Layered and partitioned decoupling control method and system

Abstract

The invention discloses a layered and partitioned decoupling control method and system. The method comprises the following steps: establishing a first control framework according to a target distribution network regulation and control requirement; establishing a first division logic based on the first control framework, and dividing a target problem into a first control model and a second control model according to the first division logic; and solving the first control model and the second control model, and performing layered and partitioned decoupling control according to a solving result. The method can effectively reduce the calculation complexity of a large-scale optimization problem, improves the calculation efficiency, and achieves the real-time optimization control of a power distribution network. The efficiency of global optimization is ensured, and the flexibility of regional autonomous control is improved. The first control model and the second control model are solved in parallel, the solving time can be remarkably shortened, and the requirement for responding to network changes of the power distribution network in real time is met. The method can adapt to power distribution networks of different scales and types, and has good applicability and expansibility.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated control of active distribution networks, and particularly to a hierarchical and zonal decoupling control method and system. Background Art

[0002] With the continuous development of the low-carbon process of the power system and the large-scale development and utilization of clean energy, the penetration rate of new energy is continuously increasing. The multi-level access of a large number of distributed energy sources has gradually evolved the distribution network from the traditional unidirectional power flow mode into an operation mode with bidirectional power flow, prompting the distribution network to transform towards an active and proactive form, and giving rise to various operation optimization means such as economic optimal dispatch and reactive power voltage optimization to achieve the consumption and optimization of distributed energy. The research basis of these operation optimization means is the power flow model of the distribution network. Due to the addition of a large number of distributed energy sources, the resulting final model will become very complex, the number of decision variables for optimization calculation will increase sharply, and it often requires a long calculation time and cannot achieve real-time optimization. To supplement the operation optimization function, coordinated control technology has been proposed, which is mainly divided into three categories: centralized control, distributed control, and hierarchical and zonal control. Centralized control generally adopts a global optimization control strategy based on the optimal power flow method. However, with the access of high-penetration distributed energy sources, the number of decision variables of the optimization algorithm increases sharply, and the computational complexity increases exponentially. Eventually, the optimization result lags significantly behind the actual operation state and cannot respond to network changes in real time. Distributed control adopts a "decentralized" control structure, enabling each autonomous unit in the distribution network to independently complete internal optimization control, with good real-time performance, but lacking an understanding of global information, which may lead to the optimization falling into a local optimum. Hierarchical and zonal control combines the advantages of centralized control and distributed control. Based on the architecture of hierarchical optimization and zonal coordination, it can achieve global optimization while quickly responding to the real-time changes of the distribution network. Hierarchical and zonal control is one of the most advanced control modes for dealing with the access of a large number of distributed energy sources to the distribution network.

[0003] The layering of the distribution network is to achieve the global optimization of the system, and the regional division is to reduce the computational and communication burdens of the system through distributed processing among autonomous regions. Currently, most of the layering methods for distribution network systems only consider single-layer control such as substation area layer control, feeder layer control, or double-layer control modes such as feeder layer - substation area layer control, and the zoning methods also only target a specific scenario, without forming a unified division rule. In addition, there are couplings in the optimization problems between regions, resulting in low final computational efficiency. Reducing the complexity of large-scale optimization problems is an issue that cannot be ignored.

[0004] It can be seen that the existing hierarchical and zonal control methods still have defects and cannot meet the common requirements of flexibility, effectiveness, and applicability. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this section, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0006] In view of the existing problems mentioned above, the present invention is proposed.

[0007] Therefore, the present invention provides a hierarchical and partitioned decoupling control method and system, which can solve the problems mentioned in the background art.

[0008] To solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, the present invention provides a hierarchical and partitioned decoupling control method, including:

[0010] Establish a first control framework according to the target distribution network regulation requirements;

[0011] Establish a first partitioning logic based on the first control framework, and divide the target problem into a first control model and a second control model according to the first partitioning logic;

[0012] Solve the first control model and the second control model, and perform hierarchical and partitioned decoupling control according to the solution results.

[0013] As a preferred solution of the hierarchical and partitioned decoupling control method of the present invention, wherein: the first control framework includes:

[0014] The first control framework includes at least three layers of control structures;

[0015] The three layers of control structures include at least one layer of control structure corresponding to the target problem, one layer of control structure corresponding to the first control model, and one layer of control structure corresponding to the second control model.

[0016] As a preferred solution of the hierarchical and partitioned decoupling control method of the present invention, wherein: the target problem includes:

[0017] The target problem includes a first objective function and a first set of constraint conditions;

[0018] The first objective function is any function for solving the exchange power between multiple regions and the main grid and the optimal mutual assistance power between regions.

[0019] As a preferred solution of the hierarchical and partitioned decoupling control method of the present invention, wherein: the first partitioning logic includes any partitioning logic for dividing the target problem into an inter-region model and an intra-region model.

[0020] As a preferred solution of the hierarchical and partition decoupling control method described in the present invention, wherein: the three-layer control structure further includes:

[0021] The one-layer control structure corresponding to the target problem is used for global optimization operations;

[0022] The one-layer control structure corresponding to the first control model is used for inter-region power coordination operations;

[0023] The one-layer control structure corresponding to the second control model is used for in-region autonomous control operations.

[0024] As a preferred solution of the hierarchical and partition decoupling control method described in the present invention, wherein: the solution of the first control model and the second control model includes: the solution of the first control model and the second control model is a parallel solution.

[0025] As a preferred solution of the hierarchical and partition decoupling control method described in the present invention, wherein: the target problem at least includes a first objective function for solving the day-ahead optimization objective, and a first constraint condition set including at least operation constraints and economic constraints.

[0026] In a second aspect, the present invention provides a hierarchical and partition decoupling control system, including:

[0027] A framework establishment module, configured to establish a first control framework according to the target distribution network regulation requirements;

[0028] A partitioning module, configured to establish a first partitioning logic based on the first control framework, and partition the target problem into a first control model and a second control model according to the first partitioning logic;

[0029] A control module, configured to solve the first control model and the second control model, and perform hierarchical and partition decoupling control according to the solution results.

[0030] In a third aspect, the present invention provides a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the method described above are implemented.

[0031] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes a hierarchical and partitioned decoupling control method and system. According to the target distribution network regulation requirements, a first control framework is established; a first partitioning logic based on the first control framework is established, and the target problem is divided into a first control model and a second control model according to the first partitioning logic; the first control model and the second control model are solved, and hierarchical and partitioned decoupling control is performed according to the solution results. Through the hierarchical and partitioned decoupling control method, the computational complexity of large-scale optimization problems can be effectively reduced, and the computational efficiency can be improved, thereby realizing the real-time optimization control of the distribution network. By establishing a three-layer control structure, this method realizes the organic combination of global optimization and regional autonomous control, ensuring both the efficiency of global optimization and the flexibility of regional autonomous control. By solving the first control model and the second control model in parallel, the solution time can be significantly shortened to meet the requirements of real-time response to the changes in the distribution network. The hierarchical and partitioned decoupling control method and system of the present invention can adapt to distribution networks of different scales and types, and have good applicability and scalability. By optimizing the design of the objective function and the constraint condition set, the present invention can achieve precise control of the power exchanged between multiple regions and the main network and the optimal mutual assistance power between regions, and optimize the economy and reliability of the distribution network. The hierarchical and partitioned decoupling control method and system of the present invention provide a new technical means for optimizing the power flow model of the distribution network, which helps to improve the consumption rate of new energy and promote the low-carbon development of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0034] Figure 1 is a method flow chart of a hierarchical and partitioned decoupling control method and system provided by an embodiment of the present invention;

[0035] Figure 2 is a schematic diagram of the solution of the hierarchical and partitioned decoupling control method of a hierarchical and partitioned decoupling control method and system provided by an embodiment of the present invention;

[0036] Figure 3 is a flow chart of the hierarchical and partitioned decoupling control method of a hierarchical and partitioned decoupling control method and system provided by an embodiment of the present invention;

[0037] Figure 4 is a topological schematic diagram of an active distribution network with hierarchical and partitioned decoupling control according to the decoupling idea provided by an embodiment of the present invention;

[0038] Figure 5 A simple autonomous region topology diagram with two power generation units for verifying the decoupling idea of a hierarchical and zonal decoupling control method and system provided in an embodiment of the present invention;

[0039] Figure 6 A flowchart for dividing each autonomous region in the feeder layer according to the influence degree of distributed power sources on the voltage of each node of a hierarchical and zonal decoupling control method and system provided in an embodiment of the present invention;

[0040] Figure 7 A flowchart for solving the output and power interaction between autonomous regions based on the ADMM algorithm of a hierarchical and zonal decoupling control method and system provided in an embodiment of the present invention;

[0041] Figure 8 The internal structure diagram of a computer device of a hierarchical and zonal decoupling control method and system provided in an embodiment of the present invention. Detailed implementation manners

[0042] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention with reference to the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] Embodiment 1

[0044] Refer to Figures 1 - 8 , which is the first embodiment of the present invention. This embodiment provides a hierarchical and zonal decoupling control method and system, including:

[0045] In the existing related technologies, there are some problems. For example, traditional centralized control methods face problems such as high computational complexity, slow response speed, and poor system scalability when dealing with large-scale distributed energy systems. These problems limit the real-time performance and flexibility of the system and are difficult to meet the requirements of modern power systems for efficient and intelligent control. In addition, when facing system failures or anomalies, centralized control methods may lead to a decline in the performance of the entire system, and even local or global control failures may occur.

[0046] This application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to elaborate in detail how to implement this hierarchical and zonal decoupling control method;

[0047] Figure 1 The method flowchart of a hierarchical and zonal decoupling control method and system is shown, including:

[0048] S101. Establish a first control framework according to the target distribution network regulation requirements.

[0049] In an optional embodiment, the target distribution network regulation requirements may include at least one or more of the following: voltage stability, frequency stability, load balancing, fault isolation, maximized utilization of distributed energy, and optimization of power quality.

[0050] In the embodiment of the present application, the first control framework includes:

[0051] The first control framework includes at least three layers of control structures.

[0052] The three layers of control structures include at least one layer of control structure corresponding to the target problem, one layer of control structure corresponding to the first control model, and one layer of control structure corresponding to the second control model.

[0053] In an optional embodiment, the layer of control structure corresponding to the target problem is responsible for collecting and processing data related to the distribution network regulation requirements to achieve goals such as voltage stability, frequency stability, and load balancing.

[0054] In the embodiment of the present application, the three layers of control structures further include:

[0055] The layer of control structure corresponding to the target problem is used to perform global optimization operations.

[0056] The layer of control structure corresponding to the first control model is used to perform inter - area power coordination operations.

[0057] The layer of control structure corresponding to the second control model is used to perform in - area autonomous control operations.

[0058] In an optional embodiment, the layer of control structure corresponding to the first control model adopts an advanced prediction algorithm to predict the future state of the distribution network, thereby guiding the reasonable allocation of inter - area power. This control structure can predict the changes in grid load and distributed energy output based on historical data and real - time data, providing a scientific basis for inter - area power coordination.

[0059] In an optional embodiment, the layer of control structure corresponding to the first control model can also respond to the dynamic changes of the power grid and timely adjust the control strategy to ensure the stability and economy of the power grid operation.

[0060] In an alternative embodiment, a layer of control structure corresponding to the second control model adopts a distributed control strategy to achieve autonomous management of each area in the distribution network. This control structure can automatically adjust local power generation and load according to real-time data within the area to reach an optimal load balance state. At the same time, it also has fault detection and isolation functions, and can quickly take measures when an abnormality occurs to limit the scope of the fault impact and ensure the power supply reliability within the area.

[0061] In an alternative embodiment, a layer of control structure corresponding to the second control model can also exchange information with the first control model to achieve a higher level of coordination and optimization.

[0062] In the embodiment of the present application, a three-layer control structure of a distribution network master station layer - feeder layer - device layer is established according to the regulation requirements. The master station layer is a layer of control structure corresponding to the target problem, the feeder layer is a layer of control structure corresponding to the first control model, and the device layer is a layer of control structure corresponding to the second control model.

[0063] It should be noted that according to the target distribution network regulation requirements, establishing the first control framework improves the operation efficiency of the distribution network. Through the optimal scheduling of the first control framework, energy waste can be reduced, and the accuracy of power generation and load management can be improved. The flexibility and scalability of the system are enhanced. The first control framework can adapt to distribution networks of different scales and types, facilitating future upgrades and maintenance. Through the real-time monitoring and data analysis of the first control framework, abnormal situations in the distribution network can be detected and responded to in a timely manner, thereby improving the power supply stability and reliability. The implementation of the first control framework helps to achieve the intelligent management of the distribution network, and through advanced information technology and automation technology, the intelligent level of the entire distribution network is improved.

[0064] S102, establish a first partitioning logic based on the first control framework, and divide the target problem into a first control model and a second control model according to the first partitioning logic;

[0065] In the embodiment of the present application, the first partitioning logic includes any partitioning logic for dividing the target problem into an inter-region model and an intra-region model.

[0066] In an alternative embodiment, the first partitioning logic can be implemented by an intelligent algorithm. This algorithm can dynamically adjust the control strategy according to the real-time data and historical data of the distribution network. For example, machine learning technology is adopted to optimize the parameters of the control model by continuously learning the operation mode and abnormal situations of the distribution network, thereby improving the accuracy and efficiency of control.

[0067] In an alternative embodiment, the first partitioning logic can also be combined with an expert system to use domain knowledge to guide the establishment and adjustment of the control model, ensuring the rationality and effectiveness of the control strategy.

[0068] In an optional embodiment, the first partitioning logic can also combine real-time weather data and a prediction model to consider the impact of external environmental factors on the operation of the distribution network. For example, the system can integrate meteorological information, predict the possible impact of severe weather on the power grid, and adjust the control strategy in advance to prevent or mitigate potential damage. In addition, the system can also perform preset control strategy adjustments according to seasonal load changes and special events (such as holidays or large-scale events) to ensure the stable operation of the power grid under various conditions.

[0069] It should be noted that through these comprehensive control logics, the hierarchical and zonal decoupled control system can respond more flexibly and intelligently to the complex and changeable power grid operation environment.

[0070] In the embodiments of the present application, no limitation is imposed on the first partitioning logic, and those skilled in the art can design it according to actual needs. However, no matter through what partitioning logic the first control framework is divided into one layer for target problems, one layer for solving inter-region parameters, and one layer for solving intra-region parameters, it should be within the protection scope of the present application.

[0071] In the embodiments of the present application, the target problems include:

[0072] The target problems include a first objective function and a first set of constraint conditions;

[0073] The first objective function is any function for solving the exchange power between multiple regions and the main grid and the optimal mutual assistance power between regions.

[0074] In an optional embodiment, the first objective function can be set to minimize the sum of the inter-region exchange power and the intra-region power imbalance. With such an objective function, the system aims to reduce the power flow between regions, reduce the dependence on the main grid, and ensure the power balance of each node within the region.

[0075] In an optional embodiment, the first objective function can also be set to minimize the weighted sum of the power generation cost and the environmental impact. In this way, the system not only considers economy but also takes into account environmental protection, striving to achieve sustainable development while meeting the power grid operation requirements. In addition, the first objective function can also consider other factors, such as the stability and reliability of the power grid, to ensure that the power grid can maintain an efficient and safe operation state under various operating conditions.

[0076] In an optional embodiment, the first set of constraint conditions can include but is not limited to the physical constraints, safety constraints, and operation constraints of the power grid to ensure the stability and security of the power grid during the optimization process.

[0077] In practical applications, the first objective function and the first set of constraint conditions can be adjusted and optimized according to the specific conditions and operating requirements of the power grid.

[0078] In the embodiments of the present application, the target problem at least includes the first objective function for solving the day-ahead optimization objective, and the first set of constraint conditions at least including operation constraints and economic constraints.

[0079] Exemplarily, as Figure 2 shown in the specific schematic diagram of a hierarchical and partitioned decoupling control method for distributed energy accessing the distribution network. According to the decoupling idea, the present invention proposes Figure 3 the shown hierarchical and partitioned decoupling control flow chart. The decoupling idea of the present invention is summarized as: decoupling the global optimization problem and respectively assigning it to the inter-region power coordination model and the intra-region autonomous control model, so that the control function originally used to eliminate the error between the real-time operating state and the optimal operating point of the active distribution network has a secondary optimization ability close to global optimization. The following combines Figure 4 to specifically illustrate the hierarchical and partitioned decoupling method of the distribution network described in the present invention.

[0080] Specifically, write the general mathematical model of the hierarchical and partitioned control model.

[0081] Global optimization model, that is, the target problem:

[0082]

[0083] In the formula: represents the active power vector corresponding to all decision-making units in the distribution network, and F and G respectively represent the objective function and the set of constraint conditions.

[0084] Inter-region power coordination model, that is, the first model:

[0085]

[0086] In the formula: u represents the set of all controllable units in the inter-region power coordination process of the autonomous regions, corresponding to the external exchange power of each autonomous region and the active power output of the controllable units not within the autonomous region, F u and G u respectively represent the objective function and the set of constraint conditions. The second equality constraint condition means is taken as the global optimal value.

[0087] Intra-region autonomous control model, that is, the second model:

[0088]

[0089] In the formula: k is the number of the autonomous region, Corresponding to the active power output of the controllable units within the autonomous region k, F k and G k respectively represent the objective function and the set of constraint conditions. The second equality constraint condition indicates that is taken as the global optimal value.

[0090] It should be noted that by comparing the mathematical models of each part, it can be seen that the optimization models of the latter two parts directly or indirectly depend on the calculation results of the global optimization model, and the control commands are sent layer by layer from the latter two parts to each controllable device.

[0091] It should also be noted that establishing the first partitioning logic based on the first control framework and partitioning the target problem into the first control model and the second control model according to the first partitioning logic can achieve hierarchical and zonal decoupling control of the distribution network, thereby improving the flexibility and response speed of control. By decomposing the complex global optimization problem into sub-problems at two levels, namely between regions and within regions, the computational complexity can be reduced and the solution speed can be accelerated. At the same time, this hierarchical and zonal control strategy helps to improve the scalability of the system, enabling the system to adapt to distribution networks of different scales and facilitating future upgrades and maintenance.

[0092] It should also be noted that the hierarchical and zonal decoupling control method can also improve the robustness of the system. Even in the face of local faults or anomalies, it can ensure the normal operation of other regions, thereby improving the power supply reliability of the entire distribution network. Through this control method, the distribution network can respond more intelligently to various operating conditions and achieve efficient, stable, and safe power supply.

[0093] S103, solve the first control model and the second control model, and perform hierarchical and zonal decoupling control according to the solution results.

[0094] In an optional embodiment, decoupled solution of the first control model and the second control model can be achieved through an algorithm based on model predictive control (MPC). This algorithm can predict the system behavior in the future for a period of time and calculate the control input by optimizing the objective function to achieve the expected control effect. During the decoupling process, the solutions of the first control model and the second control model are independent of each other, which can reduce the coupling effect between models and improve the solution efficiency.

[0095] In an optional embodiment, a feedback mechanism can also be included, which is used to monitor the operating state of the distribution network in real time and adjust the control strategy according to the monitoring results to ensure the stability and reliability of the system. Through this feedback mechanism, the system can respond in a timely manner to changes in the external environment, such as load fluctuations, equipment failures, etc., so as to achieve more accurate and flexible control.

[0096] In an alternative embodiment, decoupled solution of the first control model and the second control model can also be achieved through an algorithm based on deep learning. This algorithm uses a deep neural network to train the control model, predicts system behavior by learning a large amount of historical data, and optimizes the control strategy. This method can handle more complex nonlinear systems and can adaptively adjust model parameters to adapt to system dynamic changes.

[0097] In an alternative embodiment, the introduction of the deep learning algorithm enables the control system to learn from experience, improve the prediction ability for future uncertainties, and thus further enhance the control accuracy and system robustness.

[0098] In the embodiment of the present application, the solution of the first control model and the second control model includes: the solution of the first control model and the second control model is a parallel solution.

[0099] Specifically, the control object of hierarchical and zonal control of the distribution network is a multi-region interconnected power system, and the controllable units are scattered in each autonomous region. Each controllable unit corresponds to an independent variable in the optimization problem. Since the autonomous regions are independent of each other, the overall optimization problem can be decomposed, and each autonomous region independently solves the corresponding sub-optimization problem, and the connection between each part is maintained by the constraint relationship of the exchange power between autonomous regions and between the autonomous region and the distribution network.

[0100] Rewriting of the global optimization model:

[0101]

[0102] In the formula: represents the exchange power between the distribution network and the external power grid, and G 0 is the constraint on it; m is the number of autonomous regions;

[0103] In an alternative embodiment, considering that the variable is in the non-autonomous region of the distribution network, the variable is in the autonomous region k, and the variable at the regional boundary is subject to the exchange power constraints of two adjacent regions. According to the decoupling idea, the inter-regional and intra-regional models are decomposed into the following two groups of optimization models at this time.

[0104] Rewriting of the inter-regional power coordination model:

[0105]

[0106] Rewriting of the intra-regional autonomous control model:

[0107]

[0108] where: G k ′ represents the constraint relationship between the decision variables in region k, which is the part of the set G k that is irrelevant to P u .

[0109] It should be noted that for the solution of the rewritten model expression above, various optimization algorithms can be used, such as the interior point method, the gradient descent method, or the genetic algorithm, etc. These algorithms can effectively handle large-scale nonlinear optimization problems and find the optimal power distribution scheme on the premise of meeting the inter-regional and intra-regional constraint conditions. In this way, the hierarchical and partitioned decoupling control system can achieve efficient and stable control of the distribution network and ensure the safe operation of the power system.

[0110] Figure 4 The three black dashed circles in it represent three autonomous regions, and the black dashed lines represent the power coordination between the two autonomous regions 1 and 2. The rest is collectively called the non-autonomous region. It should be noted that if there is a connection line between the two autonomous regions 1 and 2, then this line is classified into the non-autonomous region to ensure that all autonomous regions are only connected to the non-autonomous region.

[0111] In summary, the present invention proposes a hierarchical and partitioned decoupling control method. According to the target distribution network regulation requirements, a first control framework is established; a first partitioning logic based on the first control framework is established, and the target problem is divided into a first control model and a second control model according to the first partitioning logic; the first control model and the second control model are solved, and hierarchical and partitioned decoupling control is performed according to the solution results. Through the hierarchical and partitioned decoupling control method, the computational complexity of large-scale optimization problems can be effectively reduced, and the computational efficiency can be improved, so as to realize the real-time optimization control of the distribution network. By establishing a three-layer control structure, this method realizes the organic combination of global optimization and regional autonomous control, which not only ensures the efficiency of global optimization but also improves the flexibility of regional autonomous control. By solving the first control model and the second control model in parallel, the solution time can be significantly shortened to meet the requirements of real-time response to the changes in the distribution network. The hierarchical and partitioned decoupling control method and system of the present invention can adapt to distribution networks of different scales and types, and have good applicability and scalability. By optimizing the design of the objective function and the constraint condition set, the present invention can achieve precise control of the power exchanged between multiple regions and the main network and the optimal mutual assistance power between regions, and optimize the economy and reliability of the distribution network. The hierarchical and partitioned decoupling control method and system of the present invention provide a new technical means for the optimization of the power flow model of the distribution network, which helps to improve the consumption rate of new energy and promote the low-carbon development of the power system.

[0112] A hierarchical and partitioned decoupling control method for distributed energy access to the distribution network is proposed based on a hierarchical optimization and partitioned cooperation architecture, effectively solving the problems that the centralized control mode and the distributed control mode cannot adapt to the real-time changes of the network and cannot ensure global optimality. The hierarchical and partitioned control mode can cope with the large-scale access of distributed energy in the distribution network.

[0113] A three-layer control framework of the distribution network main station layer - feeder layer - equipment layer is established. The main station layer maintains the global optimality of each partition; the feeder layer maintains the optimal coordination between autonomous regions; the equipment layer maintains and completes the real-time control within each autonomous region.

[0114] According to the influence degree of distributed power sources on the voltage of each node, the control areas of each distributed power source are reasonably divided at the feeder layer. By distributed processing between sub-regions, the burden of power system calculation and communication is reduced, and the support ability of the distribution network for multiple weak nodes is improved.

[0115] After approximately decoupling the global optimization problem, a power coordination model between regions and an autonomous control model within the region are respectively assigned, so that the control function originally used to eliminate the error between the real-time operation state and the optimal operation point of the active distribution network has a quadratic optimization ability close to global optimization. Therefore, the hierarchical and partitioned decoupling control method proposed by the present invention.

[0116] After distributed processing between sub-regions, the ADMM algorithm with excellent convergence performance and strong adaptability is used to solve the output and power interaction conditions between different levels of the main station - feeder - equipment and between autonomous regions at the same level. Compared with the centralized algorithm, the ADMM algorithm is more suitable for large-scale systems.

[0117] Embodiment 2

[0118] In a preferred embodiment, as Figure 5 shown, it is a simple autonomous region topology diagram with two power generation units for verifying the decoupling method of the present invention. The following combines Figure 5 to verify the decoupling method described in the present invention. Figure 5 It is a simple autonomous region topology containing two power generation units. P is the external output power of the autonomous region, P 1 and P 2 are the output powers of two distributed power sources, and P L is the load power. The information of each part is listed in the following two tables:

[0119] Table 1 Power Generation Unit Information

[0120] Autonomous Region Number Rated Capacity (p.u.) Objective Function of Optimization Problem 1 2 <![CDATA[0.085P 2 +2.18P+271]]> 2 1 <![CDATA[0.065P 2 +2.26P+260]]>

[0121] Table 2 Distribution Network Change Information

[0122] Power Current State Expected Final State Actual Final State P (p.u.) 0.5 0.8 0.8 <![CDATA[P L (p.u.)]]> 1 1.3 1.2

[0123] The time interval between the current state and the final state is the duration required for one-time regional autonomous control; the P corresponding to the expected final state is the power command issued by the feeder layer, and this state is achieved at the actual final state; the P corresponding to the expected final state L is the power value at the corresponding moment obtained by load forecasting.

[0124] The corresponding target programming problem:

[0125]

[0126] s.t.P 1 +P 2 -P-P L =0

[0127] Solving the optimization problem gives:

[0128]

[0129] The relationship between P and PL is not clear. According to the decoupling idea, the curves of P and PL changing with time can be approximately regarded as straight lines, then the relationship between P and PL can be approximately expressed by a linear relationship:

[0130] P L =P+0.5

[0131] From this, the solution is:

[0132]

[0133] The corresponding objective function value:

[0134]

[0135] When the final state P = 0.8, the data is shown in the following table:

[0136] Table 3 Final state data results

[0137] Index Expected Final State Actual Final State Deviation (%) P (p.u.) 1.18 1.13 3.88 <![CDATA[P L (p.u.)]]> 0.92 0.87 6.46 F 535.83 535.59 0.04

[0138] As can be seen from the above specific embodiments, the derived expected final state is close to the actual final state, indicating that the decoupling method proposed by the present invention is effective.

[0139] Embodiment 3

[0140] In a preferred embodiment, as Figure 6 shown, it is a flowchart of the present invention for dividing each autonomous region in the feeder layer according to the influence degree of distributed power sources on the voltage of each node. The following combines Figure 6 to illustrate the feeder layer zoning method of the present invention.

[0141] Step 1: Calculate the voltage sensitivity of each node after the access of distributed power sources. The relationship between the changes in the phase angle and amplitude of the bus voltage when the active power and reactive power change in the distribution network:

[0142]

[0143] In the formula: ΔP and ΔQ are the changes in active power and reactive power respectively after the distributed power source is injected into a certain node; ΔV and Δδ are the changes in the voltage and phase angle of a certain node; J is the Jacobian matrix.

[0144] It can be seen from formula (1) that the phase angle and amplitude of the voltage are state variables and change due to the disturbance of the injection power of the distributed power source at a certain node. Therefore, define J -1 as the voltage sensitivity matrix:

[0145]

[0146] Voltage–active power sensitivity matrix and voltage–reactive power sensitivity matrix respectively represent the voltage change caused by the changes in active power and reactive power after the injection of the distributed power source. Each sub-matrix is of order (n - 1)×(n - 1), where n is the number of nodes in the distribution network.

[0147] When the distributed power source is injected into feeder j, an active power disturbance ΔP DG,j and a reactive power disturbance ΔQ DG,j， are generated, and the corresponding voltage changes of each node are:

[0148]

[0149] In the formula: i is the node number (i = 1, 2, …, n), ΔVi, and are both matrices of order (n - 1)×1.

[0150] It can be seen from formula (3) that if the change in the injection power of the distributed power source is constant, the higher the voltage sensitivity, the stronger the voltage support of the distributed power source to the voltage; conversely, the lower the voltage sensitivity, the weaker the effect of the distributed power source on the voltage.

[0151] Step 2: Division of the local control area. The partition control divides the distribution network into several autonomous areas according to the voltage sensitivity proposed above. In these autonomous areas, the distributed power source only provides strong voltage support for the node voltages in the self - area, and has a small impact on the node voltages outside the autonomous area. These several autonomous areas are called local control areas.

[0152] The present invention mainly divides the local control area by the reactive power output of distributed power sources. Nodes that are greatly affected by the change in the reactive power output of distributed power sources are divided into the same control area. The variable ΔQ of the adjustable reactive power of each distributed power source DG is as follows:

[0153]

[0154] In the formula: Q res is the maximum reactive power that the distributed power source can emit or absorb, that is, the reactive power reserve. Q gen is the reactive power currently emitted by the distributed power source.

[0155] It can be seen from formula (4) that ΔQ DG . m will change in real time. Substituting formula (4) into formula (3) can obtain the voltage change of each node of feeder j. Define a voltage threshold ΔV th to divide the nodes that are strongly affected by the distributed power source control. If ΔV i >ΔV th , then node i belongs to the local control area of the m-th distributed power source at feeder j. In addition, if ΔV i <ΔV th , then node i is excluded.

[0156] When the situation of control area overlap occurs at a certain node, the analysis is as follows:

[0157] Situation 1: If a certain node belongs to more than one control area, then select the control area with the greatest influence as its local control area according to its voltage influence situation.

[0158] Situation 2: When the influence areas of two control areas contain the same distributed power source, the two control areas are merged into the same control area.

[0159] Embodiment 4

[0160] In a preferred embodiment, as Figure 7 shown, it is a flowchart of the present invention for solving the output and power interaction between autonomous regions based on the ADMM algorithm. The following combines Figure 7 to illustrate the ADMM algorithm described in the present invention.

[0161] Step 1: Set the initial values of the global variables according to the measured data of the distribution network, and set the initial values of the Lagrange multipliers and power compensation parameters of all regional boundary data to 0.

[0162] Step 2: Establish a Lagrangian augmented function corresponding to the sub-goal value of each autonomous region. Taking autonomous region a and autonomous region b as examples, there is power coordination between the two autonomous regions. Then the corresponding and As follows:

[0163]

[0164]

[0165] Where: k is the number of iterations, ρ is the penalty parameter; fa and fb are the autonomous objective functions inside regions a and b respectively; are the power interaction variables between regions a and b obtained after the k-th iteration optimization calculation inside regions a and b respectively; are the power reference values for the (k + 1)-th iteration of regions a and b respectively; are the Lagrange multipliers corresponding to the k-th iteration of regions a and b respectively.

[0166] Step 3: Update the decision variables within each autonomous region. Start the iteration from k = 1. In the (k + 1)-th iteration, parallelly optimize and solve the augmented Lagrangian function inside each region to obtain the decision variable values inside the region at the corresponding iteration step length Meanwhile, the coupled variable values at the region boundaries can be obtained and

[0167]

[0168] Step 4: Update the boundary variables between each region based on Step 3. According to the boundary values obtained in Step 3 and Use them as the reference values for the next iteration.

[0169] Step 5: Update the Lagrange multipliers of each region:

[0170]

[0171] Wherein, are the Lagrange multipliers of regions a and b corresponding to the (k + 1)-th iteration respectively.

[0172] Step 6: Determine whether the algorithm converges. The convergence criterion is whether the original residual r k+1 and the dual residual s k+1 tend to 0. δ is the convergence accuracy. If the convergence condition is satisfied, the algorithm ends; otherwise, return to Step 2.

[0173]

[0174] Example 5

[0175] In this example, a hierarchical partition decoupling control system is further provided, including:

[0176] A framework establishment module, configured to establish a first control framework according to the target distribution network regulation requirements;

[0177] A division module, configured to establish a first division logic based on the first control framework, and divide the target problem into a first control model and a second control model according to the first division logic;

[0178] A control module, configured to solve the first control model and the second control model, and perform hierarchical and zonal decoupling control according to the solution results.

[0179] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0180] This embodiment also provides a computer device, which can be a terminal, and its internal structure diagram can be as Figure 8 shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it realizes a hierarchical and zonal decoupling control method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0181] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by the processor, the following steps are realized:

[0182] Establish a first control framework according to the target distribution network regulation requirements;

[0183] Establish a first division logic based on the first control framework, and divide the target problem into a first control model and a second control model according to the first division logic;

[0184] Solve the first control model and the second control model, and perform hierarchical and zonal decoupling control according to the solution results.

[0185] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0186] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0187] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0188] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0189] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1Steps of the functions specified in one or more boxes.

[0190] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present application.

[0191] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A hierarchical partition decoupling control method, characterized in that: include: Establish the first control framework according to the target distribution network control requirements; Establishing a first division logic based on the first control framework, and dividing the target problem into a first control model and a second control model according to the first division logic; The first control model and the second control model will be solved, and hierarchical and partitioned decoupling control will be performed based on the solution results.

2. The hierarchical and partitioned decoupling control method according to claim 1, characterized in that: The first control framework includes: The first control framework includes at least three layers of control structure; The three-layer control structure at least includes a layer of control structure corresponding to the target problem, a layer of control structure corresponding to the first control model, and a layer of control structure corresponding to the second control model.

3. The hierarchical partition decoupling control method according to claim 2, characterized in that: The target questions include: The target problem includes a first target function and a first set of constraints; The first objective function is an arbitrary function for solving the exchange power between multiple regions and the main network and the optimal mutual assistance power between regions.

4. The hierarchical partition decoupling control method according to claim 3, characterized in that: The first partitioning logic includes arbitrary partitioning logic for partitioning the target problem into an inter-region model and an intra-region model.

5. The hierarchical and partitioned decoupling control method according to claim 4, characterized in that: The three-layer control structure also includes: A layer of control structure corresponding to the target problem is used to perform global optimization operations; A layer of control structure corresponding to the first control model is used to perform inter-regional power coordination operations; A layer of control structure corresponding to the second control model is used to perform autonomous control operations within the area.

6. The hierarchical and partitioned decoupling control method according to claim 5, characterized in that: The solving of the first control model and the second control model includes: solving the first control model and the second control model in parallel.

7. The hierarchical and partitioned decoupling control method according to claim 6, characterized in that: The target problem includes at least a first target function for solving a day-ahead optimization target, and a first constraint condition set including at least an operation constraint and an economic constraint.

8. A hierarchical partition decoupling control system, characterized in that: include: A framework establishment module, used to establish a first control framework according to target distribution network control requirements; A partitioning module, configured to establish a first partitioning logic based on the first control framework, and to partition the target problem into a first control model and a second control model according to the first partitioning logic; The control module is used to solve the first control model and the second control model, and perform hierarchical and partitioned decoupling control according to the solution results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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