Virtual power plant resource optimization scheduling method and system based on load control cost

Abstract

The present application relates to the technical field of virtual power plant resource scheduling, and provides a virtual power plant resource optimization scheduling method and system based on load control cost, comprising: deploying schedulable resources in a virtual power plant, obtaining basic information and real-time data of the schedulable resources; establishing an operation constraint model of the schedulable resources in the virtual power plant; establishing a control cost model for user-side loads in the virtual power plant; establishing a hierarchical optimization scheduling model of day-ahead planning and intra-day optimization of the virtual power plant, and solving a virtual power plant resource scheduling target. According to the scheme of the present application, the present application is helpful for source-load-storage resource collaborative optimization interaction, reduces virtual power plant operation cost, and proposes a method of formulating a day-ahead scheduling plan and intra-day real-time optimization, which can eliminate the influence of part of the price or power prediction error. The present application can provide technical support for virtual power plant participation in power market transactions, and can provide a solution for improving new energy consumption, meeting energy demand, and promoting energy supply quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of virtual power plant resource scheduling, and particularly relates to a virtual power plant resource optimization scheduling method and system based on load control cost. BACKGROUND

[0002] In order to meet the increasing demand of users for power, the power system needs to adapt to various complex load demands and respond quickly to these demands. In this case, a large number of adjustable resources on the user side have huge regulation potential, and a virtual power plant aggregates a large number of user-side load resources. At present, the control form of the virtual power plant on the user-side load is mainly demand response, and there are problems such as difficulty in source-load-storage cooperation, great influence on user production and life, and high virtual power plant operation cost for the direct control of the load. SUMMARY

[0003] The present application aims to solve at least one of the technical problems in the background art, and provides a virtual power plant resource optimization scheduling method and system based on load control cost.

[0004] To achieve the above-mentioned purpose, the present application provides a virtual power plant resource optimization scheduling method based on load control cost, comprising:

[0005] Deploying adjustable resources inside the virtual power plant, obtaining basic information and real-time data of the adjustable resources;

[0006] Establishing an operation constraint model of the adjustable resources inside the virtual power plant;

[0007] Establishing a control cost model for the user-side load inside the virtual power plant;

[0008] Establishing a hierarchical optimization scheduling model of day-ahead planning-intra-day optimization of the virtual power plant, and solving the virtual power plant resource scheduling target.

[0009] According to one aspect of the present application, the adjustable resources include: conventional generator units, wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, electric vehicles and air conditioners;

[0010] The basic information and real-time data include: rated capacity of the adjustable resources, operation data, weather data of the environment where the adjustable resources are located, and real-time electricity price information of the power market of the adjustable resources.

[0011] According to one aspect of the present application, the operation constraints of the adjustable resources include: power constraints, ramping constraints, capacity constraints and start-stop time constraints of conventional generator units, and power constraints and capacity constraints of wind power generation equipment and photovoltaic power generation equipment;

[0012] The operation constraint model of the adjustable resources inside the virtual power plant includes:

[0013] For the traditional generator set power generation, an operation constraint model is established, including:

[0014] The first power constraint model:

[0015]

[0016] Wherein, and are the minimum power and maximum power limits of the i-th traditional generator set DG i , is the switching state of the i-th traditional generator set DG i at time t, is the active power size of the i-th traditional generator set DG i at time t;

[0017] The ramp constraint model:

[0018]

[0019] Wherein, is the maximum ramp rate of the i-th traditional generator set DG i ;

[0020] The first capacity constraint model:

[0021]

[0022] Wherein, is the reactive power size of the i-th traditional generator set DG i at time t, is the capacity constraint of the i-th traditional generator set DG i ;

[0023] The start-stop time constraint model:

[0024]

[0025]

[0026]

[0027]

[0028] Wherein, y i,t and z i,t are the start and stop indications of the i-th traditional generator set DG i at time t, which are {0, 1} binary variables, and are the start and stop times of the i-th traditional generator set DGi Minimum start-up time and minimum shutdown time;

[0029] For wind power generation equipment and photovoltaic power generation equipment, an operation constraint model is established, including:

[0030] Second power constraint model:

[0031]

[0032] Second capacity constraint:

[0033]

[0034] in, and The i-th wind turbine WP i Active and reactive power at time t, and The i-th photovoltaic power generation equipment PV i Active and reactive power at time t, and The i-th wind turbine WP i and photovoltaic power generation equipment PV i Predict the maximum available active power at time t, and The i-th wind turbine WP i and photovoltaic power generation equipment PV i The maximum capacity.

[0035] According to one aspect of the present invention, the types of user-side loads within the virtual power plant include air conditioning loads, electric vehicle loads, and energy storage equipment loads;

[0036] The control cost model for user-side loads within the virtual power plant is established by: based on load type, the load control cost model is established based on the degree of impact caused by the deviation of equipment from user expectations when it is called; wherein the air conditioning load control cost is represented by temperature deviation, the electric vehicle load control cost is represented by the deviation between the charge amount during the charging period and the ideal charge amount, and the energy storage equipment control cost is represented by the degree of battery aging, including:

[0037] For air conditioning load, the control cost model is established based on the temperature of the air conditioner:

[0038]

[0039] Where: T is the number of load control periods; T j (t) is the temperature provided to the user during the jth temperature-controlled load t period; The temperature set for the jth temperature control load in period t;

[0040] For electric vehicle loads, the control cost model is established based on the deviation between the charging amount during the charging period and the ideal charging amount:

[0041]

[0042] Where: T is the number of load control periods; E j (t end ) is the battery capacity of the j-th electric vehicle at the end of charging; is the battery capacity expected to be achieved by the j-th electric vehicle;

[0043] For energy storage equipment, a cost control model is established based on the degree of battery aging:

[0044]

[0045] Where: T is the number of control periods; L j (t) is the unit cycle number of the battery of the jth energy storage device; L j all is the total cycle life of the battery of the jth energy storage device; d j is the depth of charge and discharge; is the battery capacity; C j For installation costs.

[0046] According to one aspect of the present invention, establishing a hierarchical optimization scheduling model of day-ahead planning and intraday optimization for a virtual power plant to solve the resource scheduling target of the virtual power plant includes:

[0047] Establish the operating cost objective function of the day-ahead dispatch virtual power plant and solve the day-ahead dispatch plan of the virtual power plant;

[0048] Establish the objective function of virtual power plant operation cost for intraday scheduling and solve the optimization results of virtual power plant intraday scheduling.

[0049] According to one aspect of the present invention, the virtual power plant operating costs include:

[0050] (1) Cost of purchasing energy from the electricity market:

[0051] (2) The i-th traditional generator set DG i Power generation cost:

[0052]

[0053] (3) Energy storage equipment ESS i Running costs:

[0054]

[0055] wherein, is the energy purchase price of the day-ahead electricity market, is the energy purchased by the virtual power plant in the day-ahead of the electricity market, i i i is the generation cost coefficient of the i-th generator DG i AP i is the operation cost coefficient of the i-th energy storage system ESS i ;

[0056] (4) Load control cost: denoted by symbol p, which is selected according to the type of user-side load participating in the optimization scheduling;

[0057] The virtual power plant operation cost objective function of the day-ahead scheduling is established as:

[0058]

[0059] According to an aspect of the present application, the virtual power plant day-ahead scheduling plan result includes: virtual power plant next-day hourly energy exchange amount and 24h power scheduling plan of traditional generator, wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, electric vehicle and air conditioner.

[0060] According to an aspect of the present application, in the intra-day scheduling process, the real-time energy exchange amount of the virtual power plant and the real-time clearing amount of the electricity market will have a deviation;

[0061] The energy purchase cost to the electricity market is:

[0062]

[0063] The virtual power plant operation cost increase deviation penalty cost is established as:

[0064]

[0065] wherein, is the clearing amount of the electricity market at time t, is the real-time energy purchase amount of the virtual power plant at time t, is the real-time energy purchase price of the electricity market, is the deviation penalty coefficient of the real-time clearing amount of the electricity market and the real-time energy purchase amount of the virtual power plant;

[0066] The virtual power plant operation cost objective function of the intra-day scheduling is established as:

[0067]

[0068] ​​According to an aspect of the present application, the virtual power plant intra-day scheduling optimization result comprises: real-time energy purchase amount of the virtual power plant to the power market, target power of the traditional generator unit, target power of wind power generation and photovoltaic power generation, target power of the energy storage device output, and target power of load tracking.

[0069] To achieve the above-mentioned purpose, the present application further provides a virtual power plant resource optimization scheduling system based on load control cost, comprising:

[0070] A schedulable resource information acquisition module acquires basic information and real-time data of schedulable resources deployed in the virtual power plant.

[0071] A running constraint model establishment module establishes a running constraint model of the schedulable resources in the virtual power plant.

[0072] A control cost model establishment module establishes a control cost model for user-side loads in the virtual power plant.

[0073] A resource scheduling calculation module establishes a hierarchical optimization scheduling model of day-ahead planning-intra-day optimization of the virtual power plant, and solves a resource scheduling target of the virtual power plant.

[0074] To achieve the above-mentioned purpose, the present application further provides an electronic device comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the computer program is executed by the processor to implement the virtual power plant resource optimization scheduling method based on load control cost as described above.

[0075] To achieve the above-mentioned purpose, the present application further provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to implement the virtual power plant resource optimization scheduling method based on load control cost as described above.

[0076] According to the scheme of the present application, the virtual power plant different types of resource running constraints are analyzed, which is helpful for source-load-storage resource collaborative optimization interaction; the user-side load control cost model is provided, which is beneficial to improve the enthusiasm of users participating in load direct control and reduce the operation cost of the virtual power plant; the method of formulating day-ahead scheduling plan and intra-day real-time optimization is proposed, which can eliminate the influence of part of the price or power prediction error; the virtual power plant participating in the power spot market transaction can be technically supported, and the solution can be provided for improving new energy consumption, meeting energy demand, and promoting energy quality. BRIEF DESCRIPTION OF DRAWINGS

[0077] Figure 1 A flow chart schematically showing the virtual power plant resource optimization scheduling method based on load control cost according to the present application;

[0078] Figure 2 Fig. 1 is a flow chart schematically illustrating a method for optimizing scheduling of virtual power plant resources based on load control cost according to an embodiment of the present application. DETAILED DESCRIPTION

[0079] The present application will now be discussed with reference to exemplary embodiments. It should be understood that the discussed embodiments are merely to provide a better understanding of and thus enable one of ordinary skill in the art to more readily make and use the present application, and are not intended to limit the scope of the present application.

[0080] As used herein, the term "includes" and its variants are to be read to be synonymous with "including, but not limited to," an open transition. The term "based on" is to be interpreted as "based, at least in part, on." The terms "one embodiment" and "an embodiment" are to be interpreted to be "at least one embodiment."

[0081] Figure 1 Fig. 1 is a flow chart schematically illustrating a method for optimizing scheduling of virtual power plant resources based on load control cost according to an embodiment of the present application. Figure 2 Fig. 1 is a flow chart schematically illustrating a method for optimizing scheduling of virtual power plant resources based on load control cost according to an embodiment of the present application. As shown in Figure 1 and Figure 2 In the embodiment, the method for optimizing scheduling of virtual power plant resources based on load control cost comprises:

[0082] a. deploying schedulable resources within the virtual power plant, and obtaining basic information and real-time data of the schedulable resources;

[0083] b. establishing a running constraint model of the schedulable resources within the virtual power plant;

[0084] c. establishing a control cost model of user-side load within the virtual power plant;

[0085] d. establishing a hierarchical optimization scheduling model of day-ahead planning-intra-day optimization, and solving a virtual power plant resource scheduling target.

[0086] According to an embodiment of the present application, in the above step a, the schedulable resources include: conventional generator units, wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, electric vehicles, and air conditioners.

[0087] The basic information and real-time data include: rated capacity of the schedulable resources, running data, weather data of an environment where the schedulable resources are located, and real-time electricity price information of the schedulable resources in the electricity market.

[0088] Specifically, the basic information and real-time data of the schedulable resources are obtained through a virtual power plant operation monitoring system, sensors, data acquisition devices, and an Internet interface to push data, including the rated power, real-time running power, start-stop state of the traditional generator set; the rated power, output power, cut-in wind speed, cut-out wind speed, real-time wind speed of the distributed wind turbine; the rated power, output power, environmental solar irradiance of the distributed photovoltaic; the capacity, cycle number, total cycle life, charge-discharge depth, installation cost of the energy storage battery; the real-time charging power, real-time battery capacity, user expected charging capacity of the electric vehicle; the real-time environmental temperature, user set temperature of the air conditioner, etc.

[0089] Further, according to an embodiment of the present application, in the above b step, the operation constraints of the schedulable resources include: power constraints, ramping constraints, capacity constraints and start-stop time constraints of the traditional generator set, and power constraints and capacity constraints of the wind power generation equipment and the photovoltaic power generation equipment;

[0090] The operation constraint model of the schedulable resources in the virtual power plant is established, including:

[0091] For the power generation of the traditional generator set, the operation constraint model is established, including:

[0092] The first power constraint model:

[0093]

[0094] wherein, and are the minimum power and maximum power limits of the ith traditional generator set DG i , is the switching state of the ith traditional generator set DG i at time t, is the active power of the ith traditional generator set DG i at time t;

[0095] The ramping constraint model:

[0096]

[0097] wherein, is the maximum ramp rate of the ith traditional generator set DG i ,

[0098] The first capacity constraint model:

[0099]

[0100] wherein, is the reactive power of the ith traditional generator set DG i at time t, C i (t) = 1 for the ith conventional generator unit DG i of the ith conventional generator unit DG

[0101] Start-up and shut-down time constraint model:

[0102]

[0103]

[0104]

[0105]

[0106] where y i,t and z i,t are the start-up and shut-down indication of the ith conventional generator unit DG i at time t, are binary variables, and are the minimum start-up time and minimum shut-down time of the ith conventional generator unit DG i , respectively;

[0107] For wind power generation and photovoltaic power generation, the operation constraint model is established, including:

[0108] Second power constraint model:

[0109]

[0110] Second capacity constraint:

[0111]

[0112] where, and are the active and reactive power of the ith wind power device WP i at time t, and are the active and reactive power of the ith photovoltaic power device PV i at time t, and are the maximum available active power of the ith wind power device WP i and photovoltaic power device PV i predicted at time t, and are the maximum capacity of the ith wind power device WP i and photovoltaic power device PV i , respectively.

[0113] Further, according to an embodiment of the present application, in the above-mentioned c step, the types of the user-side loads inside the virtual power plant include air conditioning loads, electric vehicle loads and energy storage device loads;

[0114] The control cost model of the user-side loads inside the virtual power plant is established as follows: based on the load types, the control cost model of the loads is established according to the degree of influence of the deviation of the devices from the user expectations when called; wherein the control cost of the air conditioning load is represented by temperature deviation, the control cost of the electric vehicle load is represented by the deviation of the charging amount in the charging period from the ideal charging amount, and the control cost of the energy storage device is represented by the degree of battery aging, including:

[0115] For the air conditioning load, the control cost model is established according to the temperature of the air conditioner:

[0116]

[0117] In the formula, T is the number of load regulation periods; T j (t) is the temperature provided to the user in the t period of the jth temperature-controlled load; is the temperature set for the t period of the jth temperature-controlled load;

[0118] For the electric vehicle load, the control cost model is established according to the deviation of the charging amount in the charging period from the ideal charging amount:

[0119]

[0120] In the formula, T is the number of load regulation periods; E j (t end ) is the battery capacity at the end of the jth electric vehicle charging; is the battery capacity expected to be reached by the jth electric vehicle;

[0121] For the energy storage device, the control cost model is established according to the degree of battery aging:

[0122]

[0123] In the formula, T is the number of regulation periods; L j (t) is the number of unit cycles of the battery of the jth energy storage device; L j all is the total cycle life of the battery of the jth energy storage device; d j is the depth of charge and discharge; is the battery capacity; C j is the installation cost.

[0124] Further, according to an embodiment of the present application, in the above-mentioned d step, the hierarchical optimization scheduling model of the virtual power plant day-ahead plan-intra-day optimization is established to solve the virtual power plant resource scheduling target, including:

[0125] establishing a day-ahead scheduling virtual power plant operation cost objective function, and solving a virtual power plant day-ahead scheduling plan result;

[0126] establishing a day-ahead scheduling virtual power plant operation cost objective function, and solving a virtual power plant day-ahead scheduling plan result;

[0127] Further, according to an embodiment of the present application, in the above d step, the virtual power plant operation cost comprises:

[0128] (1) energy purchase cost from the power market:

[0129] (2) the i th traditional generator DG i or homogeneous resource virtual generator power generation cost:

[0130]

[0131] (3) energy storage equipment ESS i operation cost:

[0132]

[0133] wherein, is the energy purchase price of the day-ahead power market, is the energy purchased by the virtual power plant from the day-ahead power market, a i ,b i ,c i is the power generation cost coefficient of the i th traditional generator DGi, AP i is the operation cost coefficient of the i th energy storage equipment ESS i .

[0134] (4) load control cost: denoted by symbol p, according to the type of user-side load participating in optimization scheduling, the corresponding load control cost is selected;

[0135] The day-ahead scheduling virtual power plant operation cost objective function is established as:

[0136]

[0137] Further, according to an embodiment of the present application, in the above d step, the virtual power plant day-ahead scheduling plan result comprises: virtual power plant next-day hourly energy exchange amount and 24h power scheduling plan of traditional generators, wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, electric vehicles and air conditioners.

[0138] Specifically, in the embodiment, the day-ahead optimization scheduling is a mixed integer nonlinear programming problem considering the resource operation constraints established in the b step, and is solved by using a Lingo global solver software. The virtual power plant operation monitoring system application program calls the DLL or OLE interface of the solver to obtain the decision variables (day-ahead scheduling results), including the day-ahead energy purchase amount of the virtual power plant to the power market, the power and start-stop state of the generator unit, the wind power generation and photovoltaic power generation, the energy storage device and the power of the electric vehicle and air conditioner:

[0139]

[0140] wherein the i-th conventional generator unit DG i The switch state at time t is a binary variable.

[0141] Further, according to an embodiment of the present application, in the above-mentioned d step, during the intraday scheduling process, the real-time energy exchange amount of the virtual power plant with the power system will deviate from the real-time clearing amount of the power market;

[0142] The energy purchase cost to the power market is:

[0143]

[0144] The deviation penalty cost of the virtual power plant operation cost is established as:

[0145]

[0146] wherein, is the clearing amount of the power market at time t, is the real-time energy purchase amount of the virtual power plant at time t, is the real-time energy purchase price of the power market, is the real-time clearing amount deviation penalty coefficient of the power market and the real-time energy purchase amount of the virtual power plant;

[0147] The intraday scheduling virtual power plant operation cost objective function is established as:

[0148]

[0149] Further, according to an embodiment of the present application, in the above-mentioned d step, the intraday scheduling optimization results of the virtual power plant include: the real-time energy purchase amount of the virtual power plant to the power market, the target power of the conventional generator unit, the target power of the wind power generation and photovoltaic power generation, the target power output of the energy storage device and the target power of the load tracking.

[0150] Specifically, in the embodiment, the intraday scheduling does not contain binary integer variables, and is a nonlinear programming problem, which is solved by using a Lingo global solver software, considering the resource operation constraints established in the b step.

[0151]

[0152] The start-stop state of the conventional generator unit is determined by the day-ahead scheduling of the virtual power plant, and the start-stop state of the conventional generator unit is unchanged during the intraday optimization, so the decision variable does not contain {0, 1} binary variables.

[0153] According to the above scheme of the present application, the virtual power plant operation cost objective function is established. According to the type of virtual power plant dispatchable resources, the virtual power plant energy purchase cost from the power market, the conventional generator unit power generation cost, the energy storage device operation cost, and the load control cost are calculated respectively, and the virtual power plant operation cost is the sum of the costs.

[0154] Considering the resource operation constraints, the minimum virtual power plant operation cost in day-ahead scheduling is taken as the optimization target, a commercial solver software is used to calculate the optimal solution, and the virtual power plant energy exchange amount every hour and the 24h power scheduling plan of the conventional generator unit, renewable energy generation, energy storage, air conditioner and electric vehicle are generated.

[0155] The virtual power plant operation cost objective function is established. According to the type of virtual power plant dispatchable resources, the virtual power plant energy purchase cost from the power market, the virtual power plant real-time energy exchange and the penalty cost of the power market clearing amount deviation, the conventional generator unit power generation cost, the energy storage device operation cost, and the load control cost are calculated respectively, and the virtual power plant operation cost is the sum of the costs.

[0156] Considering the resource operation constraints, the minimum virtual power plant operation cost in day-ahead scheduling is taken as the optimization target, a commercial solver software is used to calculate the optimal solution, and the virtual power plant energy exchange amount every hour and the 24h power scheduling plan of the conventional generator unit, renewable energy generation, energy storage, air conditioner and electric vehicle are generated.

[0157] According to the scheme of the application, the application analyzes different types of resource operation constraints of a virtual power plant, is helpful to source-load-storage resource collaborative optimization interaction, provides a user side load control cost model, is favorable to improving the enthusiasm of user participation in load direct control, and reduces the operation cost of the virtual power plant, the application proposes a method of formulating a day-ahead scheduling plan and intraday real-time optimization, and can eliminate the influence of part of the price or power prediction error, and the application can provide technical support for the virtual power plant participating in the power spot market transaction, and can provide a solution for improving new energy consumption, meeting energy demand, and promoting energy quality.

[0158] Further, to achieve the above object, the application further provides a virtual power plant resource optimization scheduling system based on load control cost, comprising:

[0159] A schedulable resource information acquisition module acquires basic information and real-time data of schedulable resources in the virtual power plant.

[0160] An operation constraint model establishment module establishes an operation constraint model of the schedulable resources in the virtual power plant.

[0161] A control cost model establishment module establishes a control cost model of the user side load in the virtual power plant.

[0162] A resource scheduling calculation module establishes a hierarchical optimization scheduling model of virtual power plant day-ahead planning-intraday optimization, and solves a virtual power plant resource scheduling target.

[0163] According to an embodiment of the application, in the above schedulable resource information acquisition module, the schedulable resources include traditional generator units, wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, electric vehicles and air conditioners.

[0164] The basic information and real-time data include rated capacity, operation data of the schedulable resources, weather data of the environment where the schedulable resources are located, and real-time power market price information of the schedulable resources.

[0165] Specifically, the basic information and real-time data of the schedulable resources are acquired through a virtual power plant operation monitoring system, sensors, data acquisition devices and an internet interface to push data, and the basic information and real-time data include rated power and real-time running power of the traditional generator units, start-stop state, rated power and output power of the distributed wind turbine, cut-in wind speed, cut-out wind speed and real-time wind speed, rated power and output power of the distributed photovoltaic, environmental solar irradiance, capacity, cycle number, total cycle life, charge-discharge depth and installation cost of the energy storage battery, real-time charging power, real-time power of the battery and user expected charging power of the electric vehicle, real-time environmental temperature and user set temperature of the air conditioner.

[0166] Further, according to an embodiment of the present application, in the operation constraint model establishing module, the operation constraints of the schedulable resources include: power constraints, ramping constraints, capacity constraints and start-stop time constraints of the conventional generating units, and power constraints and capacity constraints of the wind power generation devices and the photovoltaic power generation devices.

[0167] The operation constraint model of the schedulable resources in the virtual power plant is established, including:

[0168] For power generation of the conventional generating units, the operation constraint model is established, including:

[0169] The first power constraint model:

[0170]

[0171] wherein, and are minimum power and maximum power limits of the ith conventional generating unit DG i , is the switching state of the ith conventional generating unit DG i at time t, is the active power of the ith conventional generating unit DG i at time t;

[0172] The ramping constraint model:

[0173]

[0174] wherein, is the maximum ramping rate of the ith conventional generating unit DG i ,

[0175] The first capacity constraint model:

[0176]

[0177] wherein, is the reactive power of the ith conventional generating unit DG i at time t, is the capacity constraint of the ith conventional generating unit DG i ;

[0178] The start-stop time constraint model:

[0179]

[0180]

[0181]

[0182]

[0183] wherein y i,t and z i,t are the minimum start-up time and the minimum shut-down time of the i-th conventional generator unit DG i the start-up and shut-down indication at time t is a binary variable {0, 1}, and are the minimum start-up time and the minimum shut-down time of the i-th conventional generator unit DG i respectively;

[0184] For wind power generation equipment and photovoltaic power generation equipment, an operation constraint model is established, including:

[0185] A second power constraint model:

[0186]

[0187] A second capacity constraint:

[0188]

[0189] wherein, and are the active and reactive power of the i-th wind power generation equipment WP i at time t, and are the active and reactive power of the i-th photovoltaic power generation equipment PV i at time t, and are the maximum available active power of the i-th wind power generation equipment WP i and photovoltaic power generation equipment PV i predicted at time t, and are the maximum capacity of the i-th wind power generation equipment WP i and photovoltaic power generation equipment PV i .

[0190] Further, according to an embodiment of the present application, in the above-mentioned control cost model establishment module, the types of the user-side load inside the virtual power plant include air conditioning load, electric vehicle load and energy storage device load;

[0191] The control cost model for the user-side load inside the virtual power plant is established as follows: based on the load type, the load control cost model is established according to the degree of influence caused by the deviation from the user's expectation when the device is called; wherein the air conditioning load control cost is represented by temperature deviation, the electric vehicle load control cost is represented by the deviation between the charging amount in the charging period and the ideal charging amount, and the energy storage device control cost is represented by the degree of battery aging, including:

[0192] ​For air conditioning load, the control cost model is established according to the temperature of the air conditioner:

[0193]

[0194] Wherein, T is the number of load regulation periods; T j (t) is the temperature provided to the user in the jth temperature-controlled load t period; is the temperature set for the jth temperature-controlled load t period;

[0195] For electric vehicle load, the control cost model is established according to the deviation of the charging amount in the charging period from the ideal charging amount:

[0196]

[0197] Wherein, T is the number of load regulation periods; E j (t end ) is the battery capacity at the end of the jth electric vehicle charging; is the battery capacity expected to be reached by the jth electric vehicle;

[0198] For energy storage equipment, the control cost model is established according to the battery aging degree:

[0199]

[0200] Wherein, T is the number of regulation periods; L j (t) is the number of unit cycles of the battery of the jth energy storage device; L j all is the total cycle life of the battery of the jth energy storage device; d j is the charge and discharge depth; is the battery capacity; C j is the installation cost.

[0201] Further, according to an embodiment of the present application, in the above-mentioned resource scheduling calculation module, a hierarchical optimization scheduling model of virtual power plant day-ahead planning-intra-day optimization is established to solve the virtual power plant resource scheduling target, including:

[0202] A day-ahead scheduling virtual power plant operation cost objective function is established to solve the virtual power plant day-ahead scheduling plan result;

[0203] A day-ahead scheduling virtual power plant operation cost objective function is established to solve the virtual power plant day-ahead scheduling plan result;

[0204] Further, according to an embodiment of the present application, in the above-mentioned resource scheduling calculation module, the virtual power plant operation cost includes:

[0205] (1) Energy cost purchased from the power market:

[0206] (2) traditional generator set DG i or homogeneous resource virtual machine set generation cost:

[0207]

[0208] (3) energy storage device ESS i operation cost:

[0209]

[0210] wherein, is the energy purchase price of the day-ahead power market, is the energy purchased by the virtual power plant in the day-ahead market, i ,b i ,c i is the generation cost coefficient of the i-th traditional generator set DG i AP i is the operation cost coefficient of the i-th energy storage device ESS i ;

[0211] (4) load control cost: denoted by symbol p, selected according to the type of user-side load participating in the optimization scheduling;

[0212] The objective function of the day-ahead scheduling virtual power plant operation cost is established as:

[0213]

[0214] Further, according to an embodiment of the present application, in the above-mentioned resource scheduling calculation module, the virtual power plant day-ahead scheduling plan result includes: virtual power plant next-day energy exchange amount and 24h power scheduling plan of traditional generator set, wind power generation device, photovoltaic power generation device, energy storage device, electric vehicle and air conditioner.

[0215] Specifically, in the present embodiment, considering the resource operation constraints established in the resource operation constraint model establishment module, the day-ahead optimization scheduling is a mixed integer nonlinear programming problem, which is solved by using Lingo global solver software. The virtual power plant operation monitoring system application program calls the DLL or OLE interface of the solver to obtain the decision variables (day-ahead scheduling results), including the virtual power plant day-ahead energy purchase amount, the generator set power and start-stop state, the wind power generation and photovoltaic power generation, the energy storage device and the electric vehicle and air conditioner power:

[0216]

[0217] wherein, the i-th traditional generator set DG i switching state at time t is a binary variable.

[0218] Further, according to an embodiment of the present application, in the above-mentioned resource scheduling calculation module, during the intraday scheduling process, the real-time energy exchange amount of the virtual power plant and the power system will deviate from the real-time clearing amount of the power market;

[0219] The energy cost of the virtual power plant for purchasing energy from the power market is:

[0220]

[0221] The deviation penalty cost of the virtual power plant for establishing the operation cost is:

[0222]

[0223] wherein, is the clearing amount of the power market at time t, is the real-time energy purchase amount of the virtual power plant at time t, is the real-time energy purchase price of the power market, is the deviation penalty coefficient of the real-time clearing amount of the power market and the real-time energy purchase amount of the virtual power plant;

[0224] The objective function of the virtual power plant operation cost for establishing the intraday scheduling is:

[0225]

[0226] Further, according to an embodiment of the present application, in the above-mentioned resource scheduling calculation module, the intraday scheduling optimization result of the virtual power plant includes: the real-time energy purchase amount of the virtual power plant from the power market, the target power of the traditional generator, the target power of the wind power generation and the photovoltaic power generation, the output target power of the energy storage device, and the load tracking target power.

[0227] Specifically, in the present embodiment, considering the resource operation constraint established in the resource operation constraint model establishing module, the intraday scheduling does not contain binary integer variables, and is a nonlinear programming problem, which is solved by using the Lingo global solver software. The virtual power plant operation monitoring system application program calls the DLL or OLE interface of the solver to obtain the decision variables (intraday scheduling results), including the real-time energy purchase amount of the virtual power plant from the power market, the power of the traditional generator, the power of the renewable energy generation (wind power generation and photovoltaic power generation), the output and input power of the energy storage device, and the target adjustment power of the air conditioner and the electric vehicle:

[0228]

[0229] The start and stop status of traditional generator sets is determined by the day-ahead dispatch of the virtual power plant. During intraday optimization, the start and stop status of traditional generator sets remains unchanged, so the decision variables do not contain {0,1} binary variables.

[0230] According to the above-mentioned solution, the present invention establishes an objective function for the operating cost of a day-ahead dispatched virtual power plant. Based on the type of dispatchable resources in the virtual power plant, the cost of purchasing energy from the electricity market, the cost of generating electricity from traditional generators, the operating cost of energy storage devices, and the cost of load control are calculated. The operating cost of the virtual power plant is the sum of these costs.

[0231] Taking resource operation constraints into consideration and taking the minimization of the operating cost of the virtual power plant as the optimization goal, commercial solver software is used to calculate the optimal solution, generating the hourly energy exchange volume of the virtual power plant the next day and the 24-hour power dispatch plan for traditional generators, renewable energy generation, energy storage, air conditioning, and electric vehicles.

[0232] This paper establishes an objective function for the operating costs of a virtual power plant for intraday scheduling. Based on the type of dispatchable resources in the virtual power plant, the cost of purchasing energy from the power market, the penalty cost for deviations between the virtual power plant's real-time energy exchange and the power market's clearing volume, the cost of generating electricity from traditional generators, the operating costs of energy storage devices, and the cost of load control are calculated. The operating cost of the virtual power plant is the sum of these costs.

[0233] Taking resource operation constraints into consideration and taking the minimization of the operating cost of the virtual power plant during the day as the optimization goal, the commercial solver software is used to calculate the optimal solution, generating the real-time energy purchase amount of the virtual power plant from the electricity market, the target power of traditional generator sets, the target power of renewable energy generation, the target power of energy storage output, the target power of air conditioning operation, and the target power of electric vehicle charging.

[0234] According to the solution of the present invention, the present invention analyzes the operating constraints of different types of resources in virtual power plants, which is conducive to the coordinated optimization and interaction of source, load and storage resources; the present invention provides a user-side load control cost model, which is conducive to improving users' enthusiasm for participating in direct load control and reducing the operating costs of virtual power plants; the present invention proposes a method for formulating a day-ahead scheduling plan and real-time optimization within the day, which can eliminate the impact of some electricity price or electricity forecast errors; the present invention can provide technical support for virtual power plants to participate in electricity spot market transactions, and can provide solutions for improving the consumption of new energy, meeting energy demand, and promoting energy supply quality.

[0235] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and runnable on the processor. When the computer program is executed by the processor, the virtual power plant resource optimization scheduling method based on load control cost as described above is implemented.

[0236] Further, to achieve the above object, the application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the virtual power plant resource optimization scheduling method based on load control cost.

[0237] Those skilled in the art can understand that the modules and algorithm steps described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software manner depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0238] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the above-described device and equipment can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0239] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed modules can be indirect coupling or communication connection through some interface, device or module, which can be electrical, mechanical or other forms.

[0240] The modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical modules, that is, they can be located in one place, or can be distributed on a plurality of network modules. According to actual needs, some or all of the modules can be selected to achieve the purpose of the embodiment of the present application.

[0241] In addition, each functional module in the embodiments of the present application can be integrated into a processing module, or each module can exist physically independently, or two or more modules can be integrated into one module.

[0242] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the energy saving signal transmission / reception method of various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk, and various media that can store program codes.

[0243] The above description is merely preferred embodiments of the present application and a description of the principles of the technology used. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the technical solutions formed by replacing the above features with the technical features disclosed in the present application (but not limited to) having similar functions.

[0244] It should be understood that the size of the serial numbers of the steps in the summary and embodiments of the present application does not absolutely mean the order of execution, and the execution order of the processes should be determined according to their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Claims

1. A virtual power plant resource optimization scheduling method based on load control cost, characterized in that: include: Deploy dispatchable resources within the virtual power plant and obtain basic information and real-time data of dispatchable resources; Establish an operational constraint model for dispatchable resources within a virtual power plant; Establish a control cost model for user-side loads within the virtual power plant; Establish a hierarchical optimization scheduling model for virtual power plants based on day-ahead planning and intraday optimization to solve the resource scheduling objectives of virtual power plants; The types of user-side loads within the virtual power plant include air conditioning loads, electric vehicle loads, and energy storage equipment loads; The control cost model for user-side loads within the virtual power plant is established by: based on load type, the load control cost model is established based on the degree of impact caused by the deviation of equipment from user expectations when it is called; wherein the air conditioning load control cost is represented by temperature deviation, the electric vehicle load control cost is represented by the deviation between the charge amount during the charging period and the ideal charge amount, and the energy storage equipment control cost is represented by the degree of battery aging, including: For air conditioning load, the control cost model is established based on the temperature of the air conditioner: ； Where: T is the number of load control periods; is the temperature provided to the user during the jth temperature control load t period; The temperature set for the jth temperature control load in period t; For electric vehicle loads, the control cost model is established based on the deviation between the charging amount during the charging period and the ideal charging amount: ； Where: T is the number of load control periods; The battery capacity of the j-th electric vehicle at the end of charging; is the battery capacity expected to be achieved by the j-th electric vehicle; For energy storage equipment, a cost control model is established based on the degree of battery aging: ； Where: T is the number of control periods; is the unit cycle number of the battery of the jth energy storage device; is the total cycle life of the battery of the jth energy storage device; is the depth of charge and discharge; is the battery capacity; For installation costs.

2. The method for optimizing and scheduling virtual power plant resources based on load control cost according to claim 1, characterized in that: The dispatchable resources include: traditional generators, wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, electric vehicles and air conditioners; The basic information and real-time data include: rated capacity and operating data of the dispatchable resources, weather data of the environment where the dispatchable resources are located, and real-time electricity price information of the dispatchable resources in the power market.

3. The method for optimizing and scheduling virtual power plant resources based on load control cost according to claim 1, characterized in that: The operating constraints of the dispatchable resources include: power constraints, ramp constraints, capacity constraints and start-stop time constraints of traditional generator sets, as well as power constraints and capacity constraints of wind power generation equipment and photovoltaic power generation equipment; The establishment of an operation constraint model for dispatchable resources within the virtual power plant includes: For traditional generator sets, an operation constraint model is established, including: First power constraint model: ； in, and is the i-th traditional generator set DG i Minimum and maximum power limits, is the i-th traditional generator set DG i The switch state at time t, is the i-th traditional generator set DG i The active power at time t; Hill-climbing constraint model: ； in, is the i-th traditional generator set DG i Maximum ramp rate; First capacity constraint model: ； in, is the i-th traditional generator set DG i The reactive power at time t is: is the i-th traditional generator set DG i capacity constraints; Start-stop time constraint model: ； in, and is the i-th traditional generator set DG i The start and stop instructions at time t are binary variables, and are the i-th traditional generator set DG i Minimum start-up time and minimum shutdown time; For wind power generation equipment and photovoltaic power generation equipment, an operation constraint model is established, including: Second power constraint model: ； Second capacity constraint: ； in, and The i-th wind turbine WP i Active and reactive power at time t, and The i-th photovoltaic power generation equipment PV i Active and reactive power at time t, and The i-th wind turbine WP i and photovoltaic power generation equipment PV i Predict the maximum available active power at time t, and The i-th wind turbine WP i and photovoltaic power generation equipment PV i The maximum capacity.

4. The method for optimizing and scheduling virtual power plant resources based on load control cost according to claim 3 is characterized in that: The establishment of a hierarchical optimization scheduling model for virtual power plants based on day-ahead planning and intraday optimization to solve the resource scheduling objectives of virtual power plants includes: Establish the operating cost objective function of the day-ahead dispatch virtual power plant and solve the day-ahead dispatch plan of the virtual power plant; Establish the objective function of virtual power plant operation cost for intraday scheduling and solve the optimization results of virtual power plant intraday scheduling.

5. The method for optimizing and scheduling virtual power plant resources based on load control cost according to claim 4 is characterized in that: The virtual power plant operating costs include: (1) Cost of purchasing energy from the electricity market: ; (2) Traditional generator set DG i The cost of electricity generation in period t : ； (3) Energy storage equipment ESS i Running costs: ； in, is the energy purchase price in the day-ahead electricity market, The energy purchased by the virtual power plant from the electricity market on the day before. is the i-th traditional generator set DG i The empirical coefficient of power generation cost, is the i-th energy storage device Operating cost coefficient; (4) Load control cost: using symbols Indicates that the corresponding load control cost is selected according to the type of user-side load participating in the optimal scheduling; The objective function for establishing the day-ahead dispatch virtual power plant operation cost is: 。 6. The method for optimizing and scheduling virtual power plant resources based on load control cost according to claim 1, characterized in that: The day-ahead dispatch plan results of the virtual power plant include: the hourly energy exchange volume of the virtual power plant the next day and the 24-hour power dispatch plan of traditional generator sets, wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, electric vehicles and air conditioners.

7. The method for optimizing and scheduling virtual power plant resources based on load control cost according to claim 1, characterized in that: During the intraday dispatch process, the real-time energy exchange between the virtual power plant and the power system will deviate from the real-time clearing volume of the power market; The cost of purchasing energy from the electricity market is: ； The operating cost of establishing a virtual power plant plus the deviation penalty cost is: ； in, is the clearing quantity of the electricity market at time t, is the real-time energy purchase amount of the virtual power plant at time t, is the real-time energy purchase price in the electricity market, The penalty coefficient for the deviation between the real-time clearing amount of the electricity market and the real-time energy purchase amount of the virtual power plant; The objective function of the intraday dispatch virtual power plant operation cost is established as: 。 8. The method for optimizing and scheduling virtual power plant resources based on load control cost according to claim 1, characterized in that: The intraday scheduling optimization results of the virtual power plant include: the real-time energy purchase amount of the virtual power plant from the electricity market, the target power of traditional power generation units, the target power of wind power generation and photovoltaic power generation, the target power output of energy storage equipment and the load tracking target power.

9. A virtual power plant resource optimization scheduling system based on load control cost, characterized by: include: The dispatchable resource information acquisition module deploys dispatchable resources within the virtual power plant and obtains basic information and real-time data of dispatchable resources; An operation constraint model establishment module is used to establish an operation constraint model for dispatchable resources within the virtual power plant; A control cost model establishment module is used to establish a control cost model for the user-side load within the virtual power plant; The resource scheduling calculation module establishes a hierarchical optimization scheduling model for virtual power plants, combining day-ahead planning and intraday optimization, to solve the resource scheduling objectives of virtual power plants. The types of user-side loads within the virtual power plant include air conditioning loads, electric vehicle loads, and energy storage equipment loads; The control cost model for user-side loads within the virtual power plant is established by: based on load type, the load control cost model is established based on the degree of impact caused by the deviation of equipment from user expectations when it is called; wherein the air conditioning load control cost is represented by temperature deviation, the electric vehicle load control cost is represented by the deviation between the charge amount during the charging period and the ideal charge amount, and the energy storage equipment control cost is represented by the degree of battery aging, including: For air conditioning load, the control cost model is established based on the temperature of the air conditioner: ； Where: T is the number of load control periods; is the temperature provided to the user during the jth temperature control load t period; The temperature set for the jth temperature control load in period t; For electric vehicle loads, the control cost model is established based on the deviation between the charging amount during the charging period and the ideal charging amount: ； Where: T is the number of load control periods; The battery capacity of the j-th electric vehicle at the end of charging; is the battery capacity expected to be achieved by the j-th electric vehicle; For energy storage equipment, a cost control model is established based on the degree of battery aging: ； Where: T is the number of control periods; is the unit cycle number of the battery of the jth energy storage device; is the total cycle life of the battery of the jth energy storage device; is the depth of charge and discharge; is the battery capacity; For installation costs.

10. An electronic device, characterized in that It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the virtual power plant resource optimization scheduling method based on load control cost as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the virtual power plant resource optimization scheduling method based on load control cost according to any one of claims 1 to 8 is implemented.

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