New energy consumption capability assessment method and system based on network security and stability constraints

Abstract

The invention discloses a new energy consumption capability assessment method and system based on network security and stability constraints, and relates to the technical field of new energy consumption capability assessment, and the method comprises the steps: building a new energy consumption model; analyzing new energy output volatility; performing scene reduction; and calculating the new energy consumption capability. According to the new energy consumption capability evaluation method based on the network security and stability constraints, the randomness of new energy output is fitted by adopting discrete probability distribution, and new energy grid-connected multi-scene state simulation is realized; meanwhile, the probability degree distribution function value in the corresponding scene is calculated and obtained, the efficiency of calculating the new energy consumption capability is improved, and the calculation amount is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy consumption capacity assessment, and specifically to a new energy consumption capacity assessment method and system based on network security and stability constraints. Background Art

[0002] As one of the key areas to help achieve the "dual carbon" goal, the power industry's primary task is to promote low-carbon energy transformation and build a new power system with new energy as the main body. Large-scale new energy grid connection brings new problems to voltage regulation, stable and safe operation, etc. High-density distributed new energy can be connected to large AC and DC power grids from the power supply side and the load side. Due to the poor reactive support capacity and weak moment of inertia of new energy power generation systems, high-density new energy grid connection will inevitably have a certain impact on the safe and stable operation of the power grid, and has gradually become the main factor restricting the consumption of new energy.

[0003] In previous studies, there are many evaluation models for the new energy absorption capacity of the power grid, such as: analyzing the new energy absorption level from key factors such as the regulation performance of new energy grid-connected power sources, the level of interconnection and interoperability, and the response of the measurement; using the rational fraction fitting algorithm to study the impact of high-density distributed photovoltaic power sources on the transient voltage of the receiving power grid, and applying the rational approximation method to the transient voltage of large hybrid AC / DC power grids; establishing a mathematical model for new energy grid connection, and using the static voltage stability margin as a constraint to obtain the relationship between the voltage stability margin and the critical penetration rate of new energy. However, the existing technology only considers the calculation of new energy absorption capacity based on time-series production simulation, and does not consider the voltage safety constraints of the power grid after the new energy is connected to the grid. At the same time, the random production simulation is based on the probability distribution of wind speed within the research period, but the calculation time is long and the amount of calculation is too large. Therefore, this patent proposes a new energy absorption capacity evaluation method based on network security and stability constraints. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: converting the time-series production simulation calculation method into a probability distribution space, using discrete random variables that obey a certain distribution to describe the randomness of the new energy distribution and consumption space, and converting the uncertainty between random variables and the range of network security and stability constraints into deterministic calculations under probabilistic scenarios, and then quickly calculating the relationship between the new energy consumption capacity and the network security and stability constraints to evaluate the new energy consumption capacity.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a new energy consumption capacity evaluation method based on network security and stability constraints, comprising:

[0007] Establish a new energy consumption model; analyze the volatility of new energy output; perform scenario reduction; and calculate new energy consumption capacity.

[0008] As a preferred scheme of the new energy consumption capacity evaluation method based on network security and stability constraints described in the present invention, wherein: the establishment of the new energy consumption model includes calculating the difference between the minimum output of the conventional unit and the load power and the DC interconnection line power, and determining the maximum consumption capacity of the new energy access to the receiving power grid.

[0009] As a preferred solution of the new energy absorption capacity evaluation method based on network security and stability constraints described in the present invention, the analysis of the volatility of new energy output adopts mathematical methods to statistically analyze the new energy power distribution to obtain the discrete probability distribution of wind power theoretical output.

[0010] As a preferred scheme of the new energy absorption capacity assessment method based on network security and stability constraints described in the present invention, the discrete probability distribution includes statistical analysis of historical data of wind speed, light intensity and load demand, and obtains typical data of each random variable in 24 hours in spring, summer, autumn and winter. The error between the typical data and the actual data at each moment is assumed to obey the probability density function of its statistical characteristics, and a typical scenario is generated based on the above model.

[0011] As a preferred solution of the new energy consumption capacity assessment method based on network security and stability constraints described in the present invention, the scenario reduction is performed based on a heuristic synchronous reduction method.

[0012] As a preferred solution of the new energy consumption capacity evaluation method based on network security and stability constraints described in the present invention, wherein: the calculation of new energy consumption capacity includes converting random new energy output into multi-scenario output distribution, combining typical daily load characteristics, setting an output plan for known system loads, and incorporating it into the conventional energy startup plan to solve the equivalent load, P W (t) and P PV (t) are the wind power and photovoltaic power generation values ​​at time t in a certain scenario, I W (t) and I PV (t) are the wind power and photovoltaic installed capacity at time t in a certain scenario. The equivalent load capacity value converted from the new energy output to the system power balance is P eql (t);

[0013] Combined with the regulation performance of hydropower units, nuclear power units and thermal power units, and considering the forced shutdown of units, mixed integer programming is used to adjust the conventional power supply startup mode and calculate the minimum output of conventional units in the system. The objective function is to maximize the new energy consumption capacity.

[0014] As a preferred solution of the new energy consumption capacity evaluation method based on network security and stability constraints described in the present invention, the objective function is expressed as:

[0015]

[0016] Among them, F(p) is the objective function of the consumption model; η is the penetration rate of new energy; P re It is the theoretical power of new energy.

[0017] Another object of the present invention is to provide a new energy consumption capacity evaluation system based on network security and stability constraints, which can improve the efficiency of solving the new energy consumption power by constructing a new energy consumption capacity evaluation system based on network security and stability constraints.

[0018] To solve the above technical problems, the present invention provides the following technical solutions: a new energy consumption capacity assessment system based on network security and stability constraints, comprising: a consumption model module, a fluctuation analysis module, a scenario reduction module and a capacity calculation module; the consumption model module is used to establish a new energy consumption model; the fluctuation analysis module is used to analyze the volatility of new energy output; the scenario reduction module is used to perform scenario reduction; the capacity calculation module is used to calculate the new energy consumption capacity.

[0019] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for evaluating the new energy consumption capacity based on network security and stability constraints as described above are implemented.

[0020] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for evaluating the new energy absorption capacity based on network security and stability constraints as described above.

[0021] Beneficial effects of the invention: The method for evaluating new energy absorption capacity based on network security and stability constraints provided by the invention proposes a method for evaluating new energy absorption capacity based on network security and stability constraints, adopts discrete probability distribution to fit the randomness of new energy output, realizes multi-scenario state simulation of new energy grid connection, and simultaneously calculates and obtains the probability distribution function value under the corresponding scenario, thereby improving the efficiency of calculating the new energy absorption capacity and reducing the amount of calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1 An overall flow chart of a new energy consumption capacity assessment method based on network security and stability constraints provided by an embodiment of the present invention.

[0024] Figure 2 This is an overall structural diagram of a new energy consumption capacity assessment system based on network security and stability constraints provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Example 1

[0028] Reference Figure 1 , which is an embodiment of the present invention, provides a method for evaluating the new energy consumption capacity based on network security and stability constraints, including:

[0029] New Energy Consumption Modeling

[0030] The randomness of renewable energy generation weakens the regulation capacity of the power system. The regulation capacity of the system in the receiving power grid mainly depends on the regulation characteristics of conventional units, and the overall regulation performance of the units is closely related to the unit type and the corresponding rated capacity. The maximum absorption capacity of renewable energy connected to the receiving power grid corresponds to the theoretical space for renewable energy absorption, which is equal to the power difference between the minimum output of conventional units and the "load + DC interconnection line power (standby)" interval.

[0031] New energy consumption space P at time t a (t) is:

[0032]

[0033] Where: P a (t) is the theoretical difference of new energy consumption space at time t; P L (t) is the total load power and DC tie line power of the system at time t; Z i (t) is a binary variable, indicating the operating status of the i-th unit at time t, Z i (t)=1 means the unit is running, Z i (t) = 0 means the unit is shut down; Ps,i,min (t) is the minimum technical output power value of the i-th unit at time t; β i is the minimum technical output coefficient of the i-th conventional unit; P s,i,rated (t) is the rated power value of the i-th unit at time t.

[0034] Let P re (t) represents the theoretical power of new energy at time t, then the power consumed by new energy at time t is P x (t)Satisfy:

[0035]

[0036] Where: P x (t) is the new energy consumption power at time t; P re (t) is the theoretical power of the new energy at time t.

[0037] The output of new energy is highly volatile and random, and its value fluctuates between 0 and rated output. This patent uses mathematical methods to statistically analyze the power distribution of new energy to obtain the discrete probability distribution of wind power theoretical output. First, the historical data of wind speed, light intensity and load demand are statistically analyzed to obtain the typical data of each random variable in 24 hours in spring, summer, autumn and winter. It is assumed that the error between the typical data and the actual data at each moment obeys the probability density function of its statistical characteristics, and a typical scenario is generated based on the above model. Taking the generation of wind speed multi-scenario as an example, the specific execution steps are as follows.

[0038] After statistical analysis, the wind speed can be approximately considered to obey the Weibull distribution, and its probability density function can usually be expressed as:

[0039]

[0040] Where: f(v) is the Weibull probability density function; Δv is the interval of wind speed 1, m / s; h is the shape parameter of the Weibull distribution; k is the scale parameter of the Weibull distribution.

[0041] The timing constraints are:

[0042] k=(σ / μ) -1.086 (4)

[0043]

[0044] Where: σ is the standard deviation of historical wind speed and typical wind speed; μ is the mean error between historical wind speed and typical wind speed; Γ(·) is the gamma function.

[0045] 2) The Monte Carlo method is used to sample the cumulative probability density 1000 times. The corresponding error sequence is obtained according to the sampled cumulative probability density, and the sampling error sequence is reordered according to the size of the sampled cumulative probability density.

[0046] Given a confidence interval of 85%, the 100th and 850th sampling error results are obtained based on the obtained error sequence distribution, and the power prediction interval at that moment is obtained by combining the point prediction. The distribution error index refers to the difference between the selected sample and the preset value. If the actual renewable energy output falls within the predetermined interval, it means that the sample data is reliable.

[0047]

[0048] Where: r u is the confidence probability error index; R PICP is the sample error value; u is the preset value of the distribution index; W is the number of test points; k wu is a Boolean quantity, k wu =1 means it is within the given confidence level.

[0049] The reliability level of the model is judged by the positive or negative value of the indicator. When the reliability index r is greater than zero, it means that the model has a favorable deviation and meets the initial scenario and can simulate the timing and randomness of new energy. When the reliability index is less than zero, it means that the model does not meet the state screening.

[0050] 3) Scenario reduction. In order to improve the computational efficiency, it is necessary to reduce the scenarios. Let the probability of occurrence of scenarios m and n be E and m and E n , define c T (E m(t) ,E n(t) ) The distance between scenes m(t) and n(t) at time period t is as follows:

[0051]

[0052] Where: c T (E m(t) ,E n(t) ) is the distance between scenes m(t) and n(t) in time period t; E m(t) is the probability coordinate value of scene m occurring at time t; E n(t) is the probability coordinate value of scene n occurring at time t; N is the set of scenes that are finally eliminated during the scene reduction process.

[0053] In order to minimize formula (8), the heuristic-based simultaneous back reduction method is used to reduce the scene. The steps are as follows:

[0054] 1. k = 0, initialize the set N, set the initial state scene reduction scene N = N (0) Is empty.

[0055] 2. When performing k - iteration reduction, aiming at minimizing the set of scenarios, obtain the number of scenarios n reduced in each iteration k .

[0056] 3. Delete scenario m k ,

[0057] 4. During the iteration process, if the number of iterations k < N, return to step 2 to continue the iteration; otherwise, generate the set of all scenarios as in step 5.

[0058] 5. N = N (k) . The scenario m in the deleted scenario set J will be replaced by the scenario closest to m in the remaining scenario set. H n is the sum of the probabilities of all scenarios, and the probability H n of the remaining scenarios is corrected to the sum of its original probability E n and the probabilities of all scenarios it replaces, that is:

[0059]

[0060] In the formula: N(n) is the set composed of all scenarios replaced by scenario n in the deleted scenario set N.

[0061] Calculation of new - energy consumption capacity

[0062] Evaluation model

[0063] Convert the random new - energy output into a multi - scenario output distribution, combine with the typical daily load characteristics, set the output plan of the known system load, and incorporate it into the conventional - energy startup plan to solve the equivalent load. P W (t) and P PV (t) are the wind - power and photovoltaic - power values at time t in a certain scenario respectively. I W (t) and I PV (t) are the installed capacities of wind power and photovoltaic power at time t in a certain scenario respectively. Converting the new - energy output into the equivalent load - capacity value in the system power balance is P eql (t).

[0064] Combined with the regulation performance of hydropower units, nuclear - power units and thermal - power units, considering situations such as forced outages of units, use mixed - integer programming to adjust the startup mode of conventional power sources and calculate the minimum output of the system's conventional units. The objective function is to maximize the new - energy consumption capacity, and its calculation formula is:

[0065]

[0066] In the formula: F(p) is the objective function of the consumption model; η is the new - energy penetration rate; P re is the new - energy theoretical power.

[0067] Since this patent only needs to calculate the equivalent load value and DC interconnection line power in multiple scenarios of renewable energy power generation grid connection to solve the mixed integer programming problem in the system theoretical absorption space, the efficiency of solving the renewable energy absorption power is far better than the time series combination function method of random generation simulation.

[0068] 2.1 Constraints

[0069] The output of each type of unit meets the constraints:

[0070]

[0071] Where: P t s is the active power of the grid-connected node of the conventional unit at time t; P is the active power change of the conventional unit grid-connected node at time t; t re is the active power injected by the new energy at time t; is the active power change of the grid-connected node of the new energy unit at time t; C is the proportion describing the acceptable range of load power fluctuation; P t c Active power injected for DC power; is the change in active power of the DC injection power at time t.

[0072] The startup capacity of conventional power supply must meet the maximum equivalent load and system backup requirements during the startup cycle, with the following constraints:

[0073] Z i P s,i,max ≥P eql,m +R(14)

[0074] Where: P eql,m This is the maximum equivalent load during the startup cycle, including the DC interconnection line power; R is the system standby power.

[0075] Multiple DC power constraints: Under the constraints of DC power flow equation, power balance constraint and line flow constraint, the power flow equation of DC tie line power in time period t is:

[0076] B ι θ ι =P ι c (15)

[0077] Where: B ι is the system admittance matrix during period t; θ ι is the node phase angle vector; P ι cis the vector of active power injection of the DC tie line at the node. Since the line capacity is constrained, DC power regulation can only change around the operating state, so the constraints of multiple DC power regulation are reasonable.

[0078] The line power flow limit is:

[0079]

[0080] Where: is the vector of active power flow in the branch; D i is a diagonal matrix; A i is the proximity matrix; P B,max To preset active power flow limit. Appropriately preset P B,max It can leave enough safety margin for the lines and effectively reduce the safety risks caused by power congestion.

[0081] Combined with the static voltage stability standard of the power industry standard DLT 1172-2013 "Guidelines for Voltage Stability Evaluation of Power Systems", under the maximum load or maximum cross-section flow in the region, the regional active power margin k in normal operation or maintenance mode is m Should be greater than 8%, calculated by formula (18):

[0082]

[0083] k m ≥8% (19)

[0084] Where: Thevenin equivalent potential in the E system; P 10 is the active load; X S is the impedance of the transmission line from the grid-connected busbar to the grid; λ is the ratio of the impedance of the new energy unit to the impedance of the traditional unit; X SG is the impedance of the traditional unit.

[0085] Therefore, the system considers the static voltage safety constraint, and further calculation yields:

[0086]

[0087] Where: η c k m The lower limit corresponds to the new energy penetration rate.

[0088] Example 2

[0089] Reference Figure 2 , which is an embodiment of the present invention, provides a new energy consumption capacity evaluation system based on network security and stability constraints, including:

[0090] Consumption model module 100, fluctuation analysis module 200, scenario reduction module 300 and capacity calculation module 400;

[0091] The consumption model module 100 is used to establish a new energy consumption model;

[0092] The fluctuation analysis module 200 is used to analyze the fluctuation of new energy output;

[0093] The scene reduction module 300 is used to perform scene reduction;

[0094] The capacity calculation module 400 is used to calculate the new energy consumption capacity.

[0095] Example 3

[0096] An embodiment of the present invention is different from the first two embodiments in that:

[0097] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0098] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0099] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0100] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0101] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A new energy consumption capacity assessment method based on network security and stability constraints is characterized by: include: Establish a new energy consumption model; Analyze the volatility of renewable energy output; Perform scene reduction; Calculate the new energy absorption capacity.

2. The new energy consumption capacity evaluation method based on network security and stability constraints as claimed in claim 1 is characterized by: The establishment of the new energy consumption model includes calculating the difference between the minimum output of the conventional unit and the load power and the DC interconnection line power, and determining the maximum consumption capacity of the new energy access to the receiving-end power grid.

3. The new energy consumption capacity assessment method based on network security and stability constraints as claimed in claim 2 is characterized by: The analysis of the volatility of new energy output adopts a mathematical method to statistically analyze the power distribution of new energy to obtain the discrete probability distribution of wind power theoretical output.

4. The new energy consumption capacity assessment method based on network security and stability constraints as claimed in claim 3 is characterized by: The discrete probability distribution includes statistical analysis of historical data on wind speed, light intensity and load demand to obtain typical 24-hour data of each random variable in spring, summer, autumn and winter. The error between the typical data and the actual data at each moment is assumed to obey the probability density function of its statistical characteristics, and a typical scenario is generated based on the above model.

5. The new energy consumption capacity assessment method based on network security and stability constraints as claimed in claim 4 is characterized by: The scene reduction is performed based on a heuristic synchronous reduction method.

6. The new energy consumption capacity assessment method based on network security and stability constraints as claimed in claim 5 is characterized by: The calculation of the new energy consumption capacity includes converting the random new energy output into a multi-scenario output distribution, combining the typical daily load characteristics, setting the output plan of the known system load, and incorporating it into the conventional energy startup plan to solve the equivalent load, P W (t) and P PV (t) are the wind power and photovoltaic power generation values ​​at time t in a certain scenario, I W (t) and I PV (t) are the wind power and photovoltaic installed capacity at time t in a certain scenario. The equivalent load capacity value converted from the new energy output to the system power balance is P eql (t); Combined with the regulation performance of hydropower units, nuclear power units and thermal power units, and considering the forced shutdown of units, mixed integer programming is used to adjust the conventional power supply startup mode and calculate the minimum output of conventional units in the system. The objective function is to maximize the new energy consumption capacity.

7. The new energy consumption capacity assessment method based on network security and stability constraints as claimed in claim 6 is characterized by: The objective function is expressed as, Among them, F(p) is the objective function of the consumption model; η is the penetration rate of new energy; P re It is the theoretical power of new energy.

8. A system using the new energy consumption capacity assessment method based on network security and stability constraints as described in any one of claims 1 to 7, characterized in that: include: A consumption model module (100), a fluctuation analysis module (200), a scenario reduction module (300) and a capability calculation module (400); The consumption model module (100) is used to establish a new energy consumption model; The fluctuation analysis module (200) is used to analyze the fluctuation of new energy output; The scene reduction module (300) is used to perform scene reduction; The capacity calculation module (400) is used to calculate the new energy consumption capacity.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the new energy absorption capacity assessment method based on network security and stability constraints described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the new energy absorption capacity assessment method based on network security and stability constraints described in any one of claims 1 to 7 are implemented.

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