New energy power grid demand side resource scheduling optimization method and device

Abstract

The invention relates to the technical field of power grid dispatching, and discloses a new energy power grid demand side resource dispatching optimization method and a new energy power grid demand side resource dispatching optimization device. A day-ahead plan scheduling model and an intra-day scheduling model are constructed by using the difference of demand side resource responses under different time scales, demand side resource scheduling is optimized, the effectiveness of a demand side scheduling scheme is improved, and the demand side resource scheduling efficiency is improved by constructing the day-ahead plan scheduling model with the operation cost minimization as the optimization target under multiple operation scenes. Therefore, multi-scene optimization is realized, the influence caused by a source-load prediction error is reduced, and the effectiveness and the flexibility of a demand side response scheme of a user integrated energy system are ensured. And the demand side response characteristics are comprehensively considered for constraint, so that the flexibility of the demand side response of the new energy power grid can be enhanced, and the self regulation capability of the power grid can be fully excavated, thereby alleviating the problem of unbalanced supply and demand caused by wind-solar power generation fluctuation.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to a method and device for optimizing resource dispatching on the demand side of a new energy power grid. Background Art

[0002] The grid demand-side resources represented by large-scale electric vehicles and user integrated energy systems are gradually becoming an important way to absorb excess renewable energy generation due to their unique and flexible load regulation capabilities. As an emerging mobile storage unit, electric vehicles can not only be used as a means of transportation, but also participate in the peak and frequency regulation services of the grid. The charging load of electric vehicles is flexible and adjustable. According to this feature, a suitable electric vehicle charging and discharging strategy can be formulated, which can show significant regulation and storage capabilities after aggregation. The user integrated energy system integrates multiple energy forms such as electricity, heat, and natural gas, and can achieve multi-energy complementarity and coordinated optimization, so that it has high flexibility in demand-side response and can effectively respond to the load dispatching needs of the power system. The two types of grid demand-side resources, electric vehicles and user integrated energy systems, operate in coordination, increase electricity demand when the load of the power system is low, and reduce electricity demand when the load is high, which can solve the supply and demand imbalance caused by the fluctuation of renewable energy generation. Make full use of the demand-side resources that have been connected to the power grid, formulate an economical and effective demand-side response plan according to the changes in wind and photovoltaic power generation and power load, fully tap the regulation potential of the power grid, and adjust the peak and valley difference of load, which is of great significance to improving the utilization rate of renewable energy and the reliability and economy of the power grid.

[0003] At present, most of the technologies related to the coordinated operation of two types of grid demand-side resources, electric vehicles and user integrated energy systems, are focused on the research of a single demand-side resource. In addition, the high uncertainty and volatility of new energy generation and electric heat loads in actual operation easily lead to deviations between the demand-side resource scheduling plan and the actual environment, making it difficult to ensure the effectiveness of the demand-side scheduling plan. Summary of the invention

[0004] In view of this, the present invention provides a new energy power grid demand side resource scheduling optimization method and device, which solves the technical problem that the demand side resource scheduling plan deviates from the actual environment and it is difficult to ensure the effectiveness of the demand side scheduling plan.

[0005] A first aspect of the present invention provides a method for optimizing resource scheduling on the demand side of a new energy power grid, the method comprising:

[0006] Constructing an operation scenario set based on the source-load day-ahead response prediction data of the power system;

[0007] Taking the minimization of the operating cost of the power system under multiple operating scenarios as the optimization goal, the first constraint condition is determined according to the demand-side response characteristics and the grid operation characteristics, and a day-ahead scheduling model is constructed;

[0008] Taking the minimization of the total cost of power system operation within a day as the optimization goal, the second constraint is determined according to the energy balance characteristics of the day-ahead plan, the comprehensive energy operation characteristics of the user and the operation characteristics of the power grid, and the intraday dispatch model is constructed;

[0009] According to the set of operating scenarios, the day-ahead planning and scheduling model and the intraday scheduling model are respectively optimized and solved, and the day-ahead planning and scheduling optimal solutions and the intraday scheduling optimal solutions are determined according to the optimal solutions corresponding to the day-ahead planning and scheduling models respectively; wherein the day-ahead planning and scheduling optimal solutions are used to execute power grid scheduling in the day-ahead planning stage of the power system, and the intraday scheduling optimal solutions are used to execute power grid scheduling in the intraday scheduling stage of the power system.

[0010] Preferably, the step of constructing an operation scenario set according to the source-load day-ahead response prediction data of the power system comprises:

[0011] Acquire source-load historical response data of the power system, and perform day-ahead prediction based on the source-load historical response data to obtain source-load day-ahead initial prediction data;

[0012] Noise data is added to the source-load day-ahead initial prediction data to generate the source-load day-ahead response prediction data.

[0013] Preferably, the day-ahead planning and scheduling model includes a first optimization objective function;

[0014] Wherein, the first optimization objective function is:

[0015]

[0016] In the formula, is the probability of operation scenario s occurring, S is the set of operation scenarios, t is the time period, T is the total time period, is the electricity balancing cost of operating scenario s in period t, To run scenario s in time period t

[0017] demand-side response costs;

[0018] in,

[0019]

[0020]

[0021] In the formula, , , , , They represent the cost of calling up the unit grid reserve capacity, the penalty cost corresponding to the unit wind and solar power abandonment, the natural gas purchase price, the energy storage calling cost corresponding to the unit energy storage system charging and discharging power, and the battery loss cost caused by the unit electric vehicle charging and discharging power. Indicates the amount of grid reserve capacity called, , They represent the fluctuation factors of wind power node m and photovoltaic node n in period t under operation scenario s, represents the maximum power of wind power node m, represents the power of wind power node m in period t under operating scenario s, represents the maximum power of photovoltaic node n, represents the power of photovoltaic node n in period t under operating scenario s, represents the gas purchase volume of the external natural gas network in period t under operation scenario s, , They represent the charging and discharging power of the energy storage device in time period t under operating scenario s, , They represent the charging and discharging power of the battery e of the electric vehicle in the operation scenario s during the period t. They represent the node sets of wind power, photovoltaic, integrated energy system and electric vehicle loads respectively. represents the comprehensive energy node, Y represents the charging and discharging power sequence of the energy storage device, Indicates the type of energy storage device charging and discharging power, where P represents the battery charging and discharging power, and G represents the gas tank charging and discharging power. A collection of battery nodes for electric vehicles.

[0022] Preferably, the first constraint condition includes power grid operation constraints, electric vehicle charging and discharging operation constraints and user comprehensive energy operation constraints, wherein the power grid operation constraints include power grid flow constraints, power gateway constraints and power system output constraints.

[0023] Preferably, the intraday scheduling model includes a second optimization objective function;

[0024] Wherein, the second optimization objective function is:

[0025]

[0026] In the formula, , They represent the power balancing cost and demand-side response cost in period t respectively;

[0027]

[0028]

[0029] In the formula, , , They represent the fluctuation factors of wind power node m and photovoltaic node n in period t, represents the power of wind power node m in period t, represents the power of photovoltaic node n in period t, represents the gas purchase volume of the external natural gas network in period t, , They respectively represent the charging and discharging power of the energy storage device in time period t.

[0030] Preferably, the second constraint condition includes power grid operation constraint, day-ahead planned energy balance constraint and user comprehensive energy operation constraint, wherein the power grid operation constraint includes power grid flow constraint, power gateway constraint and power system output constraint.

[0031] Preferably, the method further comprises:

[0032] The optimal day-ahead planning scheduling scheme and the optimal day-ahead scheduling scheme are sequentially executed for the day-ahead planning stage and the day-ahead scheduling stage according to the scheduling cycle preset within the day;

[0033] In the case that scheduling is not completed for all scheduling cycles within the day, the optimal day-ahead scheduling scheme and the optimal intraday scheduling scheme of the current scheduling cycle are updated according to the source-load day-ahead response prediction data under the previous scheduling cycle, and based on the updated optimal day-ahead scheduling scheme and the optimal intraday scheduling scheme, the step of sequentially executing the optimal day-ahead scheduling scheme and the optimal intraday scheduling scheme for the day-ahead planning stage and the intraday scheduling stage according to the scheduling cycle preset within the day is performed until all scheduling cycles within the day are completed and the scheduling is terminated.

[0034] In a second aspect, the present invention further provides a new energy grid demand side resource scheduling optimization device, the device comprising:

[0035] A scenario set building module, used to build an operation scenario set according to the source-load day-ahead response prediction data of the power system;

[0036] The day-ahead dispatch module is used to minimize the operating cost of the power system under multiple operating scenarios as the optimization goal, determine the first constraint condition according to the demand-side response characteristics and the grid operation characteristics, and build a day-ahead planning and dispatching model;

[0037] The intraday dispatch module is used to minimize the total cost of the power system's intraday operation as the optimization goal, determine the second constraint condition based on the energy balance characteristics of the day-ahead plan, the user's comprehensive energy operation characteristics, and the power grid operation characteristics, and build an intraday dispatch model;

[0038] A scheduling optimization module is used to optimize and solve the day-ahead scheduling model and the intraday scheduling model according to the set of operating scenarios, and determine the day-ahead scheduling optimal plan and the intraday scheduling optimal plan according to the optimal solutions corresponding to the day-ahead scheduling model and the intraday scheduling model respectively; wherein the day-ahead scheduling optimal plan is used to execute power grid scheduling in the day-ahead planning stage of the power system, and the intraday scheduling optimal plan is used to execute power grid scheduling in the intraday scheduling stage of the power system.

[0039] In a third aspect, the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the new energy power grid demand side resource scheduling optimization method as described in the first aspect.

[0040] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, the steps of the new energy power grid demand side resource scheduling optimization method as described in the first aspect are implemented.

[0041] It can be seen from the above technical solutions that the present invention considers the uncertainty and fluctuation characteristics of source-load data under the actual operating environment of the power system, and uses the differences in demand-side resource responses under different time scales to construct a day-ahead scheduling model and an intraday scheduling model, optimizes demand-side resource scheduling, and improves the effectiveness of the demand-side scheduling scheme. By constructing a day-ahead scheduling model with the operation cost minimization as the optimization goal under multiple operation scenarios, multiple scenarios are optimized, the impact of source-load prediction errors is reduced, and the effectiveness and flexibility of the user's integrated energy system demand-side response scheme is guaranteed. The demand-side response characteristics are comprehensively considered for constraints, which can enhance the flexibility of the demand-side response of the new energy power grid, fully tap the grid's own regulation capabilities, and thus alleviate the supply and demand imbalance caused by wind and solar power generation fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 An application environment diagram of a new energy power grid demand side resource scheduling optimization method provided by an embodiment of the present invention;

[0043] Figure 2 A flowchart of a new energy grid demand side resource scheduling optimization method provided by an embodiment of the present invention;

[0044] Figure 3A schematic diagram of day-ahead and day-intraday prediction of wind power generation data provided by an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of the day-ahead and day-intraday prediction of photovoltaic power generation data provided by an embodiment of the present invention;

[0046] Figure 5 A schematic diagram of day-ahead and day-intraday prediction of power load data provided by an embodiment of the present invention;

[0047] Figure 6 A schematic diagram of an electric vehicle charging solution provided by an embodiment of the present invention;

[0048] Figure 7 A schematic diagram of the structure of a new energy grid demand side resource scheduling optimization device provided by an embodiment of the present invention;

[0049] Figure 8 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The embodiment of the present application provides a new energy grid demand side resource scheduling optimization method, which can be applied to Figure 1 In the application environment shown. Among them, each node of the power grid system communicates with the server 102 through the network. The data storage system can store the data that the server 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on the cloud or other network servers. The server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0052] The power grid system includes loads (such as electric vehicles), user integrated energy systems and renewable energy power plants, among which renewable energy power plants include wind power generation systems and photovoltaic power generation systems. The user integrated energy system includes equipment such as batteries, heat storage tanks and gas turbines.

[0053] like Figure 2 As shown, the embodiment of the present application provides a new energy grid demand side resource scheduling optimization method, which is applied to Figure 1The server 102 in the example is used as an example to illustrate the method, which includes the following steps S1 to S4. Among them:

[0054] Step S1: construct an operation scenario set according to the source-load day-ahead response prediction data of the power system.

[0055] The source-load day-ahead response prediction data refers to the data obtained by using the source-load day-ahead response historical data through a data prediction model, such as a time series analysis method, a random forest algorithm, and the like.

[0056] The types of source-load day-ahead response forecast data include electric field output data and load data. The electric field output data include wind farm and photovoltaic farm power generation forecast data within the next 24 hours, and the load data include electric and thermal load forecast data within the next 24 hours in the area where the power system is located.

[0057] In some embodiments, the process of constructing an operation scenario set based on the source-load day-ahead response prediction data of the power system includes:

[0058] Step S101: Acquire historical source-load response data of the power system, and perform day-ahead prediction based on the historical source-load response data to obtain initial day-ahead prediction data of the source-load.

[0059] Among them, the source-load historical response data of the power system can also include equipment parameters and economic parameters. Equipment parameters include operating parameters and operating costs of wind power generation systems, photovoltaic power generation systems, electric vehicles, and batteries, heat storage tanks, gas turbines and other equipment in the user's integrated energy system; economic parameters include time-of-use electricity prices and natural gas purchase costs in the area where the power system is located.

[0060] Step S102: adding noise data to the initial prediction data of the source-load day-ahead to generate the source-load day-ahead response prediction data.

[0061] It is understandable that for an actually operating power system, there will be interference noise from many aspects. The embodiment of the present application regards these noise data as noise data that conforms to the normal distribution, and uses the source-load day-ahead initial prediction data to add noise data to simulate the source-load data during actual operation.

[0062] Step S2: Taking minimizing the operating cost of the power system under multiple operating scenarios as the optimization goal, determining the first constraint condition according to the demand-side response characteristics and the grid operation characteristics, and constructing a day-ahead planning and dispatching model.

[0063] Among them, in the day-ahead dispatch planning stage of the power system, the day-ahead dispatch model formulates the future electric vehicle load response plan in advance, aiming to minimize the power system operating cost in multiple scenarios while meeting the charging needs of electric vehicles. Therefore, the embodiment of the present application takes minimizing the operating cost of the power system in multiple operating scenarios as the optimization goal. At the same time, the operating characteristics of the demand-side resources of the power system, such as electric vehicles and user integrated energy systems, must be guaranteed to meet the demand-side response characteristics and grid operation characteristics.

[0064] To this end, the day-ahead scheduling model includes a first optimization objective function;

[0065] Among them, the first optimization objective function is:

[0066] (1)

[0067] In the formula, is the probability of operation scenario s occurring, S is the set of operation scenarios, t is the time period, T is the total time period, is the electricity balancing cost of operating scenario s in period t, To run scenario s in time period t

[0068] demand-side response costs;

[0069] in,

[0070] (2)

[0071] (3)

[0072] In the formula, , , , , They represent the cost of calling up the unit grid reserve capacity, the penalty cost corresponding to the unit wind and solar power abandonment, the natural gas purchase price, the energy storage calling cost corresponding to the unit energy storage system charging and discharging power, and the battery loss cost caused by the unit electric vehicle charging and discharging power. Indicates the amount of grid reserve capacity called, , They represent the fluctuation factors of wind power node m and photovoltaic node n in period t under operation scenario s, represents the maximum power of wind power node m, represents the power of wind power node m in period t under operating scenario s, represents the maximum power of photovoltaic node n, represents the power of photovoltaic node n in period t under operating scenario s, represents the gas purchase volume of the external natural gas network in period t under operation scenario s, , They represent the charging and discharging power of the energy storage device in time period t under operating scenario s, , They represent the charging and discharging power of the battery e of the electric vehicle in the operation scenario s during the period t. They represent the node sets of wind power, photovoltaic, integrated energy system and electric vehicle loads respectively. represents the comprehensive energy node, Y represents the charging and discharging power sequence of the energy storage device, Indicates the type of energy storage device charging and discharging power, where P represents the battery charging and discharging power, and G represents the gas tank charging and discharging power. A collection of battery nodes for electric vehicles.

[0073] The first constraint condition includes grid operation constraints, electric vehicle charging and discharging operation constraints, and user comprehensive energy operation constraints. Among them, grid operation constraints include grid flow constraints, power gateway constraints, and power system output constraints.

[0074] Among them, for the charging and discharging operation constraints of electric vehicles, electric vehicles are parked most of the time and can be charged flexibly during the parking period. Therefore, the charging load of electric vehicles during the charging period can be adjusted and transferred to the most appropriate time, which will be beneficial to the peak load regulation of the power system and save the charging costs of car owners.

[0075] Taking a single electric vehicle as an example, the following constraints are met during the charging period:

[0076] (4)

[0077] (5)

[0078] (6)

[0079] (7)

[0080] (8)

[0081] (9)

[0082] Among them, formula (4) is the constraint of electric vehicle power change, is the SOC value of the battery in period t, is the battery self-discharge rate, is the SOC value of the battery at time t-1, is the time difference, is the battery capacity, For battery charging efficiency, is the charging power of the battery in period t-1, is the battery discharge efficiency.

[0083] Formula (5) is the upper and lower limit constraints of battery capacity. , The upper and lower limits of the battery SOC capacity.

[0084] Formula (6) is the constraint that the power reaches the owner's requirement after charging is completed. Request power for car owners, The battery capacity after charging is completed.

[0085] Equations (7) to (8) limit the upper limit of charge and discharge power and charge and discharge state respectively. is the charging power of the battery in period t, They are all 0-1 variables, indicating the charging and discharging status of the battery. Upper limit for battery charging power.

[0086] Regarding the user's comprehensive energy operation constraints, a typical user's comprehensive energy system consists of electricity, natural gas, and heating subsystems, including electric boilers, power-to-gas equipment, micro gas turbines, gas boilers and other energy conversion equipment, as well as batteries, heat storage tanks, gas storage tanks and other energy storage equipment. The user's comprehensive energy system can flexibly adjust the operating status of different internal equipment according to the external power grid conditions, respond to demand, improve user energy utilization efficiency, and reduce user energy costs. The operation of the user's comprehensive energy system needs to meet the following constraints:

[0087] (1) Electricity, heat and gas energy balance constraints

[0088] The electrical subsystem operates as a flexible load relative to the external power grid. Its external power characteristics are determined by the coordinated scheduling of the micro gas turbine, battery energy storage and multiple power consumption devices (such as typical user needs, power-to-gas equipment, and electric boilers) within the subsystem. The power balance in the integrated energy system can be expressed as:

[0089] (10)

[0090] In the formula, represents the input power of the comprehensive energy system, It represents the electrical load demand in the integrated energy system. Respectively represent the output power of electrical conversion equipment, cogeneration unit, and electric boiler, Indicates the charging and discharging power of the electrical energy storage.

[0091] The operational flexibility of the power subsystem is achieved through the electricity-to-gas energy conversion procedure. When there is sufficient electricity generation from renewable energy, the excess electricity can be converted into natural gas through the power-to-gas equipment. When electricity imports are insufficient, the stored natural gas will be released to power the gas turbine to meet internal demand. The energy balance of the natural gas subsystem in the integrated energy system can be expressed as:

[0092] (11)

[0093] In the formula, represents the gas purchase volume from the external natural gas network, Indicates the gas load demand in the integrated energy system. Respectively represent the amount of natural gas produced / consumed by electrical conversion equipment, cogeneration units, and gas boilers, Indicates the filling and discharging volume of the gas tank.

[0094] In order to improve the efficiency of gas-electricity conversion, the CCHP power generation scheme is introduced to recover the waste heat of the micro gas turbine for heating. The energy balance of the heating subsystem in the integrated energy system can be expressed as:

[0095] (12)

[0096] In the formula, represents the heat load demand in the integrated energy system. , , Respectively represent the heat produced / consumed by electric boilers, cogeneration units, and gas boilers, , Indicates the heat consumed by charging and discharging the gas tank.

[0097] (2) Energy conversion equipment operation constraints

[0098] Micro gas turbines use natural gas as fuel and convert it into electricity and heat energy, meeting the following constraints:

[0099] (13)

[0100] (14)

[0101] (15)

[0102] In the formula, They are The power and heat generation efficiency of the micro gas turbine at all times, is the maximum output power of the micro gas turbine.

[0103] A power-to-gas device is a device that converts electrical energy into gaseous fuel, satisfying the following constraints:

[0104] (16)

[0105] (17)

[0106] In the formula, They represent the maximum output power and gas production efficiency of the power-to-gas equipment respectively.

[0107] An electric boiler is a device that converts electrical energy directly into thermal energy through a resistance heating element. The energy conversion relationship satisfies the following constraints:

[0108] (18)

[0109] (19)

[0110] In the formula, They represent the maximum output power and heat production efficiency of the power-to-gas equipment respectively.

[0111] A gas boiler is a device that converts chemical energy into thermal energy by burning natural gas or other gaseous fuels. The energy conversion relationship meets the following constraints:

[0112] (20)

[0113] (twenty one)

[0114] (3) Energy storage equipment constraints

[0115] The user-side integrated energy system includes three types of energy storage devices: batteries, heat storage tanks, and gas storage tanks. They all include the following constraints: simultaneous charging and discharging restrictions (Formula (22)), charging / discharging power restrictions (Formula (23-24)), and energy storage capacity restrictions (see Formula (25-27)).

[0116] (twenty two)

[0117] (twenty three)

[0118] (twenty four)

[0119] (25)

[0120] (26)

[0121] (27)

[0122] In the above formula, It is a 0-1 variable, indicating the charging and discharging status of the energy storage device; Respectively represent the charging and discharging power of the energy storage device, Indicates the upper limit of charging and discharging power of the device; Respectively represent the energy storage loss coefficient, charging and discharging efficiency is the upper / lower limit of the energy storage device capacity, is the initial energy storage of the device, , are the capacities of the energy storage equipment in period t-1 and period t respectively.

[0123] For grid operation constraints, the power system operation with demand-side resources meets the following constraints:

[0124] 1) Power Grid Flow Constraints

[0125] (28)

[0126] (29)

[0127] (30)

[0128] (31)

[0129] (32)

[0130] In the formula, Input power for the integrated energy system of operating scenario s, is the load fluctuation factor of operating scenario s, is the upper limit of normal load in the power system, , are the charging and discharging power of electric vehicles, is the power flow value through line ij in operation scenario s during period t, To connect to a node The node set of is a regular node set, A collection of special nodes connected to the external power grid. is the grid reserve capacity call amount of operation scenario s, is a node in the special node set C A collection of connected nodes, For the node connected nodes, , They are the nodes in the running scenario respectively. , The voltage phase angle, is the impedance of the transmission line between nodes ij, , are the upper and lower limits of the power flow value of line ij, , They are the upper and lower limits of the voltage phase angle respectively.

[0131] 2) Power gateway constraints

[0132] (33)

[0133] In the formula, Indicates the operating reserve introduced from the external power grid. For operation spare upper limit.

[0134] 3) Output constraints of wind and photovoltaic power generation systems

[0135] (34)

[0136] (35)

[0137] Step S3, taking minimization of the total cost of the power system's daily operation as the optimization goal, determining the second constraint condition based on the energy balance characteristics of the day-ahead plan, the user's comprehensive energy operation characteristics, and the power grid operation characteristics, and constructing an intraday dispatch model.

[0138] Among them, the goal of the intraday dispatch plan is to absorb excess renewable energy through the demand-side response of the user's integrated energy system, ensure the stable operation of the power system, and minimize the total cost of the system's intraday operation. The intraday dispatch model includes the second optimization objective function;

[0139] (36)

[0140] In the formula, , They represent the power balancing cost and demand-side response cost in period t respectively;

[0141] (37)

[0142] (38)

[0143] In the formula, , , They represent the fluctuation factors of wind power node m and photovoltaic node n in period t, represents the power of wind power node m in period t, represents the power of photovoltaic node n in period t, represents the gas purchase volume of the external natural gas network in period t, , They respectively represent the charging and discharging power of the energy storage device in time period t.

[0144] The second constraint condition includes grid operation constraint, day-ahead planned energy balance constraint and user comprehensive energy operation constraint. Among them, grid operation constraint includes grid flow constraint, power gateway constraint and power system output constraint.

[0145] Among them, the grid operation constraints are the same as those in the above equations (28) to (35), the user comprehensive energy operation constraints are the same as those in the above equations (10) to (27), and the energy balance constraints for the day-ahead plan are:

[0146] (39)

[0147] In the formula, are the planned charging and discharging powers of electric vehicles decided in the day-ahead planning stage.

[0148] Step S4, according to the operation scenario set, respectively, the day-ahead planning and scheduling model and the intraday scheduling model are optimized and solved, and the day-ahead planning and scheduling optimal solutions and the intraday scheduling optimal solutions are determined according to the optimal solutions corresponding to the day-ahead planning and scheduling models respectively; wherein, the day-ahead planning and scheduling optimal solution is used to execute power grid scheduling in the day-ahead planning stage of the power system, and the intraday scheduling optimal solution is used to execute power grid scheduling in the intraday scheduling stage of the power system.

[0149] The power grid dispatching process is executed in a rolling manner in the day-ahead planning stage and the intra-day dispatching stage, including steps S501 to S502.

[0150] Step S501: Execute the optimal day-ahead planning scheduling scheme and the optimal day-ahead scheduling scheme in sequence for the day-ahead planning stage and the day-ahead scheduling stage according to the scheduling cycle preset within the day.

[0151] In one example, a scheduling cycle is 4 hours per day, and the operating scenario consisting of the source-load day-ahead response forecast data within 4 hours is obtained to determine the optimal day-ahead scheduling plan for the power system in the current scheduling cycle and the optimal intraday scheduling plan for execution.

[0152] The optimal plan for day-ahead scheduling includes the planned power of each wind power node, the planned power of each photovoltaic node, the planned gas purchase volume, the charging and discharging power of the energy storage equipment, and the charging and discharging power of the electric vehicle's battery.

[0153] The optimal plan for intraday scheduling includes the power adjustment of each wind power node, the power adjustment of each photovoltaic node, the gas purchase adjustment, and the charging and discharging power adjustment of the energy storage equipment.

[0154] Step S502: When all scheduling cycles within the day have not completed scheduling, the optimal day-ahead scheduling scheme and the optimal day-ahead scheduling scheme of the current scheduling cycle are updated according to the source-load day-ahead response prediction data of the previous scheduling cycle, and based on the updated day-ahead scheduling optimal scheme and the optimal day-ahead scheduling optimal scheme, go to step S501 until all scheduling cycles within the day are completed and the scheduling is ended.

[0155] During the monitoring system time, step S501 is continuously executed, and based on the advancement of the scheduling cycle, the optimal scheduling plan for the day ahead and the optimal scheduling plan for the day are updated until the 24-hour scheduling cycle ends.

[0156] It should be noted that the method for optimizing resource scheduling on the demand side of a new energy power grid provided in the embodiment of the present application considers the uncertainty and volatility characteristics of source-load data under the actual operating environment of the power system, and utilizes the differences in demand-side resource responses under different time scales to construct a day-ahead scheduling model and an intraday scheduling model, optimizes demand-side resource scheduling, and improves the effectiveness of the demand-side scheduling scheme. By constructing a day-ahead scheduling model with the minimization of operating costs under multiple operating scenarios as the optimization goal, multiple scenarios are optimized, the impact of source-load prediction errors is reduced, and the effectiveness and flexibility of the demand-side response scheme of the user's integrated energy system is ensured. The demand-side response characteristics are comprehensively considered for constraints, which can enhance the flexibility of the demand-side response of the new energy power grid, fully tap the grid's own regulation capabilities, and thus alleviate the supply and demand imbalance caused by fluctuations in wind and solar power generation.

[0157] The following is a calculation example of a new energy power grid demand side resource scheduling optimization method provided in combination with an embodiment of the present application.

[0158] In this example, a demand-side response strategy is developed for a power system with demand-side resources such as electric vehicles, user-side integrated energy systems, and renewable energy generation. Information related to demand-side response resources and wind and solar output load forecasts is obtained. Table 1 shows the main parameters of each device and electric vehicle in the user's integrated energy system.

[0159] Table 1 Main parameter values ​​of user integrated energy system and electric vehicle

[0160]

[0161] like Figures 3-5 As shown, Figures 3-5 The day-ahead and intraday forecast data of wind and solar power generation and power load are shown respectively.

[0162] By using the day-ahead scheduling model and the intraday scheduling model provided in the embodiment of the present application to solve, the electric vehicle scheduling result is obtained as follows: Figure 6As shown in the figure, the charging load of electric vehicles is mainly concentrated in 1:00-3:00 and 17:00-20:00. At this time, the power generation of renewable energy is large, while the conventional non-adjustable load is low. Electric vehicles start charging as adjustable loads to absorb excess renewable energy and reduce wind and solar power abandonment.

[0163] When executing the intraday scheduling plan, the total 24-hour power flow is shown in Table 2.

[0164] Table 2 Electric energy flow in the daily dispatching scheme

[0165]

[0166] As can be seen from Table 2, in this embodiment, the demand-side response scheme obtained by the present invention is adopted, so that the renewable energy consumption rate of the power system reaches 93.6%, of which the renewable energy consumed by demand-side resources accounts for 49.7%. Those skilled in the art can formulate a demand-side response strategy for the power system based on the optimization results to ensure the stable operation of the power system and achieve full consumption of excess renewable energy.

[0167] In addition, in engineering applications, existing regulation resources such as electric vehicles and user integrated energy systems can be configured or utilized in the power grid, and the method provided in the embodiment of the present application can be used to formulate a suitable new energy power grid demand-side response plan, thereby effectively solving the stability problems brought about by the access of a high proportion of renewable energy power generation to the power system and improving the utilization rate of renewable energy in the power system.

[0168] Based on the same inventive concept, an embodiment of the present application also provides a new energy power grid demand side resource scheduling optimization system for implementing the above-mentioned new energy power grid demand side resource scheduling optimization method.

[0169] The implementation solution for solving the problem provided by the device is similar to the implementation solution recorded in the above method. Therefore, the specific limitations in one or more embodiments of the new energy power grid demand side resource scheduling optimization device provided below can be referred to the above limitations on the new energy power grid demand side resource scheduling optimization method, and will not be repeated here.

[0170] like Figure 7 As shown, an embodiment of the present application provides a new energy power grid demand side resource scheduling optimization device, the device comprising:

[0171] A scenario set construction module 100 is used to construct an operation scenario set according to the source-load day-ahead response prediction data of the power system;

[0172] The day-ahead scheduling module 200 is used to take minimizing the operating cost of the power system under multiple operating scenarios as the optimization goal, determine the first constraint condition according to the demand-side response characteristics and the power grid operation characteristics, and construct a day-ahead planning and scheduling model;

[0173] The intraday dispatch module 300 is used to take minimization of the total cost of the power system's intraday operation as the optimization goal, determine the second constraint condition according to the energy balance characteristics of the day-ahead plan, the user's comprehensive energy operation characteristics and the power grid operation characteristics, and construct an intraday dispatch model;

[0174] The scheduling optimization module 400 is used to optimize and solve the day-ahead scheduling model and the intraday scheduling model according to the set of operating scenarios, and determine the day-ahead scheduling optimal plan and the intraday scheduling optimal plan according to the optimal solutions corresponding to the day-ahead scheduling model and the intraday scheduling model respectively; wherein, the day-ahead scheduling optimal plan is used to execute grid scheduling in the day-ahead planning stage of the power system, and the intraday scheduling optimal plan is used to execute grid scheduling in the intraday scheduling stage of the power system.

[0175] In some embodiments, the scenario set construction module 100 is also used to obtain the source-load historical response data of the power system, and perform day-ahead prediction based on the source-load historical response data to obtain the source-load day-ahead initial prediction data; add noise data to the source-load day-ahead initial prediction data to generate source-load day-ahead response prediction data.

[0176] In some embodiments, the day-ahead planning and scheduling model includes a first optimization objective function;

[0177] Among them, the first optimization objective function is:

[0178]

[0179] In the formula, is the probability of operation scenario s occurring, S is the set of operation scenarios, t is the time period, T is the total time period, is the electricity balancing cost of operating scenario s in period t, To run scenario s in time period t

[0180] demand-side response costs;

[0181] in,

[0182]

[0183]

[0184] In the formula, , , , , They represent the cost of calling up the unit grid reserve capacity, the penalty cost corresponding to the unit wind and solar power abandonment, the natural gas purchase price, the energy storage calling cost corresponding to the unit energy storage system charging and discharging power, and the battery loss cost caused by the unit electric vehicle charging and discharging power. Indicates the amount of grid reserve capacity called, , They represent the fluctuation factors of wind power node m and photovoltaic node n in period t under operation scenario s, represents the maximum power of wind power node m, represents the power of wind power node m in period t under operating scenario s, represents the maximum power of photovoltaic node n, represents the power of photovoltaic node n in period t under operating scenario s, represents the gas purchase volume of the external natural gas network in period t under operation scenario s, , They represent the charging and discharging power of the energy storage device in time period t under operating scenario s, , They represent the charging and discharging power of the battery e of the electric vehicle in the operation scenario s during the period t. They represent the node sets of wind power, photovoltaic, integrated energy system and electric vehicle loads respectively. represents the comprehensive energy node, Y represents the charging and discharging power sequence of the energy storage device, Indicates the type of energy storage device charging and discharging power, where P represents the battery charging and discharging power, and G represents the gas tank charging and discharging power. A collection of battery nodes for electric vehicles.

[0185] In some embodiments, the first constraint condition includes grid operation constraints, electric vehicle charging and discharging operation constraints, and user comprehensive energy operation constraints, wherein the grid operation constraints include grid flow constraints, power gateway constraints, and power system output constraints.

[0186] In some embodiments, the intraday scheduling model includes a second optimization objective function;

[0187]

[0188] In the formula, , They represent the power balancing cost and demand-side response cost in period t respectively;

[0189]

[0190]

[0191] In the formula, , , They represent the fluctuation factors of wind power node m and photovoltaic node n in period t, represents the power of wind power node m in period t, represents the power of photovoltaic node n in period t, represents the gas purchase volume of the external natural gas network in period t, , They respectively represent the charging and discharging power of the energy storage device in time period t.

[0192] In some embodiments, the second constraint condition includes grid operation constraint, day-ahead planned energy balance constraint and user comprehensive energy operation constraint, wherein the grid operation constraint includes grid flow constraint, power gateway constraint and power system output constraint.

[0193] In some embodiments, the device also includes: an execution module, which is used to execute the optimal day-ahead planning scheduling plan and the optimal intraday scheduling plan for the day-ahead planning stage and the intraday scheduling stage in sequence according to the scheduling cycle preset within the day; when all scheduling cycles within the day have not completed scheduling, the day-ahead planning scheduling plan and the optimal intraday scheduling plan of the current scheduling cycle are updated according to the source-load day-ahead response prediction data under the previous scheduling cycle, and based on the updated day-ahead planning scheduling plan and the optimal intraday scheduling plan, it is turned to execute the day-ahead planning scheduling plan and the optimal intraday scheduling plan for the day-ahead planning stage and the intraday scheduling stage in sequence according to the scheduling cycle preset within the day, until all scheduling cycles within the day are completed and the scheduling is ended.

[0194] like Figure 8 As shown, an embodiment of the present application provides an electronic device, the electronic device 10 includes a memory 20 and a processor 30, the memory 20 stores a computer program, and when the computer program is executed by the processor 30, the processor 30 executes the steps of the new energy power grid demand side resource scheduling optimization method as in any of the above embodiments.

[0195] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the steps of the new energy power grid demand side resource scheduling optimization method as in any of the above embodiments are implemented.

[0196] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, electronic device and computer storage medium can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0197] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatus.

[0198] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and a part of a module, a program segment or a code includes one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0199] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, electronic devices, computer storage media and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0200] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0201] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0202] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for executing all or part of the steps of the various embodiments of the present invention through a computer device (which can be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk, etc. Various media that can store program codes.

[0203] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A new energy power grid demand side resource scheduling optimization method, characterized in that: The method comprises: Constructing an operation scenario set based on the source-load day-ahead response prediction data of the power system; Taking the minimization of the operating cost of the power system under multiple operating scenarios as the optimization goal, the first constraint condition is determined according to the demand-side response characteristics and the grid operation characteristics, and a day-ahead scheduling model is constructed; Taking the minimization of the total cost of power system operation within a day as the optimization goal, the second constraint is determined according to the energy balance characteristics of the day-ahead plan, the comprehensive energy operation characteristics of the user and the operation characteristics of the power grid, and the intraday dispatch model is constructed; According to the set of operating scenarios, the day-ahead planning and scheduling model and the intraday scheduling model are respectively optimized and solved, and the day-ahead planning and scheduling optimal solutions and the intraday scheduling optimal solutions are determined according to the optimal solutions corresponding to the day-ahead planning and scheduling models respectively; wherein the day-ahead planning and scheduling optimal solutions are used to execute power grid scheduling in the day-ahead planning stage of the power system, and the intraday scheduling optimal solutions are used to execute power grid scheduling in the intraday scheduling stage of the power system.

2. The method for optimizing resource scheduling on the demand side of a new energy power grid according to claim 1, characterized in that: The step of constructing an operation scenario set according to the source-load day-ahead response prediction data of the power system comprises: Acquire source-load historical response data of the power system, and perform day-ahead prediction based on the source-load historical response data to obtain source-load day-ahead initial prediction data; Noise data is added to the source-load day-ahead initial prediction data to generate the source-load day-ahead response prediction data.

3. The method for optimizing resource scheduling on the demand side of a new energy power grid according to claim 1, characterized in that: The day-ahead planning and scheduling model includes a first optimization objective function; Wherein, the first optimization objective function is: In the formula, is the probability of operation scenario s occurring, S is the set of operation scenarios, t is the time period, T is the total time period, is the electricity balancing cost of operating scenario s in period t, is the demand-side response cost of operating scenario s in period t; in, In the formula, , , , , They represent the cost of calling up the unit grid reserve capacity, the penalty cost corresponding to the unit wind and solar power abandonment, the natural gas purchase price, the energy storage calling cost corresponding to the unit energy storage system charging and discharging power, and the battery loss cost caused by the unit electric vehicle charging and discharging power. Indicates the amount of grid reserve capacity called, , They represent the fluctuation factors of wind power node m and photovoltaic node n in period t under operation scenario s, represents the maximum power of wind power node m, represents the power of wind power node m in period t under operating scenario s, represents the maximum power of photovoltaic node n, represents the power of photovoltaic node n in period t under operating scenario s, represents the gas purchase volume of the external natural gas network in period t under operation scenario s, , They represent the charging and discharging power of the energy storage device in time period t under operating scenario s, , They represent the charging and discharging power of the battery e of the electric vehicle in the operation scenario s during the period t. They represent the node sets of wind power, photovoltaic, integrated energy system and electric vehicle loads respectively. represents the comprehensive energy node, Y represents the charging and discharging power sequence of the energy storage device, Indicates the type of energy storage device charging and discharging power, where P represents the battery charging and discharging power, and G represents the gas tank charging and discharging power. A collection of battery nodes for electric vehicles.

4. The method for optimizing resource scheduling on the demand side of a new energy power grid according to claim 1 or 3, characterized in that: The first constraint conditions include power grid operation constraints, electric vehicle charging and discharging operation constraints and user comprehensive energy operation constraints, wherein the power grid operation constraints include power grid flow constraints, power gateway constraints and power system output constraints.

5. The method for optimizing resource scheduling on the demand side of a new energy power grid according to claim 1, characterized in that: The intraday scheduling model includes a second optimization objective function; Wherein, the second optimization objective function is: In the formula, , They represent the power balancing cost and demand-side response cost in period t respectively; In the formula, , , They represent the fluctuation factors of wind power node m and photovoltaic node n in period t, represents the power of wind power node m in period t, represents the power of photovoltaic node n in period t, represents the gas purchase volume of the external natural gas network in period t, , They respectively represent the charging and discharging power of the energy storage device in time period t.

6. The method for optimizing resource scheduling on the demand side of a new energy power grid according to claim 1 or 5, characterized in that: The second constraint conditions include power grid operation constraints, day-ahead planned energy balance constraints and user comprehensive energy operation constraints, wherein the power grid operation constraints include power grid flow constraints, power gateway constraints and power system output constraints.

7. The method for optimizing resource scheduling on the demand side of a new energy power grid according to claim 1, characterized in that: Also includes: The optimal day-ahead planning scheduling scheme and the optimal day-ahead scheduling scheme are sequentially executed for the day-ahead planning stage and the day-ahead scheduling stage according to the scheduling cycle preset within the day; In the case that scheduling is not completed for all scheduling cycles within the day, the optimal day-ahead scheduling scheme and the optimal intraday scheduling scheme of the current scheduling cycle are updated according to the source-load day-ahead response prediction data under the previous scheduling cycle, and based on the updated optimal day-ahead scheduling scheme and the optimal intraday scheduling scheme, the step of sequentially executing the optimal day-ahead scheduling scheme and the optimal intraday scheduling scheme for the day-ahead planning stage and the intraday scheduling stage according to the scheduling cycle preset within the day is performed until all scheduling cycles within the day are completed and the scheduling is terminated.

8. A new energy grid demand side resource scheduling optimization device, characterized in that: The device comprises: A scenario set building module, used to build an operation scenario set according to the source-load day-ahead response prediction data of the power system; The day-ahead dispatch module is used to minimize the operating cost of the power system under multiple operating scenarios as the optimization goal, determine the first constraint condition according to the demand-side response characteristics and the grid operation characteristics, and build a day-ahead planning and dispatching model; The intraday dispatch module is used to minimize the total cost of the power system's intraday operation as the optimization goal, determine the second constraint condition based on the energy balance characteristics of the day-ahead plan, the user's comprehensive energy operation characteristics, and the power grid operation characteristics, and build an intraday dispatch model; A scheduling optimization module is used to optimize and solve the day-ahead scheduling model and the intraday scheduling model according to the set of operating scenarios, and determine the day-ahead scheduling optimal plan and the intraday scheduling optimal plan according to the optimal solutions corresponding to the day-ahead scheduling model and the intraday scheduling model respectively; wherein the day-ahead scheduling optimal plan is used to execute power grid scheduling in the day-ahead planning stage of the power system, and the intraday scheduling optimal plan is used to execute power grid scheduling in the intraday scheduling stage of the power system.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the new energy power grid demand side resource scheduling optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the steps of the new energy power grid demand side resource scheduling optimization method as described in any one of claims 1 to 7 are implemented.

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