Generation and priority sorting method of multi-scheme power transfer considering main grid and distribution network collaborative optimization
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HEYUAN POWER SUPPLY BUREAU GUANGDONG POWER GRID CO LTD
- Filing Date
- 2026-03-31
- Publication Date
- 2026-06-19
Smart Images
Figure CN122246747A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power technology, and more specifically to a method for generating and prioritizing multiple power transfer schemes that considers the coordinated optimization of the main and distribution networks. Background Technology
[0002] The main grid and distribution network are crucial components of a power system, responsible for power transmission and distribution. Their operation directly impacts the system's safety, reliability, and economy. In practice, the main and distribution networks exchange power through nodes such as substations. When the power system is under maintenance or experiencing a fault, network reconfiguration is a common operational control measure. By changing the status of some lines or switches, load can be transferred between different power supply paths, thereby reducing the scope of power outages, minimizing load loss, and improving system operational flexibility. With the continuous expansion of distribution network scale and the increasing complexity of network structures, relying solely on manual experience to formulate power transfer plans is no longer sufficient to meet operational needs. Optimization calculation methods are urgently needed to assist in decision-making during the power transfer process.
[0003] In recent years, optimal power flow methods have been widely applied in power system operation analysis and optimal dispatching. This method establishes a mathematical model incorporating power balance, voltage constraints, and line capacity constraints, and optimizes the operational objectives while satisfying system physical constraints. Introducing optimal power flow methods into distribution network reconfiguration and transfer decisions helps optimize system operating costs or risks while considering multiple operational constraints. Most existing network reconfiguration or transfer methods based on optimal power flow typically aim to obtain a single optimal solution, i.e., outputting a set of optimal network topologies and operating schemes under given objective functions and constraints. While these methods can theoretically achieve mathematically optimal results, they still have certain limitations in practical engineering applications. On the one hand, transfer schemes with different topologies often have small differences in operating costs or performance evaluation indicators, and a single optimal solution cannot reflect the differences between multiple feasible operating modes. On the other hand, dispatchers typically need to weigh factors such as safety, reliability, and operational complexity in actual decision-making, and relying solely on a single optimization result is insufficient to meet the needs of flexible dispatching.
[0004] Furthermore, in scenarios involving coordinated operation of the main and distribution networks, the mutual influence between them becomes more significant. Distribution network transfer schemes not only alter the power distribution on the distribution side but may also impact the power flow, voltage levels, and equipment load of the main network. During the development of transfer schemes, it is necessary to consider the operational constraints of both the main and distribution networks within a unified framework to avoid increasing overall operational risks due to localized optimization.
[0005] Therefore, how to systematically generate multiple feasible power transfer schemes while meeting the operational constraints of the main distribution network, and how to comprehensively evaluate and prioritize different schemes to provide operators with multi-scheme comparison and decision support, remains a problem that needs further research and improvement in the existing technology.
[0006] Currently, a common approach for load restoration during maintenance or fault scenarios is the network reconfiguration and load transfer method based on Optimal Power Flow (OPF). This type of method typically constructs an optimization model with the objective of minimizing system operating costs or losses, and then performs centralized or distributed solutions for the operation of the main and distribution networks. In this approach, the overall system operating cost is usually used as the objective function, satisfying basic operating conditions such as power balance constraints, voltage operation constraints, and line capacity constraints. After introducing line or switch state variables, a mixed-integer programming approach is typically used to solve the problem, ultimately obtaining a set of optimal network topologies and corresponding operating states that satisfy the constraints. This type of method has clear mathematical significance in theory, capable of obtaining the globally optimal or near-optimal solution of the system under given objective functions and constraints.
[0007] Although the single-scheme network reconfiguration method based on optimal power flow has achieved certain results in theoretical research and some engineering applications, it still has shortcomings in actual main and distribution network power transfer and operation decision-making scenarios, mainly in the following aspects:
[0008] 1) These methods typically output only a single optimal solution. In actual operation, the differences in operating costs or losses corresponding to different network topologies are often small, but a single optimal solution cannot reflect the differences between multiple feasible operating modes, limiting the choice space for schedulers in complex operating scenarios. 2) These methods are primarily guided by the optimality of the objective function, while paying insufficient attention to the structural differences between schemes. When the objective function is not sensitive enough to topology changes, the solution results are prone to repeatedly providing schemes with the same or highly similar structures in multiple calculations, which is not conducive to forming a comparison of multiple schemes. 3) In engineering practice, schedulers often need to weigh multiple factors such as safety, reliability, and operational complexity, and a single optimal solution cannot fully reflect the trade-offs between different operational objectives, reducing the interpretability and applicability of the model results in actual scheduling decisions.
[0009] In addition, existing technologies also employ multi-scheme transfer methods based on heuristic algorithms or empirical rules. These methods typically generate multiple feasible network topology schemes for operators to choose from, by pre-setting several transfer rules or search strategies, while meeting basic operational constraints.
[0010] In distribution network operation, common methods include enumeration strategies based on switch operation sequences, topology adjustment methods based on local search, and branch exchange strategies based on graph theory. These methods typically generate several candidate topologies by gradually changing the states of some lines or switches, and then verify their feasibility.
[0011] In some implementations, this type of method may improve the efficiency of scheme generation by simplifying power flow calculations or using approximate models to quickly evaluate candidate schemes. To a certain extent, this type of method can provide multiple alternative schemes, enhancing the flexibility of scheduling decisions.
[0012] Although heuristic or rule-based methods can generate multiple transfer schemes, these methods also have the following limitations:
[0013] 1) Heuristic methods typically lack a unified optimization objective function, and the generated candidate solutions lack clear guarantees of optimality or near-optimality. Different solutions exhibit significant performance differences in operating costs, load recovery levels, and voltage safety, making quantitative comparisons difficult. 2) Heuristic methods often rely on human experience or pre-defined rules during the search process, making their results highly sensitive to rule selection. When the network scale is large or operating conditions change, the quality and stability of the solutions are difficult to guarantee. 3) Heuristic methods usually separate solution generation from operational status analysis. The mutual influence between the main grid and distribution network is difficult to consider within the same optimization framework, easily leading to locally feasible solutions with high overall operational risks, limiting their application effectiveness in scenarios involving coordinated power transfer between the main grid and distribution network. Summary of the Invention
[0014] To address the common problems in existing power transfer decision-making in main and distribution networks, such as the availability of only a single power transfer scheme, the lack of multiple feasible schemes for dispatchers to choose from, and the tendency to generate repetitive or highly similar schemes during multiple solutions, making it difficult to reflect different operational risk characteristics, this invention provides a multi-scheme power transfer generation and priority ranking method that considers the coordinated optimization of main and distribution networks. This method can provide multiple power transfer schemes with different structures and distinguishable operational characteristics for power transfer decision-making in main and distribution networks, and provide clear priority ranking criteria, thereby improving the engineering applicability and reliability of power transfer decision-making in main and distribution networks, and ensuring the safety and reliability of the power system.
[0015] To achieve the above objectives, the technical solution of this invention is as follows:
[0016] A multi-scheme power transfer generation and priority ranking method considering primary and secondary network coordinated optimization includes:
[0017] Obtain main and distribution network operation data and maintenance and faulty lines to construct an optimal power flow model that considers the transfer of power from the main and distribution networks; the optimal power flow model considers main network operation constraints, distribution network operation constraints, distributed power sources and load recovery requirements;
[0018] The optimal power flow model is solved by a dual sensitivity-guided multi-transfer scheme generation method to obtain a baseline transfer scheme. Then, multi-scheme generation constraints are constructed based on the baseline transfer scheme to generate multiple candidate transfer schemes.
[0019] The performance indicators of multiple candidate supply transfer schemes are calculated sequentially, and the performance indicators are ranked using a hierarchical ranking method based on Pareto dominance relationship to obtain the final ranking of the main and auxiliary coordinated supply transfer schemes.
[0020] Compared with the prior art, the advantages of this invention are as follows:
[0021] The multi-scheme power transfer generation and priority ranking method considering the coordinated optimization of the main and distribution networks provided by this invention can provide a variety of power transfer schemes with different structures and distinguishable operating characteristics for the power transfer decision of the main and distribution networks, and provide clear priority ranking basis for them, thereby improving the engineering applicability and reliability of the power transfer decision of the main and distribution networks, and ensuring the safety and reliability of the power system. Attached Figure Description
[0022] Figure 1 The main flowchart of the multi-scheme power transfer generation and priority ranking method considering the coordinated optimization of the main and distribution networks provided in the embodiments of this application is shown. Detailed Implementation
[0023] Example:
[0024] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.
[0025] See Figure 1 As shown, the multi-scheme power transfer generation and priority ranking method considering the coordinated optimization of the main and distribution networks provided in this embodiment mainly includes the following steps:
[0026] 110. Obtain main and distribution network operation data and maintenance and fault lines to construct an optimal power flow model that considers the transfer of power from the main and distribution networks; the optimal power flow model considers main network operation constraints, distribution network operation constraints, distributed power sources and load recovery requirements.
[0027] In this step, a collaborative optimal power flow model is constructed that simultaneously considers the operating characteristics and constraints of the main grid and the distribution network. The model's constraints uniformly cover the physical operating limitations of both the main grid and the distribution network.
[0028] 120. A multi-transfer scheme generation method based on dual sensitivity is used to solve the optimal power flow model to obtain a baseline transfer scheme. Multiple scheme generation constraints are then constructed based on the baseline transfer scheme to generate multiple candidate transfer schemes.
[0029] In this step, a multi-transfer scheme generation method based on dual sensitivity is used to solve the optimal power flow model. Without changing the original constraints and optimization framework of the main distribution network transfer problem, the differences between the multiple transfer schemes in multiple aspects such as operating performance, network structure and the dominant structure of operating constraints are realized. This results in a set of feasible transfer schemes that are distinguishable in terms of operating mechanism and meet the requirements of safe operation and engineering implementation, providing richer and more reliable alternatives for power system dispatching decisions.
[0030] 130. Calculate the performance indicators of multiple candidate supply transfer schemes sequentially. Then, apply a hierarchical ranking method based on Pareto dominance relationships to these performance indicators to obtain the final ranking of the primary and secondary coordinated supply transfer schemes.
[0031] In this step, by sequentially calculating the evaluation performance indicators of multiple candidate supply transfer schemes, a hierarchical ranking method based on Pareto dominance is adopted for the evaluation performance indicators. This enables the systematic hierarchical ranking of multiple schemes on multi-dimensional evaluation performance indicators while ensuring the safety, economy and operability of the schemes. This avoids the problem of indicator conflicts being masked by single weighted scoring, and at the same time provides the scheduling personnel with a priority implementation sequence with engineering interpretability and technical transparency.
[0032] Therefore, the multi-scheme power transfer generation and priority ranking method considering the coordinated optimization of the main and distribution networks provided in this embodiment can provide a variety of power transfer schemes with different structures and distinguishable operating characteristics for the power transfer decision of the main and distribution networks, and provide clear priority ranking basis for them, thereby improving the engineering applicability and reliability of the power transfer decision of the main and distribution networks, and ensuring the safety and reliability of the power system.
[0033] In one specific embodiment, the objective function of the optimal power flow model considering the interaction and transfer between the main and distribution networks is as follows:
[0034] In the formula, The overall objective function for optimizing the main distribution network. , , , , , These represent the main grid generator generation cost, main grid switch operation cost, distribution network switch operation cost, distribution network loss cost, renewable energy curtailment penalty cost, and load shedding penalty cost, respectively. , , All are power generation cost coefficients. , These are the main grid switch operation cost coefficient and the distribution network switch operation cost coefficient, respectively. This is the distribution network loss cost coefficient. , These are the costs of curtailment penalties for solar power curtailment and wind power curtailment, respectively. Penalty cost for loss of load; For generator sets During the period Those who have made contributions For the line During the period The switch 0-1 state variable, For the line During the period The current, For the line The resistance, , They are nodes During the period The curtailed power of solar power and the curtailed power of wind power, For nodes During the period The power of the load shedding.
[0035] The mainnet's operational constraints include:
[0036] (1) Power balance constraints of power systems;
[0037] In the formula , They are nodes During the period The active and reactive power output of photovoltaic power. , They are nodes During the period The active and reactive power output of wind power. , Mainnet The sum of active load and the sum of reactive load during a given time period.
[0038] (2) Generator output constraints;
[0039] In the formula , Generator sets exist The upper and lower limits of contribution at any given time , Generator sets exist The upper and lower limits of reactive power output at any given moment.
[0040] (3) Upper and lower limits of climbing;
[0041] in For the unit The rate of ascent.
[0042] (4) Power flow constraints of the line;
[0043] In the formula , The lines are respectively Active power and reactive power during a given time period , The lines are respectively The upper and lower limits of active power, , The lines are respectively The lower limit and upper limit of reactive power
[0044] (5) DC power flow constraint;
[0045] In the formula, For line admittance, For nodes exist Phase angle at any given moment.
[0046] (6) Phase angle constraint; ; ;;
[0047] In the formula, The phase angle of the balancing node.
[0048] (7) Output constraints of photovoltaic units;
[0049] In the formula, , They are nodes exist At any given moment, photovoltaic power generation is both active and reactive. , Each node exist The upper and lower limits of photovoltaic active power output at any given time. , Each node exist The upper and lower limits of photovoltaic reactive power output at any given time.
[0050] (8) Wind turbine output constraints ;
[0051] In the formula, , They are nodes exist At any given moment, wind power outputs both active and reactive power. , Each node exist The upper and lower limits of wind power active power output at any given time. , Each node exist The upper and lower limits of wind power reactive power output at any given time.
[0052] The network constraints include:
[0053] (1) Distribution network Distflow power flow constraints; ; ; ; ; ;
[0054] In the formula, , Distribution network nodes exist The power received from the main network at all times. , They are nodes The square of the voltage and current values.
[0055] (2) Voltage safety constraints;
[0056] In the formula, For nodes exist Voltage at time, , They are nodes The upper and lower limits of the permissible voltage.
[0057] (3) Branch power and current constraints ; ;
[0058] In the formula, For the line exist Apparent power flowing through the moment, For the line Maximum transportable capacity For the line The maximum current that is allowed to flow.
[0059] Thus, the above method can be used to construct a collaborative optimal power flow model that simultaneously considers the operating characteristics and constraints of the main grid and distribution network. The objective function of this model integrates the generation cost of the main grid, the switching operation cost of the main and distribution networks, the network loss cost of the distribution network, the penalty for abandoning new energy and the penalty for load shedding. The model's constraints uniformly cover the physical operating limitations of the main grid and distribution network.
[0060] In scenarios involving coordinated operation of the main and distribution networks and fault transfer, to improve the reliability and flexibility of system operation decisions, it is usually necessary to generate multiple feasible transfer schemes while meeting safety operation constraints, allowing dispatchers to select based on different operational objectives. Since power system operation is subject to multiple constraints such as power flow balance, equipment capacity, and voltage safety, generating different transfer schemes by enumerating network topology, adjusting branch switch states, or limiting the number of switch operations often results in similar power flow distributions for different topology schemes in actual operation. This leads to a lack of substantial differences in power flow direction among the generated transfer schemes, making it difficult to fully reflect the potential operating state of the system under different dominant operational constraints.
[0061] To address the aforementioned issues, this method employs a dual-sensitivity-guided multi-transfer scheme generation method in step 120 to solve the optimal power flow model. Without altering the main distribution network transfer optimization model structure or the original security constraint system, it introduces operational constraint dual sensitivity to guide the multi-transfer schemes towards topological dispersion, thereby obtaining a set of feasible transfer schemes with operational differentiation. Specifically, step 120 includes:
[0062] The main distribution network power transfer problem can be represented as a constrained optimization model containing continuous operating variables and discrete topology decision variables, and its general form is:
[0063] in, These represent continuously operating variables of the system, including node voltages, branch power flow, and power distribution. Indicates the state variables of the branch switch. It is the objective function for system operation, used to characterize the load loss, operational risk, or overall operational cost.
[0064] The above optimization model must satisfy the following constraints: ;
[0065] Equation (21) represents system power flow balance and main distribution network coupling, etc., and Equation (22) represents the system inequality operation constraint set, including branch capacity constraints, node voltage upper and lower limit constraints, etc. In the process of solving the power transfer optimization model shown in Equations (20)–(22), the commercial solver will provide a set of dual variables. In order to rigorously describe and explain their physical meaning from a mathematical perspective, the Lagrangian function associated with the model constraints is introduced:
[0066] in, These are the dual variables corresponding to the equality constraints. For each inequality constraint The corresponding dual variable is used to characterize the marginal impact of the corresponding operational constraint on the system's operational objective in the current transfer scheme. Its value reflects the tension of the operational constraint in the current operating state. The larger the value, the stronger the constraint's restrictive effect on the current scheme.
[0067] In obtaining the benchmark supply plan and its corresponding dual variables Subsequently, to generate multiple supply transfer schemes, this method introduces the following four collaborative constraints during the multi-scheme generation process:
[0068] (1) Near-optimal solution constraints
[0069] To ensure the overall acceptability of the multi-supply scheme in terms of operational performance, a near-optimal constraint is constructed, the expression of which is:
[0070] in, This is the allowable performance degradation coefficient. This constraint limits the generation process of multiple transfer schemes to a high-quality feasible solution region, avoiding the introduction of transfer schemes with significantly degraded operational performance in the pursuit of scheme diversity.
[0071] (2) Topological inconsistency constraints
[0072] This constraint is used to avoid repeatedly generating transfer schemes with completely identical or highly similar topologies, and its expression is:
[0073] in, Indicates the number of generated [items]. One supply transfer plan, This is the set of operable branches. This constraint ensures that the newly generated transfer scheme differs from the existing scheme in network topology, resulting in differences in the transfer paths between the new and original schemes.
[0074] (3) Key operational variance constraints
[0075] Relying solely on topological inconsistency constraints may lead to similar power flow distributions among different power transfer schemes during operation. To further enhance the distinguishability of multiple power transfer schemes, this invention introduces operational constraint difference constraints based on dual sensitivity. A set of key operational constraints is constructed based on the magnitude of the dual variables corresponding to existing power transfer schemes.
[0076] in, In order to be with the first In the optimization model corresponding to the existing supply transfer scheme, the first... Inequality constraints The corresponding dual variable; This is the dual significance screening coefficient, and its value range is... ,when When the value is close to 1, the set of critical operational constraints This will include only a very small number of constraints with the largest and strongest dual variable values, guiding the new solution to deviate from the core constraints; when When the value is small, the set will contain more constraints with certain limiting effects, allowing the new scheme to be adjusted within a wider range. By adjusting this coefficient, the focus of multiple scheme generation can be flexibly controlled.
[0077] Based on the aforementioned set of key operational constraints And its corresponding dual variable, constructing a sensitivity-based operational constraint difference condition. The mathematical expression of this condition is as follows:
[0078] in, and Representing the new scheme and the first The existing solution is in the first The absolute value of the difference between the values of each running constraint function. Quantify the degree of deviation between the two schemes in the physical quantities corresponding to the constraint, such as line power, node voltage amplitude, generator output, etc. As a weight, it means that the stronger the constraint on the original scheme, the higher the weight of the corresponding deviation of the physical quantity in the evaluation of the new scheme. The difference threshold parameter sets the minimum threshold for the total weighted deviation. The larger the value, the more thoroughly the new solution is required to separate from the state of the existing solution in the key constraint-driven operation scheme, forcing the optimization search process to jump out of the current optimal solution.
[0079] (4) Constraint on the number of switching operations
[0080] To ensure the feasibility of the multi-power supply scheme in actual operation, a constraint on the number of switching operations is introduced, the expression of which is as follows:
[0081] in, This indicates the branch state in the initial operating state of the system. This is the maximum allowed number of switching operations. This constraint prevents the introduction of excessive switching operations in pursuit of different solutions, ensuring the safety and operability of the power transfer solution during field implementation.
[0082] No. Each supply transfer scheme is obtained by solving the following multi-scheme generative model:
[0083] By using the above methods, without changing the original constraints and optimization framework of the main distribution network power transfer problem, the differences between multiple power transfer schemes in terms of operating performance, network structure and dominant structure of operating constraints are realized. This results in a set of feasible power transfer schemes that are distinguishable in terms of operating mechanism and meet the requirements of safe operation and engineering implementation, providing richer and more reliable alternatives for power system dispatching decisions.
[0084] After obtaining multiple candidate power transfer schemes for the main distribution network that meet operational constraints and economic requirements, it is necessary to comprehensively evaluate and rank these schemes to assist dispatchers in quickly determining the priority power transfer schemes. Therefore, in step 130, the performance indicators of multiple candidate power transfer schemes are calculated sequentially, and a hierarchical ranking method based on Pareto dominance is used to obtain the final ranking of the main distribution network coordinated optimization power transfer schemes. Specifically, step 130 includes:
[0085] From multiple perspectives, including system security, power restoration capability, operational economy, and operational complexity, a multi-dimensional performance index system for evaluating solutions is constructed. Let the... The decision variables corresponding to the candidate solutions are: Its performance evaluation indicators include, but are not limited to, the following categories:
[0086] (1) Performance indicators for evaluating the level of load loss
[0087] The load shedding level is a core indicator for measuring the power restoration capability of a power transfer scheme. The smaller the load shedding level, the stronger the scheme's power restoration capability for users, and the higher its priority. Definition 1 The total load shedding for each scheme during the scheduling period is:
[0088] In the formula, Indicates the first Nodes under each scheme exist The amount of load loss at any given moment.
[0089] (2) Performance indicators for voltage over-limit risk assessment
[0090] Voltage exceedance is a crucial indicator for assessing the operational safety of a power system. The voltage exceedance risk index reflects the impact of a proposed scheme on node voltage stability; a lower value indicates a lower operational risk. - Definition The voltage exceedance level of each scheme is as follows:
[0091] In the formula, Indicates the first Nodes under each scheme exist Voltage at a given moment.
[0092] (3) Performance indicators for evaluating the level of line over-limit
[0093] Line overload directly impacts equipment safety and system reliability. The line overload level reflects the utilization of line operating margins by different solutions; the smaller the overload level, the higher the safety of the solution. Definition 1 The degree of line violation for each scheme is as follows:
[0094] In the formula, For the first The route under each option In time The active power.
[0095] (4) Performance indicators for evaluating the complexity of switch operation
[0096] In emergency power transfer or maintenance scenarios, the feasibility of the solution is equally crucial. The fewer the number of switching operations, the lower the difficulty of implementation and the smaller the scheduling risk. (Definition of the first...) The number of switching operations for each scheme relative to the initial operating state is:
[0097] To achieve unified evaluation and prioritization of multiple options, this paper proposes a Pareto hierarchical ranking method based on dominance relationships. Without relying on manual weighting or a single comprehensive score, it systematically hierarchically classifies multi-dimensional option indicators, thereby providing schedulers with a clear and interpretable priority implementation sequence.
[0098] In the specific implementation process, the dominance relationship between schemes is defined. For two schemes P and Q, if all indicators of scheme P are not inferior to those of scheme Q, and scheme P is strictly superior to scheme Q in at least one indicator, then scheme P is said to dominate scheme Q. Based on this, all generated schemes are sorted by non-dominance, and all schemes not dominated by any other scheme are identified, forming the first Pareto front layer. After removing the above schemes from the candidate set, the remaining schemes dominate the non-dominated solution set again, forming the second Pareto front layer. This process is iterated until all schemes are assigned to a specific front layer. In this process, the objective dominance relationship between each scheme is strictly relied upon, demonstrating the hierarchical structure of the multi-scheme group in the multi-dimensional indicator space.
[0099] Subsequently, to achieve a fine-grained ranking of non-dominant schemes within the same Pareto layer, a weighted scoring system was introduced to rank the schemes within the same layer. For the... The overall score of the solutions in the layer. Calculated by the following formula:
[0100] In the formula, , , , The weighting coefficients for each evaluation indicator are: , , , This serves as the normalized baseline value for the corresponding indicator. This step provides a more refined order reference for schemes within the same layer, while ensuring that inter-layer priorities are not compromised. Finally, the multiple Pareto fronts are arranged in strata order, and within each layer, a priority sequence of candidate schemes can be output by incorporating weighted fine-tuning results.
[0101] in, This indicates a fine-tuning and reordering of the Pareto front schemes at the th level. The Pareto level is used to determine the number of Pareto levels. Through the above method, this invention enables the systematic stratification and ranking of multiple solutions across multi-dimensional evaluation indicators, while ensuring the safety, economy, and operability of the solutions. This avoids the problem of indicator conflicts masking issues caused by single-weighted scoring, and provides schedulers with a sequence of preferred implementation schemes that offers engineering interpretability and technical transparency.
[0102] In summary, compared with the prior art, the technical solution provided by the present invention has the following significant advantages:
[0103] (1) Improved decision-making flexibility. This invention breaks through the limitation of traditional methods that only provide a single optimal solution. It can systematically generate multiple high-quality transfer schemes with significant differences in topology and operation mechanism, providing dispatchers with rich decision-making options and effectively responding to different operation scenarios and risk preferences.
[0104] (2) The invention ensures the substantial differences between the schemes. By introducing operational mechanism differences based on dual sensitivity, the invention guides the optimization model to generate alternative schemes with different operating conditions. These schemes are independent and reliable backups for each other, which enhances the robustness of the transfer strategy.
[0105] (3) The scientific nature and interpretability of the ranking were optimized. A hierarchical ranking method based on Pareto dominance relationship was adopted to establish an objective and transparent priority sequence of schemes, making the decision-making basis clearer and more reliable.
[0106] (4) The scheme takes into account both economic efficiency and engineering practicality. By using near-optimal solution constraints and operational complexity constraints, the generated scheme is ensured to maintain excellent economic efficiency while being feasible on-site, thus achieving the unity of theoretical value and engineering application.
[0107] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the content of the present invention should be covered within the scope of protection of the present invention.
Claims
1. A method for generating and prioritizing multiple power transfer schemes considering coordinated optimization of primary and secondary distribution networks, characterized in that, include: Obtain main and distribution network operation data and maintenance and fault lines to construct an optimal power flow model that takes into account the power transfer of the main and distribution network; The optimal power flow model considers main grid operation constraints, distribution network operation constraints, distributed power sources and load recovery requirements; The optimal power flow model is solved by a dual sensitivity-guided multi-transfer scheme generation method to obtain a baseline transfer scheme. Then, multi-scheme generation constraints are constructed based on the baseline transfer scheme to generate multiple candidate transfer schemes. The performance indicators of multiple candidate supply transfer schemes are calculated sequentially, and the performance indicators are ranked using a hierarchical ranking method based on Pareto dominance relationship to obtain the final ranking of the main and auxiliary coordinated supply transfer schemes.
2. The multi-scheme power transfer generation and priority ranking method considering primary and secondary network coordinated optimization as described in claim 1, characterized in that, The objective function of the optimal power flow model is as follows: ； In the formula, The overall objective function for optimizing the main distribution network. , , , , , These represent the main grid generator generation cost, main grid switch operation cost, distribution network switch operation cost, distribution network loss cost, renewable energy curtailment penalty cost, and load shedding penalty cost, respectively. , , All are power generation cost coefficients. , These are the main grid switch operation cost coefficient and the distribution network switch operation cost coefficient, respectively. This is the distribution network loss cost coefficient. , These are the costs of curtailment penalties for solar power curtailment and wind power curtailment, respectively. Penalty cost for loss of load; For generator sets During the period Those who have made contributions For the line During the period The switch 0-1 state variable, For the line During the period The current, For the line The resistance, , They are nodes During the period The curtailed power of solar power and the curtailed power of wind power, For nodes During the period The power of the load shedding.
3. The multi-scheme power transfer generation and priority ranking method considering primary and secondary network coordinated optimization as described in claim 1, characterized in that, The main network operation constraints include: (1) Power balance constraints of power system ； In the formula , They are nodes During the period The active and reactive power output of photovoltaic power. , They are nodes During the period The active and reactive power output of wind power. , Mainnet The sum of active load and the sum of reactive load during a given time period; (2) Generator output constraints ； In the formula , Generator sets exist The upper and lower limits of contribution at any given time , Generator sets exist The upper and lower limits of reactive power output at any given moment; (3) Upper and lower limits of climbing constraints ； in For the unit The rate of ascent; (4) Power flow constraints of the line ； In the formula , The lines are respectively Active power and reactive power during a given time period , The lines are respectively The upper and lower limits of active power, , The lines are respectively The lower limit and upper limit of reactive power; (5) DC power flow constraint ； In the formula, For line admittance, For nodes exist Phase angle at any given moment; (6) Phase angle constraint ； ； In the formula, The phase angle of the balancing node; (7) Output constraints of photovoltaic units ； In the formula, , They are nodes exist At any given moment, photovoltaic power generation is both active and reactive. , Each node exist The upper and lower limits of photovoltaic active power output at any given time. , Each node exist The upper and lower limits of photovoltaic reactive power output at any given time; (8) Wind turbine output constraints ； In the formula, , They are nodes exist At any given moment, wind power outputs both active and reactive power. , Each node exist The upper and lower limits of wind power active power output at any given time. , Each node exist The upper and lower limits of wind power reactive power output at any given time.
4. The multi-scheme power transfer generation and priority ranking method considering primary and secondary network coordinated optimization as described in claim 1, characterized in that, The power distribution network operation constraints include: (1) Distribution network Distflow power flow constraints ； ； ； ； ； ； In the formula, , Distribution network nodes exist The power received from the main network at all times. , They are nodes The squares of voltage and current values; (2) Voltage safety constraints ； In the formula, For nodes exist Voltage at time, , They are nodes The upper and lower limits of the permissible voltage; (3) Branch power and current constraints ； ； In the formula, For the line exist Apparent power flowing through the moment, For the line Maximum transportable capacity For the line The maximum current that is allowed to flow.
5. The multi-scheme power transfer generation and priority ranking method considering primary and secondary network coordinated optimization as described in claim 1, characterized in that, The method for generating multiple power transfer schemes based on dual sensitivity guidance solves the optimal power flow model, including: The main distribution network power transfer problem can be represented as a constrained optimization model containing continuous operating variables and discrete topology decision variables, as follows: ； in, These represent continuous operating variables of the power system, including node voltage, branch power flow, and power distribution. Indicates the state variables of the branch switch. It is the objective function for power system operation, used to characterize load shedding, operational risks, or overall operational costs; The above-mentioned constrained optimization model must satisfy the following constraints: ； ； Equation (21) represents the power system power flow balance and main distribution network coupling equality constraints, and Equation (22) represents the set of power system inequality operation constraints, including branch capacity constraints and node voltage upper and lower limit constraints. In solving the power transfer optimization model shown in Equations (20)–(22), the Lagrangian function associated with the model constraints is introduced: ； in, These are the dual variables corresponding to the equality constraints; For each inequality constraint The corresponding dual variables are used to characterize the marginal impact of the corresponding operational constraints on the power system's operational objectives in the current power transfer scheme; Obtain the benchmark supply solution and its corresponding dual variables .
6. The multi-scheme power transfer generation and priority ranking method considering primary and secondary network coordinated optimization as described in claim 5, characterized in that, The constraints for generating multiple solutions include near-optimal solution constraints, topological inconsistency constraints, key operational difference constraints, and switching operation count constraints.
7. The multi-scheme power transfer generation and priority ranking method considering primary and distribution network coordinated optimization as described in claim 6, characterized in that, The near-optimal solution constraint is: ； in, The allowable performance degradation factor; The topological inconsistency constraint is: ； in, Indicates the number of generated [items]. One supply transfer plan, It is the set of operable branches.
8. The multi-scheme power transfer generation and priority ranking method considering primary and distribution network coordinated optimization as described in claim 7, characterized in that, The set of key operational constraints is as follows: in, In order to be with the first In the optimization model corresponding to the existing supply transfer scheme, the first... Inequality constraints The corresponding dual variable; This is the dual significance screening coefficient, and its value range is... ; Based on the aforementioned set of key operational constraints And its corresponding dual variables, construct the sensitivity-based operational constraint difference condition, the mathematical expression of which is as follows: ；； in, and Representing the new scheme and the first The existing solution is in the first The absolute value of the difference between the values of each running constraint function. Quantify the physical quantities corresponding to the constraint for the two schemes; As weight; This is the difference threshold parameter; The constraint on the number of switching operations is: ； in, This indicates the branch state in the initial operating state of the system. The maximum number of allowed switching operations; The first Based on the generated transfer schemes, and taking into account near-optimal solution constraints, topological inconsistency constraints, key operational difference constraints, and switching operation count constraints, candidate transfer schemes are obtained by solving a multi-scheme generation model. The overall scheme solution model is as follows: 。 9. The multi-scheme power transfer generation and priority ranking method considering primary and secondary network coordinated optimization as described in claim 1, characterized in that, The performance metrics for evaluating the multiple candidate supply transfer schemes include: Performance indicators for load shedding level evaluation, voltage over-limit risk evaluation, line over-limit level evaluation, and switch operation complexity evaluation.
10. The multi-scheme power transfer generation and priority ranking method considering primary and secondary network coordinated optimization as described in claim 1, characterized in that, The hierarchical ranking method based on Pareto dominance includes: Define the dominance relationship between schemes: for two schemes P and Q, if all indicators of scheme P are not inferior to those of scheme Q, and scheme P is strictly superior to scheme Q in at least one indicator, then scheme P is said to dominate scheme Q. Sort all generated schemes into non-dominated categories and identify all schemes that are not dominated by any other scheme, forming the first Pareto front layer. After removing the above schemes from the candidate set, the remaining schemes dominate the non-dominated solution set again, forming the second Pareto front layer. Iterate in this way until all schemes are assigned to a specific front layer. Introduce weighted scoring to rank schemes within the same layer; for the... The overall score of the solutions in the layer. Calculated by the following formula: ； In the formula, , , , The weighting coefficients for each evaluation indicator are: , , , This is the normalized benchmark value for the corresponding indicator; Arrange the multi-layered Pareto fronts in hierarchical order, and within each layer, combine the weighted fine-tuning results to output a priority sequence of candidate solutions: ； in, This indicates a fine-tuning and reordering of the Pareto front schemes at the th level. This represents the number of Pareto layers.