Multi-objective optimization dispatching system and method for virtual power plants in grid-connected microgrids

Abstract

The present invention discloses a virtual power plant multi-objective optimization scheduling system and method for a grid-connected microgrid in the field of power grid scheduling technology, including: obtaining a virtual power plant system including distributed entities on the demand side; optimizing the scheduling of the virtual power plant system based on carbon emission costs and power generation costs; connecting the optimized virtual power plant system to an external power grid for information and energy interaction. The present invention can effectively control regional distributed energy, so that distributed power sources can obtain good economic benefits and power quality through economic optimization scheduling while serving as low-carbon peak-shaving and frequency-regulating resources, providing technical support for the configuration and consumption of distributed power sources under the background of "dual carbon".

Description

Technical Field

[0001] The present invention relates to a virtual power plant multi-objective optimization dispatching system and method for a grid-connected microgrid, and belongs to the technical field of power grid dispatching. Background Art

[0002] Microgrid refers to a system that combines DG, energy storage units, grid-connected interface converters and load units into a grid-connected and autonomous system, i.e., a distributed energy system. Energy storage units can be used to smooth the fluctuations in the output power of distributed power units, thereby significantly improving the power quality of the system and solving the grid connection problem of distributed power sources to a certain extent. Virtual power plants aggregate a variety of distributed power generation units, controllable loads and energy storage units through advanced measurement technology, control technology, communication technology and other means, and participate in the coordinated control and management of the power market and power grid operation as an entity similar to a real power plant, so as to achieve the stability and reliability of the overall output of distributed power sources and provide safe and efficient power supply for the power grid. In the context of "dual carbon", carbon emissions will become a key factor in restricting the various power generation entities in the power system, and the thermal power units, distributed energy storage and other units included in the virtual power plant will be subject to the total carbon emission quota of the virtual power plant.

[0003] Existing regional distributed power systems are mostly constructed in the form of grid-connected microgrids. The application and practice of the newly proposed virtual power plant concept are still in the initial exploratory and experimental stage. Distributed power sources that have been widely used under the background of "dual carbon" have problems such as high single-machine access costs and difficult management. At the same time, they also bring many technical difficulties to the stable operation of the power grid, such as power flow changes, line blockages, and voltage flickers. Due to geographical restrictions, the existing grid-connected microgrids have certain limitations for the effective utilization of large-scale, multi-regional distributed power sources. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a multi-objective optimization scheduling system and method for a virtual power plant for a grid-connected microgrid, which can effectively control regional distributed energy, so that distributed power sources can obtain good economic benefits and power quality through economic optimization scheduling while serving as low-carbon peak-shaving and frequency-regulating resources, thereby providing technical support for the configuration and consumption of distributed power sources under the background of "dual carbon".

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention provides a multi-objective optimization scheduling method for a virtual power plant for a grid-connected microgrid, comprising:

[0007] Obtaining a virtual power plant system including demand-side distributed entities;

[0008] Optimize the scheduling of virtual power plant systems based on carbon emission costs and power generation costs;

[0009] The virtual power plant system after optimized scheduling is connected to the external power grid for information and energy interaction.

[0010] Furthermore, the demand-side distributed entities include photovoltaic power generation systems, wind power generation systems, distributed power generation systems and electric energy storage systems, wherein:

[0011] The photovoltaic power generation output power of the photovoltaic power generation system is:

[0012] g st =g stc (I r，t / I stc )[1+α θ (θ t -θ stc )]

[0013] Where: g st is the photovoltaic power output, g stc is the output power of the module under standard test conditions, α θ is the temperature coefficient of the component, θ t is the photovoltaic panel temperature at time t, θ stc is the photovoltaic panel temperature under standard test conditions, I r，t is the actual solar radiation intensity at time t, I stc is the solar radiation intensity under standard test conditions;

[0014] The output power of the wind power generation system is:

[0015]

[0016] Where: is the original simulated output of the wind farm, m t 、m in 、m rated With m out are the actual wind speed at time t, the cut-in wind speed of the wind turbine, the rated wind speed and the cut-out wind speed, respectively, and R is the rated output power of the wind turbine;

[0017] The distributed power generation system includes a gas turbine power generation system, an internal combustion engine power generation system for peak load regulation, and a diesel engine power generation system. The main power generation cost of the distributed power generation system is:

[0018]

[0019] Where: C MT is the main cost of distributed generation system, P MT(t) is the power generation of the distributed generation system at time t; ρ MT , γ MT , is the unit operating cost coefficient of the distributed generation system;

[0020] The mathematical models of the electric energy storage system in the charging state and the discharging state are respectively:

[0021]

[0022]

[0023] Where: S B (t) is the mathematical model of the electric energy storage system at time t, B represents the battery energy storage device, δ B is the consumption rate during the energy storage process, and are the input conversion efficiency and the output conversion efficiency respectively, Δt is the time step, and They are energy input and energy output respectively.

[0024] Furthermore, the carbon emission quota calculation formula of the virtual power plant system is:

[0025]

[0026] Where: P t is the total power generation of the controllable distributed generators in the virtual power plant at time t, P MT，i，t is the output of controllable distributed generator set i at time t, M is the number of controllable distributed generator sets, P bat，j，t is the electric power of energy storage unit j at time t, P WT is the power generation of the wind power generation system at time t, P PV is the power generation of the photovoltaic power generation system at time t, N is the number of energy storage units, E L is the total carbon emission quota of the system, and ε is the emission quota per unit of electricity;

[0027] The carbon emissions calculation formula of the virtual power plant system is:

[0028]

[0029] Where: E p is the total carbon emissions of the system in a scheduling cycle, δ MT，i is the carbon emission intensity of controllable distributed generator set i, δ bat，j is the carbon emission intensity of energy storage unit j;

[0030] The relationship between the carbon emission cost and carbon emissions of the virtual power plant system is:

[0031]

[0032] Where: C g is the system carbon emission cost, μ is the transaction unit price of the system each time it participates in carbon market transactions, d is the carbon emission step range coefficient, and k is the increase in the carbon trading price when the carbon emissions increase by one step.

[0033] Furthermore, the virtual power plant system performs optimal scheduling with the minimum total power generation cost and carbon emission cost of the system as the optimization objectives. The specific objective function is as follows:

[0034] minC=C PV +C WT +C GT +C IC +C g

[0035] Where: C is the total cost of the virtual power plant, C PV is the cost of photovoltaic power generation system, C WT is the cost of wind power generation system, C GT is the power generation cost of the gas turbine system, C IC is the power generation cost of the peak-shaving internal combustion unit, C g is the system carbon emission cost.

[0036] Furthermore, the optimization scheduling process satisfies the equipment output power constraint, the energy storage equipment operation constraint, the ramp rate constraint, the electric power balance constraint, the tie line capacity constraint and the system load loss rate constraint, wherein:

[0037] The device output power constraint is:

[0038]

[0039] In the formula, is the active power output of microgrid energy supply unit i at time t, P i，max and P i，min are the upper and lower limits of active output of microgrid energy supply unit i respectively;

[0040] The energy storage device operation constraints are:

[0041]

[0042]

[0043] Where: (t) is the charging power at time t, is the maximum charging power, (t) is the energy release power at time t, is the maximum discharge power, X and Y are state variables, T is the charge-discharge cycle, It is the power consumed by the energy storage device during the energy storage process;

[0044] The climbing rate constraint is:

[0045]

[0046] Where: g i，t is the active output of microgrid energy supply unit i in period t, Δg i + and Δg i - are its maximum climbing power and climbing power respectively;

[0047] The electric power balance constraint is:

[0048] P load (t) = P PV (t)+P WT (t)+P GT (t)+P IC (t)+P ST (t)-P cut (t)

[0049] Among them, P load (t), P PV (t), P WT (t), P GT (t), P IC (t), P ST (t) are the power of user-side electric load, photovoltaic power generation system, wind power generation system, gas turbine power generation system, internal combustion engine power generation system and electric energy storage system, respectively. cut (t) is the load shedding amount of the system in period t;

[0050] The tie line capacity constraint is:

[0051] P grid (t)≤P grid，max

[0052] Where P grid (t) is the transmission power of the tie line between the microgrid and the large power grid at time t, P grid，max The maximum transmission power allowed by the interconnection line between the microgrid and the large power grid;

[0053] The system load loss rate constraint is:

[0054]

[0055] Where:LPSP It represents the load loss rate of the grid-connected microgrid system. The maximum allowable load loss rate value, T sch is a scheduling cycle, W LPSP is the total load loss in one scheduling cycle.

[0056] Furthermore, the optimization scheduling process also includes model linearization processing, including:

[0057] The product of a binary variable b and a continuous variable x can be expressed as a continuous variable y:

[0058] y=b*x

[0059] Then, add the following constraints to the model:

[0060]

[0061] At this time, x min 、x max are the minimum and maximum values ​​of continuous variables, respectively.

[0062] Furthermore, the optimization scheduling process includes: after defining two evaluation indicators to respectively evaluate the benefits of the virtual power plant in economic operation and carbon emissions, a multi-objective particle swarm algorithm is used to optimize the virtual power plant system to obtain a Pareto solution set including a series of feasible Pareto solutions, and the system scheduling cost and operating benefit under multiple solutions in the Pareto solution set are compared and calculated to obtain the optimal solution. The multi-objective particle swarm algorithm includes a multi-objective evolutionary algorithm, a second-generation non-dominated sorting genetic algorithm and a multi-objective particle swarm algorithm.

[0063] In a second aspect, the present invention provides a virtual power plant multi-objective optimization scheduling system for a grid-connected microgrid, comprising:

[0064] Receiving module: used to obtain a virtual power plant system including distributed entities on the demand side;

[0065] Optimization scheduling module: used to optimize the scheduling of the virtual power plant system based on carbon emission costs and power generation costs;

[0066] Interaction module: used to connect the virtual power plant system after optimized scheduling to the external power grid for information and energy interaction.

[0067] In a third aspect, the present invention provides a multi-objective optimization scheduling device for a virtual power plant for a grid-connected microgrid, including a processor and a storage medium;

[0068] The storage medium is used to store instructions;

[0069] The processor is used to operate according to the instructions to execute the steps of any of the methods described above.

[0070] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] The present invention provides a virtual power plant multi-objective optimization scheduling system and method for grid-connected microgrids under the background of "dual carbon". Based on comprehensive consideration of carbon emission costs, carbon trading prices, system output and other influencing factors, a multi-objective optimization scheduling model for virtual power plants that considers carbon emission costs is constructed. This can effectively control regional distributed energy, so that distributed power sources can obtain good economic benefits and power quality through economic optimization scheduling while serving as low-carbon peak-shaving and frequency-regulating resources, thereby providing technical support for the configuration and consumption of distributed power sources under the background of "dual carbon". BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a virtual power plant scheduling architecture diagram considering carbon emission costs provided in the first embodiment of the present invention;

[0074] Figure 2 It is a flow chart of the multi-objective particle swarm algorithm optimization method provided in the first embodiment of the present invention;

[0075] Figure 3 is a prediction curve diagram of electric load, wind power generation power and solar irradiance provided by the first embodiment of the present invention;

[0076] Figure 4 This is a distributed power output distribution diagram provided by the first embodiment of the present invention;

[0077] Figure 5 It is a line graph of system carbon emission costs before and after scheduling provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0078] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0079] Embodiment 1:

[0080] A multi-objective optimization scheduling method for virtual power plants for grid-connected microgrids. This method integrates distributed power sources such as photovoltaic power generation, wind power generation, and gas turbines, distributed energy storage, and internal loads into a virtual power plant. During the operation of the virtual power plant, user-side regulation is coordinated with distributed power sources and energy storage systems (Coordination control) to establish a virtual power plant operation architecture that takes carbon emission costs into account. Figure 1 As shown in the figure, in a typical virtual power plant system, the demand-side distributed entities include photovoltaic power generation, wind power generation, gas turbines, energy storage, etc., and the unified dispatching center of the virtual power plant (VPP dispatching center) ensures the safe operation. The virtual power plant is connected to the external power grid (External Power Grid) through the interconnection line, and can exchange information and energy with other connected virtual power plants, and can also exchange energy with the main grid.

[0081] 1. Energy supply unit model

[0082] (1) Photovoltaic power generation system

[0083] The output power of the photovoltaic array (PV) system can be calculated based on the output power under standard test conditions (STC) and the actual light intensity, ambient temperature and other conditions, as shown in the following formula:

[0084] g st =g stc (I r，t / I stc )[1+α θ (θ t -θ stc )]

[0085] Where: g st is the photovoltaic power output, g stc is the output power of the module under standard test conditions (solar radiation intensity I = 1.0kW / m 2 , temperature θ stc =25°C); α θ is the temperature coefficient of the component; θ t is the photovoltaic panel temperature at time t; θ stc is the photovoltaic panel temperature under standard test conditions; I r，t is the actual solar radiation intensity at time t; I stc is the solar irradiance under standard test conditions. In actual operation, the marginal cost of photovoltaics can be approximated to 0.

[0086] (2) Wind power generation system

[0087] The relationship between the output power of wind turbines (WT) and the actual wind speed of wind turbines can be approximated as a piecewise function, namely Weibull distribution, and the scale parameter ξ and shape parameter k of the wind speed Weibull distribution are fitted according to the historical wind speed data of the wind farm. Let the random variable be v, and the wind speed conforms to the Weibull distribution with scale parameter ξ and shape parameter k respectively:

[0088]

[0089] Where: m is the wind speed. Affected by the wind turbine's own parameters (cut-in wind speed, cut-out wind speed, rated wind speed and rated output power), the output power of the wind farm can be expressed by the following formula:

[0090]

[0091] Where: is the original simulated output of the wind farm; m t , m in , m rated With m out are the actual wind speed at time t, the wind turbine cut-in wind speed, the rated wind speed and the cut-out wind speed; R is the rated output power of wind power. The cost of wind power is also mainly composed of construction cost and maintenance cost. In actual operation, its marginal cost can also be approximated to 0.

[0092] (3) Distributed power generation system

[0093] Distributed generation systems mainly include gas turbine generation systems, internal combustion engine generation systems for peak load regulation, and diesel engine generation systems. The main cost of power generation is C MT Including fuel cost, maintenance cost, etc., in system optimization, its model can be expressed by a quadratic function:

[0094]

[0095] Where: P MT (t) is the power generation of the distributed generation system at time t; ρ MT , γ MT , is the unit operating cost coefficient of the distributed generation system.

[0096] (4) Electric energy storage system

[0097] The distributed storage system can operate in three states: charging state, discharging state and shutdown state. B (t) are:

[0098]

[0099]

[0100] Where: subscript B represents battery energy storage equipment; δ B is the consumption rate during the energy storage process; and are the input conversion efficiency and the output conversion efficiency respectively; Δt is the time step; and They are energy input and energy output respectively.

[0101] (5) Carbon emission model

[0102] Carbon neutrality is an effective emission reduction mechanism. At present, my country's carbon trading quota allocation follows a free allocation scheme, specifically using two methods: historical method and baseline method. The baseline method has better fairness than the historical method and is more widely used in carbon quota allocation schemes. Therefore, this invention selects the baseline method as the carbon emission quota allocation method. The system carbon emission quota calculation formula is:

[0103]

[0104] Where: P t is the total power generation of the controllable distributed generators in the virtual power plant at time t; P MT，i，t is the output of controllable distributed generator set i at time t; M is the number of controllable distributed generator sets; P bat，j，t is the electric power of energy storage unit j at time t; P WT is the power generation of the wind power generation system at time t; P PV is the power generation of the photovoltaic power generation system at time t; N is the number of energy storage units; E L is the total carbon emission quota of the system; ε is the emission quota per unit of electricity.

[0105] In a virtual power plant, wind power and photovoltaic units are distributed clean energy, and their carbon emissions are approximately 0, so only the carbon emissions of controllable units and energy storage units are considered. The carbon emissions calculation formula of the system is:

[0106]

[0107] Where: E p is the total carbon emissions of the system in a scheduling cycle; MT，i is the carbon emission intensity of controllable distributed generator set i; δ bat，j is the carbon emission intensity of energy storage unit j.

[0108] The relationship between system carbon emission cost and carbon emissions is reflected by the following three-stage carbon emission cost model:

[0109]

[0110] Where: C g is the system carbon emission cost; μ is the transaction unit price of the system participating in the carbon market transaction each time; d is the carbon emission step range coefficient; k is the increase in the carbon trading price when the carbon emission increases by one step. It is worth noting that when the system carbon emission is less than the carbon emission quota, the carbon emission cost C g The value of will be less than 0, at which point the system can sell carbon emission quotas in the carbon trading market to obtain corresponding carbon benefits.

[0111] 2. Objective Function and Constraints

[0112] When analyzing the optimal dispatch of virtual power plants, the optimization objectives are to minimize the total power generation cost and carbon emission cost of the system. The specific objective function is as follows:

[0113] minC=C PV +C WT +C GT +C IC +C g

[0114] Where: C is the total cost of the virtual power plant; C PV is the cost of photovoltaic power generation system, C WT is the cost of wind power generation system, C GT is the power generation cost of the gas turbine system, C IC is the power generation cost of the peak-shaving internal combustion unit, C g is the system carbon emission cost.

[0115] At the same time, certain constraints need to be met during the optimization process, mainly including the equipment constraints and system operation constraints of the virtual power plant, as follows.

[0116] (1) Equipment output power constraints

[0117] P i，min ≤P i t ≤P i，max

[0118] In the formula, is the active power output of microgrid energy supply unit i at time t, P i，max and P i，min are the upper and lower limits of the active output of microgrid energy supply unit i respectively.

[0119] (2) Energy storage equipment operation constraints

[0120] The charging and discharging power of energy storage equipment can be continuously adjusted within a certain range, but the charging and discharging process cannot be carried out at the same time, so there are the following constraints:

[0121]

[0122] Where: (t) is the charging power at time t; is the maximum charging power; (t) is the energy release power at time t; is the maximum energy release power; X and Y are state variables.

[0123] Energy storage devices should be set with appropriate charge-discharge cycles, that is, the stored energy should be released in a certain cycle to avoid energy loss caused by long-term storage and cost increase caused by excessive storage capacity. Therefore, there are the following constraints:

[0124]

[0125] Where: T is the charge-discharge cycle; It is the power consumed by the energy storage device during the energy storage process.

[0126] (3) Climbing rate constraint

[0127]

[0128] Where: g i，t is the active output of microgrid energy supply unit i in period t, Δg i + and Δg i - are the maximum climbing power and climbing power respectively.

[0129] (4) Electric power balance

[0130] During the entire interactive process, the balance constraint between power supply and load must be met, as shown in the following formula:

[0131] P load (t) = P PV (t)+P WT (t)+P GT (t)+P IC (t)+P ST (t)-P cut (t)

[0132] Among them, P load (t), P PV (t), P WT (t), P GT (t), P IC (t), PST (t) are the power of user-side electric load, photovoltaic power generation system, wind power generation system, gas turbine power generation system, internal combustion engine power generation system and electric energy storage system, respectively. cut (t) is the load shedding (load loss) of the system in period t.

[0133] (5) Tie line capacity constraints

[0134] P grid (t)≤P grid，max

[0135] Where P grid (t) is the transmission power of the tie line between the microgrid and the large power grid at time t, P grid，max It is the maximum transmission power allowed by the interconnection line between the microgrid and the large power grid.

[0136] (6) System load loss rate constraint

[0137]

[0138] Where: LPSP It represents the loss of power supply probability (LPSP) of the grid-connected microgrid system, which is used to characterize the power supply reliability of the system. The maximum allowable load loss rate value, T sch is a scheduling cycle, and W LPSP is the total load loss in a dispatching cycle. When the system load is fully satisfied during period t, λ LPSP is 0, otherwise it is λ LPSP =P cut (t)Δt.

[0139] 3. Solution method and result analysis

[0140] (1) Model linearization

[0141] According to the modeling of each component above, the final result will be a mixed integer nonlinear optimization model. Considering that the nonlinear model requires a long calculation time, the model is linearized to convert it into a mixed integer linear model to improve the optimization speed. The linearization method is as follows.

[0142] The product of a binary variable b and a continuous variable x can be expressed in terms of a continuous variable y as:

[0143] y=b*x

[0144] Then, add the following constraints to the model:

[0145]

[0146] At this time, x min 、x max are the minimum and maximum values ​​of continuous variables, respectively. When b = 0, y = 0; when b = 1, y = x. Through this linearization method, the original mixed integer nonlinear model can be transformed into a mixed integer linear model without any approximation.

[0147] (2) Target Optimization Method

[0148] See also Figure 2 , the overall framework of the optimal scheduling of virtual power plants includes multi-objective optimization methods and corresponding decision-making methods. First, two evaluation indicators are defined to evaluate the benefits of virtual power plants in economic operation and carbon emissions respectively. Secondly, the multi-objective particle swarm optimization algorithm (MOPSO) is used to optimize the system and obtain a Pareto solution set including a series of feasible Pareto solutions. Finally, the system scheduling cost and operating benefit under multiple solutions in the Pareto solution set are compared and evaluated to obtain the optimal solution.

[0149] Common multi-objective optimization algorithms include multi-objective evolutionary algorithm (MOEA), second-generation non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO), etc. Among them, MOPSO has the advantages of fast convergence speed, suitability for optimizing continuous variables and short calculation time. Therefore, the present invention adopts it as the optimization algorithm.

[0150] During the optimization process, the carbon trading benchmark price was set at 50 yuan / t, the carbon emission step range coefficient d=40t, the carbon trading price increase coefficient k=25% for each step increase, the system baseline emission factor was 0.75, and the wind power operation and maintenance cost was selected as 150 yuan / MW.

[0151] The multi-objective optimization scheduling model established in the present invention can be regarded as a mixed integer linear programming problem from the classification of optimization problems, and the commercial solver CPLEX can be used to solve the problem.

[0152] (3) Results analysis

[0153] The load (electric), wind power generation (WT) and solar irradiance (solar) prediction curves of the example involved in this embodiment are as follows: Figure 3 In the virtual power plant optimization scheduling, on the one hand, it is necessary to allocate the output of gas turbines and energy storage to reduce the system operating cost and carbon emission cost, and on the other hand, it is necessary to ensure the balance of the system by adjusting the distributed energy storage. The optimization results of each energy supply system and distributed energy storage output are shown in Figure 4In order to better demonstrate the balancing effect of the energy storage system on load fluctuations and clean energy output fluctuations, the sum of the electric load and the output of the energy storage system (charging is a positive value and discharging is a negative value) is regarded as the converted load.

[0154] from Figure 4 It can be seen that the optimization result of the algorithm proposed in this embodiment can achieve better tracking of user loads. However, the output composition of each time period is different, which is jointly determined by the unit cost and the carbon emission cost. Since the output cost and carbon neutrality cost of different gas turbines are different, the output of gas turbines with lower carbon neutrality costs is given priority in the optimization and scheduling process. Before 19:00, due to the upper and lower limits of the unit output, the power inside the virtual power plant is borne by different gas turbines, and the peak-shaving task is mainly undertaken by internal combustion engine generator sets with more flexible power regulation. At the same time, since the marginal cost of distributed photovoltaic and wind power is 0, the system always gives priority to the use of distributed photovoltaic and wind power output. In the evening, due to the high output of distributed power sources and the large number of adjustable flexible loads, all power demands of the system are mainly borne by clean energy power sources and power sources with lower carbon emission costs.

[0155] like Figure 5 As shown in the figure, after optimized scheduling, the system's carbon emission cost is significantly improved, from the original positive value to a negative value, while the output cost remains basically unchanged. That is, the system can benefit from selling carbon emission quotas, which significantly improves the system's operating efficiency. At the same time, it is found that the introduction of energy storage equipment can reduce the operating cost of virtual power plants, improve energy utilization, and smooth the randomness and volatility of clean energy and load.

[0156] Embodiment 2:

[0157] The multi-objective optimization scheduling system for a virtual power plant for a grid-connected microgrid can implement the multi-objective optimization scheduling method for a virtual power plant for a grid-connected microgrid described in the first embodiment, including:

[0158] Receiving module: used to obtain a virtual power plant system including distributed entities on the demand side;

[0159] Optimization scheduling module: used to optimize the scheduling of the virtual power plant system based on carbon emission costs and power generation costs;

[0160] Interaction module: used to connect the virtual power plant system after optimized scheduling to the external power grid for information and energy interaction.

[0161] Embodiment three:

[0162] The embodiment of the present invention also provides a virtual power plant multi-objective optimization scheduling device for a grid-connected microgrid, which can implement the virtual power plant multi-objective optimization scheduling method for a grid-connected microgrid described in the first embodiment, including a processor and a storage medium;

[0163] The storage medium is used to store instructions;

[0164] The processor is used to operate according to the instructions to perform the steps of the following method:

[0165] Obtaining a virtual power plant system including demand-side distributed entities;

[0166] Optimize the scheduling of virtual power plant systems based on carbon emission costs and power generation costs;

[0167] The virtual power plant system after optimized scheduling is connected to the external power grid for information and energy interaction.

[0168] Embodiment 4:

[0169] The embodiment of the present invention further provides a computer-readable storage medium, which can implement the multi-objective optimization scheduling method for a virtual power plant for a grid-connected microgrid as described in the first embodiment, and a computer program is stored thereon, and when the program is executed by a processor, the steps of the following method are implemented:

[0170] Obtaining a virtual power plant system including demand-side distributed entities;

[0171] Optimize the scheduling of virtual power plant systems based on carbon emission costs and power generation costs;

[0172] The virtual power plant system after optimized scheduling is connected to the external power grid for information and energy interaction.

[0173] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0174] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0175] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0176] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0177] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A multi-objective optimization scheduling method for virtual power plants for grid-connected microgrids, characterized by: include: Obtaining a virtual power plant system including demand-side distributed entities; Optimize the scheduling of virtual power plant systems based on carbon emission costs and power generation costs; Connecting the optimized and scheduled virtual power plant system to an external power grid for information and energy interaction; The demand-side distributed entities include photovoltaic power generation systems, wind power generation systems, distributed power generation systems and electric energy storage systems; The distributed power generation system includes a gas turbine power generation system, an internal combustion engine power generation system for peak load regulation, and a diesel engine power generation system. The power generation cost of the distributed power generation system is: Where: C MT is the power generation cost of the distributed generation system, P MT (t) is the power generation of the distributed generation system at time t; ρ MT , γ MT , is the unit operating cost coefficient of the distributed generation system; The carbon emissions calculation formula of the virtual power plant system is: Where: E p is the total carbon emissions of the system in a scheduling cycle, δ MT,i is the carbon emission intensity of controllable distributed generator set i, δ bat,j is the carbon emission intensity of energy storage unit j; P MT,i,t is the output of controllable distributed generator set i at time t, M is the number of controllable distributed generator sets, P bat,j,t is the electric power of energy storage unit j at time t, and N is the number of energy storage units; The relationship between the carbon emission cost and carbon emissions of the virtual power plant system is: Where: C g is the system carbon emission cost, μ is the transaction unit price of each carbon market transaction, E L is the total carbon emission quota of the system, d is the carbon emission step range coefficient, and k is the increase in carbon trading price when carbon emissions increase by one step; The virtual power plant system performs optimal scheduling with the minimum total power generation cost and carbon emission cost of the system as the optimization objectives. The specific objective function is as follows: minC=C PV +C WT +C GT +C IC +C g Where: C is the total cost of the virtual power plant, C PV is the cost of photovoltaic power generation system, C WT is the cost of wind power generation system, C GT is the power generation cost of the gas turbine system, C IC The cost of power generation by peak-shaving internal combustion units.

2. The multi-objective optimization scheduling method for a virtual power plant for a grid-connected microgrid according to claim 1 is characterized in that: The photovoltaic power generation output power of the photovoltaic power generation system is: g st =g stc (I r，t / I stc )[1+a θ (i t -θ stc )] Where: g st is the photovoltaic power output, g stc is the output power of the module under standard test conditions, α θ is the temperature coefficient of the component, θ t is the photovoltaic panel temperature at time t, θ stc is the photovoltaic panel temperature under standard test conditions, I r,t is the actual solar radiation intensity at time t, I stc is the solar radiation intensity under standard test conditions; The output power of the wind power generation system is: Where: is the original simulated output of the wind farm, m t 、m in 、m rated With m out are the actual wind speed at time t, the cut-in wind speed of the wind turbine, the rated wind speed and the cut-out wind speed, respectively, and R is the rated output power of the wind turbine; The mathematical models of the electric energy storage system in the charging state and the discharging state are respectively: Where: S B (t) is the mathematical model of the electric energy storage system at time t, B represents the battery energy storage device, δ B is the consumption rate during the energy storage process, and are the input conversion efficiency and the output conversion efficiency respectively, Δt is the time step, and They are energy input and energy output respectively.

3. The multi-objective optimization scheduling method for a virtual power plant for a grid-connected microgrid according to claim 2 is characterized in that: The carbon emission quota calculation formula of the virtual power plant system is: Where: P t is the total power generation of the controllable distributed generators in the virtual power plant at time t, P WT is the power generation of the wind power generation system at time t, P PV is the power generation of the photovoltaic power generation system at time t, and ε is the emission quota per unit of electricity.

4. The multi-objective optimization scheduling method for a virtual power plant for a grid-connected microgrid according to claim 2 is characterized in that: The process of the optimization scheduling satisfies the equipment output power constraint, the energy storage equipment operation constraint, the ramp rate constraint, the electric power balance constraint, the tie line capacity constraint and the system load loss rate constraint, wherein: The device output power constraint is: In the formula, is the active power output of microgrid energy supply unit i at time t, P i,max and P i,min are the upper and lower limits of active output of microgrid energy supply unit i respectively; The energy storage device operation constraints are: Where: is the charging power at time t, is the maximum charging power, is the energy release power at time t, is the maximum discharge power, X and Y are state variables, T is the charge-discharge cycle, It is the power consumed by the energy storage device during the energy storage process; The climbing rate constraint is: Where: g i,t is the active output of microgrid energy supply unit i in period t, and are its maximum climbing power and climbing power respectively; The electric power balance constraint is: P load (t)=P PV (t)+P WT (t)+P GT (t)+P IC (t)+P ST (t)-P cut (t) Among them, P load (t), P PV (t), P WT (t), P GT (t), P IC (t), P ST (t) are the power of user-side electric load, photovoltaic power generation system, wind power generation system, gas turbine power generation system, internal combustion engine power generation system and electric energy storage system, respectively. cut (t) is the load shedding amount of the system in period t; The tie line capacity constraint is: P grid (t)≤P grid，max Where P grid (t) is the transmission power of the tie line between the microgrid and the large power grid at time t, P grid,max The maximum transmission power allowed by the interconnection line between the microgrid and the large power grid; The system load loss rate constraint is: Where: LPSP represents the load loss rate of the grid-connected microgrid system, The maximum allowable load loss rate value, T sch is a scheduling cycle, W LPSP is the total load loss in one scheduling cycle.

5. The multi-objective optimization scheduling method for virtual power plants for grid-connected microgrids according to claim 1 is characterized in that: The optimization scheduling process also includes model linearization processing, including: The product of binary variable b and continuous variable x is expressed as a continuous variable y: y=b*x Then, add the following constraints to the model: At this time, x min 、x max are the minimum and maximum values ​​of continuous variables, respectively.

6. The multi-objective optimization scheduling method for virtual power plants for grid-connected microgrids according to claim 1 is characterized in that: The optimization scheduling process includes: after defining two evaluation indicators to respectively evaluate the benefits of the virtual power plant in economic operation and carbon emissions, a multi-objective particle swarm algorithm is used to optimize the virtual power plant system to obtain a Pareto solution set including a series of feasible Pareto solutions, and the system scheduling cost and operating benefit under multiple solutions in the Pareto solution set are compared and calculated to obtain the optimal solution. The multi-objective particle swarm algorithm includes a multi-objective evolutionary algorithm, a second-generation non-dominated sorting genetic algorithm and a multi-objective particle swarm algorithm.

7. A multi-objective optimization dispatching system for a virtual power plant for a grid-connected microgrid, characterized by including: Receiving module: used to obtain a virtual power plant system including distributed entities on the demand side; Optimization scheduling module: used to optimize the scheduling of the virtual power plant system based on carbon emission costs and power generation costs; Interaction module: used to connect the virtual power plant system after optimized scheduling to the external power grid for information and energy interaction; The demand-side distributed entities include photovoltaic power generation systems, wind power generation systems, distributed power generation systems and electric energy storage systems; The distributed power generation system includes a gas turbine power generation system, an internal combustion engine power generation system for peak load regulation, and a diesel engine power generation system. The power generation cost of the distributed power generation system is: Where: C MT is the power generation cost of the distributed generation system, P MT (t) is the power generation of the distributed generation system at time t; ρ MT , γ MT , is the unit operating cost coefficient of the distributed generation system; The carbon emissions calculation formula of the virtual power plant system is: Where: E p is the total carbon emissions of the system in a scheduling cycle, δ MT,i is the carbon emission intensity of controllable distributed generator set i, δ bat,j is the carbon emission intensity of energy storage unit j; P MT,i,t is the output of controllable distributed generator set i at time t, M is the number of controllable distributed generator sets, P bat,j,t is the electric power of energy storage unit j at time t, and N is the number of energy storage units; The relationship between the carbon emission cost and carbon emissions of the virtual power plant system is: Where: C g is the system carbon emission cost, μ is the transaction unit price of each carbon market transaction, E L is the total carbon emission quota of the system, d is the carbon emission step range coefficient, and k is the increase in carbon trading price when carbon emissions increase by one step; The virtual power plant system performs optimal scheduling with the minimum total power generation cost and carbon emission cost of the system as the optimization objectives. The specific objective function is as follows: minC=C PV +C WT +C GT +C IC +C g Where: C is the total cost of the virtual power plant, C PV is the cost of photovoltaic power generation system, C WT is the cost of wind power generation system, C GT is the power generation cost of the gas turbine system, C IC The cost of power generation by peak-shaving internal combustion units.

8. A multi-objective optimization dispatching device for a virtual power plant for a grid-connected microgrid, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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