated based on the risk coefficient of the equipment in the distributed smart grid, and the risk defense database is updated based on the emergency plan.

[0304] Preferably, the prevention module is further used for:

[0305] Based on the abnormal operation information, identify the abnormal component and disconnect the switch corresponding to the abnormal component;

[0306] Determine whether there is an emergency plan in the risk defense database that matches the abnormal operation information;

[0307] If so, retrieve the emergency response plan and perform anomaly elimination based on the emergency response plan;

[0308] If not, then extract abnormal features based on the abnormal operation information, generate an abnormality elimination decision based on the abnormal features, and perform abnormality elimination based on the abnormality elimination decision.

[0309] Example 4:

[0310] like Figure 8 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0311] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to realize the steps of the distributed smart grid hierarchical optimization scheduling method in the above embodiments.

[0312] Example 5

[0313] Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the distributed smart grid hierarchical optimization scheduling method described in the above embodiments.

[0314] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0315] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0316] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0317] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0318] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A hierarchical optimization scheduling method for distributed smart grids, characterized in that, include: Acquire system input data and distribution network input data, wherein the system input data includes grid operation cost data and grid over-limit risk data, and the distribution network input data includes load compensation cost data and energy storage cost data of each distribution network; Using the system input data and the distribution network input data as input, the pre-constructed upper-level system optimization model and lower-level distribution network optimization model are iteratively optimized and solved to obtain the target optimization scheduling result; The upper-level system optimization model is constructed with the goal of minimizing the operating cost and grid over-limit risk within the power grid system scheduling cycle, and the output is the coordinated scheduling scheme of the distribution network. The lower-level distribution network optimization model is constructed with the goal of minimizing the operating cost of each distribution network and outputs the distribution network scheduling scheme of each distribution network.

2. The method according to claim 1, characterized in that, The process of constructing the upper-level system optimization model includes: A distributed smart grid is constructed based on the smart grid to be optimized, wherein the distributed smart grid includes multiple distribution networks containing new energy sources, energy storage devices and flexible loads; The first system objective function of the upper-level system optimization model is constructed based on the system input data to minimize the operating cost within the dispatch cycle of the distributed smart grid. The second system objective function of the upper-level system optimization model is constructed based on the lowest risk of exceeding the limit during the dispatch cycle of the distributed smart grid, and system constraints are constructed for the first system objective function and the second system objective function. The upper-level system optimization model outputs the coordinated scheduling scheme of the distribution network and iteratively optimizes it based on the distribution network scheduling scheme of the lower-level distribution network optimization model.

3. The method according to claim 2, characterized in that, The formula for calculating the objective function of the first system is as follows: minF1=C1+C2+C3 Among them, F1 is the operating cost within the system scheduling cycle; C1 is the net cost of purchasing electricity from the upper-level power grid; C2 is the cost of purchasing electricity from distributed power operators; and C3 is the network loss cost, which is calculated based on the distribution network scheduling scheme of each distribution network.

4. The method according to claim 2, characterized in that, The objective function of the second system is calculated as follows: minF2=K SVR Where F2 represents the risk of exceeding operational limits during the system scheduling cycle, and K SVR This refers to the system's risk index value for exceeding operational limits.

5. The method according to claim 2, characterized in that, The construction process of the lower-level distribution network optimization model includes: Based on the coordinated scheduling scheme of the distribution network output by the upper-level system optimization model and the distribution network input data of each distribution network, the distribution network objective function of the lower-level distribution network optimization model is constructed with the lowest cost of the distribution network in the distributed smart grid, and distribution network constraints are constructed for the distribution network objective function. The lower-level distribution network optimization model is constructed based on the distribution network and takes the input data of the distribution network as input.

6. The method according to claim 5, characterized in that, The formula for calculating the objective function of the power distribution network is as follows: minF3=C ESS +C cut +C sl Where F3 is the objective function of each distribution network; C ESS The charging and discharging cost of energy storage is calculated based on the coordinated scheduling scheme; C cut To compensate for the reduced load; C sl This is the cost of compensating for transferable loads.

7. The method according to claim 5, characterized in that, The system constraints and distribution network constraints include at least one or more of the following: line power flow constraints, controllable load constraints, distributed power source operation constraints, energy storage operation constraints, and system security constraints.

8. The method according to claim 1, characterized in that, The power grid purchase cost data includes the net power purchase cost from the upper-level power grid, the power purchase cost from distributed power operators, and network loss costs. The power grid over-limit risk data includes system operation over-limit risk index values; The load compensation cost data for each distribution network includes compensation costs for load reduction and compensation costs for load transfer. The energy storage cost data for each distribution network includes the charging and discharging cost data of the energy storage devices.

9. The method according to claim 2, characterized in that, The step of iteratively optimizing and solving the pre-constructed upper-level system optimization model and lower-level distribution network optimization model using the system input data and distribution network input data as inputs to obtain the target optimization scheduling result includes: Based on a preset distributed optimization scheduling strategy, the system input data is input into the pre-built upper-level system optimization model for coordinated optimization to generate a coordinated scheduling scheme for the power distribution network; Based on the coordination and scheduling scheme of the distribution network, optimize the objective function of each distribution network, and optimize and solve the pre-constructed lower-level distribution network optimization model based on the optimized objective function and the input data of the distribution network, so as to generate the distribution network scheduling scheme of each distribution network. The distribution network scheduling schemes of each distribution network are fed back to the upper-level system optimization model for further coordination and optimization, generating the next coordination and scheduling scheme for the distribution network. This process continues until a preset number of iterations is met, at which point the distributed distribution network scheduling schemes corresponding to each distribution network are obtained. The distributed distribution network scheduling schemes corresponding to each of the aforementioned distribution networks are used as the target optimization scheduling results.

10. The method according to claim 9, characterized in that, The optimization of the pre-constructed lower-level distribution network optimization model is performed based on the optimized objective function of the distribution network and the input data of the distribution network, generating a distribution network scheduling scheme for each distribution network, including: According to the preset distributed optimization scheduling strategy, the distribution networks in the distributed smart grid are decoupled based on the augmented Lagrange function; Given that each decoupled distribution network satisfies the preset boundary constraints, the pre-constructed lower-level distribution network optimization model is optimized and solved using the alternating direction multiplier algorithm and the optimized distribution network objective function based on the input data of the distribution network, thereby generating a distribution network scheduling scheme for each distribution network.

11. The method according to claim 9, characterized in that, The step of feeding back the distribution network scheduling schemes of each distribution network to the upper-level system optimization model for further coordination and optimization, generating the next coordination and scheduling scheme for the distribution network, until a preset number of iterations is met, and then obtaining the distributed distribution network scheduling schemes corresponding to each distribution network, includes: The power distribution network scheduling scheme of each power distribution network is fed back to the upper-level system optimization model; Based on the distribution network scheduling schemes of each distribution network, the first system objective function and the second system objective function are optimized, and the next coordination and scheduling scheme of the distribution network is generated based on the optimized first system objective function and the optimized second system objective function. The next coordination and scheduling scheme is fed back to the lower-level distribution network optimization model for iterative optimization. This process is repeated alternately until a preset number of iterations is met, and then the distributed distribution network scheduling scheme corresponding to each distribution network is obtained.

12. The method according to claim 2, characterized in that, After generating the target optimized scheduling result, the process also includes: Determine the current operating status of the distributed smart grid, and when the current operating status of the distributed smart grid is determined to be a normal operating status, acquire the real-time operating data of the distributed smart grid; Risk assessment is performed based on the real-time operation data of the distributed smart grid, and risk decision-making is carried out based on the risk assessment results. When the current operating state of the distributed smart grid is determined to be an abnormal operating state, abnormal operating information is obtained, and an abnormal control strategy is determined based on the abnormal operating information to eliminate the abnormal operation.

13. The method according to claim 12, characterized in that, The risk decision-making process based on risk assessment results includes: When the risk assessment result indicates that the distributed smart grid is in a risky state, a risk defense control decision is initiated, and the risky state is lifted based on the risk defense control decision. When the risk assessment result indicates that the distributed smart grid is not in a risky state, an emergency plan is formulated based on the risk coefficient of the equipment in the distributed smart grid, and the risk defense database is updated based on the emergency plan.

14. The method according to claim 12, characterized in that, The step of determining anomaly control strategies based on the abnormal operation information to eliminate abnormal operation includes: Based on the abnormal operation information, identify the abnormal component and disconnect the switch corresponding to the abnormal component; Determine whether there is an emergency plan in the risk defense database that matches the abnormal operation information; If so, retrieve the emergency response plan and perform anomaly elimination based on the emergency response plan; If not, then extract abnormal features based on the abnormal operation information, generate an abnormality elimination decision based on the abnormal features, and perform abnormality elimination based on the abnormality elimination decision.

15. A distributed smart grid hierarchical optimization scheduling system, characterized in that, include: The acquisition module is used to acquire system input data and distribution network input data. The system input data includes grid operation cost data and grid over-limit risk data. The distribution network input data includes load compensation cost data and energy storage cost data for each distribution network. The optimization module is used to iteratively optimize and solve the pre-constructed upper-level system optimization model and lower-level distribution network optimization model using the system input data and distribution network input data as inputs, so as to obtain the target optimization scheduling result; The upper-level system optimization model is constructed with the goal of minimizing the operating cost and grid over-limit risk within the power grid system scheduling cycle, and the output is the coordinated scheduling scheme of the distribution network. The lower-level distribution network optimization model is constructed with the goal of minimizing the operating cost of each distribution network and outputs the distribution network scheduling scheme of each distribution network.

16. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the distributed smart grid hierarchical optimization scheduling method as described in any one of claims 1 to 14 is implemented.

17. A readable storage medium, characterized in that, It contains an execution program, which, when executed, implements the distributed smart grid hierarchical optimization scheduling method as described in any one of claims 1 to 14.

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