Multi-time scale distributed cluster optimization regulation and control method, device, equipment and medium

Abstract

The invention relates to a multi-time-scale distributed cluster optimization regulation and control method, device and equipment and a medium, and the method comprises the steps: constructing a load-storage-source low-voltage transformer area system model; a distributed cluster dynamic aggregation model is constructed according to the load-storage-source low-voltage transformer area system model, and the adjustable potential of transformer area resources is evaluated; establishing a day-ahead scheduling model by taking photovoltaic maximization absorption as a target according to the adjustable potential of the transformer area resources, and solving to obtain the maximum photovoltaic absorption amount of the system; on the basis of the maximum photovoltaic consumption amount of the system, an intra-day scheduling model with the minimum regulation and control cost as the target is established and solved, and the power interval of the intra-day flexibility adjustable and controllable resources in the low-voltage transformer area system is obtained; and formulating an optimal power regulation scheme in intra-day scheduling optimization by using the power interval, and issuing a regulation instruction to each distributed cluster. The method can adapt to the demand of non-uniform power transmission and distribution of a power system in a new situation.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated control of distributed energy, and in particular to a multi-time scale distributed cluster optimization control method, device, equipment and medium. Background Art

[0002] With the rapid development of renewable energy and the widespread application of distributed energy in power systems, the intermittent and volatile distributed photovoltaic power generation in large-scale areas has brought uncertainty and instability to the operation of the power system. At the same time, the random fluctuations of power load and electricity demand are also growing. The centralized management mode of traditional power systems and their power balance and reliable operation are facing unprecedented challenges. The main manifestations are as follows:

[0003] Since distributed photovoltaic power generation has obvious intermittent characteristics and is greatly affected by external natural factors, and its output power will fluctuate with changes in environmental conditions, the uncertainty of power transmission and distribution in time and space in the substation area will increase, making power regulation more difficult.

[0004] With the development of economy and the improvement of living standards, the demand for electricity continues to grow. The popularity of adjustable flexible loads such as electric vehicles has continuously aggravated the load pressure of low-voltage substations in the power system, making the randomness of load regulation increasingly increasing, which has led to an increase in the complexity of transmission and distribution regulation in the substations.

[0005] The low-voltage power supply and distribution in the traditional power system is completely dependent on the control instructions planned by the superior substation. However, with the widespread access of distributed energy and the increasing popularity of adjustable loads, in order to cope with the intermittent and volatile nature of renewable energy, the low-voltage substation system's own flexible resource regulation capabilities also play a certain dominant role, so as to cater to the superior's day-ahead control curve when the electric energy is constantly changing.

[0006] At present, a large amount of distributed photovoltaic energy has been integrated in low-voltage substations. Although its flexibility and decentralization provide clean and renewable electricity for the power grid, it is almost impossible to achieve unified and centralized management due to the complexity of its regulation. There is an urgent need to develop a more flexible distributed regulation strategy.

[0007] The traditional low-voltage substation mode of "centralized regulation and control" has been difficult to adapt to the needs of stable operation of the power grid under the new situation. How to propose a solution for substation regulation optimization to meet the needs of uneven power transmission and distribution in the new situation has become one of the technical problems that technicians in this field need to solve urgently.

[0008] It should be noted that the information disclosed in the background technology section of the invention is only intended to deepen the understanding of the general background technology of the invention, and should not be regarded as acknowledging or suggesting in any form that such information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0009] The technical problem to be solved by the present invention is to provide a multi-time scale distributed cluster optimization control method, device, equipment and medium, which can adapt to the needs of uneven power transmission and distribution in the power system under the new situation.

[0010] The technical solution adopted by the present invention to solve the technical problem is: to provide a multi-time scale distributed cluster optimization control method, comprising the following steps:

[0011] Constructing a load-storage-source low-voltage area system model, wherein the low-voltage area system model includes a controllable adjustable load model, an energy storage system device model, and a distributed photovoltaic model;

[0012] Constructing a distributed cluster dynamic aggregation model according to the load-storage-source low-voltage substation system model, and evaluating the adjustable potential of substation resources based on the distributed cluster dynamic aggregation model;

[0013] According to the adjustable potential of the substation resources, with the goal of maximizing photovoltaic absorption, a day-ahead dispatching model is established in combination with the day-ahead interactive power curve, and the day-ahead dispatching model is solved to obtain the maximum photovoltaic absorption capacity of the system;

[0014] Based on the maximum photovoltaic absorption capacity of the system and the adjustable potential of the resources in the substation, an intraday dispatch model with the goal of minimizing the regulation cost is established, and the intraday dispatch model is solved to obtain the power range of the intraday flexible adjustable resources in the low-voltage substation system;

[0015] The intraday flexible controllable resources are used to formulate the optimal power regulation plan in the intraday scheduling optimization in the power range in the low-voltage station system and issue regulation instructions to each distributed cluster.

[0016] The controllable adjustable load model is expressed as: Among them, PD EV,i,t is the power consumption of electric vehicles in cluster i at time t, is the adjustable flexibility adjustment coefficient of electric vehicles, P EV,i,t is the base load of electric vehicles in cluster i at time t.

[0017] The energy storage system device model is expressed as: Among them, P ES,+i,t is the upward flexibility power that the energy storage system in cluster i can provide at time t, is the output of the energy storage system in the maximum operation mode, P dis,i,t is the discharge output of the energy storage system in cluster i at time t, P ES,-i,t is the downward flexibility power that the energy storage system in cluster i can provide at time t, It is the limiting output of the energy storage system during discharge.

[0018] The distributed photovoltaic model is expressed as: Among them, P PV,+i,t is the upward flexibility power that the distributed photovoltaics in cluster i can provide at time t, is the output of distributed photovoltaic in the maximum operation mode, P PV,i,t is the output of distributed photovoltaic power generation in cluster i at time t, P PV,-i,t is the downward flexibility power that the distributed photovoltaics in cluster i can provide at time t, It is the output limit value of distributed photovoltaic.

[0019] The distributed cluster dynamic aggregation model is constructed according to the load-storage-source low-voltage area system model, and the adjustable potential of the area resources is evaluated based on the distributed cluster dynamic aggregation model. Specifically, the output and flexibility aggregation power trajectories of the adjustable flexibility resources in the area are integrated together using Minkowski and to obtain a distributed cluster dynamic aggregation model. Evaluate the power boundary of the feasible set of regional flexibility by The energy boundary of the feasible set of zone flexibility, where and They are respectively for the aggregation upward flexibility power constraint and downward flexibility power constraint, and is the upward flexibility power and downward flexibility power of distributed photovoltaic at time t; and is the upward flexibility power and downward flexibility power provided by the energy storage system device at time t; and is the upward flexibility power and downward flexibility power of the adjustable load at time t; j∈g indicates that the flexibility resource j is the distributed photovoltaic in the cluster; j∈E indicates that the flexibility resource j is the energy storage system device in the cluster; j∈l indicates that the flexibility resource j is the adjustable load in the cluster; and They are the downward adjustment constraint and upward adjustment constraint of the flexibility energy of cluster i at time t+1 respectively; and They are the upward flexibility power and downward flexibility power of distributed photovoltaics; and They are the upward and downward flexibility powers provided by the energy storage device; is the upward flexibility power of the adjustable load; Δt represents unit time.

[0020] The objective function of the day-ahead scheduling model is expressed as: Among them, F is the objective function of the day-ahead scheduling model, t represents the time period, and D represents the set of all nodes installed with photovoltaic power generation systems. is the amount of abandoned light in the photovoltaic power generation system at node n at time t.

[0021] The objective function of the intraday scheduling model is expressed as: Among them, C j is the cost function under flexibility aggregation power, expressed as: γ j , b j and c j is the cost-effectiveness coefficient of distributed photovoltaics; α E,1,j and α E,2,j The cost-effectiveness coefficient of the energy storage system backup device; β D,j P is the cost-effectiveness coefficient of adjustable load reserve; g,j and P D,j are the source side output and the load side load demand respectively; P E,j is the output of the energy storage system device, is the maximum output of the energy storage system; P l,j is the known load that has not been processed by demand response, j∈g means that the flexibility resource j is the distributed photovoltaic in the cluster; j∈l means that the flexibility resource j is the adjustable load in the cluster, The profit function obtained by providing spare capacity is expressed as: and are the upper reserve profit coefficient and the lower reserve profit coefficient, and They are respectively used to solve the intraday upward reserve capacity and the intraday downward reserve capacity under the flexibility aggregation power. is the power value predicted in the day-ahead phase.

[0022] The constraints of the intraday scheduling model include a power balance constraint of the substation area, and the power balance constraint of the substation area is expressed as: Where D is the set of load-side clusters, G is the set of source-side clusters, and P G,i,t is the source-side power output of cluster i at time t, P D,i,t is the load demand of cluster i at time t.

[0023] The technical solution adopted by the present invention to solve the technical problem is: to provide a multi-time scale distributed cluster optimization control device, comprising:

[0024] A construction module is used to construct a load-storage-source low-voltage area system model, wherein the low-voltage area system model includes a controllable adjustable load model, an energy storage system device model, and a distributed photovoltaic model;

[0025] Constructing an evaluation module, which is used to construct a distributed cluster dynamic aggregation model according to the load-storage-source low-voltage substation system model, and evaluate the adjustable potential of substation resources based on the distributed cluster dynamic aggregation model;

[0026] The first modeling and solving module is used to establish a day-ahead scheduling model based on the adjustable potential of the substation resources and the goal of maximizing photovoltaic absorption, combined with the day-ahead interactive power curve, and solve the day-ahead scheduling model to obtain the maximum photovoltaic absorption capacity of the system;

[0027] The second modeling and solving module is used to establish a daily dispatch model with the goal of minimizing the regulation cost based on the maximum photovoltaic absorption capacity of the system and the adjustable potential of the resources in the substation, and solve the daily dispatch model to obtain the power range of the flexible and adjustable resources in the low-voltage substation system.

[0028] The control module is used to formulate the optimal power regulation plan in the intra-day scheduling optimization by utilizing the power range of the intra-day flexible and adjustable resources in the low-voltage substation system and issue regulation instructions to each distributed cluster.

[0029] The controllable and adjustable load model constructed by the building module is expressed as: Among them, PD EV,i,t is the power consumption of electric vehicles in cluster i at time t, is the adjustable flexibility adjustment coefficient of electric vehicles, P EV,i,t is the base load of electric vehicles in cluster i at time t.

[0030] The energy storage system device model constructed by the building blocks is expressed as: Among them, P ES,+i,t is the upward flexibility power that the energy storage system in cluster i can provide at time t, is the output of the energy storage system in the maximum operation mode, P dis,i,t is the discharge output of the energy storage system in cluster i at time t, P ES,-i,t is the downward flexibility power that the energy storage system in cluster i can provide at time t, It is the limiting output of the energy storage system during discharge.

[0031] The distributed photovoltaic model constructed by the building block is expressed as: Among them, P PV,+i,t is the upward flexibility power that the distributed photovoltaics in cluster i can provide at time t, is the output of distributed photovoltaic in the maximum operation mode, P PV,i,t is the output of distributed photovoltaic power generation in cluster i at time t, P PV,-i,t is the downward flexibility power that the distributed photovoltaics in cluster i can provide at time t, It is the output limit value of distributed photovoltaic.

[0032] The evaluation module uses Minkowski and integrates the output and flexibility aggregation power trajectories of the adjustable flexibility resources in the substation area to obtain a distributed cluster dynamic aggregation model. Evaluate the power boundary of the feasible set of regional flexibility by The energy boundary of the feasible set of zone flexibility, where and They are respectively for the upward and downward flexibility power constraints of aggregation, and is the upward flexibility power and downward flexibility power of distributed photovoltaic at time t; and is the upward flexibility power and downward flexibility power provided by the energy storage system device at time t; and is the upward flexibility power and downward flexibility power of the adjustable load at time t; j∈g indicates that the flexibility resource j is the distributed photovoltaic in the cluster; j∈E indicates that the flexibility resource j is the energy storage system device in the cluster; j∈l indicates that the flexibility resource j is the adjustable load in the cluster; and They are the downward constraint and upward constraint of the flexibility energy of cluster i at time t+1 respectively; and They are the upward flexibility power and downward flexibility power of distributed photovoltaics; and They are the upward and downward flexibility powers provided by the energy storage device; is the upward flexibility power of the adjustable load; Δt represents unit time.

[0033] The objective function of the day-ahead scheduling model established by the first modeling and solving module is expressed as: Among them, F is the objective function of the day-ahead scheduling model, t represents the time period, and D represents the set of all nodes installed with photovoltaic power generation systems. is the amount of abandoned light in the photovoltaic power generation system at node n at time t.

[0034] The objective function of the intraday scheduling model established by the second modeling and solving module is expressed as: Among them, C j is the cost function under flexibility aggregation power, expressed as: γ j , b j and c j is the cost-effectiveness coefficient of distributed photovoltaics; α E,1,j and α E,2,j The cost-effectiveness coefficient of the energy storage system backup device; β D,j is the cost-effectiveness coefficient of adjustable load reserve; P g,j and P D,j are the source side output and the load side load demand respectively; P E,j is the output of the energy storage system device, is the maximum output of the energy storage system; P l,j is the known load that has not been processed by demand response, j∈g means that the flexibility resource j is the distributed photovoltaic in the cluster; j∈l means that the flexibility resource j is the adjustable load in the cluster, The profit function obtained by providing spare capacity is expressed as: and are the upper reserve profit coefficient and the lower reserve profit coefficient, and They are respectively used to solve the intraday upward reserve capacity and the intraday downward reserve capacity under the flexibility aggregation power. is the power value predicted in the day-ahead phase.

[0035] The constraints of the intraday scheduling model established by the second modeling and solving module include a power balance constraint of the substation area, and the power balance constraint of the substation area is expressed as: Where D is the set of load-side clusters, G is the set of source-side clusters, and P G,i,t is the source-side power output of cluster i at time t, P D,i,t is the load demand of cluster i at time t.

[0036] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned multi-time-scale distributed cluster optimization and control method when executing the computer program.

[0037] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned multi-time-scale distributed cluster optimization and control method are implemented.

[0038] Beneficial Effects

[0039] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art: the present invention constructs a distributed cluster dynamic aggregation model, and effectively solves the problem that it is difficult to realize centralized control of power transmission and distribution in low-voltage substations under the influence of distributed photovoltaics and load diversity at this stage through cluster division. At the same time, the establishment of a virtual battery model in the cluster gives full play to the adjustable potential of flexible resources, ensuring the real-time feasibility of the power regulation process. The present invention can cater well to the day-ahead interactive power curve of the substation issued by the superior through the self-regulation of resources in the substation, and realize the maximum photovoltaic absorption of the substation by multi-time scale optimization and control within the day-ahead. According to the control priority of the load-storage source, the photovoltaic absorption is further corrected to fit the power curve, and the control cost is minimized to meet the actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flowchart of a multi-time-scale distributed cluster optimization and control method according to a first embodiment of the present invention;

[0041] Figure 2 is a schematic diagram of a distributed cluster in a first embodiment of the present invention;

[0042] Figure 3 Schematic diagram of Minkowski summation in the first embodiment of the present invention. DETAILED DESCRIPTION

[0043] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0044] The first embodiment of the present invention relates to a multi-time scale distributed cluster optimization control method, such as Figure 1 As shown, the following steps are included:

[0045] Step 1: construct a low-voltage area system model of load-storage-source. The distributed cluster in this implementation is as follows: Figure 2 As shown, the low-voltage area system model constructed therefore includes a controllable adjustable load model, an energy storage system device model and a distributed photovoltaic model.

[0046] The controllable and adjustable load model constructed in this step is a controllable and adjustable load with electric vehicles as the main body, which can be expressed as:

[0047]

[0048] Among them, PD EV,i,t is the power consumption of electric vehicles in cluster i at time t, P is the adjustable flexibility coefficient of the electric vehicle, which is used to adjust the load up and down. EV,i,t is the base load of electric vehicles in cluster i at time t, that is, the load level when it is not regulated.

[0049] The base load P of electric vehicles in cluster i at time t in the controllable load model EV,i,t The constraints can be described as:

[0050]

[0051] In the formula, is the maximum charging power of the electric vehicles in cluster i at time t, which is limited by the power of the charging pile and the charging capacity of the electric vehicles themselves.

[0052] Adjustable flexibility coefficient of electric vehicles in controllable load model Affected by market prices or incentive mechanisms, it indicates the load regulation capability of electric vehicles when participating in demand response, and its value range is:

[0053]

[0054] when When the electric vehicle stops charging completely; when When the electric vehicle charging power reaches the maximum

[0055] The energy storage system device model constructed in this step can be expressed as:

[0056]

[0057] Among them, P ES,+i,t is the upward flexibility power that the energy storage system in cluster i can provide at time t, is the output of the energy storage system in the maximum operation mode, P dis,i,t is the discharge output of the energy storage system in cluster i at time t, P ES,-i,t is the downward flexibility power that the energy storage system in cluster i can provide at time t, It is the limiting output of the energy storage system during discharge.

[0058] The charging and discharging power constraints of the energy storage system device model are:

[0059]

[0060] Where P dis,i,t is the discharge power of the energy storage system device in cluster i at time t, is the maximum discharge power of the energy storage system, Pcharge,i,t is the charging power of the energy storage system device in cluster i at time t, is the maximum discharge power of the energy storage system, S SOC,i,t It indicates the state of the energy storage system at time t. 0 indicates that the energy storage system is completely discharged and has no remaining power. 1 indicates that the energy storage system is fully charged and the battery power reaches the maximum value. It reflects the current charging level of the energy storage system.

[0061] The distributed photovoltaic model constructed in this step can be expressed as:

[0062]

[0063] Among them, P PV,+i,t is the upward flexibility power that the distributed photovoltaics in cluster i can provide at time t, is the output of distributed photovoltaic in the maximum operation mode, P PV,i,t is the output of distributed photovoltaic power generation in cluster i at time t, P PV,-i,t is the downward flexibility power that the distributed photovoltaics in cluster i can provide at time t, It is the output limit value of distributed photovoltaic.

[0064] The output constraint of the distributed photovoltaic model is expressed as:

[0065]

[0066] Step 2: construct a distributed cluster dynamic aggregation model according to the load-storage-source low-voltage substation system model, and evaluate the adjustable potential of substation resources based on the distributed cluster dynamic aggregation model.

[0067] In this step, the Minkowski sum is used to integrate the load-storage-source low-voltage area system model, such as Figure 3 As shown in the figure, the specific process is to use Minkowski and to convert the output P of the adjustable flexibility resource j in the substation into j and flexibility aggregate power trajectory P agg Putting them together, we get the flexible feasible set of the whole area, which is described by the formula:

[0068]

[0069] Among them, P D and P DR They represent the reserve energy of adjustable load and energy storage and the reserve capacity of distributed photovoltaic. pro It is a flexible and feasible set of the overall adjustable resources in the substation.

[0070] The power boundary of the feasible set of area flexibility can be evaluated and expressed by the virtual battery model, and its calculation formula is:

[0071]

[0072] in, and They are respectively for the upward and downward flexibility power constraints of aggregation, and is the upward flexibility power and downward flexibility power of distributed photovoltaic at time t; and is the upward flexibility power and downward flexibility power provided by the energy storage system device at time t; and It is the upward flexibility power and downward flexibility power of the adjustable load at time t; j∈g means that the flexibility resource j is the distributed photovoltaic in the cluster; j∈E means that the flexibility resource j is the energy storage system device in the cluster; j∈l means that the flexibility resource j is the adjustable load in the cluster.

[0073] The energy boundary of the feasible set of area flexibility can also be evaluated and expressed by the virtual battery model, and its calculation formula is:

[0074]

[0075] in, and They are the downward constraint and upward constraint of the flexibility energy of cluster i at time t+1 respectively; and They are the upward flexibility power and downward flexibility power of distributed photovoltaics; and They are the upward and downward flexibility powers provided by the energy storage device; is the upward flexibility power of the adjustable load; Δt represents unit time.

[0076] The feasible set of regional resources based on the virtual battery model is used to obtain the actual feasible region P where multiple flexible resources can provide flexible power to the system. agg ∈R N , and the distribution network security domain is introduced, and its description formula is:

[0077]

[0078] In the formula, R N Represents the N-dimensional time period space within 24 hours before the current day; and They represent the boundaries of upward and downward flexibility power in time period T respectively; S safe Represents a multi-dimensional constraint space to limit the upward and downward adjustment range of flexible power, ensuring that power regulation does not exceed the carrying capacity of the distribution network; Pmin (t) is the minimum power allowed by the upper distribution network in time period t, P max (t) is the maximum power allowed by the upper distribution network in time period t.

[0079] Step 3: Based on the adjustable potential of the substation resources and with the goal of maximizing photovoltaic absorption, a day-ahead scheduling model is established in combination with the day-ahead interactive power curve, and the day-ahead scheduling model is solved to obtain the maximum photovoltaic absorption capacity of the system.

[0080] In this step, a day-ahead control model is established with the objective function of maximizing the amount of photovoltaic power generation consumed within one day. The expression of the objective function is:

[0081]

[0082] Among them, F is the objective function of the day-ahead scheduling model, t represents the time period, corresponding to 24 hours within a day, and D represents the set of all nodes installed with photovoltaic power generation systems. is the amount of abandoned light in the photovoltaic power generation system at node n at time t.

[0083] The low voltage 380V improved IEEE21 node is used as the substation simulation model, and the power flow constraint model is used to calculate the power flow. The operation safety constraints under the power flow equation are as follows:

[0084]

[0085] Where P ti,j , Q ti,j is the active and reactive power at the branch i and j, P tj.L.fore , Q tj.L.fore represents the predicted value of active and reactive power of the load at node j, P tj.L.in , Q tj.L.in represents the active and reactive power flowing into node j from other branches, P tj.L.out , Q tj.L.out represents the active and reactive power flowing out of node j to other branches, P tj.PV , Q tj.PV is the active and reactive power of photovoltaic power generation at node j, P tj.PV.cur , Q tj.PV.cur is the active and reactive power reduction of the photovoltaic node j, ω(j) is the set of terminal nodes connected to node j, θ j is the power factor angle at node j, Q tj.C is the reactive power compensation of node j, I ti,j is the current in branches i and j, V ti is the voltage at node i, x ij 、r ij is the reactance and resistance of branch ij.

[0086] Relax the above current square equation and convert it into the standard second-order cone form:

[0087]

[0088] The power flow constraint model has nonlinear constraints. As described in the above formula, the second-order cone programming method is used to transform the model. By transforming the problem into a convex problem, the model solving process is simplified, and finally the maximum photovoltaic power generation that the system can absorb under given constraints is obtained.

[0089] Step 4: Based on the maximum photovoltaic absorption capacity of the system and in accordance with the adjustable potential of the substation resources, a daily scheduling model is established with the goal of minimizing the regulation cost, and the intraday scheduling model is solved to obtain the power range of the intraday flexible and adjustable resources in the low-voltage substation system.

[0090] The objective function of the intraday scheduling model established in this step is expressed as:

[0091]

[0092] Among them, C j is the cost function under flexibility aggregation power, The revenue function for providing spare capacity is: and They are respectively the intraday upward reserve capacity and the intraday downward reserve capacity under the flexible aggregate power solution, which must meet the elastic power range based on Minkowski and the solution of the virtual battery model in the day-ahead regulation. Constraints, is the power value predicted in the day-ahead phase.

[0093] Among them, the cost function C under the flexibility aggregation power j It can be expressed as:

[0094]

[0095] Among them, γ j , b j and c j is the cost-effectiveness coefficient of distributed photovoltaics; α E,1,j and α E,2,j The cost-effectiveness coefficient of the energy storage system backup device; β D,j P is the cost-effectiveness coefficient of adjustable load reserve; g,j and P D,j are the source side output and the load side load demand respectively; P E,j is the output of the energy storage system device, is the maximum output of the energy storage system; P l,jis the known amount of load that has not been handled by demand response.

[0096] Profit function for providing spare capacity The specific expression is:

[0097]

[0098] in, and They are the upper reserve profit coefficient and the lower reserve profit coefficient respectively.

[0099] The constraints of the intraday dispatch model established in this step also include the power balance constraints of the substation area, which can be expressed as:

[0100]

[0101] Where D is the set of load-side clusters, G is the set of source-side clusters, and P G,i,t is the source-side power output of cluster i at time t, P D,i,t is the load demand of cluster i at time t.

[0102] This step uses a distributed consistency algorithm to solve the intraday scheduling model, namely:

[0103]

[0104] In the formula, k is the number of iterations, λ i (k-1) is the consistent term, is the target asymptotic term, whose function value is the ideal value of the consistency variable and the actual value of the consistency variable λ i The difference between (k) i (k), the actual value of the driving consistency variable λ i (k) tends to the ideal value

[0105] When , there is Δλ1(k)=Δλ2(k)=…=Δλ n (k) = 0, at this time the optimal solution of distributed economic regulation can be obtained, that is, the power range of the flexible and controllable resources in the low-voltage station system within the day.

[0106] Step 5: Utilize the power range of the intraday flexible adjustable resources in the low-voltage area system to formulate the optimal power adjustment plan in the intraday scheduling optimization and issue adjustment instructions to each distributed cluster. This step is as follows:

[0107] When the consistency algorithm is used to solve the intraday scheduling model, a new power range appears for the flexible and controllable resources. The system promptly corrects the power limit before issuing instructions and sets the output power error constraint of the system operation and control curve. The optimal power regulation plan for the next unit time interval is determined by referring to the economic response of the flexible services of each resource, and is controlled according to the timing priority of "load-storage-source".

[0108] It is not difficult to find that the present invention constructs a distributed cluster dynamic aggregation model, and effectively solves the problem that it is difficult to achieve centralized control of power transmission and distribution in low-voltage areas under the influence of distributed photovoltaics and load diversity at this stage through cluster division. At the same time, the establishment of a virtual battery model in the cluster fully utilizes the adjustable potential of flexible resources to ensure the real-time feasibility of the power regulation process. The present invention can cater well to the interactive power curve of the area sent by the superior through the self-regulation of resources in the area, and realize the maximum photovoltaic consumption of the area through multi-time scale optimization and control within the day before the day. According to the control priority of the load storage source, the photovoltaic consumption is further corrected to fit the power curve, and the control cost is minimized to meet the actual needs.

[0109] A second embodiment of the present invention relates to a multi-time scale distributed cluster optimization control device, comprising:

[0110] A construction module is used to construct a load-storage-source low-voltage area system model, wherein the low-voltage area system model includes a controllable adjustable load model, an energy storage system device model, and a distributed photovoltaic model;

[0111] Constructing an evaluation module, which is used to construct a distributed cluster dynamic aggregation model according to the load-storage-source low-voltage substation system model, and evaluate the adjustable potential of substation resources based on the distributed cluster dynamic aggregation model;

[0112] The first modeling and solving module is used to establish a day-ahead scheduling model based on the adjustable potential of the substation resources and the goal of maximizing photovoltaic absorption, combined with the day-ahead interactive power curve, and solve the day-ahead scheduling model to obtain the maximum photovoltaic absorption capacity of the system;

[0113] The second modeling and solving module is used to establish a daily dispatch model with the goal of minimizing the regulation cost based on the maximum photovoltaic absorption capacity of the system and the adjustable potential of the resources in the substation, and solve the daily dispatch model to obtain the power range of the flexible and adjustable resources in the low-voltage substation system.

[0114] The control module is used to formulate the optimal power regulation plan in the intra-day scheduling optimization by utilizing the power range of the intra-day flexible and adjustable resources in the low-voltage substation system and issue regulation instructions to each distributed cluster.

[0115] The controllable and adjustable load model constructed by the building module is expressed as: Among them, PD EV,i,t is the power consumption of electric vehicles in cluster i at time t, is the adjustable flexibility adjustment coefficient of electric vehicles, P EV,i,t is the base load of electric vehicles in cluster i at time t.

[0116] The energy storage system device model constructed by the building blocks is expressed as: Among them, P ES,+i,t is the upward flexibility power that the energy storage system in cluster i can provide at time t, is the output of the energy storage system in the maximum operation mode, P dis,i,t is the discharge output of the energy storage system in cluster i at time t, P ES,-i,t is the downward flexibility power that the energy storage system in cluster i can provide at time t, It is the limiting output of the energy storage system during discharge.

[0117] The distributed photovoltaic model constructed by the building block is expressed as: Among them, P PV,+i,t is the upward flexibility power that the distributed photovoltaics in cluster i can provide at time t, is the output of distributed photovoltaic in the maximum operation mode, P PV,i,t is the output of distributed photovoltaic power generation in cluster i at time t, P PV,-i,t is the downward flexibility power that the distributed photovoltaics in cluster i can provide at time t, It is the output limit value of distributed photovoltaic.

[0118] The evaluation module uses Minkowski and integrates the output and flexibility aggregation power trajectories of the adjustable flexibility resources in the substation area to obtain a distributed cluster dynamic aggregation model. Evaluate the power boundary of the feasible set of regional flexibility by The energy boundary of the feasible set of zone flexibility, where and They are respectively for the aggregation upward flexibility power constraint and downward flexibility power constraint, and is the upward flexibility power and downward flexibility power of distributed photovoltaic at time t; and is the upward flexibility power and downward flexibility power provided by the energy storage system device at time t; and is the upward flexibility power and downward flexibility power of the adjustable load at time t; j∈g indicates that the flexibility resource j is the distributed photovoltaic in the cluster; j∈E indicates that the flexibility resource j is the energy storage system device in the cluster; j∈l indicates that the flexibility resource j is the adjustable load in the cluster; and They are the downward adjustment constraint and upward adjustment constraint of the flexibility energy of cluster i at time t+1 respectively; and They are the upward flexibility power and downward flexibility power of distributed photovoltaics; and They are the upward and downward flexibility powers provided by the energy storage device; is the upward flexibility power of the adjustable load; Δt represents unit time.

[0119] The objective function of the day-ahead scheduling model established by the first modeling and solving module is expressed as: Among them, F is the objective function of the day-ahead scheduling model, t represents the time period, and D represents the set of all nodes installed with photovoltaic power generation systems. is the amount of abandoned light in the photovoltaic power generation system at node n at time t.

[0120] The objective function of the intraday scheduling model established by the second modeling and solving module is expressed as: Among them, C j is the cost function under flexibility aggregation power, expressed as: γ j , b j and c j is the cost-effectiveness coefficient of distributed photovoltaics; α E,1,j and α E,2,j The cost-effectiveness coefficient of the energy storage system backup device; β D,j P is the cost-effectiveness coefficient of adjustable load reserve; g,j and P D,j are the source side output and the load side load demand respectively; P E,j is the output of the energy storage system device, is the maximum output of the energy storage system; P l,j is the known load that has not been processed by demand response, j∈g means that the flexibility resource j is the distributed photovoltaic in the cluster; j∈l means that the flexibility resource j is the adjustable load in the cluster, The profit function obtained by providing spare capacity is expressed as: and are the upper reserve profit coefficient and the lower reserve profit coefficient, and They are respectively used to solve the intraday upward reserve capacity and the intraday downward reserve capacity under the flexibility aggregation power. is the power value predicted in the day-ahead phase.

[0121] The constraints of the intraday scheduling model established by the second modeling and solving module include a power balance constraint of the substation area, and the power balance constraint of the substation area is expressed as: Where D is the set of load-side clusters, G is the set of source-side clusters, and P G,i,t is the source-side power output of cluster i at time t, P D,i,t is the load demand of cluster i at time t.

[0122] A third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the multi-time-scale distributed cluster optimization and control method of the first embodiment when executing the computer program.

[0123] A fourth embodiment of the present invention is a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the multi-time-scale distributed cluster optimization and control method of the first embodiment.

[0124] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0125] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0128] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A multi-time scale distributed cluster optimization control method, characterized in that: The following steps are involved: Constructing a load-storage-source low-voltage area system model, wherein the low-voltage area system model includes a controllable adjustable load model, an energy storage system device model, and a distributed photovoltaic model; Constructing a distributed cluster dynamic aggregation model according to the load-storage-source low-voltage substation system model, and evaluating the adjustable potential of substation resources based on the distributed cluster dynamic aggregation model; According to the adjustable potential of the substation resources, with the goal of maximizing photovoltaic absorption, a day-ahead dispatching model is established in combination with the day-ahead interactive power curve, and the day-ahead dispatching model is solved to obtain the maximum photovoltaic absorption capacity of the system; Based on the maximum photovoltaic absorption capacity of the system and the adjustable potential of the resources in the substation, an intraday dispatch model with the goal of minimizing the regulation cost is established, and the intraday dispatch model is solved to obtain the power range of the intraday flexible adjustable resources in the low-voltage substation system; The intraday flexible controllable resources are used to formulate the optimal power regulation plan in the intraday scheduling optimization in the power range in the low-voltage station system and issue regulation instructions to each distributed cluster.

2. The multi-time scale distributed cluster optimization control method according to claim 1 is characterized in that: The controllable adjustable load model is expressed as: Among them, PD EV,i,t is the power consumption of electric vehicles in cluster i at time t, is the adjustable flexibility adjustment coefficient of electric vehicles, P EV,i,t is the base load of electric vehicles in cluster i at time t.

3. The multi-time scale distributed cluster optimization control method according to claim 1 is characterized in that: The energy storage system device model is expressed as: Among them, P ES,+i,t is the upward flexibility power that the energy storage system in cluster i can provide at time t, is the output of the energy storage system in the maximum operation mode, P dis,i,t is the discharge output of the energy storage system in cluster i at time t, P ES,-i,t is the downward flexibility power that the energy storage system in cluster i can provide at time t, It is the limiting output of the energy storage system during discharge.

4. The multi-time scale distributed cluster optimization control method according to claim 1, characterized in that: The distributed photovoltaic model is expressed as: Among them, P PV,+i,t is the upward flexibility power that the distributed photovoltaics in cluster i can provide at time t, is the output of distributed photovoltaic in the maximum operation mode, P PV,i,t is the output of distributed photovoltaic power generation in cluster i at time t, P PV,-i,t is the downward flexibility power that the distributed photovoltaics in cluster i can provide at time t, It is the output limit value of distributed photovoltaic.

5. The multi-time scale distributed cluster optimization control method according to claim 1, characterized in that: The distributed cluster dynamic aggregation model is constructed according to the load-storage-source low-voltage area system model, and the adjustable potential of the area resources is evaluated based on the distributed cluster dynamic aggregation model. Specifically, the output and flexibility aggregation power trajectories of the adjustable flexibility resources in the area are integrated together using Minkowski and to obtain a distributed cluster dynamic aggregation model. Evaluate the power boundary of the feasible set of regional flexibility by The energy boundary of the feasible set of zone flexibility, where and They are respectively for the upward and downward flexibility power constraints of aggregation, and is the upward flexibility power and downward flexibility power of distributed photovoltaic at time t; and is the upward flexibility power and downward flexibility power provided by the energy storage system device at time t; and is the upward flexibility power and downward flexibility power of the adjustable load at time t; j∈g indicates that the flexibility resource j is the distributed photovoltaic in the cluster; j∈E indicates that the flexibility resource j is the energy storage system device in the cluster; j∈l indicates that the flexibility resource j is the adjustable load in the cluster; and They are the downward constraint and upward constraint of the flexibility energy of cluster i at time t+1 respectively; and They are the upward flexibility power and downward flexibility power of distributed photovoltaics; and They are the upward and downward flexibility powers provided by the energy storage device; is the upward flexibility power of the adjustable load; Δt represents unit time.

6. The multi-time scale distributed cluster optimization control method according to claim 1, characterized in that: The objective function of the day-ahead scheduling model is expressed as: Among them, F is the objective function of the day-ahead scheduling model, t represents the time period, and D represents the set of all nodes installed with photovoltaic power generation systems. is the amount of abandoned light in the photovoltaic power generation system at node n at time t.

7. The multi-time scale distributed cluster optimization control method according to claim 1 is characterized in that: The objective function of the intraday scheduling model is expressed as: Among them, C j is the cost function under flexibility aggregation power, expressed as: γ j 、b j and c j is the cost-effectiveness coefficient of distributed photovoltaics; α E,1,j and α E,2,j Cost-effectiveness factor for installing backup for energy storage system; β D,j P is the cost-effectiveness coefficient of adjustable load reserve; g,j and P D,j are the source side output and the load side load demand respectively; P E,j is the output of the energy storage system device, is the maximum output of the energy storage system; P l,j is the known load that has not been processed by demand response, j∈g means that the flexibility resource j is the distributed photovoltaic in the cluster; j∈l means that the flexibility resource j is the adjustable load in the cluster, The profit function obtained by providing spare capacity is expressed as: and are the upper reserve profit coefficient and the lower reserve profit coefficient, and They are respectively used to solve the intraday upward reserve capacity and the intraday downward reserve capacity under the flexibility aggregation power. is the power value predicted in the day-ahead phase.

8. The multi-time scale distributed cluster optimization control method according to claim 7 is characterized in that: The constraints of the intraday scheduling model include a power balance constraint of the substation area, and the power balance constraint of the substation area is expressed as: Where D is the set of load-side clusters, G is the set of source-side clusters, and P G,i,t is the source-side power output of cluster i at time t, P D,i,t is the load demand of cluster i at time t.

9. A multi-time scale distributed cluster optimization control device, characterized in that: include: A construction module is used to construct a load-storage-source low-voltage area system model, wherein the low-voltage area system model includes a controllable adjustable load model, an energy storage system device model, and a distributed photovoltaic model; Constructing an evaluation module, which is used to construct a distributed cluster dynamic aggregation model according to the load-storage-source low-voltage substation system model, and evaluate the adjustable potential of substation resources based on the distributed cluster dynamic aggregation model; The first modeling and solving module is used to establish a day-ahead scheduling model based on the adjustable potential of the substation resources and the goal of maximizing photovoltaic absorption, combined with the day-ahead interactive power curve, and solve the day-ahead scheduling model to obtain the maximum photovoltaic absorption capacity of the system; The second modeling and solving module is used to establish a daily dispatch model with the goal of minimizing the regulation cost based on the maximum photovoltaic absorption capacity of the system and the adjustable potential of the resources in the substation, and solve the daily dispatch model to obtain the power range of the flexible and adjustable resources in the low-voltage substation system. The control module is used to formulate the optimal power regulation plan in the intra-day scheduling optimization by utilizing the power range of the intra-day flexible and adjustable resources in the low-voltage substation system and issue regulation instructions to each distributed cluster.

10. The multi-time scale distributed cluster optimization control device according to claim 9, characterized in that: The controllable and adjustable load model constructed by the building module is expressed as: Among them, PD EV,i,t is the power consumption of electric vehicles in cluster i at time t, is the adjustable flexibility adjustment coefficient of electric vehicles, P EV,i,t is the base load of electric vehicles in cluster i at time t.

11. The multi-time scale distributed cluster optimization control device according to claim 9, characterized in that: The energy storage system device model constructed by the building blocks is expressed as: Among them, P ES,+i,t is the upward flexibility power that the energy storage system in cluster i can provide at time t, is the output of the energy storage system in the maximum operation mode, P dis,i,t is the discharge output of the energy storage system in cluster i at time t, P ES,-i,t is the downward flexibility power that the energy storage system in cluster i can provide at time t, It is the limiting output of the energy storage system during discharge.

12. The multi-time scale distributed cluster optimization control device according to claim 9, characterized in that: The distributed photovoltaic model constructed by the building block is expressed as: Among them, P PV,+i,t is the upward flexibility power that the distributed photovoltaics in cluster i can provide at time t, is the output of distributed photovoltaic in the maximum operation mode, P PV,i,t is the output of distributed photovoltaic power generation in cluster i at time t, P PV,-i,t is the downward flexibility power that the distributed photovoltaics in cluster i can provide at time t, It is the output limit value of distributed photovoltaic.

13. The multi-time scale distributed cluster optimization control device according to claim 9, characterized in that: The evaluation module uses Minkowski and integrates the output and flexibility aggregation power trajectories of the adjustable flexibility resources in the substation area to obtain a distributed cluster dynamic aggregation model. Evaluate the power boundary of the feasible set of regional flexibility by The energy boundary of the feasible set of zone flexibility, where and They are respectively for the upward and downward flexibility power constraints of aggregation, and is the upward flexibility power and downward flexibility power of distributed photovoltaic at time t; and is the upward flexibility power and downward flexibility power provided by the energy storage system device at time t; and is the upward flexibility power and downward flexibility power of the adjustable load at time t; j∈g indicates that the flexibility resource j is the distributed photovoltaic in the cluster; j∈E indicates that the flexibility resource j is the energy storage system device in the cluster; j∈l indicates that the flexibility resource j is the adjustable load in the cluster; and They are the downward constraint and upward constraint of the flexibility energy of cluster i at time t+1 respectively; and They are the upward flexibility power and downward flexibility power of distributed photovoltaics; and They are the upward and downward flexibility powers provided by the energy storage device; is the upward flexibility power of the adjustable load; Δt represents unit time.

14. The multi-time scale distributed cluster optimization control device according to claim 9, characterized in that: The objective function of the day-ahead scheduling model established by the first modeling and solving module is expressed as: Among them, F is the objective function of the day-ahead scheduling model, t represents the time period, and D represents the set of all nodes installed with photovoltaic power generation systems. is the amount of abandoned light in the photovoltaic power generation system at node n at time t.

15. The multi-time scale distributed cluster optimization control device according to claim 9, characterized in that: The objective function of the intraday scheduling model established by the second modeling and solving module is expressed as: Among them, C j is the cost function under flexibility aggregation power, expressed as: γ j , b j and c j is the cost-effectiveness coefficient of distributed photovoltaics; α E,1,j and α E,2,j Cost-effectiveness factor for installing backup for energy storage system; β D,j P is the cost-effectiveness coefficient of adjustable load reserve; g,j and P D,j are the source side output and the load side load demand respectively; P E,j is the output of the energy storage system device, is the maximum output of the energy storage system; P l,j is the known load that has not been processed by demand response, j∈g means that the flexibility resource j is the distributed photovoltaic in the cluster; j∈l means that the flexibility resource j is the adjustable load in the cluster, The profit function obtained by providing spare capacity is expressed as: and are the upper reserve profit coefficient and the lower reserve profit coefficient, and They are respectively used to solve the intraday upward reserve capacity and the intraday downward reserve capacity under the flexibility aggregation power. is the power value predicted in the day-ahead phase.

16. The multi-time scale distributed cluster optimization control device according to claim 15, characterized in that: The constraints of the intraday scheduling model established by the second modeling and solving module include a power balance constraint of the substation area, and the power balance constraint of the substation area is expressed as: Where D is the set of load-side clusters, G is the set of source-side clusters, and P G,i,t is the source-side power output of cluster i at time t, P D,i,t is the load demand of cluster i at time t.

17. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the multi-time-scale distributed cluster optimization and control method as described in any one of claims 1-8 are implemented.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-time-scale distributed cluster optimization and control method as described in any one of claims 1 to 8 are implemented.

Images