WO2020063144A1 - Method and system for evaluating energy delivery capacity in flexible dc electrical grid - Google Patents

Abstract

Provided are a method and a system for evaluating energy delivery capacity in a flexible DC electrical grid, the method comprising: a pre-established AC/DC hybrid system is divided into a power supplying region and a power receiving region; on the basis of the absorption limit of the power receiving region, a limiting condition and a target function of the power supplying region are defined, and an optimization model for combined operation of wind, solar, and pumped-storage hydroelectricity power is created, said optimization model comprising the limiting condition and the target function of the power supplying region; the interior point method is used to solve the optimization model to obtain a maximum delivery power of the power supplying region.

Description

Method and system for evaluating energy delivery capability of flexible DC grid

This disclosure claims the priority of a Chinese patent application filed with the Chinese Patent Office on September 30, 2018, with application number 201811154591.2. The entire contents of the above application are incorporated herein by reference.

Technical field

This article belongs to the field of power system automation, for example, it relates to a method and system for evaluating energy delivery capacity of a flexible DC grid.

Background technique

With the adjustment of the energy structure, the scale of new energy development has gradually increased. The application of new energy is increasingly widespread all over the world. By the end of 2016, China's wind and solar grid-connected installed capacity reached 147 million kilowatts and 78 million kilowatts, respectively. However, traditional technologies face many practical problems when new energy is connected to power systems on a large scale. Therefore, flexible DC transmission technology with fast and flexible controllability, high compactness, and excellent environmental adaptability is widely used in the grid connection of new energy sources. The fluctuating and intermittent nature of wind power and photovoltaic output has brought challenges to the safe and stable operation of the power system. Using energy storage systems in conjunction with wind farms and photovoltaic power plants is an effective way to improve wind and solar power output characteristics and energy efficiency. Among them, pumped storage has attracted much attention due to its low cost, large capacity, and long life, and has become one of the most mature energy storage technologies. Studying the power supply capacity of the combined operation system of wind power, photovoltaic, and pumped storage, and how to coordinate the dispatch of wind and solar power to obtain a stable output of the combined system, and to maximize the use of wind and solar energy, are of great significance to promote the large-scale integration of wind power and photovoltaics.

At this stage, research on new energy power stations and pumped storage combined operation systems is mainly focused on two directions: power station capacity planning and operation optimization scheduling. Among them, the research on the operation and scheduling of the joint system mainly focuses on improving the economics of system operation, or optimizing the power output characteristics of the joint system to improve the system stability. There is no analysis and research on the system operation to collect new energy delivery capabilities. The dispatching method is not convincing in the effect of sending new energy across regions and improving the utilization of clean energy. At present, there is no relevant analysis and calculation method for the maximum delivery limit of new energy such as wind power, photovoltaics, and pumped storage power generation in flexible DC grids. And the related technology lacks research on the power supply capacity of the combined operation system of wind power photovoltaic new energy and pumped storage.

Summary of the Invention

This paper proposes a method and system for assessing the energy delivery capacity of a flexible DC power grid. The research focuses on the power supply capability of a wind-solar pumping combined power supply system connected via a multi-terminal flexible DC power grid. Through the optimized operation of the wind and solar pumping operation to improve the utilization ratio of wind and solar energy, the maximum delivery capacity of the wind and solar pumping operation is obtained.

This article adopts the following technical solutions:

A method for evaluating energy delivery capacity of a flexible DC power grid, the method includes:

Dividing a pre-established AC / DC hybrid system into a power supply area and a power reception area;

Based on the consumption limit of the power receiving area, the constraint conditions and objective function of the power supply area are defined, and an optimized model for combined operation of wind and light extraction is constructed; the optimized model for combined operation of wind and light extraction includes the constraint conditions and objective function of the supply area The power supply area includes: connecting a wind farm, a photovoltaic power station, and a pumped storage power station to each other through a flexible DC line;

An interior point method is used to solve the wind-solar pumping operation optimization model to obtain the maximum delivered power of the power supply area.

An energy delivery capability evaluation system for a flexible DC grid includes:

Area division module configured to divide a pre-established AC / DC hybrid system into a power supply area and a power reception area;

A building module configured to define a constraint condition and an objective function of the power supply area based on the consumption limit of the power receiving area, and construct an optimization model for combined operation of scenery and light extraction including the constraint condition and the objective function of the power supply area; The power supply area includes: connecting wind farms, photovoltaic power stations and pumped storage power stations to each other through flexible DC lines;

The analysis module is configured to solve the model using the interior point method to obtain the maximum delivered power in the power supply area.

An electronic device includes:

At least one processor;

Memory, configured to store at least one program,

When the at least one program is executed by the at least one processor, the at least one processor implements the method as described above.

A computer-readable storage medium stores computer-executable instructions, where the computer-executable instructions are used to perform the method described above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of a method for evaluating an energy transmission capability of a flexible DC power grid according to an embodiment of the present invention; FIG.

2 is a schematic diagram of segmentation provided in an embodiment of the present invention;

Among them, Fig. 2 (a) is a schematic diagram of the working state of a pumped storage power station, Fig. 2 (b) is a graph showing the full power of the wind and wind after segmentation, and Fig. 2 (c) is the daily maximum power supply of the combined power supply system is less than the output power Schematic diagram, Figure 2 (d) is a schematic diagram of the situation where the maximum daily power supply of the combined power supply system is greater than the output power;

FIG. 3 is a schematic diagram of a case of consumption limit provided in an embodiment of the present invention; FIG.

Among them, FIG. 3 (a) is a diagram of the digestion curve, and FIG. 3 (b) is a schematic diagram of the tracking digestion curve by the segmentation method;

FIG. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

detailed description

The content of this article will be further described below in conjunction with the drawings and examples of the description.

In order to overcome the related technologies, the combined operation system of wind power, photovoltaic and pumped storage power stations is more concerned with economic operation and based on planning and design. There is no real-time dispatch level calculation method for transmission capacity, and the lack of calculation methods for flexible power grid integrated power supply capacity calculation. The storage capacity constraints of pumped storage at the end of sunrise and the slow calculation speed using the traditional repeated power flow method. This paper proposes a method and system for evaluating the energy delivery capacity of a flexible DC power grid, which can be effectively used for multi-terminal flexible DC power grids with integrated wind power, photovoltaic, and pumped storage power stations. Through the 24-hour segmented modeling of wind power photovoltaics, the curve of maximum output sum of wind and light in each period is obtained. Using the maximum daily power generation as the objective function, and taking into account the storage capacity constraints at the end of sunrise of the pumped storage, a mathematical model of the pumped storage power station is established. The wind and solar power generation is used as the independent variable and calculated by the improved continuous power flow calculation method. The flexible DC power grid aggregates the analysis results of the new energy delivery capacity to obtain the output power and maximum delivery power of the wind and solar pumping, which is convenient for subsequent dispatchers to make scheduling plans and make real-time decisions.

A method for evaluating the energy delivery capacity of a flexible DC power grid as shown in FIG. 1 includes:

S1 divides a pre-established AC / DC hybrid system into a power supply area and a power reception area;

S2 uses the interior point method to solve the optimization model of combined operation of wind and solar pumping to obtain the maximum delivered power in the power supply area;

The optimization model for combined operation of wind and light pumping includes a constraint condition and an objective function of a power supply area defined based on the consumption limit of the power receiving area.

In step S1, dividing the pre-established AC / DC hybrid system into a power supply area and a power reception area includes:

Integrate wind farms, photovoltaic power plants, pumped storage power plants and flexible DC systems into a grid to form an AC / DC hybrid system;

The AC / DC hybrid system includes: a combined power supply system and an AC / DC system;

The AC and DC system is defined as a power receiving area, and the combined power supply system is defined as a power supply area. The combined power supply system includes: a flexible DC line connecting a wind farm, a photovoltaic power station, and a pumped storage power station to each other.

The consumption limit of the power receiving area in step S2 is modeled in advance by segmenting multiple times in a single day in advance, and the power receiving area corresponding to multiple times obtained through power flow calculation is used to draw the consumption limit of the power receiving area. Nano limit curve. Based on this, step S2 defines the power supply area's constraints and objective function based on the power consumption area's consumption limit. The objective function of the power supply area is the maximum output of the combined power supply system in a single day, and based on the impact of the power supply area. Factor equations add inequality constraints for flexible DC systems, inequality constraints for pumped storage power stations, inequality constraints for actual output from wind farms and photovoltaic power plants, and inequality constraints for the output power of the combined power supply system as constraints for the power supply area;

Among them, the inequality constraints of the flexible DC system include: DC node voltage constraints, inverter AC-side voltage constraints, modulation ratio constraints, voltage source converter (VSC) thermal capacity constraints, and DC line maximum allowable current constraints;

The inequality constraints of pumped storage power stations include: upper and lower storage capacity constraints, storage capacity constraints at the end of sunrise, and power constraints of pumped storage power stations.

The influencing factor equations in the power supply area include: the DC network equation, the VSC converter (ie, voltage source converter) equation, and the storage capacity variation equation for pumped storage power stations;

The DC network equation is as follows:

In the formula, P _dv and U _dv are the node injection power and node voltage of the DC node v, N _d is the set of DC nodes, and Y _dvk is the v-th and k-th elements of the admittance matrix Y _d of the flexible DC network node;

The VSC converter equation is as follows:

P _c = P _d

In the formula, R is the phase resistance, X is the phase inductance, U _s is the voltage amplitude of the AC side of the VSC converter, U _c is the voltage amplitude of the valve side, and δ _sc is the voltage between the valve side voltage and the AC side voltage of the VSC converter. Angle difference, P _s is the active power injected into the AC side of the VSC converter, Q _s is the active power injected into the AC side of the VSC converter, P _c is the active power output from the valve side of the VSC converter, and P _d is the VSC converter Active power injected into the DC line; I _s is the current flowing from the AC system into the VSC converter transformer (ie, VSC converter transformer), U _d is the unipolar voltage on the DC side of the VSC converter, M is the modulation degree, and μ is the DC voltage Utilization rate; the VSC converter transformer is a transformer of the VSC converter.

The storage capacity change equation of the pumped storage power station includes:

Variation equation of the storage capacity of the upper and lower reservoirs when the pumped storage power station is generating power:

The equation of storage capacity change of the upper and lower reservoirs when pumped by a pumped storage power station:

Wherein the superscript t represents time, V ^u of the reservoir capacity, V ^d of the reservoir capacity, active power P _h as given pumped storage power station, η _1, η ₂ respectively generated power of the pumped storage power station Conversion factor between pumping power and storage capacity.

Among them, the objective function of the power supply area is determined by the following formula:

Where

Is the active power transmitted between nodes i and j at time t, A is the power supply area, B is the power reception area; N is the number of sampling points in a day, Δt is the sampling time interval, and f is the maximum of the power supply area. Delivery power.

The inequality constraint of the flexible DC system is determined by the following formula:

Where U _kd is the d-th DC node voltage, and k is the number of DC nodes.

with

The upper and lower limits of the d-th DC node voltage,

with

The upper and lower limits of the AC-side voltage of the c-th VSC converter, respectively. U _nc , M _n , and I _nc are the AC-side voltage, modulation ratio, and thermal capacity of the c-th VSC converter.

with

Modulation ratio maximum, minimum and thermal capacity upper limit of the c-th VSC converter, n is the collection of VSC converters, I _kv is the maximum allowable current of the v-th flexible DC line,

Is the rated current of the vth flexible DC line, and z is the number of flexible DC lines.

Determine the upper and lower storage capacity constraints by:

Determine the storage capacity constraint at the end of the sunrise by the following formula:

Determine the power constraint by:

In the formula, P _h is the active power from the pumped storage power station, and V _t ^u and V _t ^d are the storage capacity of the upper reservoir and the storage capacity of the lower reservoir at time _t , respectively.

with

Are the maximum and minimum storage capacity of the upper reservoir,

with

The maximum and minimum storage capacity of the lower reservoir, respectively

with

The storage capacity of the upper reservoir at the beginning of the day and the end of the day;

with

P _omax and _P omin are pumped storage power station of the maximum and minimum power output;; P _imax and P _imin pumped storage are first day, the last day the reservoir capacity, [mu] is the coefficient of variation of the maximum capacity were allowed 1 day Maximum and minimum pumping output of the power station.

Determine the actual output inequality constraints of the wind farm and photovoltaic power plant by the following formula:

0≤P _f ≤P ′ _f

0≤P _g ≤P ′ _g

In the formula, P _f and P _g are the actual output of the wind farm and the photovoltaic power plant, respectively, and P ′ _f and P ′ _g are the maximum output of the wind farm and the photovoltaic power plant, respectively.

Determine the inequality constraint of the output power of the joint power supply system by the following formula:

Where

The output power of the joint power supply system at time t,

It is the limit of the power receiving area at time t.

Step S2, using the interior point method to solve the model, and obtaining the maximum delivered power in the power supply area includes:

a) Obtain the sum of the maximum output of the wind farm and the photovoltaic power plant corresponding to each sampling point on a typical day;

b) calculating the difference between the sum of the maximum output of the wind farm and the photovoltaic power station and the dissipation limit of the power receiving area;

c) If the difference corresponding to the current sampling point and the difference corresponding to the sampling point at the next moment are both greater than 0 or less than 0, the two sampling points are classified into the same time period;

d) According to the time period, the equivalent transformation of the optimization model for combined operation of wind and light extraction is performed.

In step b) above, the difference between the sum of the maximum output of the wind farm and the photovoltaic power station and the dissipation limit of the power receiving area is determined by the following formula:

Where N _t is the total number of typical daily sampling points,

Is the dissipation limit of the power receiving area at time t,

Represents the maximum output variable of the wind farm at time t,

Represents the maximum output variable of the photovoltaic power plant at time t,

Is the difference between the sum of the maximum output of the wind farm and the photovoltaic power station and the dissipation limit of the power receiving area.

In step d), equivalently transforming the optimization model of the combined operation of scenery and light extraction according to the time period includes:

Update the maximum output of the wind farm and the photovoltaic power plant in the wind-solar pumping combined operation optimization model;

Obtaining the consumption limit curve of the power receiving area drawn based on the consumption limit of the power receiving area at each moment in a single day; converting the consumption limit curve of the power receiving area into a stepped curve according to the time period;

Update the optimization model of wind-solar pumping combined operation and inequality constraints of pumped storage power stations;

The interior point method is used to solve the updated combined optimization model of wind and solar pumping operation to obtain the maximum delivered power in the power supply area.

Among them, the maximum output of the wind farm and the photovoltaic power plant in the wind-solar pumping combined operation optimization model is updated by the following formula:

In the formula, P ″ _f and P ″ _g are the average values of the maximum output of the wind farm and the photovoltaic power plant in a unit time period, respectively; T _m is the length of the m-th period; N _Tm is the number of sampling points included in the m-th period .

The consumption limit curve of the power receiving area is transformed into a stepped curve by the following formula:

In the formula, a and b are sampling point numbers, and m is a period number after segmentation.

Update the optimization model of combined operation of scenery and pumping by the following formula:

Where

M periods between active power transfer node i, j's, A represents the power supply region, B represents a power receiving region, N _T is the number of sampling points m period.

Update the inequality constraints of pumped storage power stations by:

In the formula, P ' _h is the active power from the pumped storage power station, and P _s is the maximum delivered power in the power supply area.

Example:

For the wind-solar pumping combined operation system, if only the cross-section transmission power limit of one time point is calculated, it is only necessary to make the pumped storage power station generate as much power (or less pump) as possible under various safety constraints. This cannot reflect the role of pumping in the concept of time to eliminate peaks and fill valleys, nor can it fully reflect the working characteristics of pumping and its requirements for storage capacity. For a two-area interconnected system, the maximum transmission power on the cross-section between the supply and reception areas should be the smaller of the maximum supply power in the supply area and the maximum dissipation power in the reception area. That is, formula (1):

P _TTC = min (P _Smax , P _Rmax ) (1)

In the formula: P _Smax is the delivery limit of the power supply area, and P _Rmax is the _absorption limit of the power reception area.

Based on the above ideas, a method for calculating the maximum power supply capacity of a wind-solar pumping combined operation system is proposed for an AC / DC hybrid system where the wind-solar pumping system is connected to the grid. Specific steps are as follows.

1: Partition the hybrid system. The combined power supply system consisting of wind power, photovoltaics, pumped storage power stations and flexible grids is divided into power supply areas, and the rest are divided into power receiving areas.

2: Calculate the maximum dissipation limit of the power receiving area. With the load distribution of the power receiving area and its changing curve known, the power supply area in the hybrid system is reduced to an infinite power source. A mathematical model of the power receiving area's absorption limit is established for the transformed system to obtain the power receiving area. Digestion limit at each moment.

3: Calculate the maximum outgoing power in the power supply area. Comprehensively consider factors such as the consumption limit of the power receiving area, the wind and power output curve, the pumped storage capacity, and the VSC constraint, and set up an optimal dispatching model with the maximum external power transmission as the goal, and use the interior point method to solve the model to obtain a period of time (1 day). The maximum power output of the hybrid system where the scenery is pumped through the grid system and the corresponding scheduling scheme.

Optimized model for combined operation of scenery and pumping

Objective function: Using the function of peak storage and valley filling in pumped storage power stations, the spatiotemporal transfer of wind and solar energy can be realized. Therefore, the time period concept should be introduced when studying the transmission capacity of the wind-solar pumping combined operation system. According to the predicted wind-solar power generation, in order to make full use of the wind, light, and storage (pumped-storage power station) power generation base's electrical energy, highlight the pumping effect and transfer it to a distant load center, and combine the power supply system within a period of time (one day) The maximum delivered power is the objective function, which is shown in equation (2):

In the formula:

Is the active power transmitted between nodes i and j at time t; A is the power supply area, that is, the wind and solar power supply system; B is the power receiving area; N is the number of sampling points in a day;

There is a loss between pumped storage and power generation in a pumped storage power station. The more pumped water loses the more wind and solar energy and the more power it delivers. Therefore, taking the maximum power supply of the joint system as the objective function, its physical meaning contains two requirements: 1. Less wind and light; 2. Pumped storage power station requires less water. And the objective function integrates the scenery extraction power of every hour throughout the day into an optimization, always taking into account the storage capacity constraints.

In summary, the use of the objective function can make up for the above-mentioned shortcomings of the traditional optimization model. In the case of insufficient scenery resources, it can avoid pumping a large amount of water at the end of the day to meet the storage capacity requirements, and thus obtain more outbound power. The transmission power curve is more stable.

Equality constraint

A hybrid AC / DC system with grid-connected wind and solar power through a flexible DC system is researched, and an optimized model is established for the power supply area of the system (wind power-photovoltaic-pumped energy storage through a flexible line connected to each other). Including the following three aspects.

(1) The DC network equation is shown in equation (3):

In the formula: P _dv and U _dv are the node injection power and node voltage of the DC node v, N _d is the set of DC nodes, and Y _dvk is the v-th row and k-th column elements of the admittance matrix Y _d of the DC sub-network node.

(2) The equation of the VSC converter is shown in equations (4) to (7):

P _c = P _d (4)

(3) Change equation of storage capacity of pumped storage power station

When the pumped storage power station generates electricity, the storage capacity change of the upper and lower reservoirs satisfies Equation (8):

When the pumped storage power station is pumping, the storage capacity change of the upper and lower reservoirs satisfies Equation (9):

In the formula: t represents the time t, V ^u is the storage capacity of the upper reservoir; V ^d is the storage capacity of the lower reservoir; _Ph is the active power from the pumped storage power station; η ₁ and η ₂ are the power generation and pumping power of the pumped storage power station, respectively. Conversion factor between power and storage capacity, and η ₁ / η ₂ = 0.75.

Inequality constraint

The inequality constraints of the flexible system include: DC node voltage constraints, converter AC side voltage constraints, modulation ratio constraints, VSC thermal capacity constraints, and DC line maximum allowable current constraints, as shown in equation (10):

In the formula: The subscript "n" is the VSC number, and NC is the set of all VSCs in the system.

The pumped storage power station inequality constraint adopts the 2.3 section pumped storage power generation mathematical model: upper and lower storage capacity constraints (formula 11), storage capacity constraints (12) and power constraints (13) at the end of sunrise.

In the formula:

with

The maximum and minimum storage capacity of the upper reservoir respectively;

with

The maximum and minimum storage capacities of Xia Reservoir, respectively.

In the formula:

with

The storage capacity of the upper reservoir at the beginning of the day and the end of the day;

with

The storage capacity of Xia Reservoir at the beginning of the day and the end of the day, respectively; μ is the maximum variation coefficient of storage capacity allowed in one day, and this article takes 0.05.

In the formula: P _omax and _Pomin are the maximum and minimum output when pumping and generating, respectively; P _imax and P _imin are the maximum and minimum output when pumping and pumping respectively.

The constraints of the actual inequality of wind power and photovoltaic output are shown in the following formula (14):

In the formula: P ′ _f and P ′ _g respectively indicate the full power output of the wind farm and photovoltaic power station under the condition of not giving up wind and light, and the output values are obtained from the wind farm and photovoltaic power plant power analysis respectively.

The output power constraint of the joint system is given by (15):

In the formula:

The output power of the wind-solar pumping combined operation system at time t,

Is the consumption limit of the power receiving area at time t, which is calculated by the calculation method of the new energy consumption limit of the power receiving area.

Piecewise method

If the maximum daily power output of the wind-solar pumping system is the objective function, and the wind, light, and pumped storage power generation powers as independent variables at each time of day, then this problem is a dynamic optimization problem. If the sampling interval is one hour in a day, 3 × 24 = 72 sets of variables are required in the entire dynamic optimization. The number of optimization variables is large, and the speed of optimization is slow. Therefore, the segmentation method is adopted to reduce the number of optimization variables, thereby shortening the model solving time.

The specific model and detailed steps of the segmentation method are as follows:

1) Time segmentation of typical days

In order to obtain the maximum power supply of the combined system, it is hoped that the pumped-storage power station will pump as much as possible when the sum of full wind and solar power (P ′ _f ± P ′ _g ) is greater than the absorption limit (P _r ) of the power receiving area, and vice versa. Pumping produces more power, so the working state of a pumped storage power station can be roughly divided by the relationship between (P ′ _f ± P ′ _g ) and _Pr (as shown in Figure 2 (a)), and based on this, the typical daily Time (horizontal axis in Figure 2). The specific method of segmentation is as follows:

Calculate the difference ΔPc between the sum of full wind and light corresponding to each sampling point on a typical day and the dissipation limit of the power receiving area, as shown in formula (16).

In the formula: superscript t is the number of sampling points, and N _t is the total number of typical daily sampling points.

Starting from the first sampling point (t = 1) of the day, judge in order

versus

Whether they are both greater than 0 (or less than 0). If the two numbers are the same, the two sampling points are classified into the same period. If the numbers are different, the next sampling period is started from the later sampling point and then t = t is set. +1, and so on. With this method, the two lines L1 and L2 in Figure 2 (a) can be used, where L1 represents the maximum output of the wind and scenery, that is, the full power of the scenery, and L2 represents the absorption limit of the power receiving area. The intersection is time-segmented for this typical day. After segmentation, three periods of ab, bc, and cd are obtained as shown in FIG. 2 (b).

2) Model transformation and reduction of the number of independent variables

Because the objective function in the optimization model is the amount of power supply, here the principle is to convert the full power of the wind and light equivalently before and after conversion for a period of time. The power is a constant constant over a period of time. The stepped dashed line in FIG. 2 (b) is the equivalent full-scenery power curve. Taking the cd period as an example, the rectangular area enclosed by the equivalent line segment ab and the horizontal axis cd should be equal to the area of the shaded area.

Correspondingly, it is necessary to reset the upper limit of wind and light output in the original optimization model, and replace the wind power and photovoltaic full power P ′ _f and P ′ _{g in} formula (14) with the average of a period of wind power and photovoltaic full power values, respectively. The values P ″ _f and P ″ _{g are} as follows:

In the formula: T _m is the duration of the m-th segment; N _Tm is the set of sampling points contained in the m-th segment.

At the same time, the dissipation limit curve of the power receiving area is also changed into a stepped polyline, and the dissipation limit in the segment remains unchanged. Its value is taken as the minimum value of the dissipation limit in the segment, as shown by the thick dashed line in Figure 2 (b). As shown. Change formula (15) to (18).

In the formula: a and b are the sampling point numbers; m is the period number after segmentation.

After the above two equivalent processing transformations, it can be considered that the wind power, photovoltaic, and pumped output power remain unchanged for each period after the segmentation, and then a set of wind and solar output force variables can be set in one segment. In other words, the number of optimization variables of the demand solution in the entire optimization model after the segmentation will be reduced to: "3 x number of periods" group. Correspondingly change the objective function formula (2) to formula (19).

In order to ensure the reliability of the calculated maximum power supply after segmentation, it is necessary to properly restrict the actual pumped output. However, by the above transformation model, cramps output from the variable P _h has deviated from the actual value. Therefore, on the premise of ignoring the network loss, based on the principle of using as much wind and solar energy as possible, the actual pumped power P ′ _h should be derived to replace the original pumped power constraint formulas (11), (12), (13 P _{h in)} , as shown in formula (20)

In the formula: P _s is the total output power of the wind-solar pumping combined system. This formula can be used as the basis for 3) back-pushing the actual scheduling scheme of wind and light pumping.

Using the interior point method to solve the optimized model after the above transformation, the daily maximum power supply of the wind-solar pumping system and the corresponding output power curve are obtained; as shown in Figure 2 (c), where the curve L3 is the output power curve of the joint system. The thin dashed line represents the sum of the full power of the equivalent wind and light, and the thick dashed line represents the dissipation limit after the transformation.

3) Reverse the scenery to extract the actual output

According to the output power of the joint system obtained after the optimization, the wind, light, and extraction force are reversed (the network loss is ignored), and the actual scheduling scheme is given (as shown in Figure 2 (cd)).

When the full wind power is less than the outgoing power (as shown by ae and cd in Figure 2 (cd)), make the wind full; when the full power is greater than the outgoing power (see eb and bc in Figure 2 (cd)) Paragraph), it is necessary to determine whether their difference exceeds the maximum power allowed by the pumped storage power station. If it does not exceed, the wind power and photovoltaic power will be fully generated. In summary, the typical day-to-day wind power and photovoltaic power shown in Figure 2 should be output along the curve L2 where the dots are located. The difference between this curve and the output power curve L3 is the actual power output of the pumped storage power station, as shown in Figure 2 (cd). The shaded area indicates.

Attention should be paid to the following two points when applying the segmentation method:

(1) The effect of the segmentation method on the optimization model's solution speed mainly depends on the number of segments after segmentation. The smaller the number of periods after segmentation, the fewer the number of optimization variables, and the more obvious the speed-up effect. Then, if the wind-power photovoltaic full-generation curve fluctuates frequently near the absorption limit curve of the power receiving area, it will make the number of periods after segmentation larger, and then the segmentation method can only be shortened by a small amount, or even the optimization solution time. For example, in the power receiving area consumption limit case shown in FIG. 3, in FIG. 3 (a), L1 represents the maximum output of the wind and the sun, that is, the full power of the wind and light, and L2 represents the power receiving area. If sampled in hours, any

versus

Both have different numbers. After segmentation, 24 periods are obtained. It is still required to solve 3 × 24 = 72 groups of variables. Then the segmentation method cannot improve the solution speed of the typical daily maximum power supply calculation model.

(2) Because formula (18) lowers the maximum daily power supply limit of the combined system, even if the wind and solar energy in this typical day is sufficient, it cannot be reused, for example, as shown by the shaded area with dots in Figure 2 (b) The digestion limit. Then, when the amplitude of the fluctuation of the absorption limit curve in the power receiving area is large, the area of the shadow area is enlarged (as shown in Figure 3 (b)). At this time, the calculation accuracy of the segmentation method is poor, and the calculation result is likely to be much smaller than the actual value of the joint system. Maximum power supply.

In response to this situation, the above-mentioned segmentation method can be adjusted. In the process of 2) model transformation and reduction of the number of independent variables, only the equivalent wind and full power curve is used, and the consumption limit curve is no longer transformed. The formula is still used (15) Constrain the outgoing power. Correspondingly, it is no longer required to keep the output power constant in the segment, that is, when setting independent variables, a set of output power variables is set for each sampling point, and a set of wind power output variables and a set of photovoltaic output variables are set in each segment. . The adjusted segmentation method has a stronger ability to track the consumption curve, but the number of independent variables that can be reduced before adjustment is reduced, so the calculation speed is relatively slow. In addition, if the wind and solar pumping capacity is properly configured in the planning stage, in general, compared with the drastic fluctuations in wind and solar output, the change in the power consumption area's absorption limit is relatively gentle. This article does not conduct a detailed study on the extreme conditions of the power receiving area as shown in Figure 3 (b).

Based on the same inventive concept, this application also proposes an energy delivery capability evaluation system for a flexible DC power grid, including:

Area division module configured to divide a pre-established AC / DC hybrid system into a power supply area and a power reception area;

A construction module configured to construct a wind and light pumping combined operation optimization model including defining a constraint condition of a power supply area and an objective function based on the consumption limit of the power reception area;

The analysis module is configured to solve the model using the interior point method to obtain the maximum delivered power in the power supply area.

Those skilled in the art should understand that the embodiments of the present application may be provided as a method, a system, or a computer program product. Therefore, this application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application may take 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.) containing computer-usable program code.

This application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and combinations of processes and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, so that the instructions generated by the processor of the computer or other programmable data processing device are used to generate instructions Means for implementing the functions specified in one or more flowcharts and / or one or more blocks of the block diagrams.

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 work in a particular manner such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, the instructions The device implements the functions specified in one or more flowcharts and / or one or more blocks of the block diagram.

These computer program instructions can also be loaded on a computer or other programmable data processing device, so that a series of steps can be performed on the computer or other programmable device to produce a computer-implemented process, which can be executed on the computer or other programmable device. The instructions provide steps for implementing the functions specified in one or more flowcharts and / or one or more blocks of the block diagrams.

FIG. 4 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention. As shown in FIG. 4, the electronic device includes one or more processors 210 and a memory 220. A processor 210 is taken as an example in FIG. 4.

The electronic device may further include an input device 230 and an output device 240.

The processor 210, the memory 220, the input device 230, and the output device 240 in the electronic device may be connected through a bus or other manners. In FIG. 4, the connection through the bus is taken as an example.

The memory 220 is a computer-readable storage medium, and may be configured to store software programs, computer-executable programs, and modules. The processor 210 executes various functional applications and data processing by running software programs, instructions, and modules stored in the memory 220 to implement any one of the methods in the above embodiments.

The memory 220 may include a storage program area and a storage data area, wherein the storage program area may store an operating system and application programs required for at least one function; the storage data area may store data created according to the use of the electronic device, and the like. In addition, the memory may include volatile memory such as Random Access Memory (RAM), and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device.

The memory 220 may be a non-transitory computer storage medium or a transient computer storage medium. The non-transitory computer storage medium, for example, at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 220 may optionally include a memory remotely disposed with respect to the processor 210, and these remote memories may be connected to the electronic device through a network. Examples of the above network may include the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The input device 230 may be configured to receive inputted numeric or character information and generate key signal inputs related to user settings and function control of the electronic device. The output device 240 may include a display device such as a display screen.

This embodiment also provides a computer-readable storage medium storing computer-executable instructions, where the computer-executable instructions are used to execute the foregoing method.

All or part of the processes in the method of the above embodiment can be completed by executing related hardware through a computer program. The program can be stored in a non-transitory computer-readable storage medium. When the program is executed, the method can include the method described above. The process of the embodiment, wherein the non-transitory computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a RAM.

This paper relates to a method and system for evaluating the energy delivery capacity of a flexible DC power grid, focusing on how to improve the utilization rate of wind and solar energy through the optimized operation of wind and solar pumping combined operation. The pre-established AC / DC hybrid system is divided into a power supply area and a power receiving area, and the interior point method is used to solve the wind and solar pumping combined operation optimization model to obtain the maximum outbound power supply in the power supply area. The maximum supply of new energy is fed into the outbound end of the flexible grid, so as to evaluate the outbound capacity of the combined operation of wind and solar pumping, which provides a strong basis for subsequent dispatchers to formulate scheduling plans and real-time decisions.

Among them, the optimization model for combined operation of wind and light extraction includes the constraints and objective functions of the power supply area defined based on the power consumption area's consumption limit; the use of the objective function can make up for the shortcomings of the traditional optimization model iterative flow, which reduces the number of model optimization variables. , Improve the model solving speed. In the case of insufficient scenery resources, it is possible to avoid a large amount of pumping at the end of the day in order to meet the storage capacity requirements, thereby obtaining more outbound power, and at the same time, make the outbound power curve of the combined system more stable.

Claims (19)

1. A method for evaluating energy delivery capacity of a flexible DC power grid, the method includes:

   Dividing a pre-established AC / DC hybrid system into a power supply area and a power reception area;

   Based on the consumption limit of the power receiving area, the constraint conditions and objective function of the power supply area are defined, and an optimized model for combined operation of wind and light extraction is constructed; the optimized model for combined operation of wind and light extraction includes the constraint conditions and objective function of the supply area The power supply area includes: connecting a wind farm, a photovoltaic power station, and a pumped storage power station to each other through a flexible DC line;

   An interior point method is used to solve the wind-solar pumping operation optimization model to obtain the maximum delivered power of the power supply area.
2. The method according to claim 1, wherein the dividing the pre-established AC / DC hybrid system into a power supply area and a power reception area comprises:

   Integrate wind farms, photovoltaic power plants, pumped storage power plants and flexible DC systems into a grid to form an AC / DC hybrid system;

   The AC / DC hybrid system includes: a combined power supply system and an AC / DC system;

   The AC / DC system is defined as a power receiving area, and the combined power supply system is defined as a power supply area.
3. The method according to claim 1, wherein the defining a constraint condition and an objective function of the power supply area based on the consumption limit of the power receiving area comprises:

   Take the maximum output power of the power supply area in a single day as the objective function of the power supply area, and add a flexible DC system inequality constraint, a pumped storage power station inequality constraint, a wind farm and a photovoltaic power plant actual output inequality based on the influence factor equation of the power supply area Constraints and constraints on the inequality of the power delivered to the power supply area, as the constraints of the power supply area;

   The flexible DC system inequality constraints include: DC node voltage constraints, converter AC-side voltage constraints, modulation ratio constraints, voltage source converter thermal capacity constraints, and DC line maximum allowable current constraints;

   The inequality constraints of the pumped storage power station include: upper and lower storage capacity constraints, end of sunrise storage capacity constraints, and pumped storage power constraints.
4. The method according to claim 3, wherein the influencing factor equations of the power supply area include: a DC network equation, a voltage source converter equation, and a storage capacity change equation of a pumped storage power station; wherein,

   The DC network equation is as follows:

   

   In the formula, P _dv and U _dv are the node injection power and node voltage of the DC node v, N _d is the set of DC nodes, and Y _dvk is the v-th and k-th elements of the admittance matrix Y _d of the flexible DC network node;

   The voltage source converter equation is as follows:

   P _c = P _d

   

   

   

   

   Where R is the phase resistance, X is the phase inductance, U _s is the voltage amplitude of the AC side of the voltage source converter, U _c is the voltage amplitude of the valve side, and δ _sc is the valve side voltage and the AC side of the voltage source converter The angle of the voltage difference, P _s is the injected active power on the AC side of the voltage source converter, Q _s is the injected reactive power on the AC side of the voltage source converter, P _c is the active power flowing out of the valve side of the voltage source converter, P _d Active power injected into the DC line for the voltage source converter; I _s is the current flowing from the AC system into the voltage source converter transformer, U _d is the unipolar voltage on the DC side of the voltage source converter, M is the modulation degree, and μ is the DC voltage Utilization

   The storage capacity change equation of the pumped storage power station includes:

   Variation equation of the storage capacity of the upper and lower reservoirs when the pumped storage power station is generating power:

   

   The equation of storage capacity change of the upper and lower reservoirs when pumped by a pumped storage power station:

   

   Wherein the superscript t represents time, V ^u of the reservoir capacity, V ^d of the reservoir capacity, active power P _h as given pumped storage power station, η _1, η ₂ respectively generated power of the pumped storage power station Conversion factor between pumping power and storage capacity.
5. The method according to claim 4, further comprising: determining an objective function of the power supply area by the following formula:

   

   Where

   

   Is the active power transmitted between nodes i and j at time t, A is the power supply area, B is the power reception area, N is the number of sampling points in a day, Δt is the sampling time interval, and f is the Maximum outgoing power in the power supply area.
6. The method according to claim 4, further comprising: determining the flexible DC system inequality constraint by:

   

   Where U _dv is the v-th DC node voltage and N _d is the number of DC nodes.

   

   with

   

   The upper and lower limits of the k-th DC node voltage,

   

   with

   

   The upper and lower limits of the AC-side voltage of the i-th voltage source converter, U _ci and I _ci are the AC-side voltage and heat capacity of the i-th voltage source converter, and M _c is the modulation of the voltage source converter ratio,

   

   The maximum and minimum modulation ratios of the voltage source converter,

   

   Is the upper limit of the thermal capacity of the i-th voltage source converter, Nc is the collection of voltage source converters, and I _{kv '} is the maximum allowable current of the v' flexible DC line,

   

   Is the rated current of the v 'flexible DC line, and k is the number of flexible DC lines.
7. The method according to claim 4, further comprising: determining the upper and lower storage capacity constraints by:

   

   Determine the storage capacity constraint at the end of the sunrise by the following formula:

   

   Determine the power constraint by:

   

   In the formula, P _h is the active power from the pumped storage power station, and V _t ^u and V _t ^d are the storage capacity of the upper reservoir and the storage capacity of the lower reservoir at time _t , respectively.

   

   with

   

   Are the maximum and minimum storage capacity of the upper reservoir,

   

   with

   

   The maximum and minimum storage capacity of the lower reservoir, respectively

   

   with

   

   The storage capacity of the upper reservoir at the beginning of the day and the end of the day;

   

   with

   

   P _omax and _P omin are pumped storage power station of the maximum and minimum power output;; P _imax and P _imin pumped storage are first day, the last day the reservoir capacity, [mu] is the coefficient of variation of the maximum capacity were allowed 1 day Maximum and minimum pumping output of the power station.
8. The method according to claim 4, further comprising: determining an actual output inequality constraint of the wind farm and the photovoltaic power plant by the following formula:

   0≤P _f ≤P ′ _f

   0≤P _g ≤P ′ _g

   In the formula, P _f and P _g are the actual output of the wind farm and the photovoltaic power plant, respectively, and P ′ _f and P ′ _g are the maximum output of the wind farm and the photovoltaic power plant, respectively.
9. The method according to claim 4, wherein the method further comprises: determining an inequalities constraint on the output power of the joint power supply system by the following formula:

   

   Where

   

   The output power of the joint power supply system at time t,

   

   It is the limit of the power receiving area at time t.
10. The method according to claim 1, wherein the solution of the wind-solar pumping combined operation optimization model by using an interior point method to obtain the maximum outbound power of the power supply area comprises:

    Obtain the sum of the maximum output of the wind farm and the photovoltaic power plant corresponding to each sampling point on a typical day;

    Calculating the difference between the sum of the maximum output of the wind farm and the photovoltaic power station and the dissipation limit of the power receiving area;

    Based on the judgment result that the difference corresponding to the current sampling point and the difference corresponding to the sampling point at the next moment are both greater than 0 or less than 0, the two sampling points are classified into the same time period;

    According to the time period, an equivalent transformation is performed on the optimization model of the combined operation of scenery and light extraction.
11. The method according to claim 10, further comprising: determining a difference between a sum of maximum output of the wind farm and a photovoltaic power plant and a dissipation limit of the power receiving area by the following formula:

    

    Where N _t is the total number of typical daily sampling points,

    

    Is the dissipation limit of the power receiving area at time t,

    

    Represents the maximum output variable of the wind farm at time t,

    

    Represents the maximum output variable of the photovoltaic power plant at time t,

    

    Is the difference between the sum of the maximum output of the wind farm and the photovoltaic power station and the dissipation limit of the power receiving area.
12. The method according to claim 10, wherein the equivalently transforming the optimized combination model of scenery and light extraction according to the time period comprises:

    Update the maximum output of the wind farm and the photovoltaic power plant in the wind-solar pumping combined operation optimization model;

    Obtain the consumption limit curve of the power receiving area drawn based on the consumption limit of the power receiving area at multiple moments in a single day; convert the consumption limit curve of the power receiving area into a step shape according to the time period curve;

    Update the optimization model of the combined operation of wind and solar power and the inequality constraints of the pumped storage power station, the constraint conditions include the inequality constraints of the pumped storage power station;

    The updated point-to-point combined operation optimization model is solved by using the interior point method to obtain the maximum delivered power of the power supply area.
13. The method according to claim 12, further comprising: updating the maximum output of a wind farm and a photovoltaic power plant in the wind-solar pumping joint operation optimization model by the following formula:

    

    In the formula, P ″ _f and P ″ _g are the average values of the maximum output of the wind farm and the photovoltaic power plant in a unit time period, respectively; T _m is the length of the m-th period; N _Tm is the number of sampling points included in the m-th period .
14. The method according to claim 12, further comprising: converting the consumption limit curve of the power receiving area into a stepped curve by the following formula:

    

    In the formula, a and b are sampling point numbers, and m is a period number after segmentation.
15. The method according to claim 12, further comprising: updating an optimization model of joint operation of updating scenery and light extraction through the following formula:

    

    Where

    

    M periods between active power transfer node i, j's, A represents the power supply region, B represents a power receiving region, N _T is the number of sampling points m period.
16. The method according to claim 7 or 12, further comprising: updating an inequality constraint of the pumped storage power station by the following formula:

    

    Wherein, P _'h is active emitted pumped storage power station, P _s is the maximum transmitted power of the external power supply region, P' _f and P 'are respectively the maximum wind farm output _g of the photovoltaic plant, P _imax pumped storage the maximum pumping power plant output, active power P _h issued for the pumped storage power station.
17. An energy delivery capability evaluation system for a flexible DC grid includes:

    Area division module configured to divide a pre-established AC / DC hybrid system into a power supply area and a power reception area;

    A building module configured to define a constraint condition and an objective function of the power supply area based on the consumption limit of the power receiving area, and construct an optimization model for combined operation of scenery and light extraction that includes the constraint condition and the objective function of the power supply area; The power supply area includes: connecting wind farms, photovoltaic power stations and pumped storage power stations to each other through flexible DC lines;

    The analysis module is configured to use the interior point method to solve the wind-solar pumping combined operation optimization model to obtain the maximum delivered power in the power supply area.
18. An electronic device includes:

    At least one processor;

    Memory, configured to store at least one program,

    When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1-16.
19. A computer-readable storage medium stores computer-executable instructions, where the computer-executable instructions are used to perform the method according to any one of claims 1-16.

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