A method and system for determining a source-load collaborative optimization model for a virtual power plant system

Abstract

The present application discloses a method and system for determining a source-load collaborative optimization model of a virtual power plant system. The method includes: determining the uncertainty of wind power output, photovoltaic output and load according to the pre-collected parameters of the virtual power plant system; determining the electric energy subsystem, thermal energy subsystem and gas energy subsystem according to the uncertainty of wind power output, photovoltaic output and load; determining the electric energy subsystem as an upper-layer collaborative optimization model, and determining the thermal energy subsystem and gas energy subsystem as lower-layer collaborative optimization models, and the upper-layer collaborative optimization model and the lower-layer collaborative optimization model influence each other; determining the objective function of the upper-layer collaborative optimization model, the constraints of the upper-layer collaborative optimization model, the objective function of the lower-layer collaborative optimization model and the constraints of the lower-layer collaborative optimization model; determining the source-load double-layer collaborative optimization model of the virtual power plant system.

Description

Technical Field

[0001] The present application relates to the technical field of virtual power plant systems, and in particular to a method and system for determining a source-load collaborative optimization model for a virtual power plant system. Background Art

[0002] With the continuous development of my country's economic situation, the energy supply model has shifted from traditional fossil energy to the large-scale adoption of renewable energy such as wind power and photovoltaics, which has become the main means to improve the utilization rate of virtual power plants. As an important manifestation of the energy Internet, the virtual power plant system includes different energy subsystems such as electricity, heat, cold, and gas, which are strongly coupled with each other. However, due to the randomness of the output of renewable energy, mainly wind power and photovoltaics on the power supply side and the volatility of electricity consumption habits on the load side, the absorption of renewable energy and the safe and economic operation of the power grid system are directly restricted. Therefore, in order to optimize the power supply structure and promote the coordinated and orderly development of renewable energy, how to reduce the uncertainty brought by both the source and the load has brought great challenges and concerns to the coordinated operation of the virtual power plant system.

[0003] With regard to the technical problems existing in the above-mentioned prior art, such as the randomness of the output of renewable energy, mainly wind power and photovoltaic power, on the power supply side and the volatility of electricity consumption habits on the load side, which directly limit the absorption of renewable energy and the safe and economical operation of the power grid system, no effective solution has been proposed so far. Summary of the invention

[0004] The embodiments of the present disclosure provide a method and system for determining a source-load collaborative optimization model for a virtual power plant system, so as to at least solve the technical problem existing in the prior art that the randomness of the output of renewable energy, mainly wind power and photovoltaic power, on the power supply side and the volatility of electricity consumption habits on the load side directly limit the absorption of renewable energy and the safe and economical operation of the power grid system.

[0005] According to one aspect of an embodiment of the present disclosure, a method for determining a source-load collaborative optimization model of a virtual power plant system is provided, comprising: determining wind power output uncertainty, photovoltaic output uncertainty and load uncertainty according to pre-collected parameters of the virtual power plant system; determining an electric energy subsystem, a thermal energy subsystem and a gas energy subsystem according to the wind power output uncertainty, the photovoltaic output uncertainty and the load uncertainty; determining the electric energy subsystem as an upper-layer collaborative optimization model, and determining the thermal energy subsystem and the gas energy subsystem as lower-layer collaborative optimization models, the upper-layer collaborative optimization model and the lower-layer collaborative optimization model mutually influencing each other; determining an objective function of the upper-layer collaborative optimization model, constraints of the upper-layer collaborative optimization model, an objective function of the lower-layer collaborative optimization model and constraints of the lower-layer collaborative optimization model; determining a source-load two-layer collaborative optimization model of the virtual power plant system according to the objective function of the upper-layer collaborative optimization model, constraints of the upper-layer collaborative optimization model, objective function of the lower-layer collaborative optimization model and constraints of the lower-layer collaborative optimization model.

[0006] According to another aspect of the embodiment of the present disclosure, a system for determining a source-load collaborative optimization model of a virtual power plant system is also provided, including: an uncertainty determination module, used to determine wind power output uncertainty, photovoltaic output uncertainty and load uncertainty according to pre-collected parameters of the virtual power plant system; a subsystem determination module, used to determine an electric energy subsystem, a thermal energy subsystem and a gas energy subsystem according to wind power output uncertainty, photovoltaic output uncertainty and load uncertainty; a collaborative optimization model determination module, used to determine the electric energy subsystem as an upper-layer collaborative optimization model, and the thermal energy subsystem and the gas energy subsystem as lower-layer collaborative optimization models, the upper-layer collaborative optimization model and the lower-layer collaborative optimization model mutually influencing each other; a target function determination module, used to determine the target function of the upper-layer collaborative optimization model, the constraints of the upper-layer collaborative optimization model, the target function of the lower-layer collaborative optimization model and the constraints of the lower-layer collaborative optimization model; a double-layer collaborative optimization model determination module, used to determine the source-load double-layer collaborative optimization model of the virtual power plant system according to the target function of the upper-layer collaborative optimization model, the constraints of the upper-layer collaborative optimization model, the target function of the lower-layer collaborative optimization model and the constraints of the lower-layer collaborative optimization model.

[0007] In the present invention, the present invention proposes a virtual power plant system source-load collaborative two-layer optimization model for the problem of uncertainty in distributed energy wind power, photovoltaic output and load. Among them, the upper-layer collaborative optimization model takes the minimum operating cost of the electric energy subsystem and the maximum renewable energy consumption rate as multi-objective functions, and uses interval form to describe the uncertainty of wind power, photovoltaics and load in the electric energy subsystem. The system decision maker and the renewable energy output decision maker are set to play a game, and the optimal collaborative solution of the upper-layer collaborative optimization model is obtained by solving the Nash equilibrium. The lower-layer collaborative optimization model adopts random chance constrained programming theory for modeling, considers the uncertainty of energy prices, and describes the objective function and constraints in a probabilistic form. This two-layer model can effectively improve the safety and robustness of the operation of virtual power plants, and provide a reliable method for virtual power plant managers in the process of weighing benefits and risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present application. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0009] Figure 1 It is a flowchart for implementing a method for determining a source-load collaborative optimization model of a virtual power plant system according to an embodiment of the present disclosure;

[0010] Figure 2 is a schematic diagram of determining an electric energy subsystem, a thermal energy subsystem, and a gas energy subsystem according to an embodiment of the present disclosure;

[0011] Figure 3 A system for determining a source-load collaborative optimization model for a virtual power plant system according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0013] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0014] According to a first aspect of this embodiment, a method for determining a source-load coordinated optimization model of a virtual power plant system is provided. Figure 1 As shown, the method includes:

[0015] S102: determining wind power output uncertainty, photovoltaic output uncertainty, and load uncertainty according to pre-collected parameters of the virtual power plant system;

[0016] S104: Determine an electric energy subsystem, a thermal energy subsystem, and a gas energy subsystem according to wind power output uncertainty, photovoltaic output uncertainty, and load uncertainty;

[0017] S106: Determine the electric energy subsystem as an upper-layer collaborative optimization model, and determine the thermal energy subsystem and the gas energy subsystem as lower-layer collaborative optimization models, and the upper-layer collaborative optimization model and the lower-layer collaborative optimization model influence each other;

[0018] S108: Determine the objective function of the upper collaborative optimization model, the constraints of the upper collaborative optimization model, the objective function of the lower collaborative optimization model, and the constraints of the lower collaborative optimization model;

[0019] S110: Determine the source-load two-layer collaborative optimization model of the virtual power plant system according to the objective function of the upper-layer collaborative optimization model, the constraints of the upper-layer collaborative optimization model, the objective function of the lower-layer collaborative optimization model, and the constraints of the lower-layer collaborative optimization model.

[0020] Specifically, this embodiment targets the influence of the uncertainty of renewable energy output and load on the source-load coordinated operation of virtual power plants. First, the source-load characteristics of virtual power plants involving large-scale distributed energy are analyzed; secondly, based on the system operation structure of the virtual power plant, a multi-objective function is constructed taking into account the lowest system operation cost and the highest renewable energy consumption rate, and a virtual power plant source-load coordinated two-layer optimization model is established; then, taking into account the output of wind power and photovoltaic distributed energy, as well as the uncertainty of load, robust optimization theory and random chance constrained programming theory are introduced to model the uncertainty of the upper and lower collaborative optimization models of the virtual power plant source-load coordinated optimization; thirdly, a solution process is designed for the established virtual power plant system source-load coordinated two-layer optimization model.

[0021] Therefore, in view of the impact of distributed energy output uncertainty on the virtual power plant system, a multi-objective function with the minimum system operating cost and the highest renewable energy consumption rate was constructed. The uncertainty of wind power, photovoltaics and load in the power subsystem was described in interval form, and the system decision maker and renewable energy output decision maker were set up to play a game. The optimization coordination scheme of the upper-level collaborative optimization model was obtained by solving the Nash equilibrium. In view of the impact of virtual power plant load uncertainty, random chance constrained programming theory was used for modeling, considering the uncertainty of energy prices, and describing the objective function and constraints in probabilistic form. Comprehensively considering the uncertainty of distributed energy wind power and photovoltaic output on the source side, and the energy price on the load side, the robust optimization theory was used in the upper-level collaborative optimization model, and the random chance constrained programming was used in the lower-level collaborative optimization model, realizing the source-load coordination of the virtual power plant and further improving the overall benefits of the system.

[0022] Optionally, the wind power output uncertainty, the photovoltaic output uncertainty and the load uncertainty are determined according to the pre-collected parameters of the virtual power plant system, including: determining the wind power output uncertainty according to the probability density of the wind speed and the available output of the wind turbine generator set; the calculation formula for determining the probability density of the wind speed is:

[0023]

[0024] Among them, f WT (v) is the probability density of the Weibull distribution fitting wind speed, v is the wind speed, c and k are f respectively. WT (v) is the state shape parameter of the Weibull distribution, and c is the scale parameter of the Weibull distribution; the calculation formula for determining the available output of the wind turbine generator set is:

[0025]

[0026] Among them, P WT (t) is the available output of the wind turbine generator set at time t, P r,W is the rated power of the wind turbine generator set, v in , v r , v out are the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine respectively, and v is the real-time wind speed at time t.

[0027] Specifically, in a virtual power plant operation system including wind turbines, wind speed determines the output of the turbines, and the randomness and volatility of its own performance make the output of wind turbines uncertain. In this embodiment, Weibull distribution is selected to fit the probability density of wind speed.

[0028] Optionally, determining the wind power output uncertainty, photovoltaic output uncertainty and load uncertainty based on the pre-collected parameters of the virtual power plant system also includes: determining the photovoltaic output uncertainty based on the probability density of light intensity and the maximum output power of the photovoltaic generator set; the calculation formula for determining the probability density of light intensity is: Among them, f PV (t) is the probability density of light intensity fitted by Beta distribution, r is the light irradiance at time t, r max is the maximum irradiance at time t, α and β are the shape parameters of Beta distribution, α and β are obtained by the mathematical expectation (μ) and variance (δ) of the light intensity at time t, The calculation formula for determining the maximum output power of the photovoltaic generator set at time t is: P PV (t) = η PV s PV θ(t), where P PV (t) is the maximum output power of the photovoltaic generator set at time t, η PV is the radiation efficiency of light intensity, s PV is the light intensity radiation area, and θ(t) is the light intensity at time t.

[0029] Specifically, in a virtual power plant operation system including a photovoltaic generator set, the light intensity determines the output of the generator set, and the randomness and volatility of its own performance make the photovoltaic power generation output have uncertain characteristics. This embodiment chooses to use Beta distribution to fit the probability density of light intensity.

[0030] Optionally, determining the wind power output uncertainty, photovoltaic output uncertainty and load uncertainty based on the pre-collected parameters of the virtual power plant system also includes: determining the load uncertainty based on the electric load power, the heat load power, the cooling load power and the gas load power; determining the calculation formulas for the electric load power, the heat load power, the cooling load power and the gas load power as follows: Among them, P electricity (t), P thermal (t), P cool (t), P gas (t) are the electric load power, heat load power, cooling load power and gas load power at time t, They are the regular change functions of the electric load power, heat load power, cooling load power and gas load power, respectively, and each load is related to time. ΔP electricity (t), ΔP thermal (t), ΔP cool (t), ΔP gas(t) are the random uncertain parts of the electric load power, thermal load power, cooling load power and gas load power respectively.

[0031] Specifically, in the source-load coordinated virtual power plant operation system, the supply situation is adjusted in real time according to the end-user's demand for various loads such as electricity, heat, cooling and gas, and the power generation side and the user side are further coordinated and optimized. Usually, the dynamic change of terminal load includes two processes: random uncertainty and regular change.

[0032] Optionally, based on the uncertainty of wind power output, the uncertainty of photovoltaic output and the uncertainty of load, the electric energy subsystem, the thermal energy subsystem and the gas energy subsystem are determined, including: based on the uncertainty of wind power output, the uncertainty of photovoltaic output and the uncertainty of load, determining that the electric energy subsystem includes wind power renewable energy, photovoltaic renewable energy, electric energy storage, demand-side resources, electric load, P2G equipment, gas turbine and fuel cell; based on the uncertainty of wind power output, the uncertainty of photovoltaic output and the uncertainty of load, determining that the thermal energy subsystem includes an external heating network, a waste heat boiler, a thermal energy storage device and a thermal load; based on the uncertainty of wind power output, the uncertainty of photovoltaic output and the uncertainty of load, determining that the gas energy subsystem includes an external gas network, P2G equipment, a waste heat boiler, a gas turbine, a fuel cell and a gas load.

[0033] Specifically, refer to Figure 2 As shown in the figure, the electric energy subsystem is the core network for the whole system to achieve multi-energy complementary synergy. It plays a key role in the formulation of the optimized operation plan, and serves as an upper-level collaborative optimization model. At the same time, considering the management resources on the load side demand side, it can achieve source-load synergy with the power supply side to further improve the system efficiency, and the uncertainty of the output of renewable distributed energy resources connected to the grid will affect the system decision-making. It will act as a decision maker to play a game with the system decision maker to form a robust optimization model. In addition, the thermal energy subsystem and the gas energy subsystem need to make decisions based on the optimized operation plan formulated by the electric energy subsystem, as a lower-level collaborative optimization model.

[0034] Furthermore, the two-level optimization theory is an effective method to solve the status of different decision makers and has been widely used in the field of decision optimization. In a typical two-level optimization model, all control variables are in different positions. The upper-level decision maker, as the leader of the entire model, can influence the lower-level decision makers and determine the overall optimization of the optimization model. The formulation of control variables by the lower-level decision makers is affected by the strategies of the upper-level decision makers. Although some of the objective functions of the upper-level decision makers are affected by the lower-level decision makers or even determined by them, the upper-level decision makers can indirectly affect their own objective functions by influencing the strategies of the lower-level decision makers. The specific mathematical expression of the two-level optimization is:

[0035]

[0036] Among them, x is the control variable of the upper decision maker, y1, y2, ..., y N are the control variables of N lower-level decision makers; f(x,y1,y2,…,y N ) is the objective function of the upper decision maker, f j (x,y1,y2,…,y N ) is the objective function of the jth lower-level decision maker. and are the inequality constraints and equality constraints of the upper decision maker respectively; f j (x,y1,y2,…,y N )}≤0 and g j (x,y1,y2,…,y N )=0 are the inequality constraint and equality constraint of the lower-level decision maker respectively.

[0037] According to the above model, although the upper decision maker can only make a decision x, he can influence the decision of the lower decision maker through x, indirectly affecting his own objective function, and the whole model presents a hierarchical decision-making scenario. The upper model and the lower model together constitute the source-load collaborative optimization operation scheme of the virtual power plant system. In the source-load collaborative operation structure, the electric energy subsystem, as the core network of the entire system, plays a pivotal role in energy conversion and transmission. Therefore, the source-load coordination plan and even the optimization operation plan of the electric energy subsystem are in a key position in the optimization operation plan of the virtual power plant system, which directly determines the formulation of the source-load operation plan of the entire system, and will also serve as the upper model of the two-layer collaborative optimization model. The operation of other forms of energy subsystems and their interaction with the electric energy subsystem need to be decided according to the strategy of the upper decision maker, which will serve as the lower model of the two-layer collaborative optimization model.

[0038] Optionally, determining the objective function of the upper-layer collaborative optimization model includes: determining a first objective subfunction of the upper-layer collaborative optimization model and a second objective subfunction of the upper-layer collaborative optimization model, the first objective subfunction being used to determine the maximum value of the comprehensive absorption rate of renewable energy power generation of the virtual power plant system during the operation cycle, the comprehensive absorption rate of renewable energy power generation including the absorption rate of wind power output and the absorption rate of photovoltaic output, and the second objective subfunction being used to determine the minimum value of the source-load coordination cost of the electric energy subsystem; determining the first objective subfunction of the upper-layer collaborative optimization model as:

[0039]

[0040] Among them, f UP,1is the comprehensive consumption rate of renewable energy power generation in the upper collaborative optimization model, r is the comprehensive consumption rate of renewable energy power generation in the virtual power plant system during the operation cycle, T is the number of scheduling periods in the operation cycle, with one day as an operation cycle and 1 hour as a scheduling period, P WT (t) and P PV (t) are the actual wind power consumption and photovoltaic power consumption in the operation cycle, respectively. and They are respectively the ultra-short-term predicted output of wind power and the ultra-short-term predicted output of photovoltaic power in period t during the operation cycle;

[0041] The second objective subfunction of the upper-level collaborative optimization model is determined as:

[0042] minf UP,2 =C E =C S +C L +C IES

[0043] Among them, minf UP,2 is the minimum value of the source-load coordination cost of the power subsystem in the upper-level collaborative optimization model, C S is the power supply side operating cost of the power subsystem, C L is the demand side management cost of the power subsystem, C IES The comprehensive operating cost of the thermal energy subsystem and gas energy subsystem of the virtual power plant system;

[0044] Determine the objective function of the upper collaborative optimization model according to the first objective sub-function of the upper collaborative optimization model and the second objective sub-function of the upper collaborative optimization model by using a weighted coefficient method;

[0045] The calculation formula for determining the objective function of the upper-level collaborative optimization model is:

[0046]

[0047] Among them, λ1 is the weight coefficient of the first objective sub-function of the upper-level collaborative optimization model, λ2 is the weight coefficient of the second objective sub-function of the upper-level collaborative optimization model, and f UP,1,max is the maximum value or optimal value that can be obtained when the upper-level collaborative optimization model only takes the first objective sub-function as the objective function, f UP,2,min It is the minimum or optimal value that can be obtained when the upper-level collaborative optimization model only takes the second objective sub-function as the objective function.

[0048] Specifically, in order to improve the economy and reliability of the system, the system optimization operation plan is jointly formulated by analyzing the interactive relationship between the source-load coordination of the virtual power plant system, considering different forms of energy, different energy supply equipment and demand-side management resources on the load side in the system. The upper-level collaborative optimization model is the source-load collaborative optimization module of the electric energy subsystem. The decision-maker's control variable is the coordinated plan of distributed generation and load in the electric energy subsystem, including wind power, photovoltaic power generation output plan, and demand-side management plan. In general, the upper-level collaborative optimization model is described in the form of multiple objectives, that is, the first sub-objective is the maximum absorption rate of wind power and photovoltaic output in the electric energy subsystem; the second sub-objective is the minimum source-load coordination cost of the electric energy subsystem, which mainly includes the operating cost of the power supply side and the demand-side management cost of the load side. In summary, the multi-objective function is converted into a comprehensive satisfaction index F by using the weighted coefficient method. Considering the different attributes of the two sub-objectives, the comprehensive satisfaction index is obtained as the objective function of the upper-level collaborative optimization model.

[0049] Optionally, determining the second objective sub-function of the upper-layer collaborative optimization model includes: determining the power supply side operating cost C of the power subsystem S The calculation formula is:

[0050] C S =P GRID (t)q GRID (t)+k SB |P SB (t)|+k WT P WT (t)+k PV P PV (t)+f MT (P MT (t))+f FC (P FC (t));

[0051] Among them, P GRID (t) is the power purchased from the power subsystem during period t; q GRID (t) is the external grid time-of-use electricity price level during period t; k SB is the energy storage operation and maintenance cost coefficient; P SB (t) is the energy storage charging and discharging power in period t. When it is greater than 0, it indicates the discharging state, and when it is less than 0, it indicates the charging state; k WT and k PV are wind power operation and maintenance cost coefficient and photovoltaic operation and maintenance cost coefficient respectively; f MT () and f FC () are respectively the micro-turbine fuel cost function and the fuel cell fuel cost function; P MT (t) and P FC(t) t is the output of the micro-turbine and the output of the fuel cell during the time period;

[0052] Determine the demand side management cost C of the power subsystem L The calculation formula is:

[0053]

[0054] in, The fixed fee for shiftable loads signed between the virtual power plant system operator and the shiftable load users. is the load power for period t agreed between the operator and the load user, c de l ay P is the variable cost factor for actually shifting the shiftable load. delay (t) is the load shift power implemented in time period t, λ delay is the conversion coefficient of the power efficiency loss caused by load shifting to users, The interruptible load fixed fee is signed between the virtual power plant system operator and the interruptible load user. The interruptible load power during period t agreed between the operator and the interruptible load user, c cut P is the variable cost coefficient for the actual interruption shift of the interruptible load. cut (t) is the load interruption power implemented during period t, λ cut It is the conversion coefficient of the loss of electricity efficiency caused by load interruption to users.

[0055] Optionally, determining the constraint conditions of the upper-layer collaborative optimization model includes: determining the constraint conditions of the power balance of the electric energy subsystem as:

[0056]

[0057] in, P is the load power in period t after demand-side management is performed on the load side of the power subsystem; loss (t) is the network loss power of the electric energy subsystem during period t; P E,H (t) and P E,G (t) are the power converted from the electric energy subsystem to the thermal energy subsystem and the gas energy subsystem respectively; d(j,t) is the element of the load shift matrix, which means that the load shifted from the jth period to the tth period accounts for P delay (j) ratio;

[0058] The constraints for determining the output of renewable energy generation are:

[0059]

[0060]

[0061] P WT (t) and P PV (t) are the actual wind power consumption and photovoltaic power consumption in the operation cycle, respectively; and They are respectively the ultra-short-term predicted output of wind power and the ultra-short-term predicted output of photovoltaic power in period t during the operation cycle;

[0062] The constraints for determining the output of controllable micro-power sources are:

[0063]

[0064] Among them, R MT R is the rated ramp rate of the micro-turbine; FC is the rated ramp rate of the fuel cell; Δt is the unit time period length; and They are the minimum and maximum allowable output of the micro-turbine respectively. The constraints indicate that the output of the micro-turbine is either 0 or must satisfy the interval Same reason and are the minimum and maximum allowable outputs of the fuel cell, respectively. The constraint indicates that the output of the fuel cell is either 0 or must satisfy the interval

[0065] The constraints that determine the power limits of electronic systems are:

[0066]

[0067] in, is the maximum value of power exchanged with the external power grid;

[0068] The constraints for energy conversion are determined as follows:

[0069] P E,H (t)≤P E,H,max

[0070] P E,G (t)≤P E,G,max

[0071] Among them, P E,H,max is the maximum conversion power from the electrical energy subsystem to the thermal energy subsystem, P E,G,max It is the maximum conversion power from the electrical energy subsystem to the gas energy subsystem.

[0072] The constraints for energy storage operation are determined as follows:

[0073]

[0074] Among them, S SB (t) and S SB (t+1) are the remaining power of SB at the end of period t and period t+1, respectively, dis is the SB discharge efficiency, η c h is the energy storage charging efficiency, are the minimum and maximum remaining energy storage capacity, respectively. are the minimum and maximum values ​​of energy storage charging and discharging power, respectively, SB is the energy storage self-discharge coefficient, Q SB For the energy storage capacity.

[0075] The constraints for demand-side management are determined as follows:

[0076]

[0077]

[0078]

[0079] in, is the maximum power supply capacity of the power subsystem. The constraint in formula (1) indicates that the load level of the power subsystem after demand-side management in period t cannot exceed the system power supply capacity. The constraint in formula (2) indicates that the actual load power shifted in any period t cannot exceed the load power shiftable agreed upon between the virtual power plant system operator and the user. The constraint in formula (3) indicates that the actual load interruption power in any period t cannot exceed the load power interruptable agreed upon between the virtual power plant system operator and the user.

[0080] Specifically, as the source-load collaborative optimization part of the power subsystem, the upper-level collaborative optimization model formulates a source-load coordination plan that first needs to meet the power balance constraint of the power subsystem, otherwise the system will have power shortage or surplus, which does not meet the reliability requirements of the system operation. In addition, the power balance constraint of the power subsystem is valid for any period within the operation cycle of the virtual power plant system. When the upper-level collaborative optimization model formulates the actual consumption plan of wind power output and photovoltaic output, the actual consumption power cannot exceed the ultra-short-term predicted output. In the upper-level collaborative optimization model, the output of the controllable micro-power source in the source-load coordination plan needs to meet the output limit constraint on the one hand, and the ramp rate constraint on the other hand. In addition, as the connection point for exchanging power with the external network, the operation of the common coupling point of the power subsystem also needs to meet the power limit constraint. As the upper-level collaborative optimization model of the double-layer optimization of the entire virtual power plant system, the power subsystem source-load coordination center formulates the electric heat conversion power and the electrical conversion power, which directly affect the operation plan of the thermal energy subsystem and the operation plan of the gas energy subsystem. Energy storage is a commonly used controllable micro power source in the power subsystem. When solving the optimal operation plan, it is necessary to solve both the charging and discharging power of the energy storage in each time period and the remaining power of the energy storage in each time period. In the source-load coordinated optimization of the power subsystem, load translation constraints and interruptible load constraints are included, and the demand side management constraints need to be met in the demand side management plan.

[0081] Optionally, determining the objective function of the lower-layer collaborative optimization model includes: determining a calculation formula for the objective function of the thermal energy subsystem as:

[0082]

[0083] Among them, minC H Minimum comprehensive operating cost of thermal energy subsystem, P H (t) is the heat power purchased by the thermal energy subsystem from the external network during period t, p H (t) is the heating price of the external thermal energy system during period t, P X (t) is the charging and discharging power of the energy storage device in the period t. When it is greater than 0, it indicates the heat storage state, and when it is less than 0, it indicates the heat release state; k X is the unit power operation and maintenance cost of the energy storage device;

[0084] The calculation formula for determining the objective function of the gas energy subsystem is:

[0085]

[0086] Among them, minC G is the minimum comprehensive operating cost of the gas energy subsystem, Q G (t) is the gas power purchased by the gas energy subsystem in period t, C NG (t) is the unit price of natural gas in the natural gas spot market during period t.

[0087] Specifically, the lower-level collaborative optimization model is a module for formulating optimized operation plans for the thermal energy subsystem and the gas energy subsystem. The two decision makers are the thermal energy subsystem optimization operation center and the gas energy subsystem optimization operation center. The decisions of both are directly affected by the upper-level model, and the value of the objective function will affect the objective function of the upper-level model. The decision-making strategy of the thermal energy subsystem optimization operation center is the operation plan of the thermal energy subnet, and the objective function is to minimize the comprehensive operation cost of the thermal energy subsystem. The decision-making strategy of the gas energy subsystem optimization operation center is the operation plan of the gas energy subsystem, and the objective function is to minimize the comprehensive operation cost of the gas energy subnet.

[0088] Optionally, determining the constraint conditions of the lower-level collaborative optimization model includes:

[0089] The constraints for the thermal energy subsystem are determined as follows:

[0090]

[0091] Among them, C he is the heating coefficient of the double-effect absorption unit, and the load of the heat energy subsystem is supplied by heating according to the output power of the micro-turbine, P L,H (t) is the heat load power of the thermal energy subsystem in the tth period, P H (t) is the thermal power purchased by the thermal energy subsystem in the tth period, X(t) is the residual energy of the thermal energy storage device in the tth period, X(t-1) is the residual energy of the thermal energy storage device in the t-1th period, λ x Q is the heat loss coefficient of the thermal energy storage device; min and Q max are the minimum and maximum charging and discharging powers of the thermal energy storage device, respectively, and X min and X max are the minimum and maximum values ​​of the residual heat of the thermal energy storage device, and are the minimum power and maximum power of the thermal energy subsystem to purchase heat from the outside network;

[0092] The constraints of the gas energy subsystem are determined as follows:

[0093]

[0094] Among them, G s (t) is the gas release power of the gas storage tank during period t, P E,G (t) is the natural gas power generated by the power-to-gas conversion of the methane reaction unit during period t, P L,G (t) is the natural gas load power during period t, Q s (t) and Q s(t-1) are the remaining power of the gas tank gas in period t and period t-1 respectively, G s (t) is the gas release power of the gas storage tank during period t, and are the minimum and maximum values ​​of the remaining power of natural gas in the gas storage tank, respectively. and are the minimum and maximum values ​​of the gas release power of the gas storage tank, and They are the minimum power and maximum power of the gas energy subnet to purchase gas from the external network. When the gas energy subsystem optimization operation center formulates the operation plan, some constraints need to be considered, mainly including the power balance of the gas energy subsystem, the operation of the gas storage tank, and the power of purchasing gas from the external network. In addition, in order to facilitate the cost calculation of the electrical energy subsystem and the thermal energy subsystem, the amount of gas in the gas energy subsystem is converted into power.

[0095] Specifically, an operation plan is formulated at the thermal energy subsystem optimization operation center, taking into account some constraints, including the thermal energy balance of the thermal energy subsystem, the operation of the energy storage device, the relationship between the charging and discharging of the energy storage device and the residual heat, and the purchase of thermal power from outside.

[0096] Optionally, according to the objective function of the upper-layer collaborative optimization model, the constraints of the upper-layer collaborative optimization model, the objective function of the lower-layer collaborative optimization model, and the constraints of the lower-layer collaborative optimization model, the source-load double-layer collaborative optimization model of the virtual power plant system is determined, including: determining that the expression of the source-load double-layer collaborative optimization model of the virtual power plant system is:

[0097]

[0098] Among them, P WT is the actual consumption vector of wind power output, P PV is the actual consumption vector of photovoltaic power, P GRID P is the power vector of the power subsystem purchasing electricity from the network. SB is the energy storage charging and discharging power vector, P MT and P FC are the output vectors of the micro-turbine and the fuel cell, P delay is the load translation power vector, P cut is the interruptible load power vector, P E,H and P E,G are the power vectors converted from the electrical energy subsystem to the thermal energy subsystem and the gas energy subsystem, respectively, H is the thermal power vector of the thermal energy subsystem, P X is the thermal power vector of the thermal energy storage device, Q G is the gas power vector of the gas energy subsystem purchasing gas from the outside, G s is the gas tank charging and discharging power vector, g UP() is the equality constraint of the upper collaborative optimization model, h UP () is the inequality constraint of the upper collaborative optimization model, g DOWN,H () is the equality constraint for the decision maker of the thermal energy subsystem optimization operation in the lower collaborative optimization model, h DOWN,H () is the inequality constraint of the decision maker for the optimization operation of the thermal energy subsystem in the lower collaborative optimization model, g DOWN,G () is the equality constraint of the decision maker of the gas energy subsystem optimization operation center in the lower collaborative optimization model, h DOWN,G () is the inequality constraint for the decision maker of the optimization operation center of the gas energy subsystem in the lower-level collaborative optimization model.

[0099] Therefore, this embodiment proposes a virtual power plant system source-load collaborative two-layer optimization model for the problem of uncertainty in distributed energy wind power, photovoltaic output and load. Among them, the upper-layer collaborative optimization model takes the minimum operating cost of the electric energy subsystem and the maximum renewable energy consumption rate as multi-objective functions, and uses interval form to describe the uncertainty of wind power, photovoltaics and load in the electric energy subsystem. The system decision maker and the renewable energy output decision maker are set to play a game, and the optimal collaborative solution of the upper-layer collaborative optimization model is obtained by solving the Nash equilibrium. The lower-layer collaborative optimization model adopts the random chance constrained programming theory for modeling, considers the uncertainty of energy prices, and describes the objective function and constraints in a probabilistic form. This two-layer model can effectively improve the safety and robustness of the operation of virtual power plants, and provide a reliable method for virtual power plant managers in the process of weighing benefits and risks.

[0100] According to another aspect of this embodiment, a system 300 for determining a source-load coordinated optimization model of a virtual power plant system is also provided. Figure 3As shown, the system 300 includes: an uncertainty determination module 310, which is used to determine the wind power output uncertainty, photovoltaic output uncertainty and load uncertainty according to the parameters of the virtual power plant system collected in advance; a subsystem determination module 320, which is used to determine the electric energy subsystem, the thermal energy subsystem and the gas energy subsystem according to the wind power output uncertainty, the photovoltaic output uncertainty and the load uncertainty; a collaborative optimization model determination module 330, which is used to determine the electric energy subsystem as the upper collaborative optimization model, and the thermal energy subsystem and the gas energy subsystem as the lower collaborative optimization model, and the upper collaborative optimization model and the lower collaborative optimization model affect each other; a target function determination module 340, which is used to determine the target function of the upper collaborative optimization model, the constraint conditions of the upper collaborative optimization model, the target function of the lower collaborative optimization model and the constraint conditions of the lower collaborative optimization model; a double-layer collaborative optimization model determination module 350, which is used to determine the virtual power plant system source-load double-layer collaborative optimization model according to the target function of the upper collaborative optimization model, the constraint conditions of the upper collaborative optimization model, the target function of the lower collaborative optimization model and the constraint conditions of the lower collaborative optimization model.

[0101] Optionally, the uncertainty determination module 310 includes: a wind power output uncertainty determination submodule, which is used to determine the wind power output uncertainty according to the probability density of the wind speed and the available output of the wind turbine generator set; and a wind speed probability density determination submodule, which is used to determine the calculation formula of the probability density of the wind speed as follows:

[0102]

[0103] Among them, f WT (v) is the probability density of the Weibull distribution fitting wind speed, v is the wind speed, c and k are f respectively. WT (v) is the state shape parameter of the Weibull distribution, and c is the scale parameter of the Weibull distribution;

[0104] The calculation formula for determining the available output of a wind turbine is:

[0105]

[0106] Among them, P WT (t) is the available output of the wind turbine generator set at time t, P r,W is the rated power of the wind turbine generator set, v in , v r , v out are the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine respectively, and v is the real-time wind speed at time t.

[0107] Optionally, the uncertainty determination module 310 further includes: a photovoltaic output uncertainty determination submodule, which is used to determine the photovoltaic output uncertainty according to the probability density of the light intensity and the maximum output power of the photovoltaic generator set; and a light intensity probability density determination submodule, which is used to determine the calculation formula of the probability density of the light intensity as follows:

[0108]

[0109] Among them, f PV (t) is the probability density of light intensity fitted by Beta distribution, r is the light irradiance at time t, r max is the maximum irradiance at time t, α and β are the shape parameters of Beta distribution, α and β are obtained by the mathematical expectation (μ) and variance (δ) of the light intensity at time t,

[0110] The maximum output power submodule is used to determine the maximum output power of the photovoltaic generator set at time t:

[0111] P PV (t) = η PV s PV θ(t)

[0112] Among them, P PV (t) is the maximum output power of the photovoltaic generator set at time t, η PV is the radiation efficiency of light intensity, s PV is the light intensity radiation area, and θ(t) is the light intensity at time t.

[0113] Optionally, the uncertainty determination module 310 further includes: a load uncertainty determination submodule, which is used to determine the load uncertainty according to the electric load power, the heat load power, the cooling load power and the gas load power; and a load power determination submodule, which is used to determine the calculation formulas of the electric load power, the heat load power, the cooling load power and the gas load power as follows:

[0114]

[0115]

[0116] Among them, P electricity (t), P thermal (t), P cool (t), P gas (t) are the electric load power, heat load power, cooling load power and gas load power at time t, They are the regular change functions of the electric load power, heat load power, cooling load power and gas load power, respectively, and each load is related to time. ΔP electricity (t), ΔP thermal (t), ΔP cool (t), ΔP gas (t) are the random uncertain parts of the electric load power, thermal load power, cooling load power and gas load power respectively.

[0117] Optionally, the determination subsystem module 320 includes:

[0118] Determine the electric energy subsystem submodule, which is used to determine that the electric energy subsystem includes wind power renewable energy, photovoltaic renewable energy, electric energy storage, demand-side resources, electric load, P2G equipment, gas turbines and fuel cells based on the uncertainty of wind power output, photovoltaic output and load uncertainty; determine the thermal energy subsystem submodule, which is used to determine that the thermal energy subsystem includes external heating network, waste heat boiler, thermal energy storage device and thermal load based on the uncertainty of wind power output, photovoltaic output and load uncertainty; determine the gas energy subsystem submodule, which is used to determine that the gas energy subsystem includes external gas network, P2G equipment, waste heat boiler, gas turbine, fuel cell and gas load based on the uncertainty of wind power output, photovoltaic output and load uncertainty.

[0119] Optionally, the objective function module 340 is determined, including: determining an objective sub-function sub-module, which is used to determine a first objective sub-function of the upper-layer collaborative optimization model and a second objective sub-function of the upper-layer collaborative optimization model, wherein the first objective sub-function is used to determine the maximum value of the comprehensive absorption rate of renewable energy power generation of the virtual power plant system during the operation cycle, and the comprehensive absorption rate of renewable energy power generation includes the absorption rate of wind power output and the absorption rate of photovoltaic output, and the second objective sub-function is used to determine the minimum value of the source-load coordination cost of the electric energy subsystem; determining a first objective sub-function sub-module, which is used to determine that the first objective sub-function of the upper-layer collaborative optimization model is:

[0120]

[0121] Among them, f UP,1 is the comprehensive consumption rate of renewable energy power generation in the upper collaborative optimization model, r is the comprehensive consumption rate of renewable energy power generation in the virtual power plant system during the operation cycle, T is the number of scheduling periods in the operation cycle, with one day as an operation cycle and 1 hour as a scheduling period, P WT (t) and P PV (t) are the actual wind power consumption and photovoltaic power consumption in the operation cycle, respectively. and They are respectively the ultra-short-term predicted output of wind power and the ultra-short-term predicted output of photovoltaic power in period t during the operation cycle;

[0122] Determine the second objective sub-function sub-module, which is used to determine the second objective sub-function of the upper-layer collaborative optimization model:

[0123] minf UP,2 =C E =C S +C L +C IES

[0124] Among them, minf UP,2 is the minimum value of the source-load coordination cost of the power subsystem in the upper-level collaborative optimization model, C S is the power supply side operating cost of the power subsystem, C L is the demand side management cost of the power subsystem, C IES The comprehensive operating cost of the thermal energy subsystem and gas energy subsystem of the virtual power plant system;

[0125] An upper-layer objective function determination submodule is used to determine the objective function of the upper-layer collaborative optimization model according to the first objective subfunction of the upper-layer collaborative optimization model and the second objective subfunction of the upper-layer collaborative optimization model by using a weighted coefficient method;

[0126] Determine the upper-level objective function formula submodule, and determine the calculation formula of the objective function of the upper-level collaborative optimization model as follows:

[0127]

[0128] Among them, λ1 is the weight coefficient of the first objective sub-function of the upper-level collaborative optimization model, λ2 is the weight coefficient of the second objective sub-function of the upper-level collaborative optimization model, and f UP,1,max is the maximum value or optimal value that can be obtained when the upper-level collaborative optimization model only takes the first objective sub-function as the objective function, f UP,2,min It is the minimum or optimal value that can be obtained when the upper-level collaborative optimization model only takes the second objective sub-function as the objective function.

[0129] Optionally, determining the second target sub-function sub-module includes: determining a power supply side operation cost unit, which is used to determine the power supply side operation cost C of the electric energy subsystem. S The calculation formula is:

[0130] C S =P GRID (t)q GRID (t)+k SB |P SB (t)|+k WT P WT (t)+k PV P PV (t)+fMT (P MT (t))+f FC (P FC (t));

[0131] Among them, P GRID (t) is the power purchased from the power subsystem during period t; q GRID (t) is the external grid time-of-use electricity price level during period t; k SB is the energy storage operation and maintenance cost coefficient; P SB (t) is the energy storage charging and discharging power in period t. When it is greater than 0, it indicates the discharging state, and when it is less than 0, it indicates the charging state; k WT and k PV are wind power operation and maintenance cost coefficient and photovoltaic operation and maintenance cost coefficient respectively; f MT () and f FC () are respectively the micro-turbine fuel cost function and the fuel cell fuel cost function; P MT (t) and P FC (t) t is the output of the micro-turbine and the output of the fuel cell during the time period;

[0132] Determine the demand side management cost unit, which is used to determine the demand side management cost C of the power subsystem L The calculation formula is:

[0133]

[0134] in, The fixed fee for shiftable loads signed between the virtual power plant system operator and the shiftable load users. is the load power for period t agreed between the operator and the load user, c de l ay P is the variable cost factor for actually shifting the shiftable load. delay (t) is the load shift power implemented in time period t, λ delay is the conversion coefficient of the power efficiency loss caused by load shifting to users, The interruptible load fixed fee is signed between the virtual power plant system operator and the interruptible load user. The interruptible load power during period t agreed between the operator and the interruptible load user, c cut P is the variable cost coefficient for the actual interruption shift of the interruptible load. cut (t) is the load interruption power implemented during period t, λ cut It is the conversion coefficient of the loss of electricity efficiency caused by load interruption to users.

[0135] Optionally, the objective function determination module 340 includes: a power balance determination submodule for the electric energy subsystem, which is used to determine the constraint condition of the power balance of the electric energy subsystem as follows:

[0136]

[0137] in, P is the load power in period t after demand-side management is performed on the load side of the power subsystem; loss (t) is the network loss power of the electric energy subsystem during period t; P E,H (t) and P E,G (t) are the power converted from the electric energy subsystem to the thermal energy subsystem and the gas energy subsystem respectively; d(j,t) is the element of the load shift matrix, which means that the load shifted from the jth period to the tth period accounts for P delay (j) ratio;

[0138] The submodule for determining the renewable energy power generation output is used to determine the constraints of the renewable energy power generation output:

[0139]

[0140] P WT (t) and P PV (t) are the actual wind power consumption and photovoltaic power consumption in the operation cycle, respectively; and They are respectively the ultra-short-term predicted output of wind power and the ultra-short-term predicted output of photovoltaic power in period t during the operation cycle;

[0141] The controllable micro power output submodule is used to determine the constraints of the controllable micro power output:

[0142]

[0143] Among them, R MT R is the rated ramp rate of the micro-turbine; FC is the rated ramp rate of the fuel cell; Δt is the unit time period length; and They are the minimum and maximum allowable output of the micro-turbine respectively. The constraints indicate that the output of the micro-turbine is either 0 or must satisfy the interval Same reason and are the minimum and maximum allowable outputs of the fuel cell, respectively. The constraint indicates that the output of the fuel cell is either 0 or must satisfy the interval

[0144] The electronic system power limit determination submodule is used to determine the constraints of the electronic system power limit:

[0145]

[0146] in, is the maximum value of power exchanged with the external power grid;

[0147] Determine the energy conversion submodule, which is used to determine the constraints of energy conversion:

[0148] P E,H (t)≤P E,H,max

[0149] P E,G (t)≤P E,G,max

[0150] Among them, P E,H,max is the maximum conversion power from the electrical energy subsystem to the thermal energy subsystem, P E,G,max It is the maximum conversion power from the electrical energy subsystem to the gas energy subsystem.

[0151] The energy storage operation submodule is used to determine the constraints of energy storage operation:

[0152]

[0153] Among them, S SB (t) and S SB (t+1) are the remaining power of SB at the end of period t and period t+1, respectively, dis is the SB discharge efficiency, η ch For energy storage charging efficiency, are the minimum and maximum remaining energy storage capacity, respectively. are the minimum and maximum values ​​of energy storage charging and discharging power, respectively, SB is the energy storage self-discharge coefficient, Q SB For the energy storage capacity.

[0154] Determine the demand side management submodule, which is used to determine the constraints of demand side management:

[0155]

[0156] in, is the maximum power supply capacity of the power subsystem. The constraint in formula (1) indicates that the load level of the power subsystem after demand-side management in period t cannot exceed the system power supply capacity. The constraint in formula (2) indicates that the actual load power shifted in any period t cannot exceed the load power shiftable agreed upon between the virtual power plant system operator and the user. The constraint in formula (3) indicates that the actual load interruption power in any period t cannot exceed the load power interruptable agreed upon between the virtual power plant system operator and the user.

[0157] Optionally, determining the target function module 340 includes:

[0158] Determine the thermal energy subsystem objective function submodule, the calculation formula for determining the objective function of the thermal energy subsystem is:

[0159]

[0160] Among them, minC H Minimum comprehensive operating cost of thermal energy subsystem, P H (t) is the heat power purchased by the thermal energy subsystem from the external network during period t, p H (t) is the heating price of the external thermal energy system during period t, P X (t) is the charging and discharging power of the energy storage device in the period t. When it is greater than 0, it indicates the heat storage state, and when it is less than 0, it indicates the heat release state; k X is the unit power operation and maintenance cost of the energy storage device;

[0161] The calculation formula for determining the objective function of the gas energy subsystem is as follows:

[0162]

[0163] Among them, minC G is the minimum comprehensive operating cost of the gas energy subsystem, Q G (t) is the gas power purchased by the gas energy subsystem in period t, C NG (t) is the unit price of natural gas in the natural gas spot market during period t.

[0164] Optionally, determining the target function module 340 includes:

[0165] The submodule for determining the constraints of the thermal energy subsystem is used to determine the constraints of the thermal energy subsystem as follows:

[0166]

[0167] Among them, C he is the heating coefficient of the double-effect absorption unit, and the load of the heat energy subsystem is supplied by heating according to the output power of the micro-turbine, P L,H (t) is the heat load power of the thermal energy subsystem in the tth period, P H (t) is the thermal power purchased by the thermal energy subsystem in the tth period, X(t) is the residual energy of the thermal energy storage device in the tth period, X(t-1) is the residual energy of the thermal energy storage device in the t-1th period, λ x Q is the heat loss coefficient of the thermal energy storage device; min and Q max are the minimum and maximum charging and discharging powers of the thermal energy storage device, respectively, and Xmin and X max are the minimum and maximum values ​​of the residual heat of the thermal energy storage device, and are the minimum power and maximum power of the thermal energy subsystem to purchase heat from the outside network;

[0168] The submodule for determining the constraints of the gas energy subsystem is used to determine the constraints of the gas energy subsystem as follows:

[0169]

[0170] Among them, G s (t) is the gas release power of the gas storage tank during period t, P E,G (t) is the natural gas power generated by the power-to-gas conversion of the methane reaction unit during period t, P L,G (t) is the natural gas load power during period t, Q s (t) and Q s (t-1) are the remaining power of the gas tank gas in period t and period t-1 respectively, G s (t) is the gas release power of the gas storage tank during period t, and are the minimum and maximum values ​​of the remaining power of natural gas in the gas storage tank, respectively. and are the minimum and maximum values ​​of the gas release power of the gas storage tank, and They are respectively the minimum power and maximum power of the gas energy sub-grid purchasing gas from the external network.

[0171] Optionally, determining the two-layer collaborative optimization model module 350 includes: determining a two-layer collaborative optimization model submodule, and determining that the expression of the virtual power plant system source-load two-layer collaborative optimization model is:

[0172]

[0173] Among them, P WT is the actual consumption vector of wind power output, P PV is the actual consumption vector of photovoltaic power, P GRID P is the power vector of the power subsystem purchasing electricity from the network. SB is the energy storage charging and discharging power vector, P MT and P FC are the output vectors of the micro-turbine and the fuel cell, P delay is the load translation power vector, P cut is the interruptible load power vector, P E,H and P E,G are the power vectors converted from the electrical energy subsystem to the thermal energy subsystem and the gas energy subsystem, respectively, His the thermal power vector of the thermal energy subsystem, P X is the thermal power vector of the thermal energy storage device, Q G is the gas power vector of the gas energy subsystem purchasing gas from the outside, G s is the gas tank charging and discharging power vector, g UP () is the equality constraint of the upper collaborative optimization model, h UP () is the inequality constraint of the upper collaborative optimization model, g DOWN,H () is the equality constraint for the decision maker of the thermal energy subsystem optimization operation in the lower collaborative optimization model, h DOWN,H () is the inequality constraint of the decision maker for the optimization operation of the thermal energy subsystem in the lower collaborative optimization model, g DOWN,G () is the equality constraint of the decision maker of the gas energy subsystem optimization operation center in the lower collaborative optimization model, h DOWN,G () is the inequality constraint for the decision maker of the optimization operation center of the gas energy subsystem in the lower-level collaborative optimization model.

[0174] A system 300 for determining a source-load collaborative optimization model for a virtual power plant system in an embodiment of the present invention corresponds to a method 100 for determining a source-load collaborative optimization model for a virtual power plant system in another embodiment of the present invention, and will not be described in detail herein.

[0175] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0176] 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 1 A device that provides the functions specified in a block or multiple blocks.

[0177] 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.

[0178] 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.

[0179] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0180] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for determining a source-load collaborative optimization model of a virtual power plant system, characterized in that: include: Determine wind power output uncertainty, photovoltaic output uncertainty and load uncertainty based on pre-collected virtual power plant system parameters; Determine an electric energy subsystem, a thermal energy subsystem, and a gas energy subsystem according to the wind power output uncertainty, the photovoltaic output uncertainty, and the load uncertainty; The electric energy subsystem is determined as an upper-layer collaborative optimization model, and the thermal energy subsystem and the gas energy subsystem are determined as lower-layer collaborative optimization models, wherein the upper-layer collaborative optimization model and the lower-layer collaborative optimization model influence each other; Determine the objective function of the upper collaborative optimization model, the constraints of the upper collaborative optimization model, the objective function of the lower collaborative optimization model, and the constraints of the lower collaborative optimization model; Determine a source-load dual-layer collaborative optimization model of a virtual power plant system according to the objective function of the upper-layer collaborative optimization model, the constraints of the upper-layer collaborative optimization model, the objective function of the lower-layer collaborative optimization model, and the constraints of the lower-layer collaborative optimization model; According to the objective function of the upper-layer collaborative optimization model, the constraint conditions of the upper-layer collaborative optimization model, the objective function of the lower-layer collaborative optimization model, and the constraint conditions of the lower-layer collaborative optimization model, a virtual power plant system source-load double-layer collaborative optimization model is determined, including: The expression of the source-load double-layer collaborative optimization model of the virtual power plant system is determined as follows: Among them, P WT is the actual consumption vector of wind power output, P PV is the actual consumption vector of photovoltaic power, P GRID P is the power vector of the power subsystem purchasing electricity from the network. SB is the energy storage charging and discharging power vector, P MT and P FC are the output vectors of the micro-turbine and the fuel cell, P delay is the load translation power vector, P cut is the interruptible load power vector, P E,H and P E,G are the power vectors converted from the electrical energy subsystem to the thermal energy subsystem and the gas energy subsystem, respectively, H is the thermal power vector of the thermal energy subsystem, P X is the thermal power vector of the thermal energy storage device, Q G is the gas power vector of the gas energy subsystem purchasing gas from the outside, G s is the gas tank charging and discharging power vector, g UP () is the equality constraint of the upper collaborative optimization model, h UP () is the inequality constraint of the upper collaborative optimization model, g DOWN,H () is the equality constraint for the decision maker of the thermal energy subsystem optimization operation in the lower collaborative optimization model, h DOWN,H () is the inequality constraint of the decision maker for the optimization operation of the thermal energy subsystem in the lower collaborative optimization model, g DOWN,G () is the equality constraint of the decision maker of the gas energy subsystem optimization operation center in the lower collaborative optimization model, h DOWN,G () is the inequality constraint for the decision maker of the optimization operation center of the gas energy subsystem in the lower-level collaborative optimization model.

2. The method according to claim 1, characterized in that Based on the pre-collected parameters of the virtual power plant system, the uncertainty of wind power output, photovoltaic output and load is determined, including: Determine the uncertainty of wind power output based on the probability density of wind speed and the available output of wind turbines; The calculation formula for determining the probability density of wind speed is: Among them, f WT (v) is the probability density of the Weibull distribution fitting wind speed, v is the wind speed, c and k are f respectively. WT (v) is the state shape parameter of the Weibull distribution, and c is the scale parameter of the Weibull distribution; The calculation formula for determining the available output of a wind turbine is: Among them, P WT (t) is the available output of the wind turbine generator set at time t, P r,W is the rated power of the wind turbine generator set, v in , v r , v out are the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine respectively, and v is the real-time wind speed at time t.

3. The method according to claim 1, characterized in that Based on the pre-collected parameters of the virtual power plant system, the uncertainty of wind power output, photovoltaic output and load is determined, including: Determine the uncertainty of photovoltaic output according to the probability density of light intensity and the maximum output power of photovoltaic generator sets; The calculation formula for determining the probability density of light intensity is: Among them, f PV (t) is the probability density of light intensity fitted by Beta distribution, r is the light irradiance at time t, r max is the maximum irradiance at time t, α and β are the shape parameters of Beta distribution, α and β are obtained by the mathematical expectation (μ) and variance (δ) of the light intensity at time t, The calculation formula for determining the maximum output power of the photovoltaic generator set at time t is: P PV (t)=η PV s PV θ(t) Among them, P PV (t) is the maximum output power of the photovoltaic generator set at time t, η PV is the radiation efficiency of light intensity, s PV is the light intensity radiation area, and θ(t) is the light intensity at time t.

4. The method according to claim 1, characterized in that Based on the pre-collected parameters of the virtual power plant system, the uncertainty of wind power output, photovoltaic output and load is determined, including: Determine load uncertainty based on electrical load power, heating load power, cooling load power, and gas load power; The calculation formula for determining the electric load power, heating load power, cooling load power and gas load power is: Among them, P electricity (t), P thermal (t), P cool (t), P gas (t) are the electric load power, heat load power, cooling load power and gas load power at time t, They are the regular change functions of the electric load power, heat load power, cooling load power and gas load power, respectively, and each load is related to time. ΔP electricity (t), ΔP thermal (t), ΔP cool (t), ΔP gas (t) are the random uncertain parts of the electric load power, thermal load power, cooling load power and gas load power respectively.

5. The method according to claim 1, characterized in that: According to the uncertainty of wind power output, the uncertainty of photovoltaic output and the uncertainty of load, the electric energy subsystem, the thermal energy subsystem and the gas energy subsystem are determined, including: According to the wind power output uncertainty, photovoltaic output uncertainty and load uncertainty, determining that the electric energy subsystem includes wind power renewable energy, photovoltaic renewable energy, electric energy storage, demand-side resources, electric load, P2G equipment, gas turbines and fuel cells; According to the wind power output uncertainty, photovoltaic output uncertainty and load uncertainty, determining that the thermal energy subsystem includes an external thermal network, a waste heat boiler, a thermal energy storage device and a thermal load; According to the wind power output uncertainty, photovoltaic output uncertainty and load uncertainty, it is determined that the gas energy subsystem includes an external gas grid, P2G equipment, waste heat boilers, gas turbines, fuel cells and gas loads.

6. The method according to claim 1, characterized in that Determine the objective function of the upper-level collaborative optimization model, including: Determine a first objective subfunction of the upper collaborative optimization model and a second objective subfunction of the upper collaborative optimization model, wherein the first objective subfunction is used to determine the maximum value of the comprehensive absorption rate of renewable energy power generation of the virtual power plant system during the operation cycle, wherein the comprehensive absorption rate of renewable energy power generation includes the absorption rate of wind power output and the absorption rate of photovoltaic output, and the second objective subfunction is used to determine the minimum value of the source-load coordination cost of the electric energy subsystem; The first objective subfunction of the upper-level collaborative optimization model is determined as: Among them, f UP,1 is the comprehensive consumption rate of renewable energy power generation in the upper collaborative optimization model, r is the comprehensive consumption rate of renewable energy power generation in the virtual power plant system during the operation cycle, T is the number of scheduling periods in the operation cycle, with one day as an operation cycle and 1 hour as a scheduling period, P WT (t) and P PV (t) are the actual wind power consumption and photovoltaic power consumption in the operation cycle, respectively. and They are respectively the ultra-short-term predicted output of wind power and the ultra-short-term predicted output of photovoltaic power in period t during the operation cycle; The second objective subfunction of the upper-level collaborative optimization model is determined as: min f UP,2 =C E =C S +C L +C IES Among them, minf UP,2 is the minimum value of the source-load coordination cost of the power subsystem in the upper-level collaborative optimization model, C S is the power supply side operating cost of the power subsystem, C L is the demand side management cost of the power subsystem, C IES The comprehensive operating cost of the thermal energy subsystem and gas energy subsystem of the virtual power plant system; Determine the objective function of the upper collaborative optimization model according to the first objective sub-function of the upper collaborative optimization model and the second objective sub-function of the upper collaborative optimization model by using a weighted coefficient method; The calculation formula for determining the objective function of the upper-level collaborative optimization model is: Among them, λ1 is the weight coefficient of the first objective sub-function of the upper-level collaborative optimization model, λ2 is the weight coefficient of the second objective sub-function of the upper-level collaborative optimization model, and f UP,1,max is the maximum value or optimal value that can be obtained when the upper-level collaborative optimization model only takes the first objective sub-function as the objective function, f UP,2,min It is the minimum or optimal value that can be obtained when the upper-level collaborative optimization model only takes the second objective sub-function as the objective function.

7. The method according to claim 6, characterized in that Determine the second objective sub-function of the upper-level collaborative optimization model, including: Determine the power supply side operating cost C of the power subsystem S The calculation formula is: C S =P GRID (t)q GRID (t)+k SB |P SB (t)|+k WT P WT (t)+k PV P PV (t)+f MT (P MT (t))+f FC (P FC (t)); Among them, P GRID (t) is the power purchased from the power subsystem during period t; q GRID (t) is the external grid time-of-use electricity price level during period t; k SB is the energy storage operation and maintenance cost coefficient; P SB (t) is the energy storage charging and discharging power in period t. When it is greater than 0, it indicates the discharging state, and when it is less than 0, it indicates the charging state; k WT and k PV are wind power operation and maintenance cost coefficient and photovoltaic operation and maintenance cost coefficient respectively; f MT () and f FC () are respectively the micro-turbine fuel cost function and the fuel cell fuel cost function; P MT (t) and P FC (t) t is the output of the micro-turbine and the output of the fuel cell during the time period; Determine the demand side management cost C of the power subsystem L The calculation formula is: in, The fixed fee for shiftable loads signed between the virtual power plant system operator and the shiftable load users. is the load power for period t agreed between the operator and the load user, c delay P is the variable cost factor for actually shifting the shiftable load. delay (t) is the load shift power implemented in time period t, λ delay is the conversion coefficient of the power efficiency loss caused by load shifting to users, The interruptible load fixed fee is signed between the virtual power plant system operator and the interruptible load user. The interruptible load power during period t agreed between the operator and the interruptible load user, c cut P is the variable cost coefficient for the actual interruption shift of the interruptible load. cut (t) is the load interruption power implemented during period t, λ cut It is the conversion coefficient of the loss of electricity efficiency caused by load interruption to users.

8. The method according to claim 1, characterized in that: Determine the constraints of the upper-level collaborative optimization model, including: The constraints for determining the power balance of the electrical energy subsystem are: in, P is the load power in period t after demand-side management is performed on the load side of the power subsystem; loss (t) is the network loss power of the electric energy subsystem during period t; P E,H (t) and P E,G (t) are the power converted from the electric energy subsystem to the thermal energy subsystem and the gas energy subsystem respectively; d(j,t) is the element of the load shift matrix, which means that the load shifted from the jth period to the tth period accounts for P delay (j) ratio; The constraints for determining the output of renewable energy generation are: P WT (t) and P PV (t) are the actual wind power consumption and photovoltaic power consumption in the operation cycle, respectively; and They are respectively the ultra-short-term predicted output of wind power and the ultra-short-term predicted output of photovoltaic power in period t during the operation cycle; The constraints for determining the output of controllable micro-power sources are: Among them, R MT R is the rated ramp rate of the micro-turbine; FC is the rated ramp rate of the fuel cell; Δt is the unit time period length; and are the minimum and maximum allowable output of the micro-turbine, respectively. The constraints indicate that the output of the micro-turbine is either 0 or must satisfy the interval Same reason and are the minimum and maximum allowable outputs of the fuel cell, respectively. The constraint indicates that the output of the fuel cell is either 0 or must satisfy the interval The constraints that determine the power limits of electronic systems are: in, is the maximum value of power exchanged with the external power grid; The constraints for energy conversion are determined as follows: P E,H (t)≤P E,H,max P E,G (t)≤P E,G,max Among them, P E,H,max is the maximum conversion power from the electrical energy subsystem to the thermal energy subsystem, P E,G,max The maximum conversion power from the electric energy subsystem to the gas energy subsystem; The constraints for energy storage operation are determined as follows: Among them, S SB (t) and S SB (t+1) are the remaining power of SB at the end of period t and period t+1, respectively, dis is the SB discharge efficiency, η ch For energy storage charging efficiency, are the minimum and maximum remaining energy storage capacity, respectively. are the minimum and maximum values ​​of energy storage charging and discharging power, respectively, SB is the energy storage self-discharge coefficient, Q SB is the energy storage capacity; The constraints for demand-side management are determined as follows: in, is the maximum power supply capacity of the power subsystem. The constraint in formula (1) indicates that the load level of the power subsystem after demand-side management in period t cannot exceed the system power supply capacity. The constraint in formula (2) indicates that the actual load power shifted in any period t cannot exceed the load power shiftable agreed upon between the virtual power plant system operator and the user. The constraint in formula (3) indicates that the actual load interruption power in any period t cannot exceed the load power interruptable agreed upon between the virtual power plant system operator and the user.

9. The method according to claim 1, characterized in that: Determine the objective function of the lower-level collaborative optimization model, including: The calculation formula for determining the objective function of the thermal energy subsystem is: Among them, minC H Minimum comprehensive operating cost of thermal energy subsystem, P H (t) is the heat power purchased by the thermal energy subsystem from the external network during period t, p H (t) is the heating price of the external thermal energy system during period t, P X (t) is the charging and discharging power of the energy storage device in the period t. When it is greater than 0, it indicates the heat storage state, and when it is less than 0, it indicates the heat release state; k X is the unit power operation and maintenance cost of the energy storage device; The calculation formula for determining the objective function of the gas energy subsystem is: Among them, minC G is the minimum comprehensive operating cost of the gas energy subsystem, Q G (t) is the gas power purchased by the gas energy subsystem in period t, C NG (t) is the unit price of natural gas in the natural gas spot market during period t.

10. The method according to claim 1, characterized in that Determine the constraints of the lower-level collaborative optimization model, including: The constraints for the thermal energy subsystem are determined as follows: Among them, C he is the heating coefficient of the double-effect absorption unit, and the load of the heat energy subsystem is supplied by heating according to the output power of the micro-turbine, P L,H (t) is the heat load power of the thermal energy subsystem in the tth period, P H (t) is the thermal power purchased by the thermal energy subsystem in the tth period, X(t) is the residual energy of the thermal energy storage device in the tth period, X(t-1) is the residual energy of the thermal energy storage device in the t-1th period, λ x Q is the heat loss coefficient of the thermal energy storage device; min and Q max are the minimum and maximum charging and discharging powers of the thermal energy storage device, respectively, and X min and X max are the minimum and maximum values ​​of the residual heat of the thermal energy storage device, and are the minimum power and maximum power of the thermal energy subsystem to purchase heat from the outside network; The constraints for determining the gas energy subsystem are: Among them, G s (t) is the gas release power of the gas storage tank during period t, P E,G (t) is the natural gas power generated by the power-to-gas conversion of the methane reaction unit during period t, P L,G (t) is the natural gas load power during period t, Q s (t) and Q s (t-1) are the remaining power of the gas tank gas in period t and period t-1, G s (t) is the gas release power of the gas storage tank during period t, and are the minimum and maximum values ​​of the remaining power of natural gas in the gas storage tank, respectively. and are the minimum and maximum values ​​of the gas release power of the gas storage tank, and They are respectively the minimum power and maximum power of the gas energy sub-grid purchasing gas from the external network.

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