Experimental Microgrid Construction and Control Method for Verifying Various Microgrid Structures

Abstract

The present invention belongs to the technical field of microgrids, and particularly relates to a method for constructing and controlling a test microgrid that can be used for verifying various microgrid structures. The test microgrid of the present invention includes a double-bus structure and a sectional control structure. The present invention can simulate and verify various microgrid structures, including using a double-bus structure and sectional control to form a test microgrid for a 10 kV voltage line, overcoming the single and limited problems of the traditional single-bus structure for 10 kV voltage lines. It is possible to adjust the structure of the test microgrid for different demonstration engineering projects for simulation verification, and use a two-layer operation optimization method with economic indicators as the bottom layer and power quality indicators as the upper layer to find the best operation plan, so that the test microgrid always operates in the best working state and makes the system operation more reasonable.

Description

Technical Field

[0001] The invention belongs to the technical field of microgrids, and particularly relates to a method for constructing and controlling a test microgrid that can be used for verifying various microgrid structures. Background Art

[0002] With the increasing utilization of new energy sources, microgrids composed of distributed power sources are becoming more and more diverse, giving rise to various microgrid structures. Although microgrid structures are diverse, traditional microgrid structures have the disadvantage of being single. For different regions such as remote mountainous areas, island areas, and industrial and commercial parks in cities, fixed microgrid structures need to be adopted. This poses a great obstacle to the construction of microgrids. In each microgrid structure, the capacities of micro-sources and loads are fixed according to actual demonstration projects. Therefore, during the platform verification process, there may be no distributed power sources and loads with specified capacities. Summary of the Invention

[0003] Aiming at the deficiencies existing in the above-mentioned prior art, the invention provides a method for constructing and controlling a test microgrid that can be used for verifying various microgrid structures. Its purpose is to provide a microgrid construction and control method that can simulate and verify various microgrid structures, and overcome the problems of the singularity and limitation of the traditional 10kv voltage line with only a single bus structure.

[0004] The technical solution adopted by the invention to achieve the above purpose is as follows:

[0005] The construction of a test microgrid that can be used for verifying various microgrid structures. The test microgrid includes a double-bus structure and a sectional control structure; wherein the double-bus structure of the test microgrid includes two buses. One grid AC bus is connected with photovoltaic, wind turbines, and loads; the other test AC bus is connected with photovoltaic, wind turbines, supercapacitors, batteries, gas turbines, diesel engines, RTLAB hardware-in-the-loop equipment, and loads.

[0006] The sectional control structure of the test microgrid includes one grid AC bus, which is divided into two sections in total. There is a circuit breaker in the middle of the grid AC bus; one section of the grid AC bus is connected with supercapacitors, batteries, gas turbines, diesel engines, RTLAB hardware-in-the-loop simulation equipment, and simulated loads; the other section of the grid AC bus is connected with photovoltaic and wind turbines.

[0007] The double-bus structure of the test microgrid includes: Photovoltaic power is connected to a variable line impedance through a DC / AC inverter and is respectively connected to the grid AC bus and the test AC bus through the variable line impedance; A wind turbine is connected to a variable line impedance through an AC / DC rectifier and an inverter and is respectively connected to the grid AC bus and the test AC bus through the variable line impedance; A load is connected to the grid AC bus; A supercapacitor is connected to a variable line impedance through an inverter and then connected to the test AC bus; A storage battery is connected to a variable line impedance through an inverter and then connected to the test AC bus; A gas turbine is connected to a variable line impedance through a rectifier and an inverter and then connected to the test AC bus; A diesel engine is connected to a variable line impedance through a rectifier and an inverter and then connected to the test AC bus; An RTLAB hardware-in-the-loop device is connected to a variable line impedance through a rectifier and an inverter and then connected to the test AC bus; The load is connected to the test AC bus, and the middle of the test AC bus is disconnected by a circuit breaker.

[0008] The sectional control structure of the test microgrid includes: A test AC bus is divided into two sections, and a circuit breaker is installed in the middle of the test AC bus to separate it into two sections; One section of the test AC bus is first connected to a variable line impedance and then, through an inverter, is respectively connected to a supercapacitor and a storage battery; This section of the test AC bus is also connected to a gas turbine, a diesel engine, and an RTLAB hardware-in-the-loop simulation device through a variable line impedance, an inverter, and a rectifier respectively; This section of the test AC bus is also connected to a simulated load for fault simulation tests.

[0009] The other section of the test AC bus is connected to a variable line impedance, the other end of the variable line impedance is connected to an inverter, and the other end of the inverter is connected to the photovoltaic power; This section of the test AC bus is also connected to a variable line impedance, the other end of the variable line impedance is connected to an inverter, the other end of the inverter is connected to a rectifier, and the other end of the rectifier is connected to the wind turbine, which can not only conduct simulation tests but also be connected to the previous bus to supply power to the load; When the load capacity increases, the circuit breaker disconnects, and the bus connected to the photovoltaic power and the wind turbine is connected to the load end to achieve sectional control.

[0010] Both the test AC bus and the grid AC bus are 10 kV AC buses of the grid.

[0011] The two connection channels of the photovoltaic power and the wind turbine are that there are two output ports on the micro-source, which are respectively connected to inverters and then connected to the two test AC buses; Most of the generated power is output to the first grid AC bus through an inverter to supply power to the regional load, and a small part is output to the other test AC bus through an inverter for simulation tests. If the regional load increases, the channel for output simulation tests is disconnected by a circuit breaker, and all power is supplied to the load.

[0012] A test microgrid control method applicable to various microgrid structure verifications, comprising the following steps:

[0013] Step 1. Select variables for optimizing bottom-layer indicators and upper-layer indicators;

[0014] Step 2. Calculate the objective function;

[0015] Step 3. Use the particle swarm algorithm to obtain the optimal solution for optimizing bottom-layer indicators;

[0016] Step 4. Determine the optimization level of the upper-layer indicators through power quality evaluation indicators;

[0017] Step 5. Use the membership function evaluation score to determine the optimal solutions for optimizing bottom-layer indicators and upper-layer indicators.

[0018] The selection of variables for optimizing bottom-layer indicators and upper-layer indicators includes:

[0019] Select two optimization variables:

[0020] {P PV P WIND} 1.1

[0021] where P PV is the photovoltaic output power, and P WIND is the wind turbine output power;

[0022] The objective function for the economic optimization of the test microgrid system is expressed as:

[0023] min Z cost =λ1A + λ2B + λ3C 1.2

[0024] In the formula, Z cost is the operating objective function of the test microgrid, A is the system maintenance cost, B is the cost of resources consumed in the system operation, and C is the profit created by the system power generation;

[0025] It can be seen that the total system cost is equal to the maintenance cost minus the profit created by the resources cost; λ1 is the weight ratio of the system maintenance cost, λ2 is the weight ratio of the system operation cost, λ3 is the weight ratio of the profit created by the system power generation, and λ1 + λ2 + λ3 = 1.

[0026] The use of the particle swarm algorithm to obtain the optimal solution for optimizing bottom-layer indicators includes the following steps:

[0027] Step 31. Determine the feasible domain space. In the D-dimensional feasible domain, assume that at time t, the spatial positions of n random particles are:

[0028]

[0029] In the above formula: Indicates the spatial positions of each particle at time t;

[0030] The particle velocity is:

[0031]

[0032] In the above formula: Indicates the velocity of each particle at time t;

[0033] Step 32. Assuming that each particle is a feasible solution to the equation to be solved, calculate the fitness function value of the particle. By vertically comparing the magnitudes of the historical fitness values of the particles before time t, obtain the individual historical optimal position:

[0034]

[0035] In the above formula: Indicates the historical optimal value of the current position of each particle;

[0036] Step 33. Horizontally compare the magnitudes of the fitness values at time t to obtain the global optimal position in the current generation:

[0037]

[0038] In the above formula: Indicates the global optimal value of each particle;

[0039] Step 34. Iteratively optimize the particles. At time t+1, the spatial position coordinates x i (t) and the velocity v i (t) have the following common iterative formulas:

[0040] v i (t+1) = v i (t) + c1·r1(p i (t) - x i (t)) + c2·r2(p g (t) - x i (t)) 3.5

[0041] In the formula, c1 and c2 respectively represent the learning constants of the particles; r1 and r2 are uniformly distributed in [0,1]; p i is the individual extreme value; p g is the global extreme value;

[0042] It can be seen from the above formula that the velocity update formula consists of three parts. The first part enables the algorithm to perform global search and balance the global and local search capabilities; the second part enables the particles to perform strong local search; the third part considers the ability of the particles to learn from the particles in the entire population, reflecting the information sharing among different particles;

[0043] The particle updates its corresponding velocity and then updates its position. The position formula is as follows:

[0044] x i (t + 1)=x i (t)+v i (t + 1) 3.6

[0045] By restricting the amplitude of the velocity change, let v min <v i (t)<v max , the change in the particle position is restricted; in multiple iterations, each particle in the population is cyclically updated, making the entire population gradually approach the global optimal solution.

[0046] The optimization of the upper-layer indicators includes:

[0047] Step 41: Establish reliability and quality as first-level indicators, and establish compliance satisfaction rate, power shortage power, system capacity reserve rate, voltage deviation, harmonics, three-phase unbalance degree, and frequency deviation as second-level indicators;

[0048] Step 42: Assign weights to the first-level indicators of reliability and quality; the proportion of reliability is 0.3, and the proportion of quality is 0.7;

[0049] Step 43: Assign weights to the second-level indicators of reliability. The proportion of load satisfaction rate is 0.4, the proportion of power shortage power is 0.2, and the proportion of system capacity reserve rate is 0.4; assign weights to the second-level indicators of quality. The proportion of frequency deviation is 0.27, the proportion of voltage deviation is 0.34, the proportion of three-phase unbalance degree is 0.22, and the proportion of harmonics is 0.17;

[0050] The determination of the membership function includes:

[0051] Use the Cauchy distribution to establish the membership function of each indicator, as shown in the following formula:

[0052]

[0053] The absolute value of the deviation between the indicator and the optimal indicator; α i and β i are the coefficients of the Cauchy distribution corresponding to the i-th indicator, and β i takes the value of 1.45;

[0054] The algorithm for the optimal membership degree is as follows:

[0055] First, select a range, and then pick a number within the selected range, which is called the optimal standard value. The corresponding optimal membership degree is 1. Then, select a corresponding reference value according to the known condition range, and stipulate that its corresponding optimal membership degree is 0.5. Calculate the numerical value of the unknown parameter, and then know other optimal membership degrees. In the frequency deviation of the first-level index quality of the experimental microgrid, assume that the limit value of the frequency deviation is selected as ±0.5 Hz. Using the absolute value calculation, stipulate that when the marginal value, that is, the frequency deviation is 1%, the corresponding optimal membership degree is 0.5, and the evaluation score is 0.5. And when the frequency deviation is 0, it is the best index value, corresponding to the membership degree of 1, and the evaluation score is 1. According to x i When x i = 1, λ i = 0.5, and it is deduced that α

[0056]

[0057] In the above formula: x i represents the frequency deviation, and λ i represents the optimal membership degree;

[0058] Take ten economic optimal solutions, run the ten groups of data in the simulation program respectively, and then calculate the power quality indexes of each group according to the power quality weight distribution, and select the optimal group as the optimal group of data for this time period.

[0059] A computer storage medium stores a computer program thereon. When the computer program is executed by a processor, the steps of the experimental microgrid control method applicable to various microgrid structure verifications are implemented.

[0060] The present invention has the following beneficial effects and advantages:

[0061] The structure of the experimental microgrid of the present invention should be able to cover the current and future microgrid structure forms. According to different test schemes, the corresponding microgrid structures can be switched at will, including DC microgrid, AC microgrid, AC-DC hybrid microgrid, and multi-energy complementary microgrid. The experimental microgrid should have scalability and should include the mainstream micro-sources of the current new energy microgrid and the micro-sources with development prospects in the future. According to the requirements of different experimental microgrid structures, the experimental microgrid can be switched between different structural modes to meet the corresponding structural detection conditions. Using the power quality standard and economic standard, adopting a two-level operation optimization strategy makes the system operation more reasonable.

[0062] The present invention can simulate and verify various microgrid structures. The present invention includes adopting a double-bus structure and sectional control for a 10 kV voltage line to form a test microgrid, which changes the singularity of the traditional single-bus structure of the 10 kV voltage line. The structure of the test microgrid can be adjusted for different demonstration engineering projects for simulation verification. A variable line impedance and a simulated load are added to replace the impedance and load in the microgrid. And a two-layer operation optimization method with economic indicators as the bottom layer and power quality indicators as the upper layer is used to find the optimal operation plan, so that the test microgrid always operates in the best working state.

[0063] The present invention discloses a method for constructing and controlling a test microgrid that can be used to verify the structure and operation plan of a microgrid, overcoming the problem that the traditional 10 kV bus has only a single-bus structure and can only operate singly, with limitations. In order to simulate microgrid tests in different scenarios, a double-bus structure and sectional control method are adopted. It can not only conduct simulation experiments but also supply power to regional loads. Among them, large-scale power generation equipment such as photovoltaic and wind turbines has two connection channels and can be switched arbitrarily. Due to different simulated scenarios, a variable line impedance and a simulated load are used instead. In the simulation test, two-level optimization is carried out using two criteria of power quality and economy to find the optimal solution and make the system operation more reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The above and / or additional aspects and advantages of the present invention will become apparent and easy to understand from the description of the embodiments in conjunction with the following drawings, where:

[0065] Figure 1 is a schematic diagram of the double-bus structure of the test microgrid of the present invention;

[0066] Figure 2 is a schematic diagram of the sectional control structure of the test microgrid of the present invention;

[0067] Figure 3 is a flowchart of the bottom-layer optimization of the test microgrid of the present invention;

[0068] Figure 4 is a block diagram of the upper-layer optimization operation index system of the test microgrid of the present invention.

[0069] In the figure:

[0070] Photovoltaic 1, wind turbine 2, supercapacitor 3, battery 4, gas turbine 5, diesel engine 6, RTLAB hardware-in-the-loop device 7, load 8, inverter 9, rectifier 10, grid AC bus 11, test AC bus 12, simulated load 13, variable line impedance 14, circuit breaker 15. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] In order to more clearly understand the above-mentioned objects, features, and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0072] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0073] The following refers to Figures 1 - 4 Describe the technical solutions of some embodiments of the present invention.

[0074] Embodiment 1

[0075] The present invention is a method for constructing and controlling a test microgrid that can be used for verifying various microgrid structures, such as Figure 1 shown, Figure 1 is a schematic diagram of the double-bus structure of the test microgrid of the present invention. The present invention has a double-bus structure and a sectional control structure, and can be used for simulation experiments and also supply power to regional loads.

[0076] Among them, Photovoltaic 1 is connected to the variable line impedance 14 through the DC / AC inverter 9, and the other end of the variable line impedance 14 is respectively connected to the grid AC bus 11 and the test AC bus 12.

[0077] The wind turbine 2 is connected to the variable line impedance 14 through the AC / DC rectifier 10 and the inverter 9, and the other end of the variable line impedance 14 is respectively connected to the grid AC bus 11 and the test AC bus 12.

[0078] The load 8 is connected to the grid AC bus 11. The supercapacitor 3 is connected to the test AC bus 12 through the inverter 9 and the variable line impedance 14, the battery 4 is connected to the test AC bus 12 through the inverter 9 and the variable line impedance 14, the gas turbine 5 is connected to the test AC bus 12 through the rectifier 10 and the inverter 9 and the variable line impedance 14, the diesel engine 6 is connected to the test AC bus 12 through the rectifier 10 and the inverter 9 and the variable line impedance 14, the RTLAB hardware-in-the-loop device 7 is connected to the test AC bus 12 through the rectifier 10 and the inverter 9 and the variable line impedance 14, and the load 8 is connected to the test AC bus 12. The test AC bus 12 is disconnected in the middle by the circuit breaker 15.

[0079] Among them, the load 8 is replaced by the simulated load 13.

[0080] The test AC bus 12 is a test 10 kV AC bus.

[0081] The grid AC bus 11 is the 10 kV AC bus of the grid.

[0082] The double-bus structure means there are a total of two buses. One grid AC bus 11 is connected to large-scale micro-sources such as PV 1 and wind turbine 2, directly supplying power to the load 8 in a specified area. The other test AC bus 12 is connected to simulation units such as PV 1, wind turbine 2, supercapacitor 3, battery 4, and gas turbine 5 to conduct simulation fault tests.

[0083] As Figure 2 shown, Figure 2 It is a schematic diagram of the sectional control structure of the test microgrid of the present invention. The sectional control method means that the test AC bus 12 for conducting simulation experiments is divided into two sections in total, and the middle of the bus is separated by a circuit breaker 15. One section is connected to the supercapacitor 3, battery 4, gas turbine 5, RTLAB hardware-in-the-loop simulation device 7, and simulated load 13 to conduct fault simulation tests. Among them: The test AC bus 12 is first connected to a variable line impedance 14, and then connected to the supercapacitor 3 through an inverter 9; the test AC bus 12 is also connected to another group of variable line impedances 14, and then connected to the battery 4 through an inverter 9.

[0084] This section of the test AC bus 12 is also sequentially connected to the inverter 9, rectifier 10, and gas turbine 5 through another group of variable line impedances 14; this section of the test AC bus 12 is also sequentially connected to the inverter 9, rectifier 10, and diesel engine 6 through another group of variable line impedances 14.

[0085] This section of the test AC bus 12 is also sequentially connected to the inverter 9, rectifier 10, and RTLAB hardware-in-the-loop simulation device 7 through another group of variable line impedances 14; this section of the test AC bus 12 is also connected to the simulated load 13 for conducting fault simulation tests.

[0086] The other section of the test AC bus 12 is connected to PV 1 and wind turbine 2, which can not only conduct simulation tests but also be connected to the previous bus to supply power to the load 8. When the capacity of the load 8 increases, the circuit breaker 15 disconnects, and the bus connected to PV 1 and wind turbine 2 is connected to the load 8 end, thus realizing sectional control.

[0087] Among them: The other section of the test AC bus 12 is connected to a variable line impedance 14, the other end of the variable line impedance 14 is connected to an inverter 9, and the other end of the inverter 9 is connected to PV 1. This section of the test AC bus 12 is also connected to another group of variable line impedances 14, the other end of the variable line impedance 14 is connected to an inverter 9, the other end of the inverter 9 is connected to a rectifier 10, and the other end of the rectifier 10 is connected to wind turbine 2.

[0088] The two connection channels of the photovoltaic power source 1 and the wind turbine 2 described in the present invention refer to that there are two output ports on the micro-source, which are respectively connected to the inverter 9 and then connected to two test AC buses. Most of the generated power is output to the first grid AC bus 11 through the inverter 9 to supply power to the regional load, and a small part is output to the other test AC bus 12 through the inverter 9 for simulation tests. If the regional load increases, the channel for output simulation tests will be disconnected by the circuit breaker 15, and all power will be supplied to the load 8.

[0089] The described simulated load 13 means that the simulated load 13 can adjust the capacity or power demand characteristics according to actual needs. During the construction of the experimental microgrid, due to the different microgrid structures and locations of each demonstration project, different load capacities are required. Therefore, the load simulation module of the experimental microgrid is replaced by the simulated load 13.

[0090] The variable line impedance 14 refers to adjusting the resistance value or impedance characteristics according to actual needs. Since the demonstration projects are different and the distances from each micro-source to the test AC bus 12 after being connected to the output of the inverter 9 are different, during the simulation experiment, the line impedance of the line is replaced by the variable line impedance 14. In this way, the impedance value can be adjusted in real time according to the demonstration project, saving time and cost.

[0091] Embodiment 2

[0092] The present invention is an experimental microgrid control method that can be used for verifying various microgrid structures, which is divided into two-level optimization. The two-level optimization refers to using the economic index as the bottom-level index for optimization and the power quality as the top-level index for optimization to make the system operation reach the highest efficiency.

[0093] The optimization of the bottom-level index is as Figure 3 shown Figure 3This is the flowchart of the underlying optimization process of the experimental microgrid of the present invention. Taking photovoltaic and wind turbines as examples, photovoltaic power generation converts solar energy into electrical energy. Therefore, due to the passage of time, the photovoltaic power generation capacity also changes. The 24 hours of a day are divided into six time periods, with each four hours as a time period. Then optimization is carried out every four hours. Assume that the power generated by the photovoltaic within these four hours is fixed. Then when the load capacity is fixed, the power distribution of the photovoltaic and the wind turbine is random. In order to determine the optimal one, one hundred random schemes are selected, the power that the photovoltaic and the wind turbine need to generate respectively is determined, the cost of the photovoltaic and the wind turbine is calculated, and then a comparison is made, excluding the 20 schemes with the highest cost. Then another 20 schemes are selected to form one hundred, and the economy is compared again. The 20 schemes with poor economy are excluded again. And so on, a total of 200 calculations and comparisons are made. Select the 10 groups of data with the best economy. These ten groups of data are considered to have the best economy within these four hours. Then, for the upper-layer optimization, the power quality of these ten groups of data is calculated respectively. A weight is given to each power quality standard, and after multiplying by the weights and adding them up, the group of data with the best power quality is found. Then this group of data is the optimal configuration within this time period.

[0094] Because the photovoltaic production capacity is different every four hours, optimization is carried out every four hours. This can ensure that the system is in the optimal operating state. The specific algorithm is as follows.

[0095] Step 1. Select two optimization variables:

[0096] {P PV P WIND} 1.1

[0097] Among them, P PV is the photovoltaic output power, and P WIND is the wind turbine output power.

[0098] Step 2. Calculate the objective function:

[0099] The objective function of the economic optimization of the experimental microgrid system can be expressed as:

[0100] min Z cost =λ1A + λ2B + λ3C 1.2

[0101] In the formula, Z cost is the operating objective function of the experimental microgrid, A is the system maintenance cost, B is the cost of resources consumed by the system operation, and C is the profit created by the system power generation. It can be seen that the total system cost is equal to the maintenance cost and resource cost minus the created profit; λ1 is the weight ratio of the system maintenance cost, λ2 is the weight ratio of the system operation cost, λ3 is the weight ratio of the profit created by the system power generation, and λ1 + λ2 + λ3 = 1.

[0102] Step 3. Particle Swarm Optimization Algorithm and Its Implementation:

[0103] The particle swarm optimization algorithm is a population-based stochastic search algorithm, and the specific steps of this algorithm are as follows:

[0104] First, determine the feasible domain space. In the D-dimensional feasible domain, assume that at time t, the spatial positions of n random particles are:

[0105]

[0106] In the above formula: represents the spatial positions of each particle at time t.

[0107] The particle velocity is:

[0108]

[0109] In the above formula: represents the velocity of each particle at time t.

[0110] Meanwhile, assume that each particle is a feasible solution to the equation to be solved, calculate the fitness function value of the particle, and obtain the individual historical optimal position by longitudinally comparing the magnitudes of the historical fitness values of the particles before time t:

[0111]

[0112] In the above formula: represents the historical optimal value of the current position of each particle.

[0113] Then, horizontally compare the magnitudes of the fitness values at time t to obtain the global optimal position in the current iteration:

[0114]

[0115] In the above formula: represents the global optimal value of each particle.

[0116] Iteratively optimize the particles. At time t + 1, the spatial position coordinates x i (t) and the velocity v i (t) have the following common iterative formulas:

[0117] v i (t + 1) = v i (t) + c1·r1(p i (t) - x i (t)) + c2·r2(p g (t) - x i (t)) 3.5

[0118] Wherein, c1 and c2 respectively represent the learning constants of the particles; r1 and r2 are uniformly taken values between [0, 1]; p i is the individual extreme value; p g is the global extreme value.

[0119] It can be seen from the above formula that the velocity update formula consists of three parts. The first part enables the algorithm to perform global search and balance the global and local search capabilities; the second part enables the particles to perform strong local search; the third part considers the ability of the particles to learn from the particles in the entire population, reflecting the information sharing among different particles.

[0120] The particles update their corresponding velocities and then update their positions. The position formula is shown as follows:

[0121] x i (t + 1) = x i (t) + v i (t + 1) 3.6

[0122] By restricting the amplitude of the velocity change, let v min < v i (t) < v max , the change size of the particle position can be restricted. In multiple iterations, each particle in the population is cyclically updated, making the entire population gradually approach the global optimal solution.

[0123] Step 4. Determination of power quality evaluation indicators:

[0124] As Figure 4 shown, Figure 4 is the block diagram of the upper layer optimization operation index system of the experimental microgrid of the present invention.

[0125] By studying the operation conditions of a large number of microgrids, according to the requirements of the power grid and relevant technologies, the aim is to study the relationship between the power or capacity of distributed power sources and loads in the microgrid system and the quality of various power qualities, and mainly evaluate the microgrid from two aspects of reliability and quality.

[0126] The upper layer index optimization of the experimental microgrid described in the present invention includes:

[0127] Step 41: Establish reliability and quality as the first-level indicators, and establish compliance satisfaction rate, power shortage power, system capacity reserve rate, voltage deviation, harmonics, three-phase unbalance degree, and frequency deviation as the second-level indicators.

[0128] Step 42: Allocate weights to the first-level indicators of reliability and quality. The proportion of reliability is 0.3, and the proportion of quality is 0.7.

[0129] Step 43: Assign weights to the secondary indicators of reliability. The load satisfaction rate accounts for 0.4, the power shortage power accounts for 0.2, and the system capacity reserve rate accounts for 0.4. Assign weights to the secondary indicators of quality. The frequency deviation accounts for 0.27, the voltage deviation accounts for 0.34, the three-phase unbalance degree accounts for 0.22, and the harmonic accounts for 0.17.

[0130] Step 5. Determine the membership function:

[0131] The membership function refers to the relationship between the finally obtained score and the evaluation indicators, which is a basis for judgment to obtain the scores of each indicator. In the present invention, the Cauchy distribution is used to establish the membership function of each indicator, and the Cauchy distribution is as shown in the formula.

[0132]

[0133] The absolute value of the deviation between the indicator and the optimal indicator; α i and β i are the coefficients of the Cauchy distribution corresponding to the i-th indicator. In the present invention, β i takes the value of 1.45.

[0134] The algorithm for the optimal membership degree is as follows:

[0135] First, select a range, and then take a number in the selected range, which is called the optimal standard value. Then the corresponding optimal membership degree is 1. Then, according to the known condition range, select a corresponding reference value and stipulate that its corresponding optimal membership degree is 0.5. The numerical value of the unknown parameter can be calculated, and then the other optimal membership degrees can be known. Taking the frequency deviation in the primary indicator of quality of the experimental microgrid as an example, assume that the limit value of the frequency deviation is selected as ±0.5 Hz. Using the absolute value calculation, it is stipulated that when the marginal value, that is, the frequency deviation is 1%, its corresponding optimal membership degree is 0.5, and the evaluation score is 0.5; while when the frequency deviation is 0, it is the best index value, and the corresponding membership degree is 1, and the evaluation score is 1. According to x i =1, λ i =0.5, it is deduced that α i =1, and the membership function of the frequency deviation is calculated as follows:

[0136]

[0137] In the above formula: x i represents the frequency deviation, and λ i represents the optimal membership degree.

[0138] Take ten optimal solutions of economy, and put the ten groups of data into the simulation program to run respectively. Then calculate the power quality indicators of each group according to the power quality weight distribution, and select the optimal group, which is the optimal group of data for this time period.

[0139] Example 3

[0140] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the test microgrid control method for verifying various microgrid structures described in Embodiment 2 are implemented.

[0141] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can 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.

[0142] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0143] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0144] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent substitutions can still be made to the specific implementation manners of the present invention, and any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A test microgrid construction method applicable to the verification of various microgrid structures, characterized in that: The experimental microgrid includes a dual-bus structure and a sectional control structure. The dual-bus structure of the experimental microgrid includes two buses. One grid AC bus is connected with a photovoltaic (1), a wind turbine (2), and a load (8). The other experimental AC bus is connected with a photovoltaic (1), a wind turbine (2), a supercapacitor (3), a battery (4), a gas turbine (5), a diesel engine (6), an RTLAB hardware-in-the-loop device (7), and a load (8). The sectional control structure of the experimental microgrid includes a grid AC bus, which is divided into two sections in total, and a circuit breaker (15) is provided in the middle of the grid AC bus. One section of the grid AC bus is connected with a supercapacitor (3), a battery (4), a gas turbine (5), a diesel engine (6), an RTLAB hardware-in-the-loop simulation device (7), and a simulated load (13). The other section of the grid AC bus is connected with a photovoltaic (1) and a wind turbine (2). The dual-bus structure of the experimental microgrid includes: The photovoltaic (1) is sequentially connected to a variable line impedance (14) through a DC / AC inverter (9), and is respectively connected to the grid AC bus (11) and the experimental AC bus (12) through the variable line impedance (14). The wind turbine (2) is connected to the variable line impedance (14) through an AC / DC rectifier (10) and an inverter (9), and is respectively connected to the grid AC bus (11) and the experimental AC bus (12) through the variable line impedance (14). The load (8) is connected to the grid AC bus (11). The supercapacitor (3) is connected to the variable line impedance (14) through an inverter (9), and then connected to the experimental AC bus (12). The battery (4) is connected to the variable line impedance (14) through an inverter (9), and then connected to the experimental AC bus (12). The gas turbine (5) is connected to the variable line impedance (14) through a rectifier (10) and an inverter (9), and then connected to the experimental AC bus (12). The diesel engine (6) is connected to the variable line impedance (14) through a rectifier (10) and an inverter (9), and then connected to the experimental AC bus (12). The RTLAB hardware-in-the-loop device (7) is connected to the variable line impedance (14) through a rectifier (10) and an inverter (9), and then connected to the experimental AC bus (12). The load (8) is connected to the experimental AC bus (12), and the middle of the experimental AC bus (12) is disconnected by a circuit breaker (15). The sectional control structure of the experimental microgrid includes: An experimental AC bus (12) divides the experimental AC bus (12) into two sections, and a circuit breaker (15) is provided in the middle of the experimental AC bus (12) to separate the experimental AC bus (12) into two sections. One section of the experimental AC bus (12) is first connected to the variable line impedance (14), and then respectively connected to the supercapacitor (3) and the battery (4) through an inverter (9). This section of the experimental AC bus (12) is also respectively connected to the gas turbine (5), the diesel engine (6), and the RTLAB hardware-in-the-loop simulation device (7) through the variable line impedance (14), an inverter (9), and a rectifier (10).This section of the test AC busbar (12) is also connected to a simulated load (13) for fault simulation tests; another section of the test AC busbar (12) is connected to a variable line impedance (14), the other end of the variable line impedance (14) is connected to an inverter (9), and the other end of the inverter (9) is connected to a photovoltaic (1); this section of the test AC busbar (12) is also connected to a variable line impedance (14), the other end of the variable line impedance (14) is connected to an inverter (9), and the other end of the inverter (9) is connected to a rectifier (10), the other end of the rectifier (10) is connected to a fan (2), which can not only conduct simulation tests but also be connected to the previous busbar to supply power to the load (8); when the capacity of the load (8) increases, the circuit breaker (15) disconnects, and the busbar connecting the photovoltaic (1) and the fan (2) is connected to the load (8) end to achieve sectional control; the test AC busbar (12) and the grid AC busbar (11) are both 10 kV grid AC busbars; the two connection channels of the photovoltaic (1) and the fan (2) are that there are two output ports on the micro-source respectively connected to the inverter (9), and then connected to two test AC busbars; most of the power generation is output to the first grid AC busbar (11) through the inverter (9) to supply power to the regional load, and a small part is output to the other test AC busbar (12) through the inverter (9) for simulation tests. If the regional load increases, the channel for output simulation tests is disconnected by the circuit breaker (15) and all power is supplied to the load (8).; 2. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the test microgrid construction method for verifying various microgrid structures described in claim 1 are implemented.

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