Wind and light storage-based building integrated energy microgrid capacity planning and operation optimization method

Abstract

The invention provides a wind and light storage-based building integrated energy microgrid capacity planning and operation optimization method, which comprises the following steps of: S1, constructing a system total cost model based on wind power and photovoltaic power characteristics, and determining wind power and photovoltaic capacity configuration; s2, constructing a worst operation scene power supply model according to wind power and photovoltaic uncertainty; s3, constructing an energy storage stock optimal configuration model according to the system total cost model and the worst operation scene power supply model, and calculating an energy storage stock optimal configuration strategy; and S4, evaluating the actual operation condition of the system according to the energy storage stock optimal configuration strategy, and optimizing the system according to the feedback of the actual operation condition. By means of multiple energy forms such as wind power, photovoltaic power and energy storage, the energy utilization efficiency can be improved, energy consumption and carbon emission are reduced, and clean, efficient and sustainable supply of energy is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of wind, solar and energy storage, and in particular to a method for capacity planning and operation optimization of a building integrated energy microgrid based on wind, solar and energy storage. Background Art

[0002] With the rapid development of renewable energy, wind power and photovoltaics, as the main forms of clean energy, account for an increasing proportion of the energy structure. However, due to the intermittent and uncertain output of wind power and photovoltaics, it poses a huge challenge to stable power supply. Relying on them alone is difficult to meet the continuous and stable needs of the building's integrated energy system. Therefore, introducing energy storage to participate in maintaining stability has become an effective way to solve this problem. Energy storage can balance the instability of wind and solar power generation, improve energy utilization efficiency, and reduce system operating costs. It not only helps to improve the self-sufficiency rate of building energy and reduce dependence on traditional power grids, but also significantly enhances the flexibility and resilience of building energy microgrids to cope with fluctuations and uncertainties in the external energy market. However, how to reasonably configure the capacity of wind power, photovoltaics and energy storage and optimize their operation is a technical problem that needs to be solved urgently. Summary of the invention

[0003] The purpose of this invention is to propose a method for capacity planning and operation optimization of a building integrated energy microgrid based on wind, photovoltaic and energy storage. By rationally allocating wind power, photovoltaic and energy storage capacity in the building integrated energy system and optimizing its operation, the building integrated energy system can be promoted to develop in a more efficient, economical and environmentally friendly direction.

[0004] In order to achieve the above object, the present invention provides a method for capacity planning and operation optimization of a building integrated energy microgrid based on wind, solar and storage, the method comprising: S1. Construct a system total cost model based on wind power and photovoltaic power characteristics to determine the wind power and photovoltaic capacity configuration; S2. Construct the worst-case power supply model based on the uncertainty of wind power and photovoltaic power; S3. Build an optimal configuration model for energy storage stock based on the system total cost model and the worst operation scenario power supply model to calculate the optimal configuration strategy for energy storage stock; S4. Evaluate the actual operation of the system based on the optimal configuration strategy for energy storage inventory, and optimize the system based on feedback from the actual operation.

[0005] Furthermore, the total system cost in the system total cost model includes investment cost, operation and maintenance cost and grid electricity price; the investment cost includes the initial construction cost of wind turbines, photovoltaic components and energy storage; the operation and maintenance cost includes the daily maintenance and overhaul costs of equipment.

[0006] Furthermore, the target optimization of the system total cost model is expressed as follows: ; in, is the total system cost, , and They are wind power capacity, photovoltaic capacity and energy storage capacity; , and They are the unit prices of wind power, photovoltaic power and energy storage investment costs respectively; , and They are wind power cost unit price, photovoltaic cost unit price and energy storage maintenance cost unit price; is the maximum output power of energy storage; is the unit price of energy storage power; , , , They are grid power, wind power, photovoltaic power and abandoned power respectively; The unit price of electricity purchase cost; Penalty price for power abandonment; and are the unit prices of wind power and photovoltaic operation costs respectively; Among them, wind power, photovoltaic and energy storage capacity need to meet non-zero limits, and the abandoned power, grid power and energy storage maximum charging power need to meet non-zero limits at the same time, as shown below: ; At the same time, for the grid power , Wind power , Photovoltaic power and abandoned power The constraints are as follows: ; in, and are the energy storage charging and discharging power respectively; It is the energy storage charge state; and are the minimum and maximum values ​​of the state of charge allowed for energy storage, respectively; and are the energy storage charging efficiency and discharging efficiency respectively; is the load power demand.

[0007] Furthermore, the objective optimization function of the worst operation scenario power supply model is expressed as follows: ; in, is the amount of power abandoned, t is the total duration, The power abandoned.

[0008] Furthermore, the constraints of the worst operating scenario power supply model include: ; in, and are the worst-case power of wind power and photovoltaic power respectively; and They are the uncertainty errors of wind power and photovoltaic power respectively.

[0009] Furthermore, the objective optimization function of the energy storage stock optimal configuration model is expressed as follows: ; in, It is the energy storage operating cost under the worst scenario.

[0010] Furthermore, the constraints of the optimal configuration model of energy storage stock are: ; ; in, and They are the energy storage charging efficiency and discharging efficiency respectively.

[0011] Furthermore, the optimization objective function of S4 is expressed as follows: ; Its constraints are: ; ; in, It is the sum of the cost of purchasing electricity from the power grid and the cost of abandoning electricity; and They are the actual power of wind power and photovoltaic power within a certain error range.

[0012] Furthermore, in S4, a genetic algorithm is introduced to optimize the objective function, specifically: First, Gray code is used to convert system information into machine-readable data information, and a certain number of candidate solutions are randomly generated within the pre-configured capacity range. The specific form is: ; in, represents the candidate solution after Gray code encoding, Gray code Bit, Represents the binary number Bit, Gray code Bit, Represents the binary number Bit, Represents the binary number Position Next one, represents the XOR operation, Indicates the position of a binary number. Represents the highest bit of a binary number. In binary encoding, Represents the binary number Bit, Represents the binary number Bit, Represents the binary number Bit; Next, the fitness of each candidate solution is calculated using the objective function to evaluate its quality, and the candidate solution is selected according to the fitness for subsequent crossover and mutation operations. The selected candidate solutions are randomly paired and crossover operations are performed to generate new offspring. At the same time, some parameters of the offspring are randomly mutated to increase the diversity and adaptability of the population; wherein the objective function is specifically: ; in, Indicates The probability of an individual being selected is Indicates The fitness function of an individual is Indicates the population The fitness function of each individual; In the crossover operation, the chromosomes of two individuals are crossed with probability Perform random exchange to obtain individuals with new chromosome characteristics; During the mutation operation, according to the mutation probability , a random number is generated for each gene on the encoded chromosome string ,when When the coding position changes, let the chromosome string be , then it changes to ; in, represents the mutated chromosome string, Indicates that the chromosome string remains unchanged when no compilation occurs. The first Bit, The first Bit; Subsequently, the mutated offspring are merged with the original population, and the best individuals are selected according to fitness to form a new generation of population; iterations are performed until the termination condition is met, that is, the maximum number of iterations or the fitness threshold is reached; if the condition is met, the execution will stop; otherwise, the next round of iterations will continue; Finally, after the iteration is terminated, the candidate solution with the highest fitness will be selected as the optimal strategy, and the optimal configuration plan of wind power, photovoltaics and energy storage will be output.

[0013] Furthermore, the constraint is expressed as: ; in, , and are the minimum values ​​of wind power, photovoltaic and energy storage capacity, respectively; Among them, the minimum values ​​of wind power, photovoltaic and energy storage capacity are obtained by comprehensively considering weather changes, seasonal fluctuations and technical performance, introducing a particle swarm optimization algorithm, and finding the optimal solution by simulating the social behavior of bird flocks. The particle swarm algorithm iteratively updates the positions of multiple particles in the solution space and converges to the global optimal solution. The optimal solution is used as the minimum values ​​of wind power, photovoltaic and energy storage capacity, as follows: ; in, is the particle velocity; is a random number between (0,1); is the current position of the particle; and is the learning factor; For the The best position that a particle has searched so far; is the optimal position searched by the entire particle swarm so far; is the inertia factor, and is the maximum and minimum value of the inertia factor; is the current iteration number; is the maximum number of iterations; At the same time, during the convergence process, the fitness of each particle will be evaluated. If the minimum value of wind power, photovoltaic and energy storage capacity exceeds the currently set minimum value, the value will be automatically updated to the new minimum capacity.

[0014] The beneficial technical effects of the present invention are at least as follows: The present invention effectively copes with the intermittency and uncertainty of renewable energy by rationally configuring wind power, photovoltaics and energy storage capacity. Faced with the challenge of unstable wind power and photovoltaic output and difficulty in meeting the stable power supply needs of the power grid, based on wind power and photovoltaic capacity, through the wind power and photovoltaic output conditions under the worst operating scenarios, it is ensured that energy storage has sufficient capacity to smooth out the intermittency and uncertainty of renewable energy, thereby ensuring the stable operation and reliable power supply of the building integrated energy microgrid. Comprehensively considering various economic factors such as investment cost, operation and maintenance cost and grid electricity price, it provides strong technical support and decision-making basis for building an efficient, economical and stable building integrated energy microgrid containing wind, photovoltaic and storage.

[0015] This invention takes building integrated energy microgrid as the object. In order to effectively deal with the uncertainty brought by new energy and improve the economy and stability of the power system, the reasonable configuration of wind, solar and storage capacity not only helps to balance the intermittent and volatility of new energy power generation, but also enhances the flexibility and resilience of the system, ensures the economic rationality and technical feasibility of the configuration plan, and lays a solid foundation for the future development of building integrated energy microgrid.

[0016] The present invention improves the self-sufficiency rate of the building integrated energy microgrid and reduces the dependence on the external power grid by optimizing the capacity configuration of wind power, photovoltaics and energy storage. In the case of unstable or insufficient wind power and photovoltaic output, energy storage can quickly supplement the insufficient power to ensure the stable operation of the system and avoid the risk of power outages caused by insufficient power supply, so that the building integrated energy microgrid can maintain efficient energy supply when facing the uncertainty of wind and solar power. By optimizing the operation strategy of wind, solar and storage, it is achieved that during the peak period of grid electricity price, the system gives priority to the use of energy storage power or wind power and photovoltaic power, reduces the purchase of electricity from the grid, and thus effectively reduces the cost. It not only improves the economic benefits, but also enhances the energy self-sufficiency. Under the comprehensive consideration of investment cost, operation and maintenance cost and grid electricity price, the capacity of wind, solar and storage is reasonably configured to avoid unnecessary over-investment. At the same time, by optimizing the operation strategy, the utilization rate of wind power and photovoltaic power has been improved, and the amount of abandoned power has been reduced, so that the system can achieve efficient and reliable power supply under different operating conditions, and help reduce the consumption of fossil energy, reduce carbon emissions, and have a positive impact on environmental protection. By promoting the widespread application of renewable energy, it has promoted green and low-carbon development and provided strong support for achieving sustainable development goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0018] Figure 1This is a flow chart of the capacity planning and operation optimization method of the building integrated energy microgrid based on wind, solar and storage according to the present invention.

[0019] Figure 2 This is the time-sharing electricity price diagram of the power grid of the present invention.

[0020] Figure 3 It is the load demand diagram of the present invention.

[0021] Figure 4 This is a diagram of the energy storage operation of the present invention.

[0022] Figure 5 This is a diagram of the power grid purchasing situation of the present invention. DETAILED DESCRIPTION

[0023] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0024] Embodiment 1

[0025] like Figure 1 As shown, the method for capacity planning and operation optimization of a building integrated energy microgrid based on wind, solar and storage provided by an embodiment of the present invention includes: S1. Construct a system total cost model based on wind power and photovoltaic power characteristics to determine the wind power and photovoltaic capacity configuration.

[0026] First, according to the energy consumption demand of the building and the characteristics of wind and solar resources, determine the capacity configuration of wind power and photovoltaics, and lay the foundation for the subsequent energy storage configuration; secondly, consider the uncertainty of wind power and photovoltaics, and improve the robustness of the system by identifying the worst operating scenarios, and the possible power generation shortage under extreme weather conditions, thereby providing a basis for the configuration of energy storage; then, after determining the worst operating scenario, calculate the required energy storage capacity and its maximum charging power to ensure the stability and reliability of the system under adverse conditions, and ensure that it can release electricity in time when the power demand peaks; finally, according to the results of the previous steps, formulate the system operation strategy, optimize the system output and the charging and discharging plan of the energy storage, so as to improve the energy utilization efficiency and enhance the flexibility and reliability of the system.

[0027] Furthermore, based on the power characteristics of wind power and photovoltaic power, the total system cost is considered to determine the wind power and photovoltaic capacity configuration, where the total system cost includes various economic factors such as investment cost, operation and maintenance cost and grid electricity price. Specifically, the investment cost mainly includes the initial construction cost of wind turbines, photovoltaic components and energy storage, which includes not only the equipment procurement cost, but also the additional costs that may be incurred during installation and commissioning. The operation and maintenance cost involves the daily maintenance and overhaul costs of the equipment, which is crucial to ensure the long-term stable operation of the system, including regular overhaul, fault repair, cleaning and monitoring costs. Effective operation and maintenance management can significantly extend the service life of the equipment and reduce the risk of unexpected downtime, thereby improving the economic benefits of the system. The grid electricity price will also have an impact on the total system cost. In the case of time-of-use electricity prices, the cost of purchasing electricity may be adjusted at any time, so the economic feasibility under the time-of-use electricity price scenario needs to be fully considered when planning capacity. Reasonable capacity configuration should be able to increase the purchase of electricity as much as possible when the electricity price is low, and give priority to self-generated electricity when the electricity price is high. At the same time, the penalty for power abandonment also affects the benefits of wind power and photovoltaic power generation, especially when there is an excess of renewable energy generation, how to effectively manage the power abandonment problem becomes the key. In addition, the penalty for power curtailment also affects the revenue of wind power and photovoltaic power generation. Especially when there is an excess of renewable energy generation, how to effectively manage the power curtailment problem becomes the key. The system should design a reasonable dispatching strategy to reduce the amount of power curtailment and maximize the utilization efficiency of renewable energy. The definition is as follows: ; in, is the total system cost, , and They are wind power capacity, photovoltaic capacity and energy storage capacity; , and They are the unit prices of wind power, photovoltaic power and energy storage investment costs respectively; , and They are wind power cost unit price, photovoltaic cost unit price and energy storage maintenance cost unit price; is the maximum output power of energy storage; is the unit price of energy storage power; , , , They are grid power, wind power, photovoltaic power and abandoned power respectively; The unit price of electricity purchase cost; Penalty price for power abandonment; and are the unit prices of operating costs of wind power and photovoltaic power respectively.

[0028] Based on the characteristics of wind power and photovoltaic power, the total system cost is considered, which specifically includes: (2) in, , and is the minimum capacity of wind power, photovoltaic power and energy storage. Formula (2) is to meet the non-zero limit of wind power, photovoltaic power and energy storage capacity, as well as the non-zero limit of abandoned power, grid power and maximum charging power of energy storage, which is an important guarantee to ensure that the building integrated energy microgrid can maintain stable, efficient and reliable operation in the face of complex and changing environments and demands. The certain power generation capacity of wind power, photovoltaic power and energy storage is the basis for realizing the utilization of renewable energy, so as to meet the basic power demand of building integrated energy and ensure the stability and reliability of the system. The non-zero limit of abandoned power ensures that when there is excess renewable energy generation, the system can effectively manage the excess power and avoid the waste of resources caused by excess power generation capacity. The non-zero limit of grid power ensures that the connection between the microgrid and the main grid is always effective, which not only provides an additional source of power for the microgrid, but also provides it with stable power output.

[0029] It is understandable that when establishing the minimum capacity of wind power, photovoltaic and energy storage systems, the initial step is to set the minimum capacity values ​​of wind power, photovoltaic and energy storage to 0, that is, , and .

[0030] Subsequently, key information such as historical weather data, seasonal load curves, and equipment technical parameters were introduced, taking into account weather changes, seasonal fluctuations, and technical performance to ensure accuracy and reliability. On the premise of meeting the supply and demand balance, the particle swarm optimization algorithm was introduced to find the optimal solution by simulating the social behavior of bird flocks. The particle swarm algorithm can quickly converge to the global optimal solution by iteratively updating the positions of multiple particles in the solution space, thereby effectively calculating the minimum capacity required for wind power, photovoltaics, and energy storage. The details are as follows: ; in, is the particle velocity; is a random number between (0,1); is the current position of the particle; and is the learning factor; For the The best position that a particle has searched so far; is the optimal position searched by the entire particle swarm so far; is the inertia factor, and is the maximum and minimum value of the inertia factor; is the current iteration number; is the maximum number of iterations.

[0031] In this process, the fitness of each particle is evaluated. If the minimum value of wind power, photovoltaic and energy storage capacity exceeds the currently set minimum value, the system will automatically update the value to the new minimum capacity. In this way, it can be ensured that the minimum capacity of wind power, photovoltaic and energy storage not only meets all preset constraints, but also is optimized as much as possible, providing a more efficient starting point for subsequent system optimization. Ultimately, the output minimum capacity value of wind power, photovoltaic and energy storage will be used as the input parameter of the objective function.

[0032] (3) in, and These are the charging and discharging power of energy storage. The charging power determines how much electricity the energy storage can absorb when there is excess renewable energy generation, while the discharging power affects how much electricity it can release when there is a peak in electricity demand. The energy storage charge state can effectively determine the operating status of the energy storage, so as to formulate corresponding charging and discharging plans to ensure that it operates within a safe and efficient range and avoid equipment damage caused by overcharging or discharging; and They are the minimum and maximum states of charge allowed for energy storage, ensuring that energy storage operates within a safe and efficient range and avoiding equipment damage due to overcharging or discharging; and They are respectively the energy storage charging efficiency and discharging efficiency, which effectively reflect the energy loss of energy storage during the charging and discharging process, thus providing a more accurate basis for capacity planning and operation optimization; To ensure that the energy storage and power generation equipment can meet the actual power demand of the building, the dynamic changes in load power demand require the system to have flexible scheduling capabilities to cope with fluctuations in power demand in different time periods.

[0033] By analyzing and optimizing wind power and photovoltaic power under the above constraints, their capacity configuration can be determined, which can maximize the utilization efficiency of renewable energy, reduce dependence on traditional fossil energy, and achieve efficient, stable and sustainable development of building integrated energy microgrids. However, in actual operation, wind power and photovoltaic power have uncertainty and volatility. This uncertainty mainly comes from changes in meteorological conditions, such as fluctuations in wind speed and sunshine intensity, which lead to unpredictable power generation capacity. Therefore, in the case of wind power and photovoltaic configuration, it is crucial to consider and calculate the worst operating scenario, which helps to improve the robustness of the system.

[0034] S2. Construct a power supply model for the worst operating scenario based on the uncertainties of wind power and photovoltaic power.

[0035] Specifically, in extreme weather conditions, the output of wind power and photovoltaic power will be affected to a certain extent, which will pose a challenge to the power supply of the building integrated energy microgrid. When the wind is too strong, wind power may reduce its output or stop operating completely due to the safety protection mechanism to avoid equipment damage. On the contrary, when the wind is too weak, the output of the wind power system will be reduced and may not be able to meet the power demand. Insufficient sunlight, such as continuous rainy days, will significantly reduce solar radiation, resulting in a significant drop in photovoltaic power generation. Snow covering the photovoltaic panels will also affect the reception of sunlight and reduce the efficiency of the photovoltaic panels. At the same time, temperature will also affect the aging or damage of wind power and photovoltaic equipment, resulting in reduced efficiency or equipment failure, which in turn leads to deviations in wind power and photovoltaic output. Therefore, considering the impact of extreme weather, sufficient energy storage needs to be configured to provide necessary power support when wind power and photovoltaic output are insufficient.

[0036] Furthermore, based on the configuration of wind power and photovoltaic capacity, the output of wind power and photovoltaic is determined, and the impact of wind power and photovoltaic uncertainty is considered. Within the error range of wind power and photovoltaic uncertainty, the amount of abandoned power is maximized to ensure that energy storage can be effectively used when power generation capacity is insufficient. Through dynamic monitoring and prediction of power generation capacity, the charging strategy of energy storage can be adjusted in time when there is excess power generation to avoid waste of resources caused by abandoned power, thereby determining the power curve of the worst operating conditions of wind power and photovoltaic, which helps to ensure that the system can also provide stable power supply under extreme weather conditions.

[0037] Its definition is as follows: (4) The constraints are: (5) in, The amount of abandoned electricity; and are the worst-case power of wind power and photovoltaic power respectively; and are the uncertainty errors of wind power and photovoltaic power respectively.

[0038] Furthermore, after comprehensively considering the output of wind power and photovoltaic power and its uncertainty, the power curve under the worst operating conditions can be obtained, which will show the minimum power generation capacity that the system may face under extreme weather conditions. In order to effectively deal with the uncertainty of wind power and photovoltaic output, sufficient energy storage needs to be configured. Energy storage should not only have the ability to respond quickly so that it can provide the required electricity in time when the output of wind power and photovoltaic power is insufficient, but also be optimized according to the needs of the system. By reasonably designing the energy storage capacity and charging and discharging strategies, it can be charged when the power supply is sufficient and discharged when the demand is peak or the power generation is insufficient, so as to effectively balance the supply and demand of electricity, reduce dependence on the power grid, and improve the stability and reliability of the entire building integrated energy microgrid. In addition, the flexibility and response speed of energy storage will provide important guarantees for coping with sudden changes in power demand and challenges brought by extreme weather, ensuring that the building integrated energy microgrid can maintain a stable power supply under various circumstances.

[0039] S3. Based on the system total cost model and the worst operating scenario power supply model, an optimal energy storage configuration model is constructed to calculate the optimal energy storage configuration strategy.

[0040] The uncertainty of wind power and photovoltaic power generation must be fully considered to reasonably configure the energy storage capacity, thereby effectively reducing power deviation. The capacity of energy storage must not only ensure that the building integrated energy microgrid can provide continuous and stable power supply under extreme conditions and avoid the risk of power outages due to energy shortages, but also take into account economic feasibility to prevent waste of resources due to over-investment. Therefore, under the premise of minimizing the system operating cost, the energy storage capacity and the maximum charging power of the energy storage are calculated, and then an energy storage configuration plan that takes into account both economy and efficiency is determined to meet the power demand under the worst conditions. This comprehensive strategy can not only improve the reliability of the microgrid, but also optimize the utilization efficiency of resources while ensuring power supply, and provide strong support for the sustainable development of renewable energy. It is defined as: (6) The constraints are: (7)

[0041] (8) in, It is the energy storage operating cost under the worst scenario.

[0042] Through the above definitions and constraints, the configuration of energy storage capacity can be effectively determined to meet the power demand in the worst case, and the energy storage capacity and charging power can be reasonably configured. At the same time, the economy of the system is optimized, so that investment and operating costs can be controlled, ensuring efficient and reliable power supply under various operating conditions. By reasonably configuring energy storage, the building integrated energy microgrid can flexibly respond to the volatility of renewable energy generation and improve the overall energy utilization efficiency. The comprehensive energy storage configuration improves the resilience and adaptability of the system.

[0043] S4. Evaluate the actual operation of the system based on the optimal configuration strategy for energy storage inventory, and optimize the system based on feedback from the actual operation.

[0044] Specifically, it is necessary to evaluate and optimize the operation of wind, solar and storage to ensure the stability and reliability of the system under different operating conditions. Energy storage has sufficient capacity to smooth out the intermittent and uncertain nature of renewable energy, ensuring that the microgrid can provide the required power in a timely manner when wind power and photovoltaic power generation are insufficient, and avoiding system instability caused by power supply fluctuations. It is defined as: (9) Furthermore, the system operation optimization not only focuses on the immediate supply of electricity, but also pays more attention to the improvement of overall economic benefits. Formula (9) takes the cost into consideration and effectively reduces the sum of the power purchase cost and the power abandonment cost of the power grid. It emphasizes that in the optimization process, various costs should be balanced to maximize economic benefits.

[0045] The constraints are: (10) (11) in, It is the sum of the cost of purchasing electricity from the power grid and the cost of abandoning electricity; and They are the actual power of wind power and photovoltaic power within a certain error range.

[0046] Furthermore, the actual power of wind power and photovoltaic power is considered in the constraints, ensuring that the power generation capacity of wind power and photovoltaic power can be reflected within a certain error range. Through the above measures, the system can not only minimize costs on the basis of ensuring stable operation, but also ensure long-term economic benefits and sustainable development, so that the building integrated energy microgrid can flexibly respond to changes in the external environment and enhance its adaptability to renewable energy.

[0047] Furthermore, according to the pre-configuration of wind, solar and storage capacity, a genetic algorithm is introduced to search in the population of candidate solutions through operations such as selection, crossover and mutation to find the optimal or approximately optimal solution, thereby determining the capacity of wind power, photovoltaics, and energy storage and the specific conditions of their maximum output power. First, a certain number of candidate solutions are randomly generated within the pre-configured capacity range. Then, the fitness of each candidate solution is calculated using the objective function to evaluate its pros and cons. Candidate solutions are selected based on their fitness for subsequent crossover and mutation operations. By randomly pairing the selected candidate solutions, crossover operations are performed to generate new offspring, and some parameters of the offspring are randomly mutated to increase the diversity and adaptability of the population. Subsequently, the mutated offspring are merged with the original population, and the best individuals are selected according to fitness to form a new generation of population. Iterate continuously until the termination condition is met, such as reaching the maximum number of iterations or the fitness threshold. If the condition is met, execution will stop; otherwise, the next round of iteration will continue. Specifically, the system information is converted into machine-readable data information. Considering the defects of binary encoding, Gray code encoding is selected, and the specific form is: ,in, represents the candidate solution after Gray code encoding, Gray code Bit, Represents the binary number Bit, Gray code Bit, Represents the binary number Bit, Represents the binary number Position Next one, represents the exclusive OR operation, Indicates the position of a binary number. Represents the highest bit of a binary number. In binary encoding, Represents the binary number Bit, Represents the binary number Bit, Represents the binary number According to the fitness probability Select individuals where Indicates The probability of an individual being selected is Indicates The fitness function of an individual is Indicates the population The probability of crossover and mutation operations is usually a preset fixed value, such as the crossover probability and mutation probability In the crossover operation, the chromosomes of two individuals are crossed with probability Perform random exchange to obtain new chromosome characteristic individuals. During the mutation operation, according to the mutation probability , a random number is generated for each gene on the encoded chromosome string ,when When the coding position changes, let the chromosome string be , then it changes to ,in, represents the mutated chromosome string, Indicates that the chromosome string remains unchanged when no compilation occurs. The first Bit, The first Bit; Finally, after the algorithm terminates, the candidate solution with the highest fitness will be selected as the optimal strategy, and the optimal configuration plan for wind power, photovoltaics and energy storage will be output.

[0048] For the above four steps, first, it is necessary to determine the capacity configuration of wind power and photovoltaic power. Based on the power characteristics of wind and solar power, the total system cost and power balance as well as the constraints of wind and solar power itself are considered to plan the configuration of wind power and photovoltaic power. Secondly, it is necessary to consider the adverse conditions that may occur in the operation of wind power and photovoltaic power, which will affect the stability of the building integrated energy microgrid. Based on the uncertainty of wind power and photovoltaic power, the worst operation scenario is determined to provide a basis for energy storage configuration planning; thirdly, based on the worst operation scenario, the uncertainty of wind power and photovoltaic power is compensated by energy storage output; finally, according to the capacity configuration of wind, photovoltaic and energy storage, the overall operation of the system is optimized to minimize operating costs and meet electricity demand, ensure that the capacity configuration of wind power, photovoltaic power and energy storage and the power purchase operation strategy of the power grid are coordinated with each other, when it is difficult to meet the demand by relying solely on wind power, photovoltaic power and energy storage, the lack of power demand is compensated by purchasing electricity from the power grid, and when wind power and photovoltaic power can meet the power demand, the energy storage is charged to improve the overall efficiency and achieve the optimal system operation.

[0049] Embodiment 2

[0050] Take the wind, solar and storage capacity configuration of a building integrated energy microgrid as an example, and consider that its operation time is 7 days. Figure 2 As shown, the load power demand is Figure 3 Other parameters are shown in Table 1.

[0051] Table 1 Parameters of a certain wind, solar and storage capacity configuration ; The objective function includes the total investment cost, operating cost and wind and solar curtailment cost of the system, and the solution yields wind power and photovoltaic capacities of 479.21kW and 570.98kW. Based on the wind power and photovoltaic capacities, the worst operating scenario is calculated taking into account their uncertainties. Based on the analysis results of the worst scenario, the capacity of the energy storage is further adjusted to ensure the stable operation of the system under different scenarios. In view of the uncertainty of wind power and photovoltaic output, and considering the mismatch between the daytime photovoltaic peak and the nighttime wind power peak, a storage capacity of 1085.83kWh and a maximum output power of 276.01kW are configured. This configuration ensures that the system can maintain stable operation in the worst operating scenario and enhance the flexibility and reliability of the power grid. By comprehensively considering factors such as the investment cost, operating cost and grid electricity price of wind power, photovoltaics and energy storage, the operation of wind, solar and energy storage is optimized. The operation of energy storage is as follows: Figure 4 As shown in Figure 2, the electricity purchase situation is as follows: Figure 5 The various costs are shown in Table 2. Although the initial investment is large, in the long run, by reducing the amount of electricity purchased and the amount of wind and solar power abandoned, the system can significantly reduce operating costs and improve overall economic benefits. By optimizing the configuration and operation strategy of wind, photovoltaic and storage, the system reduced the amount of wind and solar power abandoned by 32.85kWh, which not only reduced the waste of resources, but also avoided the losses caused by power abandonment. At the same time, the effective use of energy storage also reduces the dependence on the external power grid, further reducing the cost of purchasing electricity. By configuring wind power, photovoltaic and energy storage capacity and optimizing their operation, it is possible to improve energy utilization efficiency and reduce system operating costs.

[0052] Table 2 Costs of wind, solar and storage capacity configuration Electricity Purchase 18707kWh Power purchase cost 10386 yuan Amount of wind and solar power curtailment 32.85kWh Energy storage charging capacity 4317.3kWh Energy storage discharge 4799.9kWh Total Cost 30791 yuan Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0053] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a division of logical functions. There may be other division methods in actual implementation, such as multiple units 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, and the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0054] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0055] Although embodiments of the present invention have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for capacity planning and operation optimization of building integrated energy microgrid based on wind, solar and storage, characterized in that: The method comprises: S1. Construct a system total cost model based on wind power and photovoltaic power characteristics to determine the wind power and photovoltaic capacity configuration; S2. Construct the worst-case power supply model based on the uncertainty of wind power and photovoltaic power; S3. Build an optimal configuration model for energy storage stock based on the system total cost model and the worst operation scenario power supply model to calculate the optimal configuration strategy for energy storage stock; S4. Evaluate the actual operation of the system based on the optimal configuration strategy for energy storage inventory, and optimize the system based on feedback from the actual operation.

2. The method for capacity planning and operation optimization of building integrated energy microgrid based on wind, solar and storage according to claim 1 is characterized in that: The total system cost in the system total cost model includes investment cost, operation and maintenance cost and grid electricity price; the investment cost includes the initial construction cost of wind turbines, photovoltaic modules and energy storage; the operation and maintenance cost includes the daily maintenance and overhaul costs of equipment.

3. The method for capacity planning and operation optimization of building integrated energy microgrid based on wind, solar and storage according to claim 2 is characterized in that: The target optimization of the system total cost model is expressed as follows: ； in, is the total system cost, , and They are wind power capacity, photovoltaic capacity and energy storage capacity; , and They are the unit prices of wind power, photovoltaic power and energy storage investment costs respectively; , and They are wind power cost unit price, photovoltaic cost unit price and energy storage maintenance cost unit price; is the maximum output power of energy storage; is the unit price of energy storage power; , , , They are grid power, wind power, photovoltaic power and abandoned power respectively; The unit price of electricity purchase cost; Penalty price for power abandonment; and are the unit prices of wind power and photovoltaic operation costs respectively; Among them, wind power, photovoltaic and energy storage capacity need to meet non-zero limits, and the abandoned power, grid power and energy storage maximum charging power need to meet non-zero limits at the same time, as shown below: ； At the same time, for the grid power , Wind power , Photovoltaic power and abandoned power The constraints are as follows: ； in, and are the energy storage charging and discharging power respectively; It is the energy storage charge state; and are the minimum and maximum values ​​of the state of charge allowed for energy storage, respectively; and are the energy storage charging efficiency and discharging efficiency respectively; is the load power demand.

4. The method for capacity planning and operation optimization of building integrated energy microgrid based on wind, solar and storage according to claim 1 is characterized in that: The objective optimization function of the worst operating scenario power supply model is expressed as follows: ； in, is the amount of power abandoned, t is the total duration, The power abandoned.

5. The method for capacity planning and operation optimization of building integrated energy microgrid based on wind, solar and storage according to claim 4 is characterized in that: The constraints of the worst operating scenario power supply model include: ； in, and are the worst-case power of wind power and photovoltaic power respectively; and They are the uncertainty errors of wind power and photovoltaic power respectively.

6. The method for capacity planning and operation optimization of building integrated energy microgrid based on wind, solar and storage according to claim 1 is characterized in that: The objective optimization function of the energy storage stock optimal configuration model is expressed as follows: ； in, It is the energy storage operating cost under the worst scenario.

7. The method for capacity planning and operation optimization of building integrated energy microgrid based on wind, solar and storage according to claim 6 is characterized in that: The constraints of the optimal configuration model of energy storage are: ； ； in, and They are the energy storage charging efficiency and discharging efficiency respectively.

8. The method for capacity planning and operation optimization of building integrated energy microgrid based on wind, solar and storage according to claim 1 is characterized in that: The optimization objective function of S4 is expressed as follows: ； The constraints are: ； ； in, It is the sum of the cost of purchasing electricity from the power grid and the cost of abandoning electricity; and They are the actual power of wind power and photovoltaic power within a certain error range.

9. The method for capacity planning and operation optimization of building integrated energy microgrid based on wind, solar and storage according to claim 8 is characterized in that: In S4, a genetic algorithm is introduced to optimize the objective function, specifically: First, Gray code is used to convert system information into machine-readable data information, and a certain number of candidate solutions are randomly generated within the pre-configured capacity range. The specific form is: ； in, represents the candidate solution after Gray code encoding, Gray code Bit, Represents the binary number Bit, Gray code Bit, Represents the binary number Bit, Represents the binary number Position Next one, represents the XOR operation, Indicates the position of a binary number. Represents the highest bit of a binary number. In binary encoding, Represents the binary number Bit, Represents the binary number Bit, Represents the binary number Bit; Next, the fitness of each candidate solution is calculated using the objective function to evaluate its quality, and the candidate solution is selected according to the fitness for subsequent crossover and mutation operations. The selected candidate solutions are randomly paired and crossover operations are performed to generate new offspring. At the same time, some parameters of the offspring are randomly mutated to increase the diversity and adaptability of the population; wherein the objective function is specifically: ； in, Indicates The probability of an individual being selected is Indicates The fitness function of an individual is Indicates the population The fitness function of each individual; In the crossover operation, the chromosomes of two individuals are crossed with probability Perform random exchange to obtain individuals with new chromosome characteristics; During the mutation operation, according to the mutation probability , a random number is generated for each gene on the encoded chromosome string ,when When the coding position changes, let the chromosome string be , then it changes to ； in, represents the mutated chromosome string, Indicates that the chromosome string remains unchanged when no compilation occurs. The first Bit, The first Bit; Subsequently, the mutated offspring are merged with the original population, and the best individuals are selected according to fitness to form a new generation of population; iterations are performed until the termination condition is met, that is, the maximum number of iterations or the fitness threshold is reached; if the condition is met, the execution will stop; otherwise, the next round of iterations will continue; Finally, after the iteration is terminated, the candidate solution with the highest fitness will be selected as the optimal strategy, and the optimal configuration plan of wind power, photovoltaics and energy storage will be output.

10. The method for capacity planning and operation optimization of building integrated energy microgrid based on wind, solar and storage according to claim 3 is characterized in that: The constraint is expressed as: ； in, , and are the minimum values ​​of wind power, photovoltaic and energy storage capacity, respectively; Among them, the minimum values ​​of wind power, photovoltaic and energy storage capacity are obtained by comprehensively considering weather changes, seasonal fluctuations and technical performance, introducing a particle swarm optimization algorithm, and finding the optimal solution by simulating the social behavior of bird flocks. The particle swarm algorithm iteratively updates the positions of multiple particles in the solution space and converges to the global optimal solution. The optimal solution is used as the minimum values ​​of wind power, photovoltaic and energy storage capacity, as follows: ； in, is the particle velocity; is a random number between (0,1); is the current position of the particle; and is the learning factor; For the The best position that a particle has searched so far; is the optimal position searched by the entire particle swarm so far; is the inertia factor, and is the maximum and minimum value of the inertia factor; is the current iteration number; is the maximum number of iterations; At the same time, during the convergence process, the fitness of each particle will be evaluated. If the minimum value of wind power, photovoltaic and energy storage capacity exceeds the currently set minimum value, the value will be automatically updated to the new minimum capacity.

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