A method for variable-step dynamic simulation of integrated energy systems

Abstract

The present invention discloses a method for variable-step-size dynamic simulation of an integrated energy system, which belongs to the field of dynamic simulation of energy systems; a method for variable-step-size dynamic simulation of an integrated energy system, comprising the following steps: Step 1: According to the specific situation of the fault (i.e., the role of the fault point in the operation of the heat network and the severity of the fault), use a fault hazard assessment strategy to assess its hazard, and obtain two-stage hazard assessment quantitative data of this fault; through certain reasonable mathematical changes, convert it into a quantitative value that can be directly weighted; Step 2: According to whether the fault is a single fault, use a corresponding weight determination method to determine the precision driving weight under this fault. The present invention adopts the basic idea of ​​variable-step-size dynamic simulation to realize the pre-optimization of the time step of the dynamic simulation of the fault state of the integrated energy system, effectively improves the simulation efficiency, balances the simulation accuracy and speed, and provides a strong guarantee for the safety verification of the integrated energy system.

Description

Technical Field

[0001] The invention belongs to the technical field of energy system simulation analysis, and in particular relates to a method for variable-step-size dynamic simulation of an integrated energy system. Background Art

[0002] Traditional energy systems are mostly based on discrete operation. Most mainstream energy systems such as coal, oil, natural gas, electricity, and cooling / heating systems operate independently, forming relatively closed industries and interest barriers. Due to the existence of these barriers, multiple energy sources cannot play their complementary and interactive characteristics and cascade utilization potential, thus reducing the comprehensive utilization efficiency of energy, and to a certain extent increasing carbon emissions and exacerbating environmental pollution problems.

[0003] In the context of the urgent need for transformation and upgrading of the world's energy system, the concept of Integrated Energy System (IES) has been widely supported by academia and industry. The Integrated Energy System can break the barriers of the discrete operation of the traditional energy system, give full play to the complementary and interactive characteristics and cascade utilization potential of various forms of energy (including coal, oil, natural gas, electricity, heat / cold, wind, light, water, hydrogen, etc.), thereby improving the comprehensive energy efficiency of the energy system, reducing carbon emissions and pollutant emissions, improving the stability and reliability of the energy supply system, and promoting the consumption of renewable energy. Therefore, it has a bright development prospect.

[0004] The dynamic simulation of the integrated energy system emphasizes the description of the changes in the working conditions over time. The model considered in the simulation process is more refined. By analyzing the changes in the working conditions under disturbance, problems such as pipe crossing and overload can be judged, which helps to improve the safety and stability of the system. Therefore, the unique advantages of dynamic simulation give it certain advantages in fault analysis. High-precision dynamic simulation of fault states can provide operators with greater scheduling adjustment space.

[0005] In dynamic simulation, the choice of simulation step size has a crucial impact on simulation speed and accuracy. If a larger simulation step size is selected, although simulation time can be saved, there will be large numerical errors in the simulation results; if the simulation step size is reduced, the simulation accuracy advantage can be improved but a certain simulation speed will be lost, and the real-time performance of the simulation cannot be guaranteed. In other words, the existing dynamic simulation technology cannot achieve both fast simulation speed and high simulation accuracy at the same time. Summary of the invention

[0006] The purpose of the present invention is to provide a method for variable-step dynamic simulation of an integrated energy system.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method for variable-step-size dynamic simulation of an integrated energy system comprises the following steps:

[0009] Step 1: According to the specific situation of the fault (i.e. the role of the fault point in the operation of the heating network and the severity of the fault), the fault hazard assessment strategy is used to assess its hazard, and the quantitative data of the two-stage hazard assessment of this fault is obtained; through certain reasonable mathematical changes, it is converted into a quantitative value that can be directly weighted;

[0010] Step 2: According to whether the fault is a single fault, the corresponding weight determination method is used to determine the precision driving weight under this fault;

[0011] Step 3: Determine the composite precision driving weight under composite faults;

[0012] Step 4: Bring the obtained weight into the step size optimization model to obtain the dynamic simulation step size of the fault state;

[0013] Step 5: Based on the idea of ​​basic variable step size of dynamic simulation, large step size simulation is adopted in normal state, and the determined step size is adopted for simulation in fault state, so as to achieve a balance between simulation speed and accuracy.

[0014] Furthermore, the specific method of step one is: in the fault hazard evaluation strategy, a fuzzy evaluation score S considering the fault type is obtained by a comprehensive fuzzy evaluation method in the first stage; the severity of the fault is considered in the second stage to obtain the severity quantification W2; since there are large differences between the quantitative data obtained in the first and second stages and they cannot be directly combined, numerical mapping is used to convert the fuzzy evaluation score S into a fuzzy evaluation weight W1 of the same order of magnitude as the severity quantification W2, so as to facilitate the next step of calculation.

[0015] Furthermore, the specific method of step 2 is: under a single fault, the value of the precision driving weight only needs to consider the first and second stage hazard assessment results of the fault, and multiply the fuzzy evaluation weight W1 by the severity quantification W2 to obtain the precision driving weight W of the single fault.

[0016] Furthermore, the specific method of step three is:

[0017] The requirements that the precision driving weight of composite faults must meet include: the weight of composite faults must be greater than the single weights that make up the composite fault; the weight of composite faults must increase with the increase of any single weight; the weights of single faults and composite faults should be greater than 0 and less than 1;

[0018] Therefore, the composite fault weight calculation formula is selected as follows:

[0019]

[0020] In the above formula, C should be less than the minimum value of a single weight, usually C is 0.5, W 单一1 and W 单一2 They represent the weights of the two single faults that constitute the composite fault, W 复合 Represents the weight of the composite fault.

[0021] Furthermore, the specific method of step 4 is:

[0022] The basic form of the specific derivation process of the step size optimization model is as follows:

[0023]

[0024]

[0025] In the formula, s represents the target value of the optimization model, w error is the precision driving weight, w time is the simulation time weight, h is the space step, tau is the time step, v is the working fluid flow rate, F net is the number of network fusion nodes L net is the total length of the network pipeline, M net is the number of network pipelines, N net is the number of network nodes, h min and h max is the maximum and minimum value of the optional spatial step size, tau min and tau max are the maximum and minimum values ​​of the optional time step, where error and time are functions of the time step tau, which can be obtained through algorithm analysis, historical calculation data and parameter fitting. The constraints of the model are the heat network parameters, and the composite precision driving weight is taken as W error By bringing the objective function into the step size optimization model, it becomes a univariate multi-time function of tau. By finding the maximum value, we can obtain the dynamic simulation time step that best meets the requirements.

[0026] Furthermore, the specific steps of step five are:

[0027] Step (1): Before simulation, input network topology data and fault information (including fault type, severity, occurrence time and end time);

[0028] Step (2): Calculate the simulation step length under the fault state;

[0029] Step (3): Perform simulation in a normal state using a normal simulation step length until the fault occurs;

[0030] Step (4): When a fault occurs, convert the dimension of the simulation parameter of the normal state to the dimension of the corresponding fault state, and reduce the simulation step size to simulate the fault state;

[0031] Step (5): Before the fault ends, keep the fault state simulation step length to perform simulation;

[0032] Step (6): When the fault ends, the fault state simulation parameters are converted to the normal state, and the simulation is performed with the normal state simulation step size until the simulation ends.

[0033] Beneficial effects of the present invention:

[0034] The present invention adopts the basic idea of ​​variable-step dynamic simulation, determines the simulation step under the fault state according to the fault type and severity, and realizes the pre-optimization of the time step of the dynamic simulation of the fault state of the integrated energy system, which effectively improves the simulation efficiency, balances the simulation accuracy and speed, and provides a strong guarantee for the safety verification of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below in conjunction with the accompanying drawings.

[0036] Figure 1 is a system structure diagram of an embodiment of the present invention;

[0037] Figure 2 A simulation flow chart of the proposed strategy under an embodiment of the present invention;

[0038] Figure 3 This is a diagram showing the relative error results of the simulation strategy proposed in the embodiment of the present invention;

[0039] Figure 4 This is a diagram of relative error results of constant step-size simulation in an embodiment of the present invention;

[0040] Figure 5 Schematic diagram of the method steps of the present invention DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] like Figures 1 to 5As shown, in the specific embodiment, there are two energy networks, the heat network and the power grid, as well as a variety of energy coupling equipment such as water-water heat exchangers, steam-water heat exchangers, and electric boilers. The parameters of each energy network and equipment do not have a substantial impact on the method proposed in the present invention, so they will not be repeated here. In the embodiment, the total simulation time is 7560s, and the simulation step length under normal conditions is tau=10s h=12m. At 3000s, the heating power of the No. 1 heat source is limited to 1.815MW. The heating power of the heat source under normal conditions is 2.5889MW. At the same time, due to temperature changes, the No. 5 pipeline leaks at the same time, and the mass flow rate of the transmission medium becomes half of the original. This fault will affect a total of 19 water supply and return pipelines. The heat network has a total of 38 pipelines. The fault lasts for 1560s, after which the system returns to normal and runs smoothly for 3000s.

[0043] Therefore, we can first consider the above faults as two single faults and find their respective two-stage quantitative indicators as follows:

[0044]

[0045] For the fuzzy evaluation score, the following formula is used to convert it to the same order of magnitude as the severity quantitative data:

[0046]

[0047] For a single fault, the accuracy driving weight is the product of the two-stage weights, which is the following formula:

[0048] W error =W1·W2 (2)

[0049] The two-stage weights and precision driving weights under two single faults are as follows:

[0050]

[0051] The driving weight under the composite fault can be obtained by the following formula:

[0052]

[0053] The weight under this composite fault is W 复合 = 0.9356, and use this weight as W error Bring it into the step size optimization model, that is, the following model:

[0054]

[0055]

[0056] It can be found that under this composite fault, the optimal time step of dynamic simulation is 2.5s.

[0057] According to the idea of ​​variable step simulation, from 1 to 3000s, the system is simulated with tau = 10s h = 12m. At 3000s, the dimension of the parameters is converted to tau = 2.5sh = 3m, and the system parameters are changed to the corresponding fault state. The fault state simulation is carried out for 1560s. At 4560s, the parameter dimension is converted to tau = 10s h = 12m and the simulation is carried out for 3000S. The simulation process is shown in the attached Figure 2 .

[0058] To prove the effectiveness of the present invention, the simulations under the above variable step size, constant step size and basic variable step size ideas (i.e., large step size for normal state and 1s for fault state simulation) are compared, and the simulation times under the three ideas are as follows:

[0059]

[0060] At the same time, the maximum relative error of the simulation under the proposed strategy is 1.0366%, and the average relative error of the simulation is 0.0143%, while the data under constant step size are 2.4398% and 0.0794%.

[0061] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above examples. The above examples and descriptions are only for explaining the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention to be protected. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents. The specific comparison of the relative errors at each time point is as follows: Figure 3 and Figure 4 shown.

[0062] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0063] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for variable step size dynamic simulation of an integrated energy system, characterized in that: The following steps are involved: Step 1: According to the specific situation of the fault, that is, the role of the fault point in the operation of the heating network and the severity of the fault, the fault hazard assessment strategy is used to evaluate its hazard, and the quantitative data of the two-stage hazard assessment of this fault is obtained; through certain reasonable mathematical changes, it is converted into a quantitative value that can be directly weighted; Step 2: According to whether the fault is a single fault, the corresponding weight determination method is used to determine the precision driving weight under this fault; Step 3: Determine the composite precision driving weight under composite faults; Step 4: Bring the obtained weight into the step size optimization model to obtain the dynamic simulation step size of the fault state; Step 5: Based on the idea of ​​basic variable step length of dynamic simulation, large step length simulation is adopted in normal state, and the determined step length is adopted in fault state to achieve the balance between speed and accuracy of simulation; The specific method of step 1 is as follows: in the fault hazard evaluation strategy, a fuzzy evaluation score S considering the fault type is obtained by a comprehensive fuzzy evaluation method in the first stage; a severity quantification W2 is obtained by considering the severity of the fault in the second stage; and a fuzzy evaluation score S is converted into a fuzzy evaluation weight W1 of the same order of magnitude as the severity quantification W2 by using numerical mapping; The specific method of step 2 is: multiplying the fuzzy evaluation weight W1 by the severity quantification W2 to obtain the precision driving weight W of a single fault; The specific method of step three is: The requirements that the precision driving weight of composite faults must meet include: the weight of the composite fault must be greater than the single weights that make up the composite fault; The weight of compound faults should increase with the increase of any single weight. The weights of single faults and compound faults should be greater than 0 and less than 1. Therefore, the composite fault weight calculation formula is selected as follows: In the above formula, C should be less than the minimum value of a single weight, usually C is 0.5, W 单一1 and W 单一2 They represent the weights of the two single faults that constitute the composite fault, W 复合 Represents the weight of the composite fault.

2. The method for variable-step dynamic simulation of an integrated energy system according to claim 1, characterized in that: The specific method of step 4 is: The basic form of the specific derivation process of the step size optimization model is as follows: In the formula, s represents the target value of the optimization model, w error is the precision driving weight, w time is the simulation time weight, h is the space step, tau is the time step, v is the working fluid flow rate, F net is the number of network fusion nodes L net is the total length of the network pipeline, M net is the number of network pipelines, N net is the number of network nodes, h min and h max is the maximum and minimum value of the optional spatial step size, tau min and tau max are the maximum and minimum values ​​of the optional time step, where error and time are functions of the time step tau, which can be obtained through algorithm analysis, historical calculation data and parameter fitting. The constraints of the model are the heat network parameters, and the composite precision driving weight is taken as W error By bringing the objective function into the step size optimization model, it becomes a univariate multi-time function of tau. By finding the maximum value, we can obtain the dynamic simulation time step that best meets the requirements.

3. The method for variable-step dynamic simulation of an integrated energy system according to claim 2, characterized in that: The specific steps of step five are: Step (1): before simulation, input network topology data and fault information, wherein the fault information includes fault type, severity, occurrence time and end time; Step (2): Calculate the simulation step length under the fault state; Step (3): Perform simulation in a normal state using a normal simulation step length until the fault occurs; Step (4): When a fault occurs, convert the dimension of the simulation parameter of the normal state to the dimension of the corresponding fault state, and reduce the simulation step size to simulate the fault state; Step (5): Before the fault ends, keep the fault state simulation step length to perform simulation; Step (6): When the fault ends, the fault state simulation parameters are converted to the normal state, and the simulation is performed with the normal state simulation step size until the simulation ends.

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