Capacity Planning and Operation Optimization Method of Building Integrated Energy Microgrid Based on Wind, Photovoltaic and Energy Storage

Abstract

The present invention proposes a method for capacity planning and operation optimization of a building integrated energy microgrid based on wind power, photovoltaic power and energy storage, including: S1, constructing a system total cost model based on the power characteristics of wind power and photovoltaic power to determine the capacity configuration of wind power and photovoltaic power; S2, constructing a power supply model for the worst operating scenario according to the uncertainties of wind power and photovoltaic power; S3, constructing an optimal energy storage inventory configuration model based on the system total cost model and the power supply model for the worst operating scenario to calculate the optimal energy storage inventory configuration strategy; S4, evaluating the actual operation of the system for the optimal energy storage inventory configuration strategy, and at the same time optimizing the system according to the feedback of the actual operation. Through various energy forms such as wind power, photovoltaic power and energy storage, the present invention can improve energy utilization efficiency, reduce energy consumption and carbon emissions, and achieve clean, efficient and sustainable energy supply.

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:

[0005] S1. Construct a system total cost model based on wind power and photovoltaic power characteristics to determine the wind power and photovoltaic capacity configuration;

[0006] S2. Construct the worst-case power supply model based on the uncertainty of wind power and photovoltaic power;

[0007] 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;

[0008] 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.

[0009] 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.

[0010] Furthermore, the objective optimization of the total system cost model is expressed as follows:

[0011] ;

[0012] where is the total system cost, , and are the wind power capacity, photovoltaic capacity, and energy storage capacity, respectively; , and are the unit investment costs of wind power, photovoltaic, and energy storage, respectively; , and are the unit costs of wind power, photovoltaic, and energy storage maintenance, respectively; is the maximum output power of the energy storage; is the unit price of the energy storage power; , , , are the grid power, wind power, photovoltaic power, and curtailed power, respectively; is the unit cost of power purchase; is the unit price of curtailment penalty; and are the unit operating costs of wind power and photovoltaic, respectively;

[0013] wherein, the wind power, photovoltaic, and energy storage capacities are subject to non - zero constraints, and the curtailed power, grid power, and maximum charging power of the energy storage also need to meet non - zero constraints, which are expressed as follows:

[0014] ;

[0015] Meanwhile, for the grid power , wind power , photovoltaic power and curtailed power there are constraint conditions, which are expressed as follows:

[0016] ;

[0017] where and are the charging and discharging powers of the energy storage, respectively; is the state of charge of the energy storage; and are the minimum and maximum values of the allowable state of charge of the energy storage, respectively; and are the charging efficiency and discharging efficiency of the energy storage, respectively; is the load power demand.

[0018] Furthermore, the objective optimization function of the power supply model for the worst operating scenario is expressed as follows:

[0019] ;

[0020] Wherein, is the curtailed power, t is the total duration, is the curtailed power rate.

[0021] Furthermore, the constraint conditions of the power supply model for the worst operating scenario include:

[0022] ;

[0023] Wherein, and are the powers of wind power and photovoltaic power under the worst conditions respectively; and are the uncertainty errors of wind power and photovoltaic power respectively.

[0024] Furthermore, the objective optimization function of the optimal energy storage capacity allocation model is expressed as follows:

[0025] ;

[0026] Wherein, is the energy storage operation cost under the worst scenario.

[0027] Furthermore, the constraint conditions of the optimal energy storage capacity allocation model are:

[0028] ;

[0029] ;

[0030] Wherein, and are the energy storage charging efficiency and discharging efficiency respectively.

[0031] Furthermore, the optimization objective function of S4 is expressed as follows:

[0032] ;

[0033] Its constraints are:

[0034] ;

[0035] ;

[0036] Wherein, is the sum of the grid power purchase cost and the curtailed power cost; and are the actual powers of wind power and photovoltaic power within a certain error range, respectively.

[0037] Furthermore, a genetic algorithm is introduced in the step S4 for the optimization objective function, specifically:

[0038] First, the system information is converted into machine-readable data information by using the Gray code encoding method, and a certain number of candidate solutions are randomly generated within the pre-configured capacity range, and the specific form is:

[0039] ;

[0040] Among them, represents the candidate solution after Gray code encoding, represents the th bit of the Gray code, represents the th bit of the binary number, represents the th bit of the Gray code, represents the th bit of the binary number, represents the th bit of the binary number, that is, the next bit, represents the exclusive OR operation, represents the position of the binary number, represents the highest bit of the binary number, is the binary coding form, represents the th bit of the binary number, represents the th bit of the binary number, represents the th bit of the binary number;

[0041] Then, the fitness of each candidate solution is calculated by using the objective function to evaluate its quality. The candidate solutions are selected according to the fitness level for subsequent crossover and mutation operations. The selected candidate solutions are randomly paired to perform the crossover operation to generate new offspring, and at the same time, some parameters of the offspring are randomly mutated to increase the diversity and adaptability of the population; among them, the objective function is specifically:

[0042] ;

[0043] Among them, represents the probability that the th individual is selected, represents the fitness function of the th individual, represents the The fitness function of an individual;

[0044] Among them, in the crossover operation, the chromosomes of two individuals are randomly exchanged with a probability to obtain new chromosomal characteristic individuals;

[0045] During the mutation operation, according to the mutation probability , a random number is generated for each gene on the encoded chromosome string . When , the coding position mutates. Let the chromosome string be , then it mutates to

[0046] ;

[0047] Among them, represents the mutated chromosome string, represents that the chromosome string remains unchanged when no compilation occurs, represents the th position of the chromosome string, represents the th position of the chromosome string;

[0048] Subsequently, the mutated offspring are merged with the original population, and the most excellent individuals are selected according to the fitness to form a new generation of population; iteration is 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 iteration will continue;

[0049] Finally, after the iteration terminates, the candidate solution with the highest fitness will be selected as the optimal strategy, and the optimal configuration solutions of wind power, photovoltaic, and energy storage will be output.

[0050] Furthermore, the constraint is expressed as:

[0051] ;

[0052] Among them, , and are the minimum values of the wind power, photovoltaic, and energy storage capacities respectively;

[0053] Among them, the minimum values of the wind power, photovoltaic, and energy storage capacities are comprehensively considered for weather changes, seasonal fluctuations, and technical performance. The particle swarm optimization algorithm is introduced to find 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, and the optimal solution is used as the minimum values of the wind power, photovoltaic, and energy storage capacities. Specifically as follows:

[0054] ;

[0055] Among them, is the particle velocity; is a random number between (0, 1); is the current position of the particle; and are learning factors; is the th optimal position searched by the th particle so far; is the optimal position searched by the entire particle swarm so far; and are the maximum and minimum values of the inertia factor; is the current iteration number; is the maximum iteration number;

[0056] Meanwhile, during the convergence process, the fitness of each particle will be evaluated. If the minimum value of the wind power, photovoltaic, and energy storage capacities exceeds the currently set minimum value, it will be automatically updated to the new minimum capacity.

[0057] The beneficial technical effects of the present invention are at least as follows:

[0058] By reasonably configuring the wind power, photovoltaic, and energy storage capacities, the present invention can effectively address the intermittency and uncertainty of renewable energy. Facing the challenges of unstable wind power and photovoltaic power outputs and difficulty in meeting the stable power supply requirements of the power grid, based on the wind power and photovoltaic capacities, through the wind power and photovoltaic power output situations under the worst operating scenarios, it ensures that the energy storage has sufficient capacity to smooth out the intermittency and uncertainty of renewable energy, thereby guaranteeing the stable operation and reliable power supply of the building integrated energy microgrid. Considering various economic factors such as investment costs, operation and maintenance costs, and grid electricity prices, it provides strong technical support and decision-making basis for constructing an efficient, economic, and stable building integrated energy microgrid with wind, light, and storage.

[0059] Taking the building integrated energy microgrid as the object, in order to effectively address the uncertainty brought by new energy and improve the economy and stability of the power system, reasonably configuring the wind, light, and storage capacities not only helps to balance the intermittency and volatility of new energy power generation, but also enhances the flexibility and resilience of the system, ensuring the economic rationality and technical feasibility of the configuration scheme, and laying a solid foundation for the future development of the building integrated energy microgrid.

[0060] By optimizing the capacity allocation of wind power, photovoltaic power, and energy storage, the present invention improves the self-sufficiency rate of the building integrated energy microgrid and reduces the dependence on the external power grid. In the case of unstable or insufficient output of wind power and photovoltaic power, energy storage can quickly supplement the deficit power to ensure the stable operation of the system, avoid the power outage risk caused by insufficient power supply, and enable the building integrated energy microgrid to maintain efficient energy supply in the face of the uncertainty of wind and light. By optimizing the operation strategy of wind-light-storage, when the grid electricity price is at a peak, the system preferentially uses the energy storage power or wind power and photovoltaic power, reducing the power purchased from the grid, thereby effectively reducing costs. This not only improves the economic benefits but also enhances the energy self-sufficiency ability. Considering multiple aspects such as investment cost, operation and maintenance cost, and grid electricity price, the capacity of wind-light-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 is improved, and the abandoned power is reduced, enabling the system to achieve efficient and reliable power supply under different operating conditions. Moreover, it helps to reduce the consumption of fossil energy, lower carbon emissions, have a positive impact on environmental protection, promote green and low-carbon development by promoting the wide application of renewable energy, and provide strong support for achieving the sustainable development goal. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the following drawings.

[0062] Figure 1 It is a flowchart of the method for capacity planning and operation optimization of the building integrated energy microgrid based on wind-light-storage of the present invention.

[0063] Figure 2 It is a time-of-use electricity price chart of the power grid of the present invention.

[0064] Figure 3 It is a load demand chart of the present invention.

[0065] Figure 4 It is a chart of the operation status of the energy storage of the present invention.

[0066] Figure 5 It is a chart of the power grid power purchase situation of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation to the present invention.

[0068] Embodiment 1

[0069] As Figure 1 shown, the method for capacity planning and operation optimization of an integrated building energy microgrid based on wind power, photovoltaic power, and energy storage provided by an embodiment of the present invention includes:

[0070] S1. Construct a system total cost model based on the power characteristics of wind power and photovoltaic power, and determine the capacity configuration of wind power and photovoltaic power.

[0071] First, according to the energy consumption demand of the building and the characteristics of wind and light resources, determine the capacity configuration of wind power and photovoltaic power, laying a foundation for subsequent energy storage configuration; second, considering the uncertainty of wind power and photovoltaic power, improve the robustness of the system by identifying the most severe operating scenarios, and in the case of insufficient power generation that may occur under extreme weather conditions, thus providing a basis for the configuration of energy storage; then, after determining the most severe 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 electrical energy in a timely manner during peak power demand; finally, according to the results of the previous steps, formulate the operation strategy of the system, optimize the power output of the system and the charge and discharge plan of the energy storage, thereby improving energy utilization efficiency and enhancing the flexibility and reliability of the system.

[0072] Furthermore, based on the power characteristics of wind power and photovoltaic power, considering the total system cost, determine the capacity configuration of wind power and photovoltaic power. 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 costs of wind turbines, photovoltaic modules, and energy storage, including not only the equipment procurement cost, but also the additional costs that may occur during installation and commissioning. The operation and maintenance cost involves the daily maintenance and repair costs of the equipment, which is crucial for ensuring the long-term stable operation of the system, including regular maintenance, 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 shutdowns, thereby enhancing the economic benefits of the system. The grid electricity price also affects the total system cost. In the case of time-of-use electricity price, the electricity purchase cost may be adjusted at any time, so the economic efficiency under the time-of-use electricity price scenario needs to be fully considered during capacity planning. A reasonable capacity configuration should be able to increase electricity purchase as much as possible when the electricity price is low, and give priority to using self-generated electricity when the electricity price is high. At the same time, the penalty for curtailment of electricity also affects the revenue of wind power and photovoltaic power generation. Especially when the power generation of renewable energy is excessive, how to effectively manage the curtailment of electricity becomes the key. In addition, the penalty for curtailment of electricity also affects the revenue of wind power and photovoltaic power generation. Especially when the power generation of renewable energy is excessive, how to effectively manage the curtailment of electricity becomes the key. The system should design a reasonable dispatching strategy to reduce the curtailment volume and maximize the utilization efficiency of renewable energy. Define as follows:

[0073] ;

[0074] Among them, is the total system cost, , and are the wind power capacity, the photovoltaic capacity, and the energy storage capacity, respectively; , and are the unit investment costs of wind power, photovoltaic, and energy storage, respectively; , and are the unit costs of wind power, photovoltaic, and energy storage maintenance, respectively; is the maximum output power of the energy storage; is the unit price of the energy storage power; , , , are the grid power, wind power, photovoltaic power, and curtailed power, respectively; is the unit cost of power purchase; is the unit price of curtailed power penalty; and are the unit operation costs of wind power and photovoltaic, respectively.

[0075] Based on the power characteristics of wind power and photovoltaic, considering the total system cost, it specifically includes:

[0076] (2)

[0077] Among them, , and are the minimum capacities of wind power, photovoltaic, and energy storage. Equation (2) is an important guarantee to meet the non-zero limits of the wind power, photovoltaic, and energy storage capacities, as well as the non-zero limits of the curtailed power, grid power, and maximum charging power of the energy storage, ensuring that the building integrated energy microgrid can maintain stable, efficient, and reliable operation in the face of complex and changing environments and demands. The wind power, photovoltaic, and energy storage having a certain power generation capacity is the basis for realizing the utilization of renewable energy to meet the basic power demand of the building integrated energy and ensure the stability and reliability of the system. The non-zero limit of the curtailed power ensures that when the renewable energy generation is excessive, the system can effectively manage the excess power and avoid resource waste caused by excessive power generation capacity. The non-zero limit of the grid power ensures that the connection between the microgrid and the main grid remains effective at all times, providing not only an additional power source for the microgrid but also a stable power output.

[0078] It can be understood that when determining the minimum capacities of the wind power, photovoltaic, and energy storage systems, the initial step is to set the minimum capacity values of the wind power, photovoltaic, and energy storage to 0, that is, , and .

[0079] Subsequently, key information such as historical weather data, seasonal load curves, and equipment technical parameters is introduced. Considering weather changes, seasonal fluctuations, and technical performance comprehensively to ensure accuracy and reliability. On the premise of meeting the supply-demand balance, the particle swarm optimization algorithm is introduced. By simulating the social behavior of bird flocks to find the optimal solution, the particle swarm algorithm can quickly converge to the global optimal solution through iterative updating of the positions of multiple particles in the solution space, thus effectively calculating the minimum capacities required for wind power, photovoltaic, and energy storage. Specifically as follows:

[0080] ;

[0081] Among them, is the particle velocity; is a random number between (0, 1); is the current position of the particle; and are learning factors; is the optimal position found by the rd particle so far; is the optimal position found by the entire particle swarm so far; is the inertia factor, and are the maximum and minimum values of the inertia factor; is the current iteration number; is the maximum iteration number.

[0082] In this process, the fitness of each particle will be evaluated. If the minimum value of the wind power, photovoltaic, and energy storage capacities exceeds the currently set minimum value, the system will automatically update this value to the new minimum capacity. Through this method, it can be ensured that the minimum capacities of wind power, photovoltaic, and energy storage not only meet all preset constraint conditions but also are optimized as much as possible, providing a more efficient starting point for subsequent system optimization. Finally, the output minimum capacity values of wind power, photovoltaic, and energy storage will be used as input parameters for the objective function.

[0083] (3)

[0084] Among them, and are the charging and discharging powers of the energy storage respectively. The charging power determines how much electrical energy the energy storage can absorb when renewable energy generation is excessive, while the discharging power affects how much electrical energy can be released during peak power demand; is the state of charge of the energy storage, which can effectively judge the operating state of the energy storage, so as to formulate corresponding charging and discharging plans to ensure its operation within a safe and efficient range and avoid equipment damage caused by overcharging or over-discharging; and are the minimum and maximum values of the state of charge allowed for energy storage, ensuring that the energy storage operates within a safe and efficient range and avoiding equipment damage caused by overcharging or over-discharging; and are the charging efficiency and discharging efficiency of the energy storage respectively, effectively reflecting the energy loss during the charging and discharging processes of the energy storage, thus providing a more accurate basis for capacity planning and operation optimization; is the load power demand to ensure that the energy storage and power generation equipment can meet the actual electricity demand of the building. The dynamic change of the load power demand requires the system to have flexible scheduling capabilities to cope with the power demand fluctuations in different time periods.

[0085] By analyzing and optimizing wind power and photovoltaic power through the above constraints, their capacity configuration can be determined, the utilization efficiency of renewable energy can be maximized, the dependence on traditional fossil energy can be reduced, and the efficient, stable and sustainable development of the building integrated energy microgrid can be achieved. However, in actual operation, wind power and photovoltaic power have uncertainty and volatility. This uncertainty mainly comes from the change of meteorological conditions, such as the fluctuation of wind speed and sunshine intensity, resulting in the unpredictability of power generation capacity. Therefore, it is crucial to consider calculating the most severe operating scenario in the case of wind power and photovoltaic power configuration, which helps to improve the robustness of the system.

[0086] S2. Construct a power supply model for the most severe operating scenario according to the uncertainty of wind power and photovoltaic power.

[0087] Specifically, in extreme weather, the power output of wind power and photovoltaic power will be affected to a certain extent, which will pose challenges to the power supply of the building integrated energy microgrid. In the case of excessive wind, wind power may reduce its output or stop operating completely due to the safety protection mechanism to avoid equipment damage. On the contrary, in the case of too little wind, the output of the wind power system will decrease 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 substantial decrease in the power generation of photovoltaic power. Snow cover on 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, and further leading to deviations in the power output of wind power and photovoltaic power. Therefore, considering the impact of extreme weather, it is necessary to configure sufficient energy storage to provide necessary power support when the power output of wind power and photovoltaic power is insufficient.

[0088] Furthermore, based on the capacity allocation of wind power and photovoltaic power, determine the output of wind power and photovoltaic power. Considering the impact of the uncertainty of wind power and photovoltaic power, within the uncertainty error range of wind power and photovoltaic power, maximize the curtailment power to ensure that energy storage can be effectively utilized when the power generation capacity is insufficient. Through the dynamic monitoring and prediction of the power generation capacity, the charging strategy of energy storage can be adjusted in a timely manner when the power generation is excessive, avoiding resource waste caused by curtailment, thereby determining the power curve of the worst operating conditions of wind power and photovoltaic power, which helps to ensure stable power supply of the system under extreme weather conditions.

[0089] It is defined as follows:

[0090] (4)

[0091] The constraints are:

[0092] (5)

[0093] Among them, is the curtailment power; and are the powers of wind power and photovoltaic power under the worst conditions respectively; and are the uncertainty errors of wind power and photovoltaic power respectively.

[0094] Furthermore, after comprehensively considering the output of wind power and photovoltaic power and their uncertainties, the power curve under the worst operating conditions can be obtained, which will show the lowest power generation capacity that the system may face under extreme weather conditions. To effectively cope with the uncertainty of the output of wind power and photovoltaic power, sufficient energy storage needs to be configured. The energy storage should not only have the ability of rapid response so that it can provide the required power in time when the output of wind power and photovoltaic power is insufficient, but also be optimized and configured according to the system requirements. By reasonably designing the energy storage capacity and charge-discharge strategy, it can be charged when the power supply is sufficient and discharged during peak demand or insufficient power generation, thereby effectively balancing the power supply and demand, reducing the dependence on the power grid, and improving the stability and reliability of the entire building integrated energy microgrid. In addition, the flexibility and response speed of the energy storage will provide an important guarantee for coping with sudden changes in power demand and challenges brought by extreme weather, ensuring stable power supply of the building integrated energy microgrid under various conditions.

[0095] S3. Construct an optimal energy storage inventory allocation model according to the system total cost model and the power supply model of the worst operating scenario to calculate the optimal energy storage inventory allocation strategy.

[0096] The uncertainties of wind power and photovoltaic power generation must be fully considered to rationally configure the energy storage capacity, thereby effectively reducing the power deviation. The capacity of the energy storage should not only ensure that the building integrated energy microgrid can continuously and stably supply power under extreme conditions, avoiding the power outage risk caused by energy shortage, but also take into account the economic feasibility to prevent resource waste caused by over-investment. Therefore, on the premise of minimizing the system operation cost, calculate the energy storage capacity and the maximum charging power of the energy storage, and then determine an energy storage configuration plan that takes into account both economy and benefits to meet the power demand under the worst conditions. This comprehensive consideration strategy can not only improve the reliability of the microgrid, but also optimize the utilization efficiency of resources while ensuring power supply, providing strong support for the sustainable development of renewable energy. It is defined as:

[0097] (6)

[0098] The constraints are:

[0099] (7)

[0100] (8)

[0101] Among them, is the energy storage operation cost under the worst scenario.

[0102] Through the above definitions and constraints, the configuration of the energy storage capacity can be effectively determined to meet the power demand under the worst conditions, and the energy storage capacity and charging power are rationally configured. At the same time, the economy of the system is optimized, so that the investment and operation costs can be controlled to ensure efficient and reliable power supply under various operating conditions. By reasonably configuring the energy storage, the building integrated energy microgrid can flexibly cope with the volatility of renewable energy power generation, improve the overall energy utilization efficiency, and the comprehensive consideration of energy storage configuration improves the resilience and adaptability of the system.

[0103] S4. Evaluate the actual operation of the optimal configuration strategy evaluation system for energy storage stock, and at the same time optimize the system according to the feedback of the actual operation.

[0104] Specifically, it is necessary to evaluate and optimize the operation of wind-solar-storage to ensure the stability and reliability of the system under different operating conditions. The energy storage has sufficient capacity to smooth out the intermittency and uncertainty 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:

[0105] (9)

[0106] Furthermore, the operation optimization of the system not only focuses on the immediate power supply but also pays more attention to the improvement of the overall economic benefits. Equation (9) comprehensively considers costs and effectively reduces the total sum of the grid's electricity purchase cost and curtailment cost. It emphasizes that during the optimization process, various costs should be balanced to maximize economic benefits.

[0107] The constraints are:

[0108] (10)

[0109] (11)

[0110] Among them, is the total sum of the grid's electricity purchase cost and curtailment cost; and are the actual powers of wind power and photovoltaic power within a certain error range, respectively.

[0111] Furthermore, the actual powers of wind power and photovoltaic power are considered in the constraints, ensuring that within a certain error range, the power generation capabilities of wind power and photovoltaic power can be reflected. Through the above measures, the system can not only minimize costs on the basis of ensuring stable operation but also guarantee long-term economic benefits and sustainable development, enabling the building integrated energy microgrid to flexibly respond to changes in the external environment and enhancing its adaptability to renewable energy.

[0112] Furthermore, according to the pre-configured situation of the capacities of wind, light, and energy storage, a genetic algorithm is introduced. Through operations such as selection, crossover, and mutation, it searches in the population of candidate solutions to find the optimal or near-optimal solution, thereby determining the specific situations of the capacities of wind power, photovoltaic power, energy storage, and their maximum output powers. 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 quality. Candidate solutions are selected according to their fitness levels for subsequent crossover and mutation operations. By randomly pairing the selected candidate solutions, crossover operations are performed to generate new offspring, and at the same time, certain 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 most excellent individuals are selected according to the fitness to form a new generation of the population. Iterations are continuously carried out until the termination conditions are met, such as reaching the maximum number of iterations or the fitness threshold. If the conditions are met, the execution will stop; otherwise, the next round of iteration will continue. Specifically: The system information is converted into machine-recognizable data information. Considering the defects of binary coding, the Gray code coding method is selected, and its specific form is: , where represents the candidate solution after Gray code coding, represents the th bit of the Gray code, represents the bit represents the bit of the Gray code, represents the bit of the binary number, represents the bit of the binary number, that is, the next bit, represents the exclusive - or operation, represents the position of the binary number, represents the most significant bit of the binary number, is the binary coding form, represents the bit of the binary number, represents the bit of the binary number, represents the bit of the binary number. Select individuals according to the fitness probability , where, represents the probability that the th individual is selected, represents the fitness function of the th individual in the population. The probabilities of crossover and mutation operations are usually preset fixed values, such as the crossover probability and the mutation probability . In the crossover operation, the chromosomes of 2 individuals are randomly exchanged with a probability to obtain new chromosome - characteristic individuals. During the mutation operation, according to the mutation probability , a random number is formed for each gene on the encoded chromosome string. When , for the encoding position, mutation occurs. Let the chromosome string be , then it mutates to , where, represents the mutated chromosome string, represents that the chromosome string remains unchanged when no mutation occurs, represents the bit of the chromosome string, represents the bit of the chromosome string;

[0113] Finally, after the algorithm terminates, the candidate solution with the highest fitness is selected as the optimal strategy, and the optimal configuration schemes of wind power, photovoltaic, and energy storage are output.

[0114] For the above four steps, first, it is necessary to determine the capacity allocation of wind power and photovoltaic power. Based on the power characteristics of wind and light, considering the total system cost, power balance, and the constraints of wind and light themselves, plan the allocation of wind power and photovoltaic power; second, it is necessary to consider the adverse situations that may occur during 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, determine the most severe operating scenario to provide a basis for the energy storage configuration plan; third, based on the most severe operating scenario, make up for the uncertainty of wind power and photovoltaic power through the energy storage output; finally, according to the capacity allocation of wind, light, and storage, optimize the overall operation of the system to minimize the operating cost and meet the power demand, ensure the coordination of the capacity allocation of wind power, photovoltaic power, and energy storage and the power purchase operation strategy of the power grid. When it is difficult to meet the demand only relying on wind power, photovoltaic power, and energy storage, purchase power from the power grid to make up for the lack of power demand, and when wind power and photovoltaic power can meet the power demand, charge the energy storage to improve the overall efficiency and achieve the optimal system operation situation.

[0115] Embodiment 2

[0116] Taking the capacity allocation of wind, light, and storage in a certain building integrated energy microgrid as an example, consider its operating time is 7 days. The time-of-use electricity price of the power grid is as Figure 2 shown, and the load power demand is as Figure 3 shown, and other parameters are shown in Table 1.

[0117] Table 1 Parameters of the capacity allocation of wind, light, and storage for a certain time

[0118] ;

[0119] The objective function includes the total investment cost, operating cost, and curtailment cost of the system. The obtained wind power and photovoltaic power capacities are 479.21 kW and 570.98 kW. Based on the wind power and photovoltaic power capacities, calculate the most severe operating scenario considering their uncertainties. Based on the analysis results of the most severe scenario, further adjust the capacity of the energy storage to ensure the stable operation of the system under different scenarios. Considering the uncertainty of wind power and photovoltaic power output and the mismatch problem between the daytime photovoltaic peak and the nighttime wind power peak, configure an energy storage capacity of 1085.83 kWh and a maximum output power of 276.01 kW. This configuration ensures that the system can maintain stable operation under the most severe operating scenario and enhances the flexibility and reliability of the power grid. By comprehensively considering factors such as the investment cost, operating cost of wind power, photovoltaic power, and energy storage, and the power grid electricity price, optimize the operation of wind, light, and storage. The operation of the energy storage is as Figure 4 shown, and the power purchase situation is as Figure 5As shown, the respective costs are shown in Table 2. Although the initial investment is relatively large, in the long run, by reducing the electricity purchase volume and the curtailment of wind and solar power, the system can significantly reduce the operating cost and improve the overall economic efficiency. By optimizing the configuration and operation strategy of the wind-solar-storage system, the system reduces the curtailment of wind and solar power by 32.85 kWh, which not only reduces resource waste but also avoids the losses caused by curtailment. At the same time, the effective utilization of energy storage also reduces the dependence on the external power grid and further reduces the electricity purchase cost. By configuring the capacities of wind power, photovoltaic power, and energy storage and optimizing their operation, the energy utilization efficiency can be improved and the system operating cost can be reduced.

[0120] Table 2 The respective cost situations of a certain wind-solar-storage capacity configuration

[0121] Purchased electricity volume 18707 kWh Purchased electricity cost 10386 yuan Abandoned wind and solar power volume 32.85 kWh Energy storage charging volume 4317.3 kWh Energy storage discharging volume 4799.9 kWh Total cost 30791 yuan

[0122] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0123] In 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 merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, 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 couplings, direct couplings, or communication connections shown or discussed with each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0124] If the described function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0125] Although embodiments of the present invention have been shown and described, those skilled in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. 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; The target optimization of the system total cost model is expressed as follows: min F=c wt *(I wt +M wt )+c pv *(I pv +M pv )+c es *(I es +M es )+p esmax *C es +S t p grid *C grid +S t p wt *R wt +S t p pv *R pv +∑ t p waste *u Where F is the total system cost, c wt 、c pv and c es They are wind power capacity, photovoltaic capacity and energy storage capacity; I wt ,I pv and I es are the unit prices of wind power, photovoltaic power and energy storage investment costs respectively; M wt 、M pv and M es are wind power cost unit price, photovoltaic cost unit price and energy storage maintenance cost unit price respectively; p esmax is the maximum output power of energy storage; C es is the unit price of energy storage power; p grid 、p wt 、p pv 、p waste are grid power, wind power, photovoltaic power and abandoned power respectively; C grid is the unit price of electricity purchase cost; u is the unit price of power abandonment penalty; R wt and R pv 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 p grid 、Wind power p wt , photovoltaic power p pv and abandoned power p waste The constraints are as follows: Among them, p esc and p esd are the energy storage charging and discharging power respectively; soc is the energy storage charge state; soc min and soc max are the minimum and maximum states of charge allowed for energy storage; c and λ d are energy storage charging efficiency and discharging efficiency respectively; p E is the load power demand, c wtm i n 、c pvmin and c esmin are the minimum values ​​of wind power, photovoltaic power and energy storage capacity respectively; p esc (t) and p esd (t) are the current energy storage charging and current discharging power respectively; soc(t) is the current energy storage charge state, soc(t-1) is the energy storage charge state at time t-1, and t is the current time t.

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 1 is characterized in that: The objective optimization function of the worst operating scenario power supply model is expressed as follows: max Z=∑ t p waste Among them, Z is the amount of power abandoned, t is the total duration, p waste The power abandoned.

4. 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 constraints of the worst operating scenario power supply model include: Among them, p wte and p pve are the worst case power of wind power and photovoltaic power respectively; w wt and w pv are the uncertainty errors of wind power and photovoltaic power respectively; p E is the load power demand; p pv is the photovoltaic power; p wt For wind power.

5. 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: minH=c es *(I es +M es )+p esmax *C es +∑ t p grid *C grid +∑ t p waste *u Among them, H is the energy storage operating cost under the worst scenario.

6. The method for capacity planning and operation optimization of building integrated energy microgrid based on wind, solar and storage according to claim 5 is characterized in that: The constraints of the optimal configuration model of energy storage are: Among them, λ c and λ d They are the energy storage charging efficiency and discharging efficiency respectively.

7. 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: min V=∑ t p grid *C grid +∑ t p waste *u The constraints are: Where V is the sum of the power purchase cost and power abandonment cost of the power grid; wta and p pva They are the actual power of wind power and photovoltaic power within a certain error range.

8. The method for capacity planning and operation optimization of building integrated energy microgrid based on wind, solar and storage according to claim 7 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: Among them, Y represents the candidate solution after Gray code encoding, y m represents the mth bit of Gray code, x m Represents the mth bit of a binary number, y i represents the i-th bit of Gray code, x i Represents the i-th digit of a binary number, x i+1 Represents the i+1th digit of a binary number, that is, x i Next one, Represents XOR operation, i represents the position of the binary number, m represents the highest bit of the binary number, X=x m x m-1 …x2x1 is a binary encoding format, x m-1 represents the m-1th bit of a binary number, x1 represents the 1st bit of a binary number, and x2 represents the 2nd bit of a binary number; 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: Among them, P i represents the probability of the i-th individual being selected, f i represents the fitness function of the ith individual, f j represents the fitness function of the jth individual in the population; In the crossover operation, the chromosomes of two individuals are crossed with probability P. c Perform random exchange to obtain individuals with new chromosome characteristics; During the mutation operation, according to the mutation probability P m , a random number r is generated for each gene on the encoded chromosome string i , when r i <P m When the coding position changes, let the chromosome string be s=a1a2a3…a l , then it changes to Among them, a′ represents the mutated chromosome string, a represents the chromosome string remains unchanged when no compilation occurs, and a i represents the i-th position of the chromosome string, a l Indicates the lth position of the chromosome string; 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.

9. 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 constraint is expressed as: Among them, c wtm i n 、c pvmin and c esm i n 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: v i =ω×v i +c1×rand()×(pbest i -x i )+c2×rand()×(gbest i -x i ) x i =x i +v i Among them, v i is the particle speed; rand() is a random number between (0,1); x i is the current position of the particle; c1 and c2 are learning factors; pbest i gbest is the best position searched by the i-th particle so far; i is the optimal position searched by the entire particle swarm so far; ω is the inertia factor, ω max and ω min is the maximum and minimum value of the inertia factor; t is the current iteration number; T max 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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