A building flexible DC microgrid system operation optimization method, terminal and storage medium based on scenario generation
Through a scene generation method, using a variety of scene generation algorithms and optimization models, the building flexible DC microgrid system is optimized, which solves the problem that traditional methods are difficult to deal with uncertain factors and achieves a more efficient and stable operation strategy.
Patent Information
- Application Number
- CN202411706053.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-26
- Publication Date
- 2025-04-08
AI Technical Summary
The operation optimization method of traditional microgrid systems is difficult to effectively deal with uncertain factors such as wind and light resource prediction errors and load fluctuations, which leads to the disconnection of the optimization strategy from the actual situation and affects the economic and stability of the system.
A scene-based generation method is adopted to generate multiple operating scenarios through multiple scenario generation algorithms (such as Monte Carlo sampling, Latin hypercube sampling, and multi-climate building performance simulation), and a pre-constructed ISO model and two-stage random planning model are used to optimize the scenario, and finally an operation strategy aimed at the optimal economic operation is formulated.
It improves the system's ability to adapt to uncertainty, enhances the accuracy and reliability of optimization strategies, and ensures the stable and economic operation of the microgrid system when facing uncertainty factors.
Smart Images
Figure CN119787282A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of DC microgrid energy optimization, and specifically relates to a building flexible DC microgrid system operation optimization method, terminal and storage medium based on scenario generation. Background Art
[0002] Under the current global energy transformation background, the rapid development of renewable energy such as wind and solar energy has provided a rich source of power for building flexible DC microgrid systems, promoting the research boom as an important component of future smart grids. However, progress in this field is also accompanied by many challenges, especially the intermittent nature of wind and solar resources and the high uncertainty of user loads, which have brought great challenges to the economic and stable operation of microgrid systems.
[0003] Traditionally, the operation optimization of microgrid systems mostly relies on deterministic optimization models. Such models are unable to cope with complex and changeable actual operating conditions because they fail to fully consider uncertain factors such as wind and solar resource prediction errors and load fluctuations, resulting in the optimization strategy being out of touch with the actual situation, affecting the economy and stability of the system.
[0004] Although existing technologies have made some attempts, such as optimizing microgrid operation by minimizing power supply losses during the fault protection period and considering the prediction deviation of new energy, or using technologies such as probability distribution random number generation and neural networks to simulate uncertain scenarios, these methods either oversimplify uncertainty factors or have high computational complexity and are difficult to directly apply to actual systems. At the same time, when dealing with uncertainty in power grid operation, traditional methods such as simulation and point estimation methods can identify key influencing factors, but lack the ability to systematically integrate and optimize strategy formulation. Summary of the invention
[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a building flexible DC microgrid system operation optimization method, terminal and storage medium based on scenario generation to solve the above-mentioned technical problems.
[0006] In a first aspect, the present invention provides a method for optimizing the operation of a building flexible DC microgrid system based on scenario generation, comprising: Based on the uncertainty of load, several scenario generation algorithms are used to generate several corresponding operation scenarios for the building flexible DC microgrid system. Use pre-built ISO models to perform independent scenario optimization for each generated operational scenario; A pre-built two-stage stochastic programming model is used to perform a second scenario optimization on each optimized operation scenario; Based on the scenario optimization results, an operation strategy is formulated with the goal of optimizing economic operation.
[0007] Further improvements of the technical solution include that the scenario generation algorithm includes Monte Carlo sampling, Latin hypercube sampling, multi-climate building performance simulation and Monte Carlo building performance simulation, which respectively use Monte Carlo sampling, Latin hypercube sampling, multi-climate building performance simulation and Monte Carlo building performance simulation to generate corresponding operating scenarios for the building flexible DC microgrid system.
[0008] A further improvement of the technical solution is that the pre-built ISO model aims to minimize the sum of investment cost and operating cost, and the objective function expression of the pre-built ISO model is: ; ; ; in, It is the sum of investment cost and operating cost; For investment costs; For operating costs; is the energy transaction price; procure electricity for the grid; is the linear investment cost per unit capacity of the ith component; is an integer variable representing the capacity of the i-th energy device; is the interest rate of the i-th energy technology; To calculate the years.
[0009] A further improvement of the technical solution is that the pre-built ISO model is also provided with power balance constraints, energy storage constraints and flexible load constraints.
[0010] A further improvement of the technical solution is that the expression of the power balance constraint is: ; ; ; in, Purchase electricity for the grid at time t, is the photovoltaic power generation at time t, is the energy storage discharge power at time t, is the energy storage charging power at time t, is the flexible load at time t, is the inflexible load at time t, is the predicted value of photovoltaic power generation at time t, is the photovoltaic power generation capacity, is the upper limit of photovoltaic power generation capacity, is the lower limit of photovoltaic power generation capacity; The expression of energy storage constraint is: ; ; in, is the energy storage capacity at time t+1, is the energy storage capacity at time t, is the lower limit of energy storage at time t; for The expression of flexible load constraint is:
[0011] in, is the lower limit of flexible load at time t, is the upper limit of flexible load at time t.
[0012] A further improvement of the technical solution is that the objective function expression of the pre-built two-stage stochastic programming model is: ; ; in, is the expected value of operating cost; for.
[0013] Further improvements of the technical solution include using a preset dynamic time warping algorithm to perform a horizontal check on the operating scenario after the second scenario optimization, so as to obtain an optimized operating strategy for the building flexible DC microgrid system under different load uncertainty conditions.
[0014] A further improvement of the technical solution also includes generating random variables based on the probability distribution of load uncertainty, and the scene at each time point is obtained by normal distribution sampling.
[0015] In a second aspect, a terminal is provided, including: processor, memory, wherein: The memory is used to store computer programs. The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.
[0016] According to a third aspect, a computer storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the methods described in the above aspects.
[0017] The beneficial effects of the present invention are: Improve system adaptability: By introducing multiple scenario generation algorithms based on load uncertainty, the present invention can fully cover and simulate various complex scenarios that the microgrid system may encounter in actual operation, thereby enhancing the system's adaptability to uncertainty. This improved adaptability makes the optimization strategy closer to the actual operation situation, effectively reducing the deviation between the optimization results and the actual situation.
[0018] Enhanced optimization accuracy: By using the pre-built ISO model for preliminary scenario optimization and combining it with the two-stage stochastic programming model for in-depth optimization, the present invention achieves a double check on the microgrid system operation strategy. This hierarchical, multi-stage optimization method can more accurately consider uncertain factors such as wind and solar resource prediction errors and load fluctuations, and improve the accuracy and reliability of the optimization strategy.
[0019] Improve economy and stability: The operation strategy based on the scenario optimization results takes the economic operation as the goal, which can not only effectively reduce the operation cost of the microgrid system, but also ensure the stable operation of the system in the face of uncertain factors. This dual-objective optimization strategy enables the microgrid system to have stronger risk resistance and stability while ensuring economic benefits.
[0020] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0022] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.
[0023] Figure 2 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0024] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present invention.
[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.
[0026] The ISO (Independent Scenarios Optimization) model is a method that is different from the traditional deterministic optimization model. It generates independent scenarios to represent different deterministic scenarios, so that each scenario can be optimized independently. Specifically, the ISO model builds an optimization model for each independent scenario, and then optimizes each scenario separately. The advantage of this method is that it can better cope with uncertainty, because each scenario is optimized based on a specific set of conditions, which can more realistically reflect the system performance under actual conditions.
[0027] like Figure 1 As shown, the present invention provides a building flexible DC microgrid system operation optimization method based on scenario generation, comprising: Step 110, based on the uncertainty of the load, using several scenario generation algorithms to generate several corresponding operation scenarios for the building flexible DC microgrid system; Step 120, using the pre-built ISO model to perform independent scenario optimization on each generated operation scenario; Step 130, using a pre-built two-stage stochastic programming model (TSSP) to perform a second scenario optimization on each optimized operation scenario; Step 140, formulating an operation strategy with the goal of optimizing economic operation based on the scenario optimization results.
[0028] Specifically, this method takes into account the demand response curve, photovoltaic power generation curve and energy storage operating status, takes into account the uncertainty of load, takes the optimal economic operation as the goal, and provides operating strategies for the main scenarios of building flexible DC microgrid systems with wind and solar as the main power source.
[0029] Among them, the scenario generation algorithms include Monte Carlo sampling, Latin hypercube sampling, multi-climate building performance simulation and Monte Carlo building performance simulation, which respectively use Monte Carlo sampling, Latin hypercube sampling, multi-climate building performance simulation and Monte Carlo building performance simulation to generate corresponding operation scenarios for the building flexible DC microgrid system. The above scenario generation method uses a robust probability model to generate random variables based on the probability distribution of load uncertainty. The scenario at each time point is obtained by normal distribution sampling to describe the changes in short-term energy demand.
[0030] Before that, it also includes the construction of a building flexible DC microgrid system that includes energy storage systems, photovoltaic systems, flexible loads (such as electric vehicle charging stations, intelligent temperature control systems, etc.) and other loads (such as conventional lighting, office equipment, etc.). The systems are connected through a DC bus to achieve the transmission and distribution of electric energy.
[0031] After that, for each generated operation scenario, an objective function is constructed and solved. The objective function aims to minimize the sum of investment and operating costs, taking into account constraints such as power balance constraints, energy storage constraints, and flexible load constraints. The objective function is solved by optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) to obtain the optimal energy configuration and operation strategy under each scenario.
[0032] The objective function expression of the pre-built ISO model is: ; ; ; in, It is the sum of investment cost and operating cost; For investment costs; For operating costs; is the energy transaction price; procure electricity for the grid; is the linear investment cost per unit capacity of the ith component; is an integer variable representing the capacity of the i-th energy device; is the interest rate of the i-th energy technology; To calculate the years.
[0033] Among them, the expression of power balance constraint is: ; ; ; in, Purchase electricity for the grid at time t, is the photovoltaic power generation at time t, is the energy storage discharge power at time t, is the energy storage charging power at time t, is the flexible load at time t, is the inflexible load at time t, is the predicted value of photovoltaic power generation at time t, is the photovoltaic power generation capacity, is the upper limit of photovoltaic power generation capacity, is the lower limit of photovoltaic power generation capacity; The expression of energy storage constraint is: ; ; in, is the energy storage capacity at time t+1, is the energy storage capacity at time t, is the lower limit of energy storage at time t; for The expression of flexible load constraint is:
[0034] in, is the lower limit of flexible load at time t, is the upper limit of flexible load at time t.
[0035] Then, based on the optimization of independent scenarios, a two-stage stochastic programming model is constructed. The first stage determines the capacity and initial operating state of each energy component; the second stage recalibrates the variables related to the complexity of energy production operations (such as the charging and discharging power of the energy storage system, the output power of the photovoltaic system, etc.) for each time step and each scenario to adapt to the uncertainty and variability in actual operation. By solving the two-stage stochastic programming model, the global optimal solution or the optimized solution that meets specific needs is obtained.
[0036] Among them, the objective function expression of the pre-built two-stage stochastic programming model is: ; ; in, is the expected value of operating cost; for.
[0037] Finally, the operation strategy of the microgrid system is formulated according to the optimization results. This includes the charging and discharging plan of the energy storage system (such as adjusting the charging and discharging time according to electricity price fluctuations to reduce costs), the output power adjustment of the photovoltaic system (such as adjusting the tilt angle of the photovoltaic panel according to the light intensity to improve the power generation efficiency), the scheduling of flexible loads (such as adjusting the charging time of electric vehicles and the operating mode of air conditioners according to user needs to save energy), etc. These measures are used to achieve economic and stable operation of the microgrid system. Specifically, the operation optimization method also includes using a preset dynamic time warping algorithm to perform a horizontal verification of the operation scenario after the second scenario optimization to obtain the optimized operation strategy of the building flexible DC microgrid system under different load uncertainty conditions.
[0038] For example, dynamic time warping (DTW) is an important method for evaluating time series similarity, which is used to quantify the similarity between two time series. DTW operates by identifying the best alignment path in the distance matrix of two time series, thereby minimizing the sum of the cumulative element values along the path. The lower the DTW value, the greater the sequence similarity. The present invention draws on Andhini's DTW dynamic time warping algorithm. Two DTW variants are involved: absolute DTW and shape DTW. Absolute DTW takes into account both absolute distance and time offset, and shape DTW measures shape similarity while ignoring absolute metrics.
[0039] This technical solution supports four scene generation methods: The first step is scene generation: Method 1, using Monte Carlo sampling of multiple (600) discrete-time scenarios; Method 2, using Latin hypercube sampling to sample multiple (600) basic scenarios; Method 3, using multi-climate building performance simulation to integrate 200 input parameter samples with climate files to obtain 600 simulation scenarios; Method 4 uses three years with large meteorological differences and generates multiple scenarios (600) through Monte Carlo building performance simulation based on the data of these three years.
[0040] The second step is modeling and solving: Using the ISO model, each scene was optimized independently, resulting in 600 optimizations per scene and 2400 optimizations in total.
[0041] Using the TSSP method, similar to ISO, 600 independent scenario optimizations were performed for each scenario. However, the excessive number of scenarios led to an increase in model variables, which posed a challenge to achieving a reasonable computational time. The results are shown in Tables 1 and 2.
[0042] Table 1: Results using mean absolute DTW
[0044] Table 1: Results of using average shape DTW
[0046] Figure 2 A structural schematic diagram of a terminal 200 provided in an embodiment of the present invention, wherein the terminal 200 can be used to execute the building flexible DC microgrid system operation optimization method based on scenario generation provided in an embodiment of the present invention.
[0047] The terminal 200 may include: a processor 210, a memory 220 and a communication module 230. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.
[0048] The memory 220 can be used to store the execution instructions of the processor 210, and the memory 220 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 220 are executed by the processor 210, the terminal 200 can perform some or all of the steps in the following method embodiments.
[0049] The processor 210 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 220, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 210 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.
[0050] The communication module 230 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.
[0051] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).
[0052] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.
[0053] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.
[0054] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, which can be electrical, mechanical or other forms.
[0055] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0056] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.
[0057] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.
Claims
1. A method for optimizing the operation of a building flexible DC microgrid system based on scenario generation, characterized in that: include: Based on the uncertainty of load, several scenario generation algorithms are used to generate several corresponding operation scenarios for the building flexible DC microgrid system. Use pre-built ISO models to perform independent scenario optimization for each generated operational scenario; A pre-built two-stage stochastic programming model is used to perform a second scenario optimization on each optimized operation scenario; Based on the scenario optimization results, an operation strategy is formulated with the goal of optimizing economic operation.
2. The building flexible DC microgrid system operation optimization method based on scenario generation according to claim 1 is characterized in that: The scenario generation algorithms include Monte Carlo sampling, Latin hypercube sampling, multi-climate building performance simulation and Monte Carlo building performance simulation, which respectively use Monte Carlo sampling, Latin hypercube sampling, multi-climate building performance simulation and Monte Carlo building performance simulation to generate corresponding operation scenarios for the building flexible DC microgrid system.
3. The building flexible DC microgrid system operation optimization method based on scenario generation according to claim 1 is characterized in that: The pre-built ISO model aims to minimize the sum of investment cost and operating cost. The objective function expression of the pre-built ISO model is: ； ； ； in, It is the sum of investment cost and operating cost; For investment costs; For operating costs; is the energy transaction price; procure electricity for the grid; is the linear investment cost per unit capacity of the ith component; is an integer variable representing the capacity of the i-th energy device; is the interest rate of the i-th energy technology; To calculate the years.
4. The building flexible DC microgrid system operation optimization method based on scenario generation according to claim 3 is characterized in that: The pre-built ISO model is also set with power balance constraints, energy storage constraints and flexible load constraints.
5. The building flexible DC microgrid system operation optimization method based on scenario generation according to claim 4 is characterized in that: The expression of power balance constraint is: ； ； ； in, Purchase electricity for the grid at time t, is the photovoltaic power generation at time t, is the energy storage discharge power at time t, is the energy storage charging power at time t, is the flexible load at time t, is the inflexible load at time t, is the predicted value of photovoltaic power generation at time t, is the photovoltaic power generation capacity, is the upper limit of photovoltaic power generation capacity, is the lower limit of photovoltaic power generation capacity; The expression of energy storage constraint is: ； ； in, is the energy storage capacity at time t+1, is the energy storage capacity at time t, is the lower limit of energy storage at time t; For [A1] The expression of flexible load constraint is: in, is the lower limit of flexible load at time t, is the upper limit of flexible load at time t.
6. The building flexible DC microgrid system operation optimization method based on scenario generation according to claim 3 is characterized in that: The objective function expression of the pre-built two-stage stochastic programming model is: ； ； in, is the expected value of operating cost; is [A2].
7. The building flexible DC microgrid system operation optimization method based on scenario generation according to claim 1 is characterized in that: It also includes using a preset dynamic time warping algorithm to perform a horizontal check on the operating scenario after the second scenario optimization, so as to obtain the optimal operation strategy of the building flexible DC microgrid system under different load uncertainty conditions.
8. The building flexible DC microgrid system operation optimization method based on scenario generation according to claim 1 is characterized in that: It also includes generating random variables based on the probability distribution of load uncertainty, and the scenario at each time point is obtained by sampling from the normal distribution.
9. A terminal, characterized in that: include: processor; A memory for storing execution instructions of the processor; The processor is configured to execute the method according to any one of claims 1 to 8.
10. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.