Balanced responsibility area optimization scheduling method based on multi-class excitation type demand response

Abstract

The invention discloses a balance responsibility area optimal scheduling method based on multi-class excitation type demand response, and relates to the field of power system optimal scheduling, and the method comprises the steps: dividing all loads into A-class loads or B-class loads according to the adjustable capacity proportion of the loads in a balance responsibility area; the method comprises the following steps: establishing a day-ahead scheduling constraint condition based on a resource scheduling mathematical model for a class-A load, establishing a day-ahead scheduling optimization model by taking the minimum total operation cost in a balance responsibility area as a target, and then solving to obtain an optimal output result of a power supply side component and an optimal output result of a load side component which meet the day-ahead scheduling constraint condition. Day-ahead optimization scheduling is carried out on the balance responsibility area; in the same way, an intra-day scheduling optimization model is established and solved, an optimal output result meeting intra-day scheduling constraint conditions is obtained, and intra-day optimization scheduling is carried out on the balance responsibility area. According to the method and the system, the operation cost is reduced while optimal scheduling is realized, and the overall benefit is maximized.

Description

Technical Field

[0001] The present application relates to the field of optimal power system dispatching, and particularly to an optimal dispatching method for a balancing responsibility area based on multi-class incentive-based demand response. Background Art

[0002] Carrying out the construction project of the balancing responsibility area of the new power system has alleviated the pressure of centralized dispatching to a certain extent. However, the incentive mechanism for flexible resources within the area and the coordinated optimal dispatching method for multi-type resources matching the balancing responsibility area are still lacking. Considering the current situation that the large-scale access of multi-type flexible resources such as distributed power sources and new loads has led to increasingly complex adjustment characteristics within the area, insufficient exploration of the adjustment potential of flexible resources, and the increasing difficulty of active power balance, the previous balancing mode can no longer meet the new control requirements. Summary of the Invention

[0003] The purpose of the present application is to provide an optimal dispatching method for a balancing responsibility area based on multi-class incentive-based demand response, which reduces the operating cost while achieving optimal dispatching and maximizes the overall benefit.

[0004] To achieve the above purpose, the present application provides the following solutions:

[0005] The present application provides an optimal dispatching method for a balancing responsibility area based on multi-class incentive-based demand response, including:

[0006] Establish a resource dispatching mathematical model for participating in the operation of the balancing responsibility area; the resource dispatching mathematical model includes a data model of power supply side components and a data model of load side components; the load side components include multi-type loads and participate in incentive-based demand response;

[0007] All loads are divided into type A loads or type B loads according to the adjustable capacity ratio of each type of load; different incentive mechanisms correspond to the type A loads and the type B loads;

[0008] For the type A loads, based on the resource dispatching mathematical model, establish day-ahead dispatching constraint conditions, and establish a day-ahead dispatching optimization model with the minimum total operating cost within the balancing responsibility area as the objective; for the type B loads, based on the resource dispatching mathematical model, establish intra-day dispatching constraint conditions, and establish an intra-day dispatching optimization model with the minimum total operating cost within the balancing responsibility area as the objective;

[0009] Solve the day-ahead dispatching optimization model to obtain the optimal output results of the power supply side components and the optimal output results of the load side components that meet the day-ahead dispatching constraint conditions, and perform day-ahead optimal dispatching on the balancing responsibility area;

[0010] Solve the intraday scheduling optimization model to obtain the optimal output results of the power supply side components and the optimal output results of the load side components that meet the intraday scheduling constraint conditions, and perform intraday optimal scheduling for the balancing responsibility area.

[0011] According to the specific embodiments provided by the present application, the present application has the following technical effects: The present application provides an optimization scheduling method for the balancing responsibility area based on multiple types of incentive-based demand response, divides the load into type A load or type B load, designs different incentive mechanisms for different types of loads to participate in incentive-based demand response, and proposes day-ahead and intraday optimization scheduling strategies with the goal of minimizing the total operating cost within the balancing responsibility area. Based on the above settings, the present application not only considers the balancing ability of each flexible resource to meet response regulation, but also considers the peak shaving and valley filling peak regulation ability brought by the energy storage system and demand response, and at the same time considers the environmental protection ability of reducing pollutant emissions and reducing pollutant treatment costs to achieve green and low-carbon. And, different incentive mechanisms are corresponding to type A load and type B load, which can effectively stimulate the scheduling potential of each flexible resource in the balancing responsibility area, reduce the total operating cost, and maximize the overall benefits of the operation of the responsibility area. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0013] Figure 1 It is an application environment diagram of an optimization scheduling method for a balancing responsibility area based on multiple types of incentive-based demand response in an embodiment of the present application.

[0014] Figure 2 It is a schematic flowchart of an optimization scheduling method for a balancing responsibility area based on multiple types of incentive-based demand response provided by an embodiment of the present application.

[0015] Figure 3 It is a schematic structural diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0017] Evaluating the capabilities of each entity from a single dimension is difficult to reflect its operating potential and impact on the power system. To fully quantify and evaluate the balancing capabilities of flexible resources, meet the diverse optimization goals of the new power system, and achieve multi-entity collaborative optimization, by formulating a reasonable and effective demand response incentive mechanism, the scheduling potential of the load side in the balancing responsibility area can be effectively stimulated, maximizing the overall social benefits of the operation of the responsibility area.

[0018] Therefore, this application starts from three aspects: the balancing capabilities, peak shaving capabilities, and environmental protection capabilities of multiple types of flexible resources, and proposes a multi-time scale optimal scheduling strategy for the balancing responsibility area that collaborates multiple types of flexible resources based on incentive-based demand response. In this strategy, the day-ahead scheduling considers the predicted values of distributed power sources and loads on the day-ahead, taking into account Type A loads participating in incentive-based demand response, so as to minimize the total operating cost in the balancing responsibility area. The intra-day scheduling then considers Type B loads participating in incentive-based demand response, and based on the difference between the intra-day and day-ahead prediction results, obtains the model of incentive-based demand response, effectively stimulating the potential of various types of flexible resources in the balancing responsibility area in power scheduling, improving the endogenous driving force of the system to participate in trading scheduling, and maximizing the overall benefits of the operation of the balancing responsibility area.

[0019] To make the above objects, features, and advantages of this application more obvious and understandable, the following further details this application in conjunction with the accompanying drawings and specific embodiments.

[0020] The optimal scheduling method for the balancing responsibility area based on multiple types of incentive-based demand response provided by the embodiments of this application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process, such as the operating and maintenance costs of each flexible resource unit, the output of wind power and photovoltaic power, the penalty cost coefficient for wind and light curtailment, the load power, the start-stop cost of micro gas turbines, the relevant parameters of fuel costs, the relevant parameters of pollutant treatment costs, the time-of-use electricity price for selling and purchasing electricity, the scheduling price of incentive-based demand response, and other information. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send an optimal scheduling request to the server 104, establish a resource scheduling mathematical model for participating in the operation of the balancing responsibility area in the server 104, and combine the data stored in the data storage system to construct and solve the day-ahead scheduling optimization model and construct and solve the intra-day scheduling optimization model. The optimal output results of the power supply side components and the optimal output results of the load side components can be fed back to the terminal 102 for display, or the optimal scheduling can be directly performed.

[0021] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0022] In an exemplary embodiment, as Figure 2 shown, a method for optimizing the scheduling of a balancing responsibility area based on multi-class incentive-based demand response is provided. This method is executed by a computer device, and specifically can be executed alone by a computer device such as a terminal or a server, or can be jointly executed by a terminal and a server. In the embodiments of the present application, taking this method applied to Figure 1 the server 104 in it as an example for illustration, it includes the following steps 201 to step 205.

[0023] Step 201, establish a mathematical model for resource scheduling participating in the operation of the balancing responsibility area; the resource scheduling mathematical model includes a data model of power supply side components and a data model of load side components. Among them, the balancing responsibility area is an artificially planned area; the power supply side components include wind turbines, photovoltaic generators, micro gas turbines, and energy storage systems; the load side components include multiple types of loads and participate in incentive-based demand response.

[0024] In a specific application, establish the following mathematical model for micro gas turbine power generation considering the balancing ability:

[0025]

[0026] Among them, c mt (t) is the fuel cost consumed by the micro gas turbine at time t; C ng is the natural gas price; P MT (t) is the output power of the micro gas turbine at time t; L is the calorific value of natural gas; η MT is the power generation efficiency of the micro gas turbine.

[0027] In a specific application, establish a mathematical model of an energy storage system considering the balancing ability and peak shaving ability. Among them, the state of charge K soc of the energy storage system can be expressed as the following function:

[0028]

[0029] The charging state of the energy storage system can be expressed as the following function:

[0030]

[0031] The discharging state of the energy storage system can be expressed as the following function:

[0032]

[0033] Among them, C remain is the remaining charge quantity in the energy storage system; C is the rated charge capacity of the energy storage system. K soc (t) is the state of charge of the energy storage system at the end of time t; K soc (t + 1) is the state of charge of the energy storage system at the end of time t + 1; δ is the self-discharge rate of the energy storage system; P ch (t) is the charging power of the energy storage system at time t; P dis (t) is the discharging power of the energy storage system at time t; E is the rated power of the energy storage system; η css is the charging efficiency of the energy storage system; η ess is the discharging efficiency of the energy storage system.

[0034] Step 202: Divide all loads into type A loads or type B loads according to the adjustable capacity ratio of each type of load; the type A loads and the type B loads correspond to different incentive mechanisms. For the loads participating in incentive-based demand response in the balance responsibility area, their adjustment capabilities can be evaluated. Specifically: Loads with an adjustable capacity ratio greater than the preset adjustable capacity ratio are classified as type A loads and participate in day-ahead scheduling; loads with an adjustable capacity ratio less than or equal to the preset adjustable capacity ratio are classified as type B loads and participate in intra-day scheduling. Different incentive mechanisms are formulated for type A loads or type B loads to perform incentive-based demand response for different types of loads. Especially for type B loads, in order to further motivate them to provide more adjustable capacity, a stepped incentive price method is adopted to encourage users to reduce their electricity loads.

[0035] In an application example, the preset adjustable capacity ratio can be 50%.

[0036] Considering the balancing ability, peak shaving ability and environmental protection ability of multiple types of flexible resources, a day-ahead scheduling optimization model and an intra-day scheduling optimization model with the goal of minimizing the total operating cost in the balance responsibility area can be established. Among them, the balancing ability of flexible resources refers to that under constraint conditions, each flexible resource participates in active power balance and demand response according to load changes; the peak shaving ability is the ability of the energy storage system and incentive-based demand response to participate in peak shaving and valley filling and surplus power sales; at the same time, considering the power generation method dominated by clean energy, the pollutant emission and treatment costs are reduced, so as to achieve the environmental protection ability of green and low-carbon. Based on this, step 203 is also included in this application.

[0037] Step 203: For the Class A loads, based on the resource scheduling mathematical model, establish the day-ahead scheduling constraint conditions, and establish a day-ahead scheduling optimization model with the goal of minimizing the total operating cost within the balancing responsibility area; for the Class B loads, based on the resource scheduling mathematical model, establish the intra-day scheduling constraint conditions, and establish an intra-day scheduling optimization model with the goal of minimizing the total operating cost within the balancing responsibility area.

[0038] In a specific application, when constructing the day-ahead scheduling optimization model or the intra-day scheduling optimization model, the total operating cost within the balancing responsibility area includes the wind power generation cost, the photovoltaic power generation cost, the micro gas turbine power generation cost, the energy storage system power generation cost, the demand-side response scheduling cost, the power purchase cost, and the power sale cost. Among them, according to the adjustable capacity ratio of the load, the participation response time, and the response speed, determine the demand-side response scheduling cost for different types of loads participating in the incentive-based demand response.

[0039] The day-ahead scheduling constraint conditions include the day-ahead scheduling power balance constraint, the wind power generation output constraint, the photovoltaic power generation output constraint, the micro gas turbine power generation output constraint, the energy storage system operation constraint, the tie-line power constraint, and the Class A load demand response call capacity constraint.

[0040] The intra-day scheduling constraint conditions include the intra-day scheduling power balance constraint, the wind power generation output constraint, the photovoltaic power generation output constraint, the micro gas turbine power generation output constraint, the energy storage system operation constraint, the tie-line power constraint, and the Class B load demand response call capacity constraint.

[0041] In another specific application, the day-ahead scheduling optimization model is:

[0042]

[0043] where, f 1 is the total operating cost within the balancing responsibility area in the day-ahead scheduling, and the Class A loads participate in the incentive-based demand response; T is the time duration; C WT (t) is the wind power generation cost, C PV (t) is the photovoltaic power generation cost, C MT (t) is the micro gas turbine power generation cost, C ESS (t) is the energy storage system power generation cost, C A ,i(t) is the actual scheduling cost of the i-th Class A load participating in the incentive-based demand response at time t when the Class A loads participate in the incentive-based demand response, C buy (t) is the power purchase cost, C sell (t) is the power sale cost.

[0044] The function of the wind power generation cost C WT (t) is:

[0045]

[0046] Among them, c yw1 is the operation and maintenance cost of the wind turbine generator; K F is the curtailment penalty cost coefficient; is the maximum available power of the wind turbine generator at time t; P WT (t) is the day-ahead output value or intra-day output value of the wind turbine generator at time t.

[0047] The function of the photovoltaic power generation cost C PV (t) is:

[0048]

[0049] Among them, c yw2 is the operation and maintenance cost of the photovoltaic generator set; K G is the curtailment penalty cost coefficient; is the maximum available power of the photovoltaic generator set at time t; P PV (t) is the day-ahead output value or intra-day output value of the photovoltaic generator set at time t.

[0050] The function of the micro gas turbine power generation cost C MT (t) is:

[0051] C MT (t) = (c yw3 + c qt + c mt + c wr )P MT (t).

[0052]

[0053] Among them, c yw3 、c qt 、c mt and c wr are the operation and maintenance cost, start-stop cost, fuel cost and pollutant treatment cost of the micro gas turbine respectively; P MT (t) is the output power of the micro gas turbine at time t; c s is the cost of treating s-type pollutants; γ mt,s is the emission coefficient of s-type pollutants generated by the micro gas turbine.

[0054] The function of the energy storage system power generation cost C ESS (t) is: C ESS (t) = c yw4 (P dis (t) + P ch(t)).

[0055] Among them, c yw4 is the operation and maintenance cost of the energy storage system. P ch (t) and P dis (t) are the discharge power and charge power of the energy storage system at time t, respectively.

[0056] When Class A loads participate in incentive-based demand response, the function of the actual scheduling cost of the i-th Class A load participating in the response at time t is: C A,i (t) = P A,i (t)k 1 k 2 c A,i (t); where P A,i (t) is the schedulable capacity of the i-th Class A load participating in the response at time t; k 1 and k 2 are the regulation time coefficient and response speed coefficient of Class A loads, respectively; c A,i (t) is the unit scheduling price of the i-th Class A load at time t.

[0057] The capacity demand elasticity coefficient is used to describe the impact of the change in the incentive price on the demand response capacity of Class B loads. The function is as follows:

[0058]

[0059] Among them, ε is the capacity demand elasticity coefficient; ΔP B and Δc B are the change amounts of the schedulable capacity P B (t) and the incentive price c B (t) of the Class B loads participating in the response at time t, respectively.

[0060] For Class B loads, the adjustment amount of their incentive price is related to the deviation between the predicted power of the day-ahead and intra-day. The power deviation ΔP b (t) is defined by the difference between the predicted values of wind power, photovoltaic power generation and electrical load of the day-ahead and intra-day. The function is as follows:

[0061] ΔP b (t) = [P’ L (t) - P’ WT (t) - P’ PV (t)] - [P L (t)” - P WT (t)” - P PV (t)”].

[0062] Among them, P’ L (t) and P L(t)” represent the in-day power and day-ahead power of the load at time t; P’ WT (t) and P WT (t)” represent the in-day power and day-ahead power of the wind power at time t; P’ PV (t) and P PV (t)” represent the in-day power and day-ahead power of the photovoltaic power at time t.

[0063] When ΔP b (t) > 0, it indicates that the load at this moment is greater than the output of the wind power and photovoltaic power. Therefore, the incentive price is increased to encourage users to reduce the electricity load, making the call capacity of type B load sufficient. According to the magnitude of ΔP b (t), the adjustment amount of the incentive price is formulated. When ΔP b (t) < 0, it indicates that the load at this moment is less than the output of the wind power and photovoltaic power. The incentive price remains unchanged, and the excess electric energy is used for energy storage in the energy storage system or sold to the distribution network.

[0064] When type B load participates in the incentive-based demand response, the function of the actual scheduling cost of the i-th type B load participating in the response at time t is: C B,i (t) = P B,i (t)k 3 k 4 c B,i (t); where P B,i (t) is the scheduling capacity of the i-th type B load participating in the response at time t; k 3 and k 4 are the regulation time coefficient and response speed coefficient of type B load respectively; c B,i (t) is the incentive price of the i-th type B load participating in the response at time t.

[0065] The power purchase cost C buy (t) has the function: C buy (t) = P buy (t)c b ; The power selling cost C sell (t) has the function: C sell (t) = P sell (t)c s ; where P buy (t) is the electric energy purchased from the distribution network at time t; c b is the time-of-use electricity price for power purchase, P sell (t) is the electric energy sold to the distribution network at time t; c s is the time-of-use electricity price for power selling.

[0066] The day-ahead scheduling power balance constraint is:

[0067] P” WT(t) + P” PV (t) + P MT (t) + P dis (t) - P ch (t) + P buy (t) - P sell (t) = P L (t)” - P Ai , t( ).

[0068] Among them, P dis (t) is the discharge power of the energy storage system at time t, P ch (t) is the charging power of the energy storage system at time t, P A,i (t) is the schedulable capacity of the i-th Class A load participating in the response at time t.

[0069] The capacity constraint for the demand response invocation of the Class A load is:

[0070]

[0071] Among them, and are respectively the maximum invocation capacity and the minimum invocation capacity of the i-th Class A load participating in the response.

[0072] The constraint for the wind power output is: The constraint for the photovoltaic power output is:

[0073] The constraint for the micro gas turbine power output:

[0074]

[0075] Among them, P MT (t - 1) is the output power of the micro gas turbine at time t - 1, and are respectively the upper limit and the lower limit of the output of the micro gas turbine at time t; and are respectively the maximum up-ramp rate and the maximum down-ramp rate of the micro gas turbine at time t, and Δt is the time step.

[0076] The operating constraint of the energy storage system is:

[0077]

[0078] Among them, U ch (t) and U dis (t) respectively represent the charging state and the discharging state of the energy storage battery at time t; and respectively represent the maximum charging power and the maximum discharging power of the energy storage battery at time t; and respectively represent the minimum capacity and the maximum capacity of the energy storage battery, and K soc (t) is the state of charge of the energy storage system at the end of time t.

[0079] The tie-line power constraint is:

[0080]

[0081] where is the upper limit of the electric energy purchased from the distribution network, is the upper limit of the electric energy sold to the distribution network.

[0082] In another specific application, the intra-day scheduling optimization model is:

[0083]

[0084] where f 2 is the total operating cost in the balancing responsibility area during intra-day scheduling, and type-B loads participate in incentive-based demand response.

[0085] The intra-day scheduling power balance constraint is:

[0086] P’ WT (t) + P’ PV (t) + P MT (t) + P dis (t) - P ch (t) + P buy (t) - P sell (t) = P’ L (t) - P B,i (t).

[0087] where P B,i (t) is the dispatchable capacity of the i-th type-B load participating in the response at time t.

[0088] The type-B load demand response call capacity constraint is:

[0089]

[0090] where and are respectively the maximum call capacity and the minimum call capacity of the i-th type-B load participating in the response. Other constraints for intra-day scheduling are the same as those recorded in the day-ahead scheduling.

[0091] Step 204: Solve the day-ahead scheduling optimization model to obtain the optimal output results of the power supply side components and the optimal output results of the load side components that meet the day-ahead scheduling constraint conditions, and perform day-ahead optimal scheduling on the balance responsibility area.

[0092] Step 205: Solve the intra-day scheduling optimization model to obtain the optimal output results of the power supply side components and the optimal output results of the load side components that meet the intra-day scheduling constraint conditions, and perform intra-day optimal scheduling on the balance responsibility area.

[0093] In a specific application, the solutions in Step 204 and Step 205 are implemented through the CPLEX solver on the MATLAB platform, so as to obtain the optimal output results of each resource that meet the constraint conditions. Specifically, the input conditions of the CPLEX solver include the operation and maintenance costs of each flexible resource unit, the output of wind power and photovoltaic power, the penalty cost coefficient for wind and photovoltaic curtailment, the load power, the start-stop cost of the micro gas turbine, the relevant parameters of the fuel cost, the relevant parameters of the pollutant treatment cost, the time-of-use electricity price for selling and purchasing electricity, the scheduling price of the incentive-based demand response, and other information. The output conditions after the CPLEX solver solves include the total day-ahead and intra-day operation costs of the balance responsibility area, the operation costs of each flexible resource, and the output values of each flexible resource, and other information.

[0094] In another exemplary embodiment, the present application sets four scenarios: only the micro gas turbine participates in active power balance; the distributed wind and photovoltaic power sources and the micro gas turbine jointly participate in active power balance; the participation of the energy storage device is considered on the basis of the second scenario; multiple types of incentive-based demand responses are added on the basis of the third scenario. By comparing the results of the above four scenarios, it can be known that the balancing ability can be reflected by the operation costs generated by the participation of flexible resources in response regulation; the peak shaving ability can be reflected on the one hand by the reduction of the wind and photovoltaic curtailment costs of the distributed wind and photovoltaic power sources by the energy storage, and on the other hand by the purchase and sale electricity costs generated by the energy storage and the incentive-based demand response for load peak shaving and valley filling; the environmental protection ability can be reflected by the reduction of the pollutant treatment costs. Thus, the feasibility and the beneficial effects achieved by the solution of the present application can be known.

[0095] In summary, according to the operating characteristics of multiple types of flexible resources, the present application designs different incentive mechanisms for different types of loads participating in demand response, and proposes a day-ahead and intra-day optimal scheduling strategy with the goal of minimizing the total operating cost in the balancing responsibility area. The present application not only considers the balancing ability of each flexible resource to meet response regulation, but also considers the peak shaving and valley filling peak regulation ability brought by the energy storage system and demand response. At the same time, for environmental protection, it considers the environmental protection ability of reducing pollutant emissions and reducing pollutant treatment costs to achieve green and low-carbon. A reasonable and targeted demand response incentive mechanism can effectively stimulate the scheduling potential of each flexible resource in the balancing responsibility area, reduce the total operating cost, obtain electricity sales revenue from the distribution network, improve the economy of the system, and maximize the overall benefit of the operation of the responsibility area.

[0096] Based on the same inventive concept, the embodiment of the present application further provides an optimal scheduling system for a balancing responsibility area based on multiple types of incentive-based demand response. The implementation solutions provided by this system to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more system embodiments provided below can refer to the limitations on the method in the above text, and will not be repeated here. The optimal scheduling system for a balancing responsibility area based on multiple types of incentive-based demand response includes the following multiple modules.

[0097] A resource scheduling mathematical model construction module, configured to establish a resource scheduling mathematical model for participating in the operation of the balancing responsibility area; the resource scheduling mathematical model includes a data model of power supply side components and a data model of load side components; the load side components include multiple types of loads and participate in incentive-based demand response.

[0098] A load classification module, configured to divide all loads into type A loads or type B loads according to the adjustable capacity ratio of each type of load.

[0099] A scheduling optimization model construction module, configured to, for the type A loads, based on the resource scheduling mathematical model, establish day-ahead scheduling constraint conditions, and establish a day-ahead scheduling optimization model with the goal of minimizing the total operating cost in the balancing responsibility area; for the type B loads, based on the resource scheduling mathematical model, establish intra-day scheduling constraint conditions, and establish an intra-day scheduling optimization model with the goal of minimizing the total operating cost in the balancing responsibility area.

[0100] A day-ahead scheduling optimization module, configured to solve the day-ahead scheduling optimization model to obtain the optimal output results of the power supply side components and the optimal output results of the load side components that meet the day-ahead scheduling constraint conditions, and perform day-ahead optimal scheduling on the balancing responsibility area.

[0101] The intraday scheduling optimization module is used to solve the intraday scheduling optimization model to obtain the optimal output results of the power supply side components and the optimal output results of the load side components that meet the intraday scheduling constraint conditions, and perform intraday optimization scheduling on the balance responsibility area.

[0102] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 3 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes the optimization scheduling method for the balance responsibility area based on multiple types of incentive-based demand response.

[0103] Those skilled in the art can understand that Figure 3 the structure shown in

[0104] is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0105] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it realizes the steps in the above method embodiments.

[0105] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, it realizes the steps in the above method embodiments.

[0106] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, it realizes the steps in the above method embodiments.

[0107] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0108] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0109] The databases involved in the various embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the various embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., and are not limited thereto.

[0110] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0111] In this text, specific examples are used to elaborate on the principles and implementation manners of this application. The descriptions of the above embodiments are only used to help understand the method and its core idea of this application. At the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. To sum up, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for optimizing the scheduling of balanced responsibility areas based on multi-type incentive demand response, characterized in that: The method for optimizing the scheduling of balanced responsibility areas based on multiple types of incentive-based demand responses includes: Establishing a resource scheduling mathematical model participating in the operation of the balancing responsibility area; the resource scheduling mathematical model includes a data model of a power supply side component and a data model of a load side component; the load side component includes multiple types of loads and participates in incentive-based demand response; According to the proportion of adjustable capacity of each type of load, all loads are divided into Class A loads or Class B loads; the Class A loads and the Class B loads correspond to different incentive mechanisms; For the Class A load, based on the resource scheduling mathematical model, a day-ahead scheduling constraint condition is established, and a day-ahead scheduling optimization model is established with the minimum total operating cost in the balancing responsibility area as the goal; for the Class B load, based on the resource scheduling mathematical model, an intraday scheduling constraint condition is established, and an intraday scheduling optimization model is established with the minimum total operating cost in the balancing responsibility area as the goal; Solving the day-ahead dispatch optimization model to obtain the optimal output results of the power supply side components and the optimal output results of the load side components that meet the day-ahead dispatch constraint conditions, and performing day-ahead optimal dispatch on the balancing responsibility area; The intraday scheduling optimization model is solved to obtain the optimal output results of the power supply side components and the optimal output results of the load side components that meet the intraday scheduling constraints, and the intraday optimization scheduling is performed on the balancing responsibility area.

2. The method for optimizing the scheduling of balanced responsibility areas based on multi-type incentive demand response according to claim 1 is characterized in that: The power supply side components include wind turbines, photovoltaic generators, micro gas turbines and energy storage systems; When constructing the day-ahead dispatch optimization model or the intraday dispatch optimization model, the total operating cost in the balancing responsibility area includes wind power generation cost, photovoltaic power generation cost, micro gas turbine power generation cost, energy storage system power generation cost, demand-side response dispatch cost, power purchase cost and power sales cost; The day-ahead dispatch constraints include day-ahead dispatch power balance constraints, wind power generation output constraints, photovoltaic power generation output constraints, micro gas turbine power generation output constraints, energy storage system operation constraints, tie line power constraints, and Class A load demand response call capacity constraints; The intraday dispatch constraints include intraday dispatch power balance constraints, wind power generation output constraints, photovoltaic power generation output constraints, micro gas turbine power generation output constraints, energy storage system operation constraints, interconnection line power constraints and Class B load demand response call capacity constraints.

3. The method for optimizing the scheduling of balanced responsibility areas based on multi-type incentive demand response according to claim 2 is characterized in that: The day-ahead scheduling optimization model is: Where f1 is the total cost of operation in the balancing responsibility area described in the day-ahead dispatch, and Class A load participates in the incentive-based demand response; T is the duration; C WT (t) is the cost of wind power generation, C PV (t) is the cost of photovoltaic power generation, C MT (t) is the power generation cost of micro gas turbine, C ESS (t) is the power generation cost of the energy storage system, C A ,i(t) is the actual dispatch cost of the i-th class A load participating in the response at time t when the class A load participates in the incentive demand response, C buy (t) is the electricity purchase cost, C sell (t) is the electricity sales cost; The intraday scheduling optimization model is: Where f2 is the total operating cost within the balancing responsibility area described in the intraday dispatch, and Class B loads participate in incentive-based demand response; C B,i (t) is the actual dispatch cost of the i-th Class B load participating in the response at time t when Class B load participates in the incentive-based demand response.

4. The method for optimizing the scheduling of balanced responsibility areas based on multi-type incentive demand response according to claim 2 is characterized in that: The wind power generation cost C WT The function of (t) is: Among them, c yw1 K is the operation and maintenance cost of the wind turbine generator set; F Penalty cost factor for wind curtailment; is the maximum available power of the wind turbine generator set at time t; P WT (t) is the day-ahead output or intraday output of the wind turbine generator set at time t; The photovoltaic power generation cost C PV The function of (t) is: Among them, c yw2 K is the operation and maintenance cost of the photovoltaic generator set; G is the penalty cost coefficient for abandoned light; is the maximum available power of the photovoltaic generator set at time t; P PV (t) is the day-ahead output or intraday output of the photovoltaic generator set at time t; The micro gas turbine power generation cost C MT The function of (t) is: C MT (t)=(c yw3 +c qt +c mt +c wr )P MT (t); Among them, c yw3 、c qt 、c mt and c wr are the operation and maintenance costs, start-up and shutdown costs, fuel costs, and pollutant treatment costs of the micro gas turbine; P MT (t) is the output power of the micro gas turbine at time t; c s is the cost of treating type s pollutants; mt,s is the emission coefficient of type S pollutants generated by the micro gas turbine; The energy storage system power generation cost C ESS The function of (t) is: C ESS (t) = c yw4 (P dis (t)+P ch (t)); Among them, c yw4 is the operation and maintenance cost of the energy storage system; P dis (t) and P ch (t) are the discharge power and charging power of the energy storage system at time t; The function of the electricity purchase cost Cbuy(t) is: buy (t) = P buy (t)c b ; The function of the electricity selling cost Csell(t) is: sell (t) = P sell (t)c s ; Among them, P buy is the electricity purchased from the distribution grid; c b is the time-of-use electricity price, P sell The electric energy sold to the distribution network; s It is the time-of-use electricity price for electricity sales.

5. The method for optimizing the scheduling of balanced responsibility areas based on multi-type incentive demand response according to claim 3 is characterized in that: When class A load participates in incentive demand response, the actual dispatch cost function of the i-th class A load participating in the response at time t is: C A,i (t) = P A,i (t)k1k2c A,i (t); where P A,i (t) is the dispatchable capacity of the i-th class A load participating in the response at time t; k1 and k2 are the regulation time coefficient and response speed coefficient of class A load respectively; c A,i (t) is the unit dispatch price of the i-th class A load at time t; When class B load participates in incentive demand response, the actual dispatch cost function of the i-th class B load participating in the response at time t is: C B,i (t) = P B,i (t)k3k4c B,i (t); where P B,i (t) is the dispatch capacity of the i-th B-type load participating in the response at time t; k3 and k4 are the regulation time coefficient and response speed coefficient of the B-type load respectively; c B,i (t) is the incentive price of the i-th type B load participating in the response at time t.

6. The method for optimizing the scheduling of balanced responsibility areas based on multi-type incentive demand response according to claim 2 is characterized in that: The day-ahead scheduling power balance constraint is: P W ” T (t)+P P ” V (t)+P MT (t)+P dis (t)-P ch (t)+P buy (t)-P sell (t)=P L (t)”-P A,i (t); Among them, P MT (t) is the output power of the micro gas turbine at time t, P dis (t) is the discharge power of the energy storage system at time t, P ch (t) is the charging power of the energy storage system at time t, P buy (t) is the electric energy purchased from the distribution network at time t, P sell (t) is the electric energy sold to the distribution network at time t, P A,i (t) is the dispatchable capacity of the i-th class A load participating in the response at time t; The class A load demand response calling capacity constraint is: in, and are the maximum call capacity and minimum call capacity of the i-th class A load participating in the response, respectively; The intraday scheduling power balance constraint is: P' WT (t)+P P ' V (t)+P MT (t)+P dis (t)-P ch (t)+P buy (t)-P sell (t)=P L '(t)-P B,i (t); Among them, P B,i (t) is the dispatchable capacity of the i-th B-type load participating in the response at time t, P L '(t) and P L (t)” represent the load intraday power and day-ahead power at time t respectively; P W ' T (t) and P WT (t)” represent the wind power intraday power and day-ahead power at time t; P P ' V (t) and P PV (t)” represent the PV intraday power and day-ahead power at time t, respectively; The class B load demand response calling capacity constraint is: in, and are respectively the maximum calling capacity and the minimum calling capacity of the i-th class B load participating in the response.

7. The method for optimizing the scheduling of balanced responsibility areas based on multi-type incentive demand response according to claim 2 is characterized in that: The wind power generation output constraint is: The photovoltaic power generation output constraint is: Among them, P WT (t) is the day-ahead output or intraday output of the wind turbine generator set at time t; is the maximum available power of the wind turbine generator set at time t; P PV (t) is the day-ahead output value or intraday output value of the photovoltaic generator set at time t; is the maximum available power of the photovoltaic generator set at time t; The power generation output constraint of the micro gas turbine is: Among them, P MT (t) is the output power of the micro gas turbine at time t, P MT (t-1) is the output power of the micro gas turbine at time t-1, and are the upper and lower output limits of the micro gas turbine at time t respectively; and are the maximum ramp-up rate and the maximum ramp-down rate of the micro gas turbine at time t, respectively, and Δt is the time step; The energy storage system operation constraints are: Among them, P dis (t) is the discharge power of the energy storage system at time t, P ch (t) is the charging power of the energy storage system at time t, U ch (t) and U dis (t) respectively represent the charging state and discharging state of the energy storage battery at time t; and They represent the maximum charging power and maximum discharging power of the energy storage battery at time t respectively; and Respectively represent the minimum capacity and maximum capacity of the energy storage battery, K soc (t) is the state of charge of the energy storage system at the end of time t; The tie line power constraint is: Among them, P buy (t) is the electric energy purchased from the distribution network at time t, P sell (t) is the electric energy sold to the distribution network at time t, is the upper limit of electricity purchased from the distribution grid, The upper limit of electricity sold to the distribution network.

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