Multi-energy collaborative virtual power plant scheduling method based on improved particle swarm optimization and related equipment

Abstract

The invention relates to the technical field of economic operation of a multi-energy collaborative virtual power plant, in particular to a multi-energy collaborative virtual power plant scheduling method and related equipment based on an improved particle swarm method.The method comprises the steps that firstly, the structure of the multi-energy collaborative virtual power plant is determined, and the structure comprises internal components and the energy flow direction; then constructing a physical model of each internal component, and determining the operation cost of each internal component based on the physical model; based on the operation cost of each internal component, a virtual power plant operation optimization model is established with the goals of cost minimization and new energy consumption maximization, and constraint conditions of the virtual power plant operation optimization model are determined; and finally, solving the virtual power plant economic operation model through an improved particle swarm algorithm so as to optimize the energy scheduling of the multi-energy collaborative virtual power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of economic operation of multi-energy collaborative virtual power plants, and in particular to a multi-energy collaborative virtual power plant scheduling method and related equipment based on an improved particle swarm algorithm. Background Art

[0002] Virtual power plants can combine technologies such as big data and artificial intelligence to flexibly control and manage distributed energy, controllable loads, etc., and achieve effective aggregation and management of distributed resources. At present, there is no efficient multi-energy collaborative virtual power plant solution in the existing technology. Summary of the invention

[0003] In view of this, the object of the present invention is to provide a multi-energy collaborative virtual power plant scheduling method and related equipment based on improved particle swarm optimization method.

[0004] To achieve the above objectives, the present invention adopts the following technical solutions:

[0005] In a first aspect, an embodiment of the present application provides a multi-energy collaborative virtual power plant scheduling method based on an improved particle swarm optimization method, comprising:

[0006] Determine a multi-energy coordinated virtual power plant structure, wherein the virtual power plant structure includes internal components and energy flows;

[0007] Constructing a physical model of each of the internal components, and determining the operating cost of each of the internal components based on the physical model;

[0008] Based on the operating costs of each of the internal components, a virtual power plant operation optimization model is established with the goals of minimizing costs and maximizing new energy consumption, and its constraints are clarified;

[0009] The economic optimization operation model of the virtual power plant is solved by using an improved particle swarm algorithm.

[0010] Further, in some embodiments of the present application, the internal components include: power source / heat source components, conversion components, and flexibility components;

[0011] The power source / heat source components include: cogeneration units, wind turbine units, photovoltaic generator sets and gas boilers; the conversion components include power-to-gas equipment; and the flexibility components include energy storage devices.

[0012] Furthermore, in some embodiments of the present application, the energy flow direction includes:

[0013] In terms of power flow: the real-time power generation of the cogeneration unit, the wind turbine unit and the photovoltaic generator unit is supplied to the load or energy storage; and during the valley period of the load, the surplus power generation of the wind turbine unit can be supplied to the power-to-gas equipment;

[0014] In terms of heat energy flow: the heat energy generated by the cogeneration unit and the gas boiler is directly supplied to the heat load; and the power-to-gas equipment generates methane from CO2 and H2O.

[0015] Furthermore, in some embodiments of the present application, the physical model includes: a physical model of a cogeneration unit, a physical model of a distributed renewable power source, a physical model of a power-to-gas equipment, a physical model of a gas boiler, and a physical model of an energy storage device; wherein the physical model of the distributed renewable power source corresponds to the wind turbine unit and the photovoltaic power generation unit.

[0016] Furthermore, in some embodiments of the present application,

[0017] The physical model of the cogeneration unit is expressed by the following formula:

[0018] P t e,CHP =η e,CHP P t CHP,gs

[0019] P t h,CHP =η h,CHP P t CHP,gs

[0020]

[0021] Among them, P t e,CHP and P t h,CHP Respectively represent the power generation power and heat generation power of the cogeneration unit at time t; P t CHP ,gs represents the gas input power of the cogeneration unit at time t; η e,CHP and η h,CHP represent the electricity and heat conversion efficiency of the cogeneration unit respectively; and is the natural gas power P input to the cogeneration unit at time t t CHP，gs The lower and upper limits of P is the natural gas power input to the cogeneration unit at time t; CHP,down and P CHP,up are the lower and upper limits of the ramp power of the cogeneration unit; and are the lower and upper limits of the power-to-heat ratio of the cogeneration unit;

[0022] The physical model of the distributed renewable power source is expressed by the following formula:

[0023]

[0024] Among them, P t WPP and P t PV are the output power of wind power and photovoltaic power at time t; v t represents the wind speed at time t; v in 、v out are the cut-in and cut-out wind speeds of the wind turbine respectively; v ra is the rated wind speed of the fan; is the rated power of the fan; S PV is the photovoltaic panel area; R t PV is the light intensity at time t; η PV is the average efficiency of the photovoltaic module; μ inv and μ sor is the conversion rate and absorption rate of solar energy; loss is the photovoltaic power generation loss;

[0025] The physical model of the power-to-gas device is expressed by the following formula:

[0026] Q pg,t =P pg,t η pg

[0027] Among them, Q pg,t is the amount of gas generated by power-to-gas conversion; P pg,t is the electricity consumption of power-to-gas conversion; η pg Represents electrical conversion efficiency;

[0028] The physical model of the gas boiler is expressed by the following formula:

[0029]

[0030] in, is the heat output of the gas boiler at time t; η h The conversion efficiency of natural gas and heat; is the amount of natural gas input to the gas boiler at time t; L gas is the calorific value of natural gas;

[0031] The energy storage device physical model includes an electric energy storage physical model and a gas storage tank physical model;

[0032] The electrical energy storage physical model is expressed by the following formula:

[0033]

[0034] Among them, ES t Represents the capacity of the energy storage device at time t; ES t+1 represents the capacity of the energy storage device at time t-1; δ chr and δ disc are the charging and discharging efficiency of the energy storage device; P chr and P disc are the charging and discharging power of the electric energy storage device respectively;

[0035] The physical model of the gas tank is expressed by the following formula:

[0036]

[0037] Among them, Q GST,t is the gas storage capacity of the gas tank at time t; Q GST,T0 is the gas storage capacity of the gas tank in the initial state; Indicates the amount of natural gas generated and stored by power-to-gas equipment; Indicates the amount of gas delivered from the gas storage tank to the gas boiler at time t; It indicates the amount of natural gas input from the gas storage tank to the cogeneration unit at time t.

[0038] Further, in some embodiments of the present application, the physical model of each of the internal components is constructed, and based on the physical model, the operating cost of each of the internal components is determined, including: based on the physical model, determining the cost model of the cogeneration unit, and determining the operating cost of the wind power and photovoltaic unit, the operating cost of the power-to-gas equipment, and the cost of wind and solar power abandonment;

[0039] The cost model of the cogeneration unit is expressed by the following formula:

[0040]

[0041] in, and Respectively represent the fuel cost and start-up and shutdown cost of the cogeneration unit; a CHP ,b CHP and c CHP is the energy consumption coefficient of thermal power unit; Q CHP,t is the power generation capacity of the combined heat and power unit; u CHP,t D is the power generation state variable of the cogeneration unit; CHP,t represents the startup cost of the CHP unit;

[0042] Furthermore, in some embodiments of the present application, the constraints include: system power balance constraints, cogeneration unit output constraints, cogeneration unit climbing constraints and start-stop constraints, wind power output constraints, photovoltaic output constraints and system reserve capacity constraints.

[0043] Furthermore, in some embodiments of the present application, solving the economic optimization operation model of the virtual power plant by using an improved particle swarm algorithm includes:

[0044] In view of the constraint conditions, the unit priority order is used as a guiding rule through a heuristic rule; wherein the heuristic rule includes:

[0045] u n =(P p,n >R1)or(P ra,n >R2)

[0046] Among them, u n is the start and stop status of unit n. p,n and P ra,n They are respectively the startup probability set according to the unit priority and the startup probability set according to the unit output range. R1 and R2 are random numbers between (0,1).

[0047] Furthermore, in some embodiments of the present application, solving the economic optimization operation model of the virtual power plant by using an improved particle swarm algorithm includes:

[0048] Processing and adjusting the initialized particles by the heuristic rule;

[0049] Calculate the fitness values ​​of the two objective functions of the two particles to further update the individual optimal values ​​and perform external archive maintenance;

[0050] Assuming k is a multiple of 10, perform selection, crossover and mutation operations, reinitialize the individual optimal value, select the optimal solution from the external archive, and update the particle's speed and position;

[0051] The updated particles are processed and adjusted according to the speed and position constraints, and the number of iterations is determined. If the number of iterations is less than the preset value, the two objective function fitness values ​​and subsequent steps of the two particles are repeatedly calculated to perform the next iteration until the number of iterations reaches the preset value.

[0052] In a second aspect, an embodiment of the present application further provides a multi-energy collaborative virtual power plant scheduling device based on an improved particle swarm method, including a processor and a memory, wherein the processor is connected to the memory:

[0053] Wherein, the processor is used to call and execute the program stored in the memory;

[0054] The memory is used to store the program, and the program is at least used to execute the multi-energy collaborative virtual power plant scheduling method based on the improved particle swarm method as mentioned above.

[0055] The present invention provides a multi-energy collaborative virtual power plant scheduling method and related equipment based on an improved particle swarm algorithm. The method comprises the following steps: firstly determining the structure of a multi-energy collaborative virtual power plant, including internal components and energy flows; then constructing a physical model of each internal component, and based on the physical model, determining the operating cost of each internal component; then, based on the operating cost of each internal component, establishing a virtual power plant operation optimization model with the goals of minimizing costs and maximizing new energy consumption, and clarifying its constraints; finally, solving the virtual power plant economic operation model through an improved particle swarm algorithm, thereby optimizing the energy scheduling of the multi-energy collaborative virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 It is a flow chart of a multi-energy collaborative virtual power plant scheduling method based on an improved particle swarm method provided in an embodiment of the present invention;

[0058] Figure 2 It is a schematic diagram of the internal structure of a virtual power plant in a multi-energy collaborative virtual power plant scheduling method based on an improved particle swarm method provided in an embodiment of the present invention;

[0059] Figure 3 It is a schematic diagram of model solution in a multi-energy collaborative virtual power plant scheduling method based on an improved particle swarm method provided in an embodiment of the present invention;

[0060] Figure 4 It is a structural schematic diagram of a multi-energy collaborative virtual power plant dispatching device based on an improved particle swarm method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0062] Multi-energy coordinated virtual power plants usually include energy types such as electricity, heat, and gas. Specifically, the cogeneration unit is the key element of the series electricity-heat connection. Combined with the cogeneration unit and the gas boiler, the heat supply is added to the real-time scheduling of the virtual power plant; at the same time, the gas unit (which can also be a cogeneration unit), the gas boiler and the power-to-gas equipment are the key elements of the electricity-gas coupling. Therefore, this technology considers the cogeneration unit, the power-to-gas equipment, etc. entering the virtual power plant, establishes the economic scheduling model of the virtual power plant, and combines it with the improved particle swarm method to solve it.

[0063] Figure 1 This is a flow chart of a multi-energy collaborative virtual power plant scheduling method based on an improved particle swarm method provided by an embodiment of the present invention. Figure 1 , this embodiment may include the following steps:

[0064] S101. Determine the virtual power plant structure with multi-energy coordination.

[0065] Among them, the virtual power plant structure includes internal components and energy flows.

[0066] Specifically, this step includes first clarifying the internal structure of the virtual power plant, including unit types and flexibility components, and then determining the flow of heat, electricity and natural gas in the multi-energy coordinated virtual power plant.

[0067] S102: construct a physical model of each internal component, and determine the operating cost of each internal component based on the physical model.

[0068] S103. Based on the operating costs of each internal component, a virtual power plant operation optimization model is established with the goals of minimizing costs and maximizing new energy consumption, and its constraints are clarified.

[0069] S104. Solve the virtual power plant operation optimization model through the improved particle swarm algorithm.

[0070] The multi-energy collaborative virtual power plant scheduling method based on the improved particle swarm algorithm provided in the embodiment of the present application first determines the structure of the multi-energy collaborative virtual power plant, including internal components and energy flows; then constructs a physical model of each internal component, and based on the physical model, determines the operating cost of each internal component; then, based on the operating cost of each internal component, establishes a virtual power plant operation optimization model with the goals of minimizing costs and maximizing new energy consumption, and clarifies its constraints; finally, solves the virtual power plant economic operation model through the improved particle swarm algorithm, thereby optimizing the energy scheduling of the multi-energy collaborative virtual power plant.

[0071] Figure 2 is a schematic diagram of the internal structure of a virtual power plant in a multi-energy collaborative virtual power plant scheduling method based on an improved particle swarm method provided in an embodiment of the present invention, such as Figure 2As shown, in the virtual power plant structure constructed in the embodiment of the present application, that is, the multi-energy collaborative virtual power plant mentioned in the present application, the constituent elements, that is, the internal components, include power supply / heat source components, conversion components, and flexibility components; wherein, the power supply / heat source components include: cogeneration units, wind turbine units, photovoltaic generator sets, and gas boilers. Conversion components include power-to-gas equipment. Flexibility components include energy storage, that is, energy storage devices.

[0072] After determining the above components, the multi-energy flow direction in the multi-energy collaborative virtual power plant is further determined.

[0073] Specifically, Figure 2 As shown, in terms of power flow, the real-time power generation of cogeneration units, wind power and photovoltaic units is supplied to the load or energy storage, and during the valley period of the load, the surplus power generation of wind power, i.e. wind turbine units and photovoltaic units (i.e. wind turbine generator units and photovoltaic generator units) can be supplied to the power-to-gas equipment; in terms of heat flow, the heat energy generated by cogeneration units and gas units such as gas boilers is directly supplied to the heat load to obtain benefits; among them, the power-to-gas equipment converts CO2 and H2O to methane (CH4). In addition, in order to further enhance the flexibility of the virtual power plant in this application, the setting of energy storage is comprehensively considered to improve the response efficiency of the virtual power plant.

[0074] It should be noted that in the virtual power plant specified in the embodiment of the present application, wind power, photovoltaic and cogeneration units are used as power sources to produce electricity. The generated electricity is supplied to the large power grid, and the excess electricity can be supplied to the power-to-gas equipment (P2G) for catalytic production of methane and energy storage equipment for storage; the heat flow is generated by the cogeneration unit and the gas boiler, and directly supplied to the heat load; the natural gas flow mainly considers the cogeneration unit, the gas boiler and the power-to-gas equipment, and its supply comes from the natural gas grid and the power-to-gas equipment, combined with the gas storage tank, to ensure the stability of the natural gas cost in the virtual power plant.

[0075] Furthermore, based on the above-mentioned embodiments of the present application, physical models of each of the internal components are constructed, including: a physical model of a cogeneration unit, a physical model of a distributed renewable power source, a physical model of a power-to-gas equipment, a physical model of a gas boiler and a physical model of an energy storage device; wherein the physical model of the distributed renewable power source corresponds to the wind turbine unit and the photovoltaic power generation unit.

[0076] Specifically, for the physical model of the cogeneration unit:

[0077] The electricity generated by the cogeneration unit is used to supply the electrical load, and waste heat is generated while generating electricity, which can be used to supply the thermal load. The heat-to-electricity ratio of the cogeneration unit can adjust the electrical and thermal output according to the real-time electrical and thermal energy demand. Its working model, that is, the physical model, is:

[0078] Pt e,CHP =η e,CHP P t CHP,gs (1)

[0079] P t h,CHP =η h,CHP P t CHP,gs (2)

[0080]

[0081] Where P t e,CHP and P t h,CHP Respectively represent the power generation power and heat generation power of the cogeneration unit at time t; P t CHP ,gs represents the gas input power of the cogeneration unit at time t; η e,CHP and η h,CHP represent the electricity and heat conversion efficiency of the cogeneration unit respectively; and The lower and upper limits of the natural gas power input to the CHP unit; P CHP ,down and P CHP,up are the lower and upper limits of the ramp power of the cogeneration unit; and It is the lower and upper limits of the electricity-to-heat ratio of the cogeneration unit.

[0082] For the physical model of distributed renewable power sources (corresponding to wind turbines and photovoltaic generators):

[0083] The output of wind power and photovoltaic power generation mainly depends on the natural wind speed and solar radiation intensity. The natural wind speed and solar radiation intensity can be regarded as obeying the Weibull function and Beta function. The physical model of distributed renewable power source, that is, the physical model of wind power and photovoltaic power generation output is:

[0084]

[0085] P t PV =S PV R t PV η PV μ inv μ sor (1-loss) (7)

[0086] Where P t WPP and Pt PV are the output power of wind power and photovoltaic power at time t; v t represents the wind speed at time t; vi n 、v out are the cut-in and cut-out wind speeds of the wind turbine respectively; v ra is the rated wind speed of the fan; is the rated power of the fan. PV is the photovoltaic panel area; is the light intensity at time t; η PV is the average efficiency of the photovoltaic module; μ inv and μ sor is the conversion rate and absorption rate of solar energy; loss is the photovoltaic power generation loss.

[0087] For the physical model of the power-to-gas plant:

[0088] The principle of power-to-gas technology is that power-to-gas technology combines surplus electricity during off-peak hours to electrolyze water and generate methane through the methanogenesis process. Its operating principle (which can be understood as the corresponding physical model including the following formula) is as follows:

[0089] Q pg,t =P pg ,tη pg (8)

[0090] In the formula, Q pg,t is the amount of gas generated by power-to-gas conversion. pg,t is the electricity consumption of power-to-gas. pg Represents electrical conversion efficiency.

[0091] For the physical model of gas boiler:

[0092] The output of a gas boiler is related to fuel consumption and its own heat conversion efficiency. Its output expression, i.e., the physical model of a gas boiler, is:

[0093]

[0094] In the formula, is the heat output of the gas boiler at time t; η h The conversion efficiency of natural gas and heat; is the amount of natural gas input to the gas boiler at time t; L gas is the calorific value of natural gas.

[0095] Correspondingly, the operating cost of the gas boiler during operation is:

[0096]

[0097] In the formula, C GBis the total cost of the gas boiler; The real-time price of natural gas.

[0098] For the physical model of energy storage device (including the physical model of electric energy storage and the physical model of gas storage tank):

[0099] Among them, the power of electric energy storage is generally related to the charging and discharging power and capacity, which can be expressed (that is, the corresponding physical model includes the following formula):

[0100]

[0101] In the formula, ES t Represents the capacity of the energy storage device at time t; δ chr and δ disc are the charging and discharging efficiency of the energy storage device; P chr and P disc are the charging and discharging power of the energy storage device, respectively. Since the energy storage device cannot be charged and discharged at the same time, there is P chr ·P disc =, that is, when one term is a non-zero positive number, the other term must be 0.

[0102] The operating constraints of the electric energy storage device (which can be used together with the above formula 11 to form the physical model of the electric energy storage):

[0103]

[0104] ES min ≤ES t ≤ES max (14)

[0105] In the formula, and are the minimum and maximum values ​​of the charging power of the electric energy storage device, respectively; and ES is the minimum and maximum value of the discharge power of the electric energy storage device; min and ES max They are the lower and upper limits of the capacity of the electric energy storage device respectively.

[0106] Gas storage tanks are used to store natural gas synthesized by power-to-gas equipment and distribute it reasonably based on prices. Natural gas can be stored at low gas prices during low load periods, and stored gas can be used to generate electricity / heat during peak load periods. The operating status of natural gas in the gas storage tank (i.e., the physical model of the gas storage tank) is as follows:

[0107]

[0108] In the formula, Q GST,t is the gas storage capacity of the gas tank at time t. is the gas storage capacity of the gas tank in its initial state. Indicates the amount of natural gas generated and stored by power-to-gas equipment. It indicates the amount of gas delivered from the gas storage tank to the gas boiler at time t. It indicates the amount of natural gas input from the gas storage tank to the cogeneration unit at time t. At the same time, it is assumed that the gas storage tank cannot store and release gas at the same time, so there is That is, when one term is a non-zero positive number, the other term must be 0.

[0109] On this basis, it is assumed that the average gas production of the power-to-gas equipment in T operating cycles is The running cost is:

[0110]

[0111] In the formula, is the overall operating cost of the power-to-gas equipment within T operating cycles; The amount of wind power used for power-to-gas equipment; is the cost price of wind power within the operation period T; is the fixed cost of the power-to-gas equipment.

[0112] Therefore, the price of natural gas produced by power-to-gas equipment It can be expressed as,

[0113]

[0114] Therefore, whether the gas storage tank in the virtual power plant stores gas depends on whether the cost gas price of the power-to-gas equipment is lower than the price of the external natural gas grid, that is:

[0115]

[0116] Furthermore, in an embodiment of the present application, a physical model of each of the internal components is constructed, and based on the physical model, the operating cost of each internal component is determined. In practical applications, the cost of operating a virtual power plant includes the operating cost of a generator set, the cost of a gas boiler, the operating cost of a power-to-gas device, the cost of energy storage, etc. The specific process may include: determining a cost model for a cogeneration unit, an operating cost model for a wind power and photovoltaic unit, and determining the operating cost of a power-to-gas device and the cost of wind and solar power abandonment based on a physical model.

[0117] First, the operating costs of each operating component within the virtual power plant are analyzed:

[0118] Specifically, it involves first determining the cost model of the cogeneration unit:

[0119]

[0120] In the formula, and Respectively represent the fuel cost and start-up and shutdown cost of the cogeneration unit. CHP ,b CHP and c CHP is the energy consumption coefficient of the thermal power unit. CHP,t is the power generation capacity of the combined heat and power unit. CHP,t D is the power generation state variable of the cogeneration unit, a 0-1 variable, 1 means the cogeneration unit is in operation, 0 means the cogeneration unit is in shutdown. CHP,t It represents the startup cost of the CHP unit, which is related to the cold start and hot start status of the CHP unit.

[0121] Then determine the operating costs of wind power and photovoltaic units:

[0122]

[0123] In the formula, and is the operating cost of wind power and photovoltaic units in period t; and is the depreciation cost of wind power and photovoltaic units; P t WPP With P t PV are the power generation of wind power and photovoltaic units respectively.

[0124] Determine the operating costs of a power-to-gas plant:

[0125] In this application, the operating cost of the power-to-gas equipment mainly considers the operating cost of the electricity used for its operation and the operating cost of the raw material conversion. t pg The main source is wind power, and the cost of this electricity is included in the cost of wind power generation.

[0126] In order to further constrain the low-carbon nature of virtual power plants, the present invention considers the cost of wind and solar power abandonment and introduces a penalty cost of energy abandonment ζ wpp and pv , the cost of wind and solar power abandonment C cur It can be expressed as:

[0127]

[0128] In the formula, and are wind power abandonment and photovoltaic power abandonment respectively; P t WPP and are the actual wind power generation and the actual wind power connected to the grid, respectively. The same is true for photovoltaic power in formula (28).

[0129] On the basis of the above-mentioned embodiments, a virtual power plant operation optimization model is established with the goals of minimizing costs and maximizing new energy consumption, and its constraints are clarified.

[0130] Specifically, in this application, in combination with the goals of minimizing the operating cost of a virtual power plant and maximizing the consumption of new energy, a virtual power plant operation optimization model is constructed as follows:

[0131]

[0132] In the formula, C VPP is the total cost of the virtual power plant.

[0133] And its operating constraints include: system power balance constraints, cogeneration unit output constraints, cogeneration unit climbing constraints and start-stop constraints, wind power output constraints, photovoltaic output constraints and system reserve capacity constraints.

[0134] Specifically, the system power balance constraint is:

[0135]

[0136] In the formula, Q CHP Indicates the active output of the CHP unit; Q j,WPP represents the active output of wind turbine j; Q m,pv represents the active output of photovoltaic unit m; D represents the system load demand value.

[0137] The output constraint of the cogeneration unit is:

[0138]

[0139] In the formula, Indicates the maximum dispatchable output of the cogeneration unit; Indicates the minimum dispatchable output of the CHP unit.

[0140] The ramping constraints and start-stop constraints of the cogeneration unit are shown in equations (4)-(5).

[0141] Wind power output constraints are:

[0142]

[0143] In the formula, represents the maximum dispatchable output of wind turbine j, Represents the minimum dispatchable output of unit j.

[0144] The photovoltaic output constraint is:

[0145]

[0146] In the formula, represents the maximum dispatchable output of photovoltaic unit m; Represents the minimum dispatchable output of PV unit m.

[0147] The system spare capacity constraint is:

[0148]

[0149] In the formula, D(t) is the load demand of the system at time t, R(t) is the reserve demand of the system at time t, l is the line loss rate of the system, θ is the self-use rate of the unit, Q max (t) is the maximum unit output at time t.

[0150] Figure 3 Schematic diagram of model solution in the multi-energy collaborative virtual power plant scheduling method based on the improved particle swarm method provided by an embodiment of the present invention. Figure 3 As shown:

[0151] In this application, based on the improved particle swarm algorithm, a solution to the multi-objective scheduling model for the above-mentioned multi-energy collaborative virtual power plant is proposed.

[0152] It should be noted that in the embodiment of the present application, with the aid of heuristic rules, the unit priority order is used as a guiding rule for a large number of constraints in the collaborative multi-objective model, specifically:

[0153] u n =(P p,n >R1)or(P ra,n >R2) (33)

[0154] In the formula, u n is the start and stop status of unit n. p,n and P ra,n They are respectively the startup probability set according to the unit priority and the startup probability set according to the unit output range. R1 and R2 are random numbers between (0,1).

[0155] The specific solution process is as follows Figure 3 As shown:

[0156] First, the initial parameters of the components of the virtual power plant constructed in the above embodiment, the priority order and output range of the computer group are input, assuming that one hour is used as the scheduling period.

[0157] Then, the initialized particles are processed and adjusted by heuristic rules, wherein the particle speed is randomly initialized and it is assumed that the initial iteration number k=1.

[0158] Calculate the fitness values ​​of the two objective functions of the two particles, further update the individual optimal values ​​and perform external file maintenance.

[0159] Assuming k is a multiple of 10, perform selection, crossover and mutation operations, reinitialize the individual optimal value, select the optimal solution from the external archive, and update the particle's speed and position.

[0160] The updated particles are processed and adjusted according to the speed and position constraints, and the number of iterations k≤K is determined, and the step of calculating the fitness values ​​of the two objective functions of the two particles is turned to perform the next iteration.

[0161] Finally, output the results.

[0162] The multi-energy collaborative virtual power plant scheduling method based on the improved particle swarm algorithm provided in the present application can be widely used in the field of economic scheduling of virtual power plants. By adding thermal energy flow and natural gas flow to the traditional virtual power plant mainly based on electricity, the multi-energy complementary flow of energy is realized; at the same time, the coupling of electricity-heat, electricity-gas, and heat-gas is also considered, which can effectively promote the multi-energy collaboration of virtual power plants; and by combining the improved particle swarm algorithm, a fast solution idea is proposed, which effectively improves the overall decision-making of the virtual power plant for uncertainty, improves its development enthusiasm and the rationality of resource scheduling.

[0163] The present invention also provides a multi-energy collaborative virtual power plant scheduling device based on an improved particle swarm method, which is used to implement the above method embodiment. Figure 4 Schematic diagram of the structure of a multi-energy collaborative virtual power plant dispatching device based on an improved particle swarm method provided by an embodiment of the present invention. Figure 4 As shown, the multi-energy collaborative virtual power plant scheduling device based on the improved particle swarm method in this embodiment includes a processor 21 and a memory 22, and the processor 21 is connected to the memory 22. The processor 21 is used to call and execute the program stored in the memory 22; the memory 22 is used to store the program, and the program is at least used to execute the multi-energy collaborative virtual power plant scheduling method based on the improved particle swarm method in the above embodiment.

[0164] The specific implementation scheme of the multi-energy collaborative virtual power plant scheduling device based on the improved particle swarm method provided in the embodiment of the present application can refer to the implementation scheme of the multi-energy collaborative virtual power plant scheduling method based on the improved particle swarm method in any of the above embodiments, which will not be repeated here.

[0165] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0166] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0167] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0168] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0169] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0170] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0171] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0172] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0173] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A multi-energy collaborative virtual power plant scheduling method based on improved particle swarm optimization method, characterized in that: include: Determine a multi-energy coordinated virtual power plant structure, wherein the virtual power plant structure includes internal components and energy flows; Constructing a physical model of each of the internal components, and determining the operating cost of each of the internal components based on the physical model; Based on the operating costs of each of the internal components, a virtual power plant operation optimization model is established with the goals of minimizing costs and maximizing new energy consumption, and its constraints are clarified; The economic optimization operation model of the virtual power plant is solved by using an improved particle swarm algorithm.

2. The multi-energy collaborative virtual power plant scheduling method based on improved particle swarm optimization method according to claim 1 is characterized in that: The internal components include: power source / heat source components, conversion components and flexibility components; The power source / heat source components include: cogeneration units, wind turbine units, photovoltaic generator sets and gas boilers; the conversion components include power-to-gas equipment; and the flexibility components include energy storage devices.

3. The multi-energy collaborative virtual power plant scheduling method based on improved particle swarm optimization method according to claim 2 is characterized in that: The energy flows include: In terms of power flow: the real-time power generation of the cogeneration unit, the wind turbine unit and the photovoltaic generator unit is supplied to the load or energy storage; and during the valley period of the load, the surplus power generation of the wind turbine unit can be supplied to the power-to-gas equipment; In terms of heat energy flow: the heat energy generated by the cogeneration unit and the gas boiler is directly supplied to the heat load; and the power-to-gas equipment generates methane from CO2 and H2O.

4. The multi-energy collaborative virtual power plant scheduling method based on improved particle swarm optimization method according to claim 2 is characterized in that: The physical model includes: a physical model of a cogeneration unit, a physical model of a distributed renewable power source, a physical model of a power-to-gas device, a physical model of a gas boiler and a physical model of an energy storage device; wherein the physical model of the distributed renewable power source corresponds to the wind turbine unit and the photovoltaic power generation unit.

5. The multi-energy collaborative virtual power plant scheduling method based on improved particle swarm optimization method according to claim 4 is characterized in that: The physical model of the cogeneration unit is expressed by the following formula: P.S t e,CHP η e,CHP P.S t CHP,gs P.S t h,CHP η h,CHP P.S t CHP,gs Among them, P t e,CHP and P t h,CHP Respectively represent the power generation power and heat generation power of the cogeneration unit at time t; P t CHP,gs represents the gas input power of the cogeneration unit at time t; η e,CHP and η h,CHP represent the electricity and heat conversion efficiency of the cogeneration unit respectively; and is the natural gas power P input to the cogeneration unit at time t t CHP，gs The lower and upper limits of P is the natural gas power input to the cogeneration unit at time t; CHP,down and P CHP,up are the lower and upper limits of the ramp power of the cogeneration unit; and are the lower and upper limits of the power-to-heat ratio of the cogeneration unit; The physical model of the distributed renewable power source is expressed by the following formula: Among them, P t WPP and P t PV are the output power of wind power and photovoltaic power at time t; v t represents the wind speed at time t; v in 、v out are the cut-in and cut-out wind speeds of the wind turbine respectively; v ra is the rated wind speed of the fan; is the rated power of the fan; S PV is the photovoltaic panel area; is the light intensity at time t; η PV is the average efficiency of the photovoltaic module; μ inv and μ sor is the conversion rate and absorption rate of solar energy; loss is the photovoltaic power generation loss; The physical model of the power-to-gas device is expressed by the following formula: Q pg,t =P pg,t the pg Among them, Q pg,t is the amount of gas generated by power-to-gas conversion; P pg,t is the electricity consumption of power-to-gas conversion; η pg Represents electrical conversion efficiency; The physical model of the gas boiler is expressed by the following formula: in, is the heat output of the gas boiler at time t; η h The conversion efficiency of natural gas and heat; is the amount of natural gas input to the gas boiler at time t; L gas is the calorific value of natural gas; The energy storage device physical model includes an electric energy storage physical model and a gas storage tank physical model; The electrical energy storage physical model is expressed by the following formula: Among them, ES t Represents the capacity of the energy storage device at time t; ES t+1 represents the capacity of the energy storage device at time t-1; δ chr and δ disc are the charging and discharging efficiency of the energy storage device; P chr and P disc are the charging and discharging power of the electric energy storage device respectively; The physical model of the gas tank is expressed by the following formula: Among them, Q GST,t is the gas storage capacity of the gas tank at time t; is the gas storage capacity of the gas tank in the initial state; Indicates the amount of natural gas generated and stored by power-to-gas equipment; It indicates the amount of gas delivered from the gas storage tank to the gas boiler at time t; It indicates the amount of natural gas input from the gas storage tank to the cogeneration unit at time t.

6. The multi-energy collaborative virtual power plant scheduling method based on improved particle swarm optimization method according to claim 5 is characterized in that: The step of constructing a physical model of each of the internal components and determining the operating cost of each of the internal components based on the physical model includes: determining a cost model of a cogeneration unit based on the physical model, and determining the operating cost of a wind power and photovoltaic unit, the operating cost of a power-to-gas device, and the cost of wind and solar power abandonment; The cost model of the cogeneration unit is expressed by the following formula: in, and Respectively represent the fuel cost and start-up and shutdown cost of the cogeneration unit; a CHP ,b CHP and c CHP is the energy consumption coefficient of thermal power unit; Q CHP,t is the power generation capacity of the cogeneration unit; u CHP,t D is the power generation state variable of the cogeneration unit; CHP,t Represents the startup cost of the CHP unit.

7. The multi-energy collaborative virtual power plant scheduling method based on improved particle swarm optimization method according to claim 1 is characterized in that: The constraints include: system power balance constraints, cogeneration unit output constraints, cogeneration unit ramp constraints and start-stop constraints, wind power output constraints, photovoltaic output constraints and system reserve capacity constraints.

8. The multi-energy collaborative virtual power plant scheduling method based on improved particle swarm optimization method according to claim 7 is characterized in that: The method of solving the economic optimization operation model of the virtual power plant by using an improved particle swarm algorithm includes: In view of the constraint conditions, the unit priority order is used as a guiding rule through a heuristic rule; wherein the heuristic rule includes: u n =(P p,n >R1)or(P ra,n >R2) Among them, u n is the start and stop status of unit n; P p,n and P ra,n They are respectively the startup probability set according to the unit priority and the startup probability set according to the unit output range. R1 and R2 are random numbers between (0,1).

9. The multi-energy collaborative virtual power plant scheduling method based on improved particle swarm optimization method according to claim 8 is characterized in that: The method of solving the economic optimization operation model of the virtual power plant by using an improved particle swarm algorithm includes: Processing and adjusting the initialized particles by the heuristic rule; Calculate the fitness values ​​of the two objective functions of the two particles to further update the individual optimal values ​​and perform external archive maintenance; Assuming k is a multiple of 10, perform selection, crossover and mutation operations, reinitialize the individual optimal value, select the optimal solution from the external archive, and update the particle's speed and position; The updated particles are processed and adjusted according to the speed and position constraints, and the number of iterations is determined. If the number of iterations is less than the preset value, the two objective function fitness values ​​and subsequent steps of the two particles are repeatedly calculated to perform the next iteration until the number of iterations reaches the preset value.

10. A multi-energy collaborative virtual power plant dispatching device based on improved particle swarm method, characterized in that: The invention comprises a processor and a memory, wherein the processor is connected to the memory: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store the program, and the program is at least used to execute the multi-energy collaborative virtual power plant scheduling method based on the improved particle swarm method as described in any one of claims 1-9.

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