Active and reactive power resource cooperative configuration method considering lean operation demand of power distribution network
By collaborating the allocation of active and reactive resources in the distribution network, the problem of difficulty in using different loss reduction transformation methods is solved, and the lean loss reduction goal of the distribution system is achieved, which improves operating efficiency and investment economy.
Patent Information
- Application Number
- CN202510106881.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-23
- Publication Date
- 2025-05-06
AI Technical Summary
In the existing technology, different loss reduction transformation methods are difficult to use in concert, resulting in limited loss reduction effects and cannot effectively help the county distribution system achieve lean loss reduction goals.
A collaborative configuration method for active and reactive resources that considers the lean operation needs of distribution networks is proposed. By traversing nodes and lines in the region, multiple types of load node sets and line data sets are generated. Based on the active and reactive load parameters of typical loads in the load, an active and reactive resource collaborative configuration model is constructed. The goal is to minimize annual active network losses in the distribution system, and consider the safe operation of the system and annualized cost constraints.
By collaborating the allocation of active and reactive resources, the best configuration plan can be determined in different operating scenarios, improving investment economy and operating efficiency, and stronger applicable capabilities, effectively improving the system trend distribution and reducing line losses.
Smart Images
Figure CN119944862A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of power system planning and operation, and in particular to a method for collaboratively configuring active and reactive resources taking into account the lean operation requirements of a distribution network. Background Art
[0002] In recent years, the energy structure transformation with the goal of "two substitutions" has alleviated the anxiety of "energy depletion" to a certain extent. At the same time, the in-depth promotion of the "dual carbon" policy has prompted the power system to put forward higher requirements in energy conservation and emission reduction. The development of energy-saving transformation technology has made the loss reduction transformation methods in county distribution systems more diversified, but different loss reduction transformation methods have their typical applicable scenarios, and different loss reduction transformation methods are difficult to use in coordination, resulting in limited loss reduction effects. Therefore, improving the loss reduction effect of active and reactive power loss reduction transformation strategies is of great significance to helping county distribution systems achieve lean loss reduction goals. Summary of the invention
[0003] The purpose of the present invention is to overcome the disadvantage that different loss reduction transformation means in the prior art are difficult to use in coordination, resulting in limited loss reduction effect, and to provide a method for coordinated configuration of active and reactive resources taking into account the lean operation requirements of distribution networks.
[0004] The purpose of the present invention is achieved through the following technical solutions: The active and reactive resource collaborative configuration method considering the lean operation requirements of the distribution network includes the following steps: Step 1, traverse all nodes and lines in the area, generate multi-type load node sets according to node load characteristics, and generate line data sets according to line parameters; Step 2: Generate four typical load days according to the seasonal characteristics of the load, each typical load day represents a season in spring, summer, autumn and winter, and then obtain the active load and reactive load of the load in the typical load day; Step 3: Taking the deployment location and capacity of the mobile energy storage as decision variables, and setting the unit capacity and unit power of the mobile energy storage; taking the deployment location and capacity of the switching capacitor as decision variables, and setting the unit capacity of the switching capacitor; taking the deployment location of the tie line as a decision variable, and setting the transmission capacity of the tie line; Step 4: Based on the active load and reactive load parameters of the load in a typical day, as well as the decision variables and fixed value parameters, an objective function of establishing an active and reactive resource collaborative configuration model with the goal of minimizing the annual active network loss in the distribution system is constructed; Step 5: Construct the constraints of the active and reactive resource collaborative configuration model, which include distribution network flow constraints, system safety operation constraints, multi-dimensional loss reduction resource configuration constraints, and annualized cost constraints; Step 6: Based on the active and reactive resource collaborative configuration model and constraint conditions, the optimal configuration scheme and the minimum annualized total cost under different operation scenarios are obtained.
[0005] Preferably, the step 1 is specifically to traverse all nodes and lines in the area and number the nodes and lines, determine the load peak value of the nodes in each stage according to the node historical data, classify all nodes according to the load characteristics, put the same type of nodes in one set, and obtain a multi-type load node set; After traversing all the lines in the area and numbering them, the first and last nodes and line length of the line are determined according to the line connection information and physical information. The first and last nodes and line length of the line are put into a set according to the line number to generate a line data set, and all line data sets are generated in sequence.
[0006] Preferably, in step 2, the time of a typical day is discretized, and each telecommunication day is discretized into 24 time periods, each time period is 60 minutes, and all states do not change in each time period.
[0007] As a preference, in step 3, the unit capacity of the mobile energy storage is uniformly The unit power of mobile energy storage is unified as The unit capacity of the switched capacitor is unified as The transmission capacity of the tie line is unified as
[0008] Preferably, the objective function in step 4 is specifically: min E NL Among them, E NL It is expressed as the annual active network loss in the distribution system, ω is the equivalent number of years for each typical day, s is the season, t is the time period, i and j are the two ends of the distribution line, Ω S ,Ω T ,Ω L Represents the season, time period, and distribution line set respectively, P ij,t,s , Q ij,t,s Represents the active and reactive power of the line in operation, U Sub Represents the voltage amplitude of the power distribution system, R ij represents the resistance of the distribution line, and △t represents the time interval.
[0009] Preferably, in step 5, the distribution network flow constraint includes a node power balance constraint and a node voltage balance constraint, and the node power balance constraint includes: Among them, u(i) and v(i) represent the upstream and downstream line sets respectively. Represent the active and reactive power of the load respectively. Respectively represent the active and reactive charging power of mobile energy storage, Respectively represent the active and reactive discharge power of mobile energy storage, Respectively represent the active and reactive output power of the substation, is the reactive output power of the switched capacitor; The node voltage balance constraint includes: Among them, X ij Represents the branch reactance, U i,t,s , U j,t,s Represent the voltage amplitude at the node, M represents a maximum value, They represent the forward and reverse operation variables of the line respectively.
[0010] Preferably, in step 5, the system safety operation constraints include node voltage amplitude constraints, branch transmission capacity constraints, substation constraints and grid constraints; The node voltage amplitude constraint includes: in, Represents the square terms of the maximum and minimum deviations of voltage; The branch transmission capacity constraints include: in, is the rated transmission capacity of the inherent line, is the rated transmission capacity of the tie line, Ω L is the intrinsic line set, Ω TL is the candidate set of contact lines to be built, Indicates the directionality of power, is equal to 1, the line is in the forward operation state, and the power value range is [0, M]. Otherwise, When it is equal to 1, the line is in reverse operation and the power range is [-M, 0]; The substation constraints include: in, is the rated capacity of the substation; The grid constraints include: in, is the line connection variable, Ω Load is the set of load nodes.
[0011] Preferably, in step 5, the multi-factor loss reduction resource configuration constraint condition includes a mobile energy storage configuration constraint, a switching capacitor configuration constraint, and a tie line configuration constraint; The mobile energy storage configuration constraints include mobile energy storage capacity constraints, mobile energy storage output constraints and mobile energy storage power constraints: the mobile energy storage capacity constraints are: in, The rated capacity of mobile energy storage deployed in different seasons. The rated power of mobile energy storage deployed in different seasons. is the deployment variable of mobile energy storage, indicating the deployment status of mobile energy storage in different seasons. is the number of mobile energy storage deployed in different seasons, is the rated capacity of a single mobile energy storage unit, is the rated power of a single mobile energy storage unit, is the minimum deployment quantity of mobile energy storage, is the maximum number of mobile energy storage deployments, the number of mobile energy storage units to be deployed in the next season; The mobile energy storage output constraint is: in, It is the charging and discharging state variable of mobile energy storage; The mobile energy storage capacity constraint is: in, To store the power of mobile energy at the next moment, is the amount of mobile energy storage at that moment, η ch , η di is the charging and discharging efficiency of mobile energy storage, α, is the lower and upper limits of the mobile energy storage capacity, θ is the capacity coefficient of the mobile energy storage at the beginning and end times; The switching capacitor configuration constraints include switching capacitor capacity constraints, switching capacitor output constraints and switching capacitor limited action constraints; The switching capacitor capacity constraint is: in, The rated capacity of the switched capacitor deployment, Represents the number of switched capacitors deployed, Represents the rated capacity of a single group of switched capacitors. Represents the minimum and maximum installation quantity of switched capacitors; The switching capacitor output constraint is: in, The number of capacitors used in each period; The switching capacitor limited action constraint is: in, is the number of switched capacitors used at the next moment, The maximum number of switching capacitor operations; The tie line configuration constraints include construction constraints and power existence constraints; The construction constraints are: in, is the construction variable of the tie line, is the connection variable of the line; The tie line power existence constraint is:
[0012] Preferably, in step 5, the annualized cost constraint condition includes annualized cost constraint considering resource synergy, construction cost, operation and maintenance cost, and power loss cost: The annualized cost constraint considering resource synergy is: C Total ≤C max C Total =C IN +C O&M +C EL C IN =C ME,IN +C SC,IN +C TL,IN C O&M =C ME,O&M +C SC,O&M +C TL,O&M C EL =C ME,EL Among them, C Total is the annualized cost upper limit of the multiple loss reduction transformation strategy, C max is the annualized cost upper limit of the multiple loss reduction transformation strategy, C IN is the construction cost of the multiple loss reduction transformation strategy, C O&M is the operation and maintenance cost of the multiple loss reduction transformation strategy, CEL is the power loss cost of the multi-layer loss reduction transformation strategy, C ME,IN , C SC,IN , C TL,IN are the construction costs of mobile energy storage, switching capacitors, and interconnection lines, respectively. ME,O&M , C SC,O&M , C TL,O&M are the operation and maintenance costs of mobile energy storage, switching capacitors, and tie lines, respectively. ME,EL The cost of power loss for mobile energy storage; The construction costs are: Among them, R ψ is the capital recovery coefficient of the multi-dimensional loss reduction and transformation strategy, d is the discount rate of the multi-dimensional loss reduction and transformation strategy, L ψ is the service life of the multiple loss reduction transformation strategy, is the construction cost coefficient of mobile energy storage per unit capacity, is the unit power construction cost coefficient, N S is the number of seasons, c SC,IN is the construction cost coefficient per unit capacity of switching capacitor, c TL,IN is the construction cost coefficient per unit length of the tie line, l ij is the line length; The operation and maintenance costs are: in, is the unit capacity operation and maintenance cost coefficient of mobile energy storage, is the unit power operation and maintenance cost coefficient of mobile energy storage, c SC,IN is the unit capacity operation and maintenance cost coefficient of the switched capacitor, is the operation and maintenance cost coefficient per unit length of the tie line; The power loss cost is: Among them, c ME,EL is the mobile energy storage power loss cost coefficient, κ ME is the power loss coefficient of mobile energy storage.
[0013] Preferably, in step 6, the solver gurobi is used to solve the active and reactive resource collaborative configuration model to obtain the minimum network loss, the deployment location, capacity and power of mobile energy storage, the deployment location and capacity of switching capacitors, the deployment location of interconnection lines, and the benefit improvement effect and cost plan of the loss reduction transformation strategy in different scenarios.
[0014] The beneficial effects of the present invention are as follows: the present invention introduces a polygonal approximation solution algorithm to linearize the branch transmission capacity constraints, and obtains an easy-to-solve mixed integer linear programming model. Finally, a case simulation is performed based on the topological structure and load operation data of the actual county distribution system. In the simulation results, the collaborative configuration scheme of the multiple loss reduction transformation strategies under different operation scenarios is demonstrated, and the operation benefits of the collaborative configuration scheme are analyzed using operation results such as system network loss, voltage deviation, line power factor, and substation outgoing line load power. Based on different cost scenarios, the collaborative configuration scheme of the multiple loss reduction transformation strategies is determined, and the investment benefit analysis of the collaborative configuration scheme is performed using cost results such as cost scheme, cost share trend, and investment benefit. The results show that the constructed active and reactive resource collaborative configuration model that considers the lean operation requirements of the distribution network can determine the optimal configuration scheme under different operation scenarios, and has the advantages of taking into account both investment economy and improved operation benefits. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 It is a schematic diagram of a method flow of the present invention; Figure 2 It is a topological diagram of a 55-node distribution network system in an embodiment of the present invention. DETAILED DESCRIPTION
[0016] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.
[0017] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.
[0018] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.
[0019] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.
[0020] Example: The coordinated configuration method of active and reactive resources considering the lean operation requirements of distribution networks, such as Figure 1 As shown, the following steps are included: Step 1, traverse all nodes and lines in the area, generate multi-type load node sets according to node load characteristics, and generate line data sets according to line parameters; Step 2: Generate four typical load days according to the seasonal characteristics of the load, each typical load day represents a season in spring, summer, autumn and winter, and then obtain the active load and reactive load of the load in the typical load day; Step 3: Taking the deployment location and capacity of the mobile energy storage as decision variables, and setting the unit capacity and unit power of the mobile energy storage; taking the deployment location and capacity of the switching capacitor as decision variables, and setting the unit capacity of the switching capacitor; taking the deployment location of the tie line as a decision variable, and setting the transmission capacity of the tie line; Step 4: Based on the active load and reactive load parameters of the load in a typical day, as well as the decision variables and fixed value parameters, an objective function of establishing an active and reactive resource collaborative configuration model with the goal of minimizing the annual active network loss in the distribution system is constructed; Step 5: Construct the constraints of the active and reactive resource collaborative configuration model, which include distribution network flow constraints, system safety operation constraints, multi-dimensional loss reduction resource configuration constraints, and annualized cost constraints; Step 6: Based on the active and reactive resource collaborative configuration model and constraint conditions, the optimal configuration scheme and the minimum annualized total cost under different operation scenarios are obtained.
[0021] The step 1 is specifically to traverse all nodes and lines in the area and number the nodes and lines, determine the load peak of the nodes in each stage according to the node historical data, classify all nodes according to the load characteristics, put the same type of nodes in one set, and obtain a multi-type load node set; After traversing all the lines in the area and numbering them, the first and last nodes and line length of the line are determined according to the line connection information and physical information. The first and last nodes and line length of the line are put into a set according to the line number to generate a line data set, and all line data sets are generated in sequence.
[0022] In this embodiment, a power distribution network in an urban area is considered, and n distribution network nodes are numbered using natural numbers 1, 2, ..., n; all lines to be planned in the grid are numbered using natural numbers 1, 2, ..., m, and the line types are divided into ordinary lines, constructed interconnection lines, and interconnection lines to be constructed.
[0023] In the step 2, the time of a typical day is also discretized, and each telecommunication day is discretized into 24 time periods, each time period is 60 minutes, and all states do not change in each time period.
[0024] In this embodiment, four typical days of spring, summer, autumn and winter are used to illustrate the scheme of the present invention, which is sufficient to clearly and completely demonstrate the scheme of the entire invention. After determining the typical day, the active load and reactive load of the seasonal load in the typical day, the parameters of each component in the power distribution system, such as the impedance of the line and the capacity of the transformer, are obtained; In order to solve the optimization model on a computer, time needs to be discretized. In this embodiment, each typical day is discretized into 24 time periods, each time period is Δt = 1 hour, and it is assumed that all states (dynamics of equipment and electrical loads, etc.) do not change in each time period, and each segment is assumed to be a certain scenario.
[0025] In step 3, the unit capacity of mobile energy storage is unified as The unit power of mobile energy storage is unified as The unit capacity of the switched capacitor is unified as The transmission capacity of the tie line is unified as
[0026] The objective function in step 4 is specifically: minE NL Among them, E NL It is expressed as the annual active network loss in the distribution system, ω is the equivalent number of years for each typical day, s is the season, t is the time period, i and j are the two ends of the distribution line, Ω S ,Ω T ,Ω L Represents the season, time period, and distribution line set respectively, P ij,t,s , Q ij,t,s Represents the active and reactive power of the line in operation, U Sub Represents the voltage amplitude of the power distribution system, R ij represents the resistance of the distribution line, and △t represents the time interval.
[0027] In step 5, the power flow constraints of the distribution network include node power balance constraints and node voltage balance constraints, and the node power balance constraints include: Among them, u(i) and v(i) represent the upstream and downstream line sets respectively. Represent the active and reactive power of the load respectively. Respectively represent the active and reactive charging power of mobile energy storage, Respectively represent the active and reactive discharge power of mobile energy storage, Respectively represent the active and reactive output power of the substation, is the reactive output power of the switched capacitor; The node voltage balance constraint includes: Among them, X ij Represents the branch reactance, U i,t,s , U j,t,s Represent the voltage amplitude at the node, M represents a maximum value, They represent the forward and reverse operation variables of the line respectively.
[0028] In step 5, the system safety operation constraints include node voltage amplitude constraints, branch transmission capacity constraints, substation constraints and grid constraints; The node voltage amplitude constraint includes: in, Represents the square terms of the maximum and minimum deviations of voltage; The branch transmission capacity constraints include: in, is the rated transmission capacity of the inherent line, is the rated transmission capacity of the tie line, Ω L is the intrinsic line set, Ω TL is the candidate set of contact lines to be built, Indicates the directionality of power, is equal to 1, the line is in the forward operation state, and the power value range is [0, M]. Otherwise, When it is equal to 1, the line is in reverse operation and the power range is [-M, 0]; The substation constraints include: in, is the rated capacity of the substation; The grid constraints include: in, is the line connection variable, Ω Load is the set of load nodes.
[0029] In the step 5, the multi-dimensional loss reduction resource configuration constraint conditions include mobile energy storage configuration constraints, switching capacitor configuration constraints, and tie line configuration constraints; The mobile energy storage configuration constraints include mobile energy storage capacity constraints, mobile energy storage output constraints and mobile energy storage power constraints: the mobile energy storage capacity constraints are: in, The rated capacity of mobile energy storage deployed in different seasons. The rated power of mobile energy storage deployed in different seasons. is the deployment variable of mobile energy storage, indicating the deployment status of mobile energy storage in different seasons. is the number of mobile energy storage deployed in different seasons, is the rated capacity of a single mobile energy storage unit, is the rated power of a single mobile energy storage unit, is the minimum deployment quantity of mobile energy storage, is the maximum number of mobile energy storage deployments, the number of mobile energy storage units to be deployed in the next season; The mobile energy storage output constraint is: in, It is the charging and discharging state variable of mobile energy storage; The mobile energy storage capacity constraint is: in, To store the power of mobile energy at the next moment, is the amount of mobile energy storage at that moment, η ch , η di is the charging and discharging efficiency of mobile energy storage, α, is the lower and upper limits of the mobile energy storage capacity, θ is the capacity coefficient of the mobile energy storage at the beginning and end times; The switching capacitor configuration constraints include switching capacitor capacity constraints, switching capacitor output constraints and switching capacitor limited action constraints; The switching capacitor capacity constraint is: in, The rated capacity of the switched capacitor deployment, Represents the number of switched capacitors deployed, Represents the rated capacity of a single group of switched capacitors. Represents the minimum and maximum installation quantity of switched capacitors; The switching capacitor output constraint is: in, The number of capacitors used in each period; The switching capacitor limited action constraint is: in, is the number of switched capacitors used at the next moment, The maximum number of switching capacitor operations; The tie line configuration constraints include construction constraints and power existence constraints; The construction constraints are: in, is the construction variable of the tie line, is the connection variable of the line; The tie line power existence constraint is:
[0030] In step 5, the annualized cost constraint condition includes the annualized cost constraint considering resource synergy, construction cost, operation and maintenance cost, and power loss cost: The annualized cost constraint considering resource synergy is: C Total ≤C max C Total =C IN +C O&M +C EL C IN =C ME,IN +C SC,IN +C TL,IN C O&M =C ME,O&M +C SC,O&M +C TL,O&M C EL =C ME,EL Among them, C Total is the annualized cost upper limit of the multiple loss reduction transformation strategy, C max is the annualized cost upper limit of the multiple loss reduction transformation strategy, C IN is the construction cost of the multiple loss reduction transformation strategy, C O&Mis the operation and maintenance cost of the multiple loss reduction transformation strategy, C EL is the power loss cost of the multi-layer loss reduction transformation strategy, C ME,IN , C SC,IN , C TL,IN are the construction costs of mobile energy storage, switching capacitors, and interconnection lines, respectively. ME,O&M , C SC,O&M , C TL,O&M are the operation and maintenance costs of mobile energy storage, switching capacitors, and tie lines, respectively. ME,EL The cost of power loss for mobile energy storage; The construction costs are: Among them, R ψ is the capital recovery coefficient of the multi-dimensional loss reduction and transformation strategy, d is the discount rate of the multi-dimensional loss reduction and transformation strategy, L ψ is the service life of the multiple loss reduction transformation strategy, is the construction cost coefficient of mobile energy storage per unit capacity, is the unit power construction cost coefficient, N S is the number of seasons, c SC,IN is the construction cost coefficient per unit capacity of switching capacitor, c TL,IN is the construction cost coefficient per unit length of the tie line, l ij is the line length; The operation and maintenance costs are: in, is the unit capacity operation and maintenance cost coefficient of mobile energy storage, is the unit power operation and maintenance cost coefficient of mobile energy storage, c SC,IN is the unit capacity operation and maintenance cost coefficient of the switched capacitor, is the operation and maintenance cost coefficient per unit length of the tie line; The power loss cost is: Among them, c ME,EL is the mobile energy storage power loss cost coefficient, κ ME is the power loss coefficient of mobile energy storage.
[0031] Specifically, in this embodiment, the minimum voltage deviation value U min is 0.9pu, the maximum voltage deviation is U max 1.1pu; rated capacity of substation is 100MVA; the maximum value of model relaxation M is 100000; the discount rate β is 0.05.
[0032] Figure 2 is a topological diagram of a 55-node distribution network system used in an embodiment of the present invention. The diagram includes different types of lines in the distribution network. Different types of lines are distinguished by different colors. The specific colors correspond to the line types shown in FIG. Figure 2 as shown in .
[0033] The large M method is used to relax the discrete variables, and the polygonal approximation is used to linearize the nonlinear constraints to obtain the limited loss reduction resource deployment candidate set. The deployment candidate set of mobile energy storage, switching capacitors, and tie lines is shown in Table 1: Table 1 Deployment candidate set of loss reduction transformation strategy This embodiment uses the commercial solver gurobi to solve the model, and the deployment location, deployment capacity, and cost of the multi-element coordinated loss reduction transformation plan for the tie line, switching capacitor, and mobile energy storage are shown in Table 2: Table 2 Configuration results of Example 1 In order to verify the effectiveness of the method proposed in the present invention, implementation example 1 and comparative implementation example 2 are provided. In comparative implementation example 1, only the deployment of tie lines is considered, and in comparative implementation example 2, the coordinated deployment of tie lines and switched capacitors is considered. The obtained results are shown in Tables 3 and 4: Table 3 compares the configuration results of implementation scheme 1 Table 4 compares the configuration results of implementation scheme 2 It can be seen from the above table that after considering the coordinated deployment of the interconnection lines, switching capacitors, and mobile energy storage of the multiple loss reduction transformation strategy, the operation benefit analysis of the coordinated configuration scheme was carried out using the operation results such as system network loss, voltage deviation, line power factor, and substation outgoing line load power. The present invention can determine the optimal configuration scheme under different operation scenarios, which has the advantage of improving the operation benefit. Not only that, under the premise of the same annualized cost, the present invention can take into account both investment economy and operation benefit improvement, and has stronger applicability. Increasing the annualized cost within a certain range can further reduce the system network loss, but as the annualized cost continues to increase, the investment benefit advantage of the coordinated configuration of the multiple loss reduction transformation strategy will be weakened. When the annualized cost increases, the investment proportion of mobile energy storage with flexible advantages and stronger reactive power regulation capabilities increases, which also reflects from the side that in the operation scenario of seasonal loads, mobile energy storage can effectively improve the system flow distribution and reduce line losses, and therefore has stronger applicability.
[0034] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.
[0035] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.
Claims
1. A method for collaborative configuration of active and reactive resources considering the lean operation requirements of distribution networks, characterized by: The following steps are involved: Step 1, traverse all nodes and lines in the area, generate multi-type load node sets according to node load characteristics, and generate line data sets according to line parameters; Step 2: Generate four typical load days according to the seasonal characteristics of the load, each typical load day represents a season in spring, summer, autumn and winter, and then obtain the active load and reactive load of the load in the typical load day; Step 3, taking the deployment location and capacity of the mobile energy storage as decision variables, and setting the unit capacity and unit power of the mobile energy storage; taking the deployment location and capacity of the switching capacitor as decision variables, and setting the unit capacity of the switching capacitor; The tie line deployment location is used as the decision variable, and the transmission capacity of the tie line is given a fixed value; Step 4: Based on the active load and reactive load parameters of the load in a typical day, as well as the decision variables and fixed value parameters, an objective function of establishing an active and reactive resource collaborative configuration model with the goal of minimizing the annual active network loss in the distribution system is constructed; Step 5: Construct the constraints of the active and reactive resource collaborative configuration model, which include distribution network flow constraints, system safety operation constraints, multi-dimensional loss reduction resource configuration constraints, and annualized cost constraints; Step 6: Based on the active and reactive resource collaborative configuration model and constraint conditions, the optimal configuration scheme and the minimum annualized total cost under different operation scenarios are obtained.
2. The method for collaboratively configuring active and reactive resources considering the lean operation requirements of distribution networks according to claim 1 is characterized in that: The step 1 is specifically to traverse all nodes and lines in the area and number the nodes and lines, determine the load peak of the nodes in each stage according to the node historical data, classify all nodes according to the load characteristics, put the same type of nodes in a set, and obtain a multi-type load node set; After traversing all the lines in the area and numbering them, the first and last nodes and line length of the line are determined according to the line connection information and physical information. The first and last nodes and line length of the line are put into a set according to the line number to generate a line data set, and all line data sets are generated in sequence.
3. The method for collaboratively configuring active and reactive resources considering the lean operation requirements of a distribution network according to claim 1 is characterized in that: In the step 2, the time of a typical day is also discretized, and each telecommunication day is discretized into 24 time periods, each time period is 60 minutes, and all states do not change in each time period.
4. The method for collaboratively configuring active and reactive resources considering the lean operation requirements of a distribution network according to claim 1 is characterized in that: In step 3, the unit capacity of mobile energy storage is unified as The unit power of mobile energy storage is unified as The unit capacity of the switched capacitor is unified as The transmission capacity of the tie line is unified as 5. The method for collaboratively configuring active and reactive resources considering the lean operation requirements of the distribution network according to claim 4 is characterized in that: The objective function in step 4 is specifically: minE NL Among them, E NL It is expressed as the annual active network loss in the distribution system, ω is the equivalent number of years for each typical day, s is the season, t is the time period, i and j are the two ends of the distribution line, Ω S ,Ω T ,Ω L Represents the season, time period, and distribution line set respectively, P ij,t,s , Q ij,t,s Represents the active and reactive power of the line in operation, U Sub Represents the voltage amplitude of the power distribution system, R ij represents the resistance of the distribution line, and △t represents the time interval.
6. The method for collaboratively configuring active and reactive resources considering the lean operation requirements of the distribution network according to claim 5 is characterized in that: In step 5, the power flow constraints of the distribution network include node power balance constraints and node voltage balance constraints, and the node power balance constraints include: Among them, u(i) and v(i) represent the upstream and downstream line sets respectively. Represent the active and reactive power of the load respectively. Respectively represent the active and reactive charging power of mobile energy storage, Respectively represent the active and reactive discharge power of mobile energy storage, Respectively represent the active and reactive output power of the substation, is the reactive output power of the switched capacitor; The node voltage balance constraint includes: Among them, X ij Represents the branch reactance, U i,t,s , U j,t,s Represent the voltage amplitude at the node, M represents a maximum value, They represent the forward and reverse operation variables of the line respectively.
7. The method for collaboratively configuring active and reactive resources considering the lean operation requirements of the distribution network according to claim 5 is characterized in that: In step 5, the system safety operation constraints include node voltage amplitude constraints, branch transmission capacity constraints, substation constraints and grid constraints; The node voltage amplitude constraint includes: in, Represents the square terms of the maximum and minimum deviations of voltage; The branch transmission capacity constraints include: in, is the rated transmission capacity of the inherent line, is the rated transmission capacity of the tie line, Ω L is the intrinsic line set, Ω TL is the candidate set of contact lines to be built, Indicates the directionality of power, is equal to 1, the line is in the forward operation state, and the power value range is [0, M]. Otherwise, When it is equal to 1, the line is in reverse operation and the power range is [-M, 0]; The substation constraints include: in, is the rated capacity of the substation; The grid constraints include: in, is the line connection variable, Ω Load is the set of load nodes.
8. The method for collaboratively configuring active and reactive resources considering the lean operation requirements of the distribution network according to claim 5 is characterized in that: In the step 5, the multi-dimensional loss reduction resource configuration constraint conditions include mobile energy storage configuration constraints, switching capacitor configuration constraints, and tie line configuration constraints; The mobile energy storage configuration constraints include mobile energy storage capacity constraints, mobile energy storage output constraints and mobile energy storage power constraints: the mobile energy storage capacity constraints are: in, The rated capacity of mobile energy storage deployed in different seasons. The rated power of mobile energy storage deployed in different seasons. is the deployment variable of mobile energy storage, indicating the deployment status of mobile energy storage in different seasons. is the number of mobile energy storage deployed in different seasons, is the rated capacity of a single mobile energy storage unit, is the rated power of a single mobile energy storage unit, is the minimum deployment quantity of mobile energy storage, is the maximum number of mobile energy storage deployments, the number of mobile energy storage units to be deployed in the next season; The mobile energy storage output constraint is: in, It is the charging and discharging state variable of mobile energy storage; The mobile energy storage capacity constraint is: in, To store the power of mobile energy at the next moment, is the amount of mobile energy storage at that moment, η ch , η di For the charging and discharging efficiency of mobile energy storage, is the lower and upper limits of the mobile energy storage capacity, θ is the capacity coefficient of the mobile energy storage at the beginning and end times; The switching capacitor configuration constraints include switching capacitor capacity constraints, switching capacitor output constraints and switching capacitor limited action constraints; The switching capacitor capacity constraint is: in, The rated capacity of the switched capacitor deployment, Represents the number of switched capacitors deployed, Represents the rated capacity of a single group of switched capacitors. Represents the minimum and maximum installation quantity of switched capacitors; The switching capacitor output constraint is: in, The number of capacitors used in each period; The switching capacitor limited action constraint is: in, is the number of switched capacitors used at the next moment, The maximum number of switching capacitor operations; The tie line configuration constraints include construction constraints and power existence constraints; The construction constraints are: in, is the construction variable of the tie line, is the connection variable of the line; The tie line power existence constraint is:
9. The method for collaboratively configuring active and reactive resources considering the lean operation requirements of a distribution network according to claim 5 is characterized in that: In step 5, the annualized cost constraint condition includes the annualized cost constraint considering resource synergy, construction cost, operation and maintenance cost, and power loss cost: The annualized cost constraint considering resource synergy is: C Total ≤C max C Total =C IN +C O&M +C EL C IN =C ME,IN +C SC,IN +C TL,IN C O&M =C ME,O&M +C SC,O&M +C TL,O&M C EL =C ME,EL Among them, C Total is the annualized cost upper limit of the multiple loss reduction transformation strategy, C max is the annualized cost upper limit of the multiple loss reduction transformation strategy, C IN is the construction cost of the multiple loss reduction transformation strategy, C O&M is the operation and maintenance cost of the multiple loss reduction transformation strategy, C EL is the power loss cost of the multi-layer loss reduction transformation strategy, C ME,IN , C SC,IN , C TL,IN are the construction costs of mobile energy storage, switching capacitors, and interconnection lines, respectively. ME,O&M , C SC,O&M , C TL,O&M are the operation and maintenance costs of mobile energy storage, switching capacitors, and tie lines, respectively. ME,EL The cost of power loss for mobile energy storage; The construction costs are: Among them, R ψ is the capital recovery coefficient of the multi-dimensional loss reduction and transformation strategy, d is the discount rate of the multi-dimensional loss reduction and transformation strategy, L ψ is the service life of the multiple loss reduction transformation strategy, is the construction cost coefficient of mobile energy storage unit capacity, is the unit power construction cost coefficient, N S is the number of seasons, c SC,IN is the construction cost coefficient per unit capacity of switching capacitor, c TL,IN is the construction cost coefficient per unit length of the tie line, l ij is the line length; The operation and maintenance costs are: in, is the unit capacity operation and maintenance cost coefficient of mobile energy storage, is the unit power operation and maintenance cost coefficient of mobile energy storage, c SC,IN is the unit capacity operation and maintenance cost coefficient of the switched capacitor, is the operation and maintenance cost coefficient per unit length of the tie line; The power loss cost is: Among them, c ME,EL is the mobile energy storage power loss cost coefficient, κ ME is the power loss coefficient of mobile energy storage.
10. The method for collaboratively configuring active and reactive resources considering the lean operation requirements of a distribution network according to any one of claims 1 to 9, characterized in that: In step 6, the solver gurobi is used to solve the active and reactive resource collaborative configuration model to obtain the minimum network loss, the deployment location, capacity and power of mobile energy storage, the deployment location and capacity of switching capacitors, the deployment location of the interconnection line, and the benefit improvement effect and cost plan of the loss reduction transformation strategy in different scenarios.