Micro-grid optimal configuration method and device, electronic equipment, medium and program product

Abstract

The invention relates to the technical field of micro-grids, in particular to a micro-grid optimal configuration method and device, electronic equipment, a medium and a program product, and the method comprises the steps: evaluating the actual power generation capability of a photovoltaic system based on a pre-established photovoltaic output model; evaluating the degradation rate of the hybrid energy storage system based on a pre-established degradation dynamic model of the hybrid energy storage system; based on a pre-established mixed integer linear programming model, calculating the total cost of the minimum micro-grid; and based on the actual power generation capacity, the degradation rate, the minimization of the total cost of the micro-grid and at least one preset constraint condition, a multi-time scale optimization configuration scheme of the target micro-grid is generated in combination with a preset day-ahead optimization scheduling model and a preset intra-day optimization scheduling model. According to the method, multi-time-scale optimization planning and flexible resource configuration of the micro-grid can be realized, the operation economy and the overall performance of a grid system are improved, and the micro-grid can keep efficient and stable operation when facing various constraint conditions to the maximum extent.

Description

Technical Field

[0001] The present application relates to the field of microgrid technology, and in particular to a microgrid optimization configuration method, device, electronic device, medium and program product. Background Art

[0002] In related technologies, with the widespread deployment of distributed renewable energy, energy storage and microgrid technologies in various countries and regions, the global electrification rate has increased significantly. These technologies not only promote the optimization of energy structure, but also improve the reliability and flexibility of energy supply. As a result, the capacity decay of batteries during long-term use brings a lot of replacement costs, and the life cycle of battery energy storage systems is usually shorter than the average life of photovoltaic modules. Frequent battery replacement not only increases maintenance costs, but also affects the stable operation of the power grid system. In addition, energy prices fluctuate due to a variety of factors, including the main grid electricity price and the cost of energy supply interruption. Non-interconnected areas (NIAs) are difficult to access the national grid due to remote geographical locations or economic barriers, resulting in uncertainty in their dependence on the main grid. This uncertainty increases the difficulty and cost of independent operation of microgrids. Supercapacitors have the advantages of fast charging and discharging, high power density and long cycle life. When used in combination with battery energy storage systems, they can significantly extend battery life and improve system performance. In the process of optimizing the dispatch of microgrids, how to coordinate and control the output of each power unit and energy storage equipment to meet load demand and minimize operating costs is an important link.

[0003] However, the microgrid optimization configuration methods in related technologies often fail to fully consider the large replacement costs and maintenance costs of batteries during long-term use, as well as the energy prices affected by various factors, which affects the stability of the power grid system operation and increases the difficulty and cost of independent operation of the microgrid. In addition, the optimization strategy of a single time scale often leads to deviations between the scheduling results and the actual situation due to the low accuracy of source and load prediction, showing great limitations and making it difficult to meet the optimization needs of the microgrid operation configuration, which needs to be solved urgently. Summary of the invention

[0004] The present application provides a microgrid optimization configuration method, device, electronic device, medium and program product to solve the problem that the microgrid optimization configuration method in the related art often fails to fully consider the large replacement cost and maintenance cost of the battery during long-term use and the energy price affected by various factors, which affects the stability of the power grid system operation and increases the difficulty and cost of independent operation of the microgrid. In addition, the optimization strategy of a single time scale often leads to a deviation between the scheduling result and the actual due to the low accuracy of source and load prediction, showing great limitations and making it difficult to meet the optimization needs of the microgrid operation configuration.

[0005] The first aspect of the present application provides a microgrid optimization configuration method, comprising the following steps: based on a pre-established photovoltaic output model, evaluating the actual power generation capacity of a photovoltaic system; based on a pre-established hybrid energy storage system degradation dynamics model, evaluating the degradation rate of a hybrid energy storage system; based on a pre-established mixed integer linear programming model, calculating the minimized total microgrid cost of the mixed integer linear programming model; based on the actual power generation capacity of the photovoltaic system, the degradation rate of the hybrid energy storage battery, the minimized total microgrid cost and at least one preset constraint, combining a preset day-ahead optimization scheduling model and a preset intraday optimization scheduling model to generate a multi-time scale optimization configuration plan for a target microgrid.

[0006] Optionally, in one embodiment of the present application, before evaluating the actual power generation capacity of the photovoltaic system, it also includes: obtaining the ambient temperature and light intensity of the target location of the photovoltaic system; and training a model using the ambient temperature and the light intensity to construct the photovoltaic output model.

[0007] Optionally, in one embodiment of the present application, before evaluating the degradation rate of the hybrid energy storage system, it also includes: calculating the total energy availability of the hybrid energy storage system, and evaluating the battery attenuation rate per unit discharge power of the hybrid energy storage system; and constructing a degradation dynamics model of the hybrid energy storage system based on the total energy availability and the battery attenuation rate.

[0008] Optionally, in one embodiment of the present application, the objective function of minimizing the total cost of the microgrid is:

[0009]

[0010] where Z is the total cost to be minimized, The sum is in the set Ω PV The relevant cost of installing PV systems on all nodes i in is, represents the initial capital cost of the ith PV system, and represents any installation-specific costs, is a binary variable indicating whether the i-th PV system is installed, The sum is in the set Ω B The cost of installing batteries on all members j in is the cost of the jth battery, is similar to But for the binary variable of battery, It is the discount of future costs over the project life (LY) at a discount rate of (1+r) y , is the battery cost, is its average lifetime energy output, represents the coefficient of degradation or efficiency loss, and the other terms in brackets reflect the associated costs of interacting with the grid over all time periods t in the set T. Specifically, Indicates the grid purchase cost, including the general grid purchase cost and the cost of purchasing electricity from the grid specifically for charging the batteries Indicates the calculation of backup power cost, represents the penalty cost of not supplying energy to meet demand, Represents the revenue from selling photovoltaic power to the grid.

[0011] Optionally, in one embodiment of the present application, the multi-time-scale optimization configuration scheme of the target microgrid is generated by combining a preset day-ahead optimization scheduling model and a preset intra-day optimization scheduling model, including: obtaining at least one system parameter of the target microgrid; determining at least one operating constraint of the target microgrid based on the at least one system parameter, so as to formulate a day-ahead optimization scheduling target according to the at least one operating constraint and the day-ahead optimization scheduling strategy; solving the preset day-ahead optimization scheduling model based on the day-ahead optimization scheduling target to obtain the output plan of the target microgrid; obtaining the intra-day optimization scheduling target according to the preset intra-day optimization scheduling model, so as to optimize the output plan according to the intra-day optimization scheduling target and the at least one operating constraint, and obtain the multi-time-scale optimization configuration scheme of the target microgrid.

[0012] The second aspect of the present application provides a microgrid optimization configuration device, including: a first evaluation module, used to evaluate the actual power generation capacity of a photovoltaic system based on a pre-established photovoltaic output model; a second evaluation module, used to evaluate the degradation rate of a hybrid energy storage system based on a pre-established hybrid energy storage system degradation dynamics model; a first calculation module, used to calculate the minimized microgrid total cost of the mixed integer linear programming model based on a pre-established mixed integer linear programming model; a generation module, used to generate a multi-time scale optimization configuration plan for a target microgrid based on the actual power generation capacity of the photovoltaic system, the degradation rate of the hybrid energy storage battery, the minimized microgrid total cost and at least one preset constraint condition, in combination with a preset day-ahead optimization scheduling model and a preset intraday optimization scheduling model.

[0013] Optionally, in one embodiment of the present application, it also includes: an acquisition module, used to obtain the ambient temperature and light intensity of the target location of the photovoltaic system before evaluating the actual power generation capacity of the photovoltaic system; a first construction module, used to train a model using the ambient temperature and the light intensity to construct the photovoltaic output model.

[0014] Optionally, in one embodiment of the present application, it also includes: a second calculation module, used to calculate the total energy availability of the hybrid energy storage system and evaluate the battery attenuation rate per unit discharge power of the hybrid energy storage system before evaluating the degradation rate of the hybrid energy storage system; a second construction module, used to construct a degradation dynamics model of the hybrid energy storage system based on the total energy availability and the battery attenuation rate.

[0015] Optionally, in one embodiment of the present application, the objective function of minimizing the total cost of the microgrid is:

[0016]

[0017] where Z is the total cost to be minimized, The sum is in the set Ω PV The relevant cost of installing PV systems on all nodes i in is, represents the initial capital cost of the ith PV system, and represents any installation-specific costs, is a binary variable indicating whether the i-th PV system is installed, The sum is in the set Ω B The cost of installing batteries on all members j in is the cost of the jth battery, is similar to But for the binary variable of battery, It is the discount of future costs over the project life (LY) at a discount rate of (1+r) y , is the battery cost, is its average lifetime energy output, represents the coefficient of degradation or efficiency loss, and the other terms in brackets reflect the associated costs of interacting with the grid over all time periods t in the set T. Specifically, Indicates the grid purchase cost, including the general grid purchase cost and the cost of purchasing electricity from the grid specifically for charging the batteries Indicates the calculation of backup power cost, represents the penalty cost of not supplying energy to meet demand, Represents the revenue from selling photovoltaic power to the grid.

[0018] Optionally, in one embodiment of the present application, the generation module includes: an acquisition unit, used to acquire at least one system parameter of the target microgrid; a formulation unit, used to determine at least one operating constraint of the target microgrid based on the at least one system parameter, so as to formulate a day-ahead optimization scheduling target according to the at least one operating constraint and the day-ahead optimization scheduling strategy; a solving unit, used to solve the preset day-ahead optimization scheduling model based on the day-ahead optimization scheduling target to obtain the output plan of the target microgrid; an optimization unit, used to obtain the intraday optimization scheduling target according to the preset intraday optimization scheduling model, so as to optimize the output plan according to the intraday optimization scheduling target and the at least one operating constraint, and obtain a multi-time scale optimization configuration scheme for the target microgrid.

[0019] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the microgrid optimization configuration method as described in the above embodiment.

[0020] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program, and when the program is executed by a processor, it implements the above microgrid optimization configuration method.

[0021] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above microgrid optimization configuration method.

[0022] The embodiment of the present application can generate a multi-time scale optimization configuration scheme for the target microgrid by combining the actual power generation capacity of the photovoltaic system, the degradation rate of the hybrid energy storage battery, the minimization of the total cost of the microgrid, the day-ahead optimization scheduling model and the intraday optimization scheduling model under various constraints. Thus, the operation economy of the power grid system is improved by considering the battery attenuation cost and the effect of the supercapacitor on the long-term operation of the microgrid, and the capacity configuration of the microgrid is optimized by the mixed integer linear programming model to improve the reliability and operation efficiency of the microgrid system, and the optimization planning and capacity configuration of the microgrid are realized under the condition of considering the battery aging, so as to improve the overall performance of the operation of the microgrid system; further, the present application can also combine the forecast data of different time scales of the day-ahead and the day, and perform rolling optimization to obtain a more reasonable scheduling plan. At the same time, considering the potential of demand-side response in improving system flexibility and promoting the consumption of new energy, it is incorporated into the optimization scheduling process, which can improve the flexibility of wind and light distributed power sources in the microgrid, promote the consumption of renewable energy, and ensure the economical, efficient, reliable and stable operation of the microgrid. Improve the scope of application of the present application. This solves the problem that the microgrid optimization configuration methods in related technologies often fail to fully consider the large amount of replacement costs and maintenance costs of batteries during long-term use, as well as energy prices affected by various factors, which affects the stability of the power grid system operation and increases the difficulty and cost of independent operation of the microgrid. In addition, the optimization strategy of a single time scale often leads to deviations between the scheduling results and the actual situation due to the low accuracy of source and load prediction, showing great limitations and making it difficult to meet the optimization needs of the microgrid operation configuration.

[0023] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0025] Figure 1 A flowchart of a microgrid optimization configuration method provided according to an embodiment of the present application;

[0026] Figure 2 This is a schematic diagram of the overall structure of a microgrid according to an embodiment of the present application;

[0027] Figure 3 A schematic diagram of wind power prediction data according to an embodiment of the present application;

[0028] Figure 4 A schematic diagram of photovoltaic prediction data according to an embodiment of the present application;

[0029] Figure 5This is a schematic diagram of electric load prediction data according to an embodiment of the present application;

[0030] Figure 6 This is a schematic diagram of heat load prediction data for one embodiment of the present application;

[0031] Figure 7 This is a schematic diagram of the optimized scheduling relationship between the day before and the day after according to an embodiment of the present application;

[0032] Figure 8 This is a flow chart of day-ahead and intra-day optimization scheduling according to an embodiment of the present application;

[0033] Fig. 9 A flowchart of a microgrid optimization configuration method based on photovoltaic and hybrid energy storage according to an embodiment of the present application;

[0034] Fig.10 A schematic diagram of a parameter curve of an embodiment of the present application;

[0035] Fig.11 A schematic diagram of the structure of a microgrid optimization configuration device according to an embodiment of the present application;

[0036] Fig.12 Schematic diagram of the structure of an electronic device according to an embodiment of the present application.

[0037] Reference numerals:

[0038] 10-Microgrid optimization configuration device: 100-first evaluation module, 200-second evaluation module, 300-first calculation module and 400-generation module; 1201-memory, 1202-processor and 1203-communication interface. DETAILED DESCRIPTION

[0039] Embodiments of the present application are described in detail below, and examples of the embodiments 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 intended to be used to explain the present application, and should not be construed as limiting the present application.

[0040] The following describes the microgrid optimization configuration method, device, electronic device, medium and program product of the embodiment of the present application with reference to the accompanying drawings. The microgrid optimization configuration method in the related art mentioned in the above background technology often does not fully consider the large replacement cost and maintenance cost of the battery during long-term use, as well as the energy price affected by various factors, which affects the stability of the power grid system operation and increases the difficulty and cost of independent operation of the microgrid. In addition, the optimization strategy of a single time scale often causes the scheduling result to deviate from the actual due to the low source-load prediction accuracy, showing great limitations and difficulty in meeting the optimization requirements of the microgrid operation configuration. The present application provides a microgrid optimization configuration method, in which a multi-time scale optimization configuration scheme for the target microgrid can be generated by combining the actual power generation capacity of the photovoltaic system, the degradation rate of the hybrid energy storage battery, the minimization of the total cost of the microgrid, the day-ahead optimization scheduling model and the intraday optimization scheduling model under various constraints. As a result, the battery attenuation cost and the impact of supercapacitors on the long-term operation of the microgrid are taken into consideration, the operating economy of the power grid system is improved, and the capacity configuration of the microgrid is optimized through a mixed integer linear programming model to improve the reliability and operating efficiency of the microgrid system. The optimal planning and capacity configuration of the microgrid are achieved while considering battery aging, thereby improving the operation and overall performance of the microgrid system. Furthermore, the present application can also combine the forecast data of different time scales on the day before and within the day to perform rolling optimization, so as to obtain a more reasonable scheduling plan. At the same time, taking into account the potential of demand-side response in improving system flexibility and promoting the consumption of new energy, incorporating it into the optimization scheduling process can improve the flexibility of wind and optical distributed power sources in the microgrid, promote the consumption of renewable energy, and ensure the economical, efficient, reliable and stable operation of the microgrid. This solves the problem that the microgrid optimization configuration methods in related technologies often fail to fully consider the large amount of replacement costs and maintenance costs of batteries during long-term use, as well as energy prices affected by various factors, which affects the stability of the power grid system operation and increases the difficulty and cost of independent operation of the microgrid. In addition, the optimization strategy of a single time scale often leads to deviations between the scheduling results and the actual situation due to the low accuracy of source and load prediction, showing great limitations and making it difficult to meet the optimization needs of the microgrid operation configuration.

[0041] Specifically, Figure 1 A flow chart of a microgrid optimization configuration method provided in an embodiment of the present application.

[0042] like Figure 1 As shown, the microgrid optimization configuration method includes the following steps:

[0043] In step S101 , the actual power generation capacity of the photovoltaic system is evaluated based on a pre-established photovoltaic output model.

[0044] It is understood that the photovoltaic output model here can be understood as a mathematical model used to describe and predict the output power of a solar photovoltaic power generation system under different conditions. These models take into account a variety of factors, including but not limited to solar radiation, meteorological conditions, photovoltaic module characteristics, etc., to provide an accurate assessment of the performance of the power grid system.

[0045] When optimizing the configuration of a microgrid system, supply and demand balance is an indispensable condition for the stable operation of the power grid system. To achieve supply and demand balance, it is necessary to consider the power supply capacity of the power grid system. Therefore, the embodiment of the present application can evaluate the actual power generation capacity of the photovoltaic system based on the pre-built photovoltaic output model, so as to optimize the configuration of the microgrid according to the actual power generation capacity.

[0046] Furthermore, when constructing a photovoltaic output model, in order to ensure the authenticity and validity of the data, the embodiment of the present application can, but is not limited to, establish an output model of the photovoltaic system based on the ambient temperature and light intensity of the target location of the photovoltaic system to accurately evaluate the actual power generation capacity of the photovoltaic system.

[0047] Next, the process of building a photovoltaic output model is described in detail.

[0048] Optionally, in one embodiment of the present application, before evaluating the actual power generation capacity of the photovoltaic system, it also includes: obtaining the ambient temperature and light intensity of the target location of the photovoltaic system; and training a model using the ambient temperature and light intensity to construct a photovoltaic output model.

[0049] It is understandable that the target location here can be understood as a location where a photovoltaic system has been installed or is to be installed.

[0050] In the actual implementation process, since the efficiency of photovoltaic cells is greatly affected by the ambient temperature, the output power usually decreases as the temperature rises. Since the light intensity has a strong impact on the ambient temperature, the embodiment of the present application can obtain the ambient temperature and light intensity of the target location of the photovoltaic system when constructing the photovoltaic output model, and thus obtain a simplified formula that approximates this influence, which can be expressed as but not limited to the following:

[0051]

[0052] Among them, P PV (I T ,T) represents the irradiance I T The photovoltaic output corresponding to the ambient temperature T, is the rated power of a single photovoltaic panel under standard conditions; γ PT Indicates the power temperature coefficient of the photovoltaic panel, I STC and T STCare the standard test condition values ​​of irradiance and ambient temperature, which can be set to 1000 W / m 2 and 25℃.

[0053] After obtaining the simplified influence formula, the influence relationship in the formula can be used to train the photovoltaic output model to ensure the authenticity and effectiveness of the constructed photovoltaic output model.

[0054] The embodiment of the present application can train and construct a photovoltaic output model based on the ambient temperature and light intensity of the target location of the photovoltaic system, thereby ensuring that the constructed light output model and the actual power generation capacity obtained by evaluation are highly reliable, thereby helping to improve the effectiveness and practicality of the microgrid optimization configuration solution obtained in the subsequent process.

[0055] Step S102 : evaluating the degradation rate of the hybrid energy storage system based on a pre-established hybrid energy storage system degradation dynamics model.

[0056] It is understandable that the degradation dynamics model of the hybrid energy storage system can be understood here as a mathematical model that describes the gradual decline in performance of its internal energy storage components such as batteries and supercapacitors during long-term use. This model is of great significance for evaluating the life of the energy storage system, optimizing energy management strategies, and conducting economic scheduling.

[0057] In some embodiments, the battery capacity of the hybrid energy storage will decrease with the use time and the number of times it is used, that is, the battery has degraded, which will have a greater impact on the operation of the power grid system. Therefore, the embodiment of the present application can construct a hybrid energy storage system degradation dynamics model, and then evaluate the battery degradation based on the Ah throughput model.

[0058] Furthermore, considering that the integration of supercapacitors has a certain effect on extending the battery life, the embodiments of the present application may also take it into consideration.

[0059] The embodiments of the present application can evaluate the degradation rate of the hybrid energy storage system based on a pre-built hybrid energy storage system degradation dynamics model, so as to identify potential performance degradation in advance according to the actual performance and development trend of the hybrid energy storage system, so as to reasonably arrange the layout and capacity configuration of the energy storage facilities when optimizing the configuration of the microgrid, so as to improve the overall performance and reliability of the system.

[0060] Next, the process of constructing the degradation dynamics model of the hybrid energy storage system in the embodiment of the present application is further explained.

[0061] Optionally, in one embodiment of the present application, before evaluating the degradation rate of the hybrid energy storage system, it also includes: calculating the total energy availability of the hybrid energy storage system, and evaluating the battery attenuation rate per unit discharge power of the hybrid energy storage system; and constructing a degradation dynamics model of the hybrid energy storage system based on the total energy availability and the battery attenuation rate.

[0062] Based on the relevant descriptions of other embodiments, it can be understood that during the optimization configuration of the microgrid, a hybrid energy storage system degradation dynamics model can be constructed to evaluate the degradation rate of the hybrid energy storage system, which is helpful to reasonably arrange the layout and capacity configuration of energy storage facilities when optimizing the configuration of the microgrid.

[0063] In some embodiments, in the process of constructing the degradation dynamics model of the hybrid energy storage system, the total energy availability of the hybrid energy storage system can be used, but is not limited to, to evaluate the battery attenuation rate per unit discharge power, thereby realizing the construction of the degradation dynamics model of the hybrid energy storage system.

[0064] For example, the Ah throughput model (the total amount of electricity that a battery can handle under given conditions through multiple charge and discharge cycles, which can usually be expressed as "battery capacity (Ah) × number of cycles") is an ideal tool for incorporating battery aging effects into energy storage system (ESS) capacity planning. In the embodiment of the present application, when the nominal energy capacity of the battery drops to 80% of the initial value, the battery's service life can be considered to have ended.

[0065] According to this standard, the embodiment of the present application can calculate the total energy availability (Eav) of a specific battery cell and then evaluate the battery attenuation rate per unit discharge power. The calculation formula can be as follows:

[0066]

[0067] Among them, E av Indicates the average energy output during the life cycle, NC B Indicates storage parameters, E B Indicates the battery energy parameter, deg ESS Indicates the battery decay rate.

[0068] Further, the embodiments of the present application may focus on the integration of a hybrid energy storage system (HESS) of supercapacitors and batteries in microgrid capacity planning. According to the Ah throughput model, using OPzV supercapacitors as only 0.1% of the total energy of the battery pack can significantly extend the battery life by about 7.7%. This aging estimation model is highly versatile and flexible because it relies entirely on parameters obtained through technical charge and discharge tests, and is therefore applicable to all types of batteries.

[0069] The embodiment of the present application can construct a degradation dynamics model of the hybrid energy storage system according to the total energy availability and battery attenuation rate of the hybrid energy storage system, optimize the cost structure of the hybrid energy storage system based on the evaluation results of the degradation dynamics model, reduce replacement costs and operating costs, and optimize the cost structure, scheduling strategies and management measures in the microgrid configuration.

[0070] Step S103, based on the pre-established mixed integer linear programming model, calculate the minimum total cost of the microgrid of the mixed integer linear programming model. Wherein, the objective function of minimizing the total cost of the microgrid can be, but is not limited to, expressed as:

[0071]

[0072]

[0073] where Z is the total cost to be minimized, The sum is in the set Ω PV The relevant cost of installing PV systems on all nodes i in is, represents the initial capital cost of the ith PV system, and represents any installation-specific costs, is a binary variable indicating whether the i-th PV system is installed, The sum is in the set Ω B The cost of installing batteries on all members j in is the cost of the jth battery, is similar to But for the binary variable of battery, It is the discount of future costs over the project life (LY) at a discount rate of (1+r) y , is the battery cost, is its average lifetime energy output, represents the coefficient of degradation or efficiency loss, and the other terms in brackets reflect the associated costs of interacting with the grid over all time periods t in the set T. Specifically, Indicates the grid purchase cost, including the general grid purchase cost and the cost of purchasing electricity from the grid specifically for charging the batteries Indicates the calculation of backup power cost, represents the penalty cost of not supplying energy to meet demand, Represents the revenue from selling photovoltaic power to the grid.

[0074] As a possible implementation method, the embodiment of the present application can also construct a mixed integer linear programming model. Specifically, a mixed integer linear programming model including components such as a photovoltaic system, a hybrid energy storage system, and a diesel generator can be constructed with the goal of minimizing the total cost of the microgrid. Among them, the mixed integer linear programming model can include but is not limited to the consideration of multiple factors such as initial investment cost, long-term operating cost, and energy supply interruption cost.

[0075] For example, the objective function of the mixed integer linear programming (MILP) model can be a cost minimization function for optimizing the size of a microgrid (MG), including but not limited to various cost elements related to photovoltaic (PV) installation, battery installation, grid interaction, backup power, and non-supplied energy (ENS). The objective function for minimizing the microgrid cost can be, but is not limited to, expressed as follows:

[0076]

[0077] where Z is the total cost to be minimized, The sum is in the set Ω PV The relevant cost of installing PV systems on all nodes i in is, represents the initial capital cost of the ith PV system, and represents any installation-specific costs, is a binary variable indicating whether the i-th PV system is installed, The sum is in the set Ω B The cost of installing batteries on all members j in is the cost of the jth battery, is similar to But for the binary variable of battery, It is the discount of future costs over the project life (LY) at a discount rate of (1+r) y , is the battery cost, is its average lifetime energy output, represents the coefficient of degradation or efficiency loss, and the other terms in brackets reflect the associated costs of interacting with the grid over all time periods t in the set T. Specifically, Indicates the grid purchase cost, including the general grid purchase cost and the cost of purchasing electricity from the grid specifically for charging the batteries Indicates the calculation of backup power cost, represents the penalty cost of not supplying energy to meet demand, Represents the revenue from selling photovoltaic power to the grid.

[0078] The embodiments of the present application can help optimize the configuration scheme to significantly reduce the total cost of the microgrid by comprehensively considering various costs such as initial investment cost, long-term operating cost, energy supply interruption cost and minimizing the total cost, thereby improving the economic feasibility of the optimized configuration scheme and making the construction and operation of the microgrid more economical.

[0079] Step S104, based on the actual power generation capacity of the photovoltaic system, the degradation rate of the hybrid energy storage battery, the minimization of the total cost of the microgrid and at least one preset constraint condition, combined with the preset day-ahead optimization scheduling model and the preset intraday optimization scheduling model, a multi-time scale optimization configuration plan for the target microgrid is generated.

[0080] It is understandable that the target microgrid here refers to a microgrid that can or needs to optimize the configuration of flexible resources at multiple time scales. The preset day-ahead optimization scheduling model here refers to a pre-established day-ahead optimization scheduling model, which can be solved using an improved particle swarm algorithm on a full-day basis to develop a 24-hour optimal output plan that comprehensively considers the economic benefits of all parameters of the target microgrid. The preset intraday optimization scheduling model here refers to a pre-established intraday optimization scheduling model, which can update the forecast data at shorter time intervals so that it can quickly respond to real-time changes, and can update the data every 15 minutes and optimize the next 4-hour plan, thereby greatly improving the robustness of wind power, photovoltaic and load forecast errors, and continuously correcting and improving the day-ahead scheduling plan through a rolling optimization strategy.

[0081] In some embodiments, by combining the actual power generation capacity of the photovoltaic system, the degradation rate of the hybrid energy storage battery, the minimization of the total cost of the microgrid, and a certain day-ahead optimization scheduling model and intraday optimization scheduling model, the present application can generate an optimal configuration scheme for the microgrid based on dynamic constraints at multiple time scales. In this process, a multi-time scale method is used to flexibly configure resources, covering short-term time scales from minutes to hours, and long-term time scale scheduling strategies from days to months, which not only considers long-term cost-effectiveness, but also responds to short-term load demand and resource changes in real time.

[0082] In the process of improving the economic and technical performance of microgrid optimization planning and capacity configuration, various constraints will be imposed, such as investment budget constraints, equipment installation constraints, power balance constraints, energy storage system operation constraints, etc.

[0083] To ensure the reliable operation and cost-effectiveness of the microgrid, the embodiments of the present application may, but are not limited to, combine at least one certain constraint condition when generating an optimal configuration scheme for the microgrid based on the actual power generation capacity of the photovoltaic system, the degradation rate of the hybrid energy storage battery and the minimization of the total cost of the microgrid, so as to ensure the practical applicability of the generated optimal configuration scheme for the microgrid.

[0084] Specifically, the constraints in the embodiments of the present application include but are not limited to the following constraints:

[0085]

[0086] Constraint (4) represents the budget constraint in the optimization problem. It stipulates that the total cost of all PV systems installed at nodes i plus the total cost of all batteries installed at nodes i must not exceed the predefined maximum budget C budget .

[0087]

[0088] Constraint (5) determines the installation status of the PV system at node i The lower limit function, i.e. function (5), indicates that the number of PV systems that can be installed is an integer value, N PVMax represents the maximum volume or capacity of photovoltaic installations that can be considered as strategic, refers to the occupied volume or space of node i. Therefore, constraint (5) can calculate the maximum number of PV systems that can be installed at a certain location based on the space availability, from inherits this installation decision while also respecting the space constraint. Similarly, in constraint (6), for the battery at position j, By binary decision variables Determine, for consideration of installing a battery on j, N BattMax Indicates the maximum total volume or capacity of the battery, is the volume or space required for a battery at location j. This equation ensures that the number of batteries installed does not exceed the actual capacity of the site. Constraint (7) sets the total area A available for PV installation at all nodes. av restrictions, represents the area required to install a PV system at node i, and the sum of all i ensures that the total allocated area does not exceed the available area.

[0089]

[0090] Constraint (8) represents the overall power balance at each time period t, and it enforces that the sum of all power generation (including renewable energy, battery discharge, external power exchange) minus losses must equal the total load demand at any given time. Constraint (9) governs the operation of the PV system and its interaction with the battery at node i at each time period t, and it ensures that the sum of power losses, generation, power sent to the battery, and power curtailment equals the theoretical maximum power generation potential of the PV system at that node and time, and is adjusted according to whether the PV system is installed.

[0091]

[0092] Constraint (10) is the power supplied by the main grid to the load at any time. and the power provided by the main grid to the BESS A limit is set that the total power from the mains cannot exceed the maximum available grid power and the available power grid at that time Constraint (11) restricts all photovoltaic systems of all nodes i in the network at time t It ensures that the total photovoltaic output does not exceed the maximum photovoltaic power generation allowed And it is adjusted according to the availability of the power grid. The parameters in constraint (12) Indicates the availability of backup diesel generators.

[0093]

[0094]

[0095]

[0096] Constraints (13)-(18) govern the operation of the battery storage system (including photovoltaic cells and grid-connected batteries) in the network, ensuring that the charging and discharging operations comply with the physical limitations of the batteries. For each battery j of the photovoltaic system connected to node i, and for each time period t, these constraints ensure that its charging / discharging is properly controlled according to the state and capacity of the battery. Among them, constraint (13) indicates that the total power of all connected photovoltaic cells at the node must not exceed the upper limit of the battery's available capacity. Constraint (14) indicates that the total power should not exceed the maximum charging rate. Constraint (15) ensures that the total power must not be lower than the maximum reverse discharge rate, and similar constraints apply to grid-connected batteries to ensure that they can also operate according to the charging / discharging state within the rated capacity range. Constraint (16) ensures that the total power exchanged with the grid-connected battery is limited by its available capacity. Constraint (17) indicates that the amount of power flowing into the grid-connected battery is limited by the maximum charging rate of the battery, which is represented by the binary variable representing the charging state Constraint (18) ensures that discharge is controlled in a similar manner, allowing discharge when the battery is not scheduled to be charged. In both cases of constraints (17) and (18), M is typically a large number to ensure that when the binary variable is allowed to charge or discharge, the inequality actually becomes an equality, reflecting the practical limitations.

[0097]

[0098] Likewise, constraints (19)-(21) impose three limits on the battery discharge power. Constraint (22) enforces that battery j can only be in one of three states during any given period of time.

[0099]

[0100] Constraints (23)-(25) define the state of charge (SoC) dynamics and constraints of the battery in an energy management system, in particular to optimize the use of the battery in interaction with renewable energy sources and the grid. Constraint (23) is the variation of SoC over time.

[0101] Constraint (24) limits the minimum and maximum SoC levels. Constraint (25) defines the initial SoC level.

[0102]

[0103] Constraints (26)-(28) extend the battery model to account for the decay of battery capacity over time and impose a limit on the total decay over the battery life. Constraint (26) restates the initial battery capacity setting, where is the capacity at the beginning of the analysis period t0, determined by the battery's binary existence and its nominal energy capacity. Constraint (27) describes the decay of capacity over time, where the capacity at each time step t is updated by subtracting the decay effect caused by the battery discharge during that time period. The decay rate is denoted by It can be a constant or a function of factors such as cycle frequency, depth of discharge and temperature. Constraint (28) ensures that the battery capacity is constant from the beginning (t0) to the end (t f ) shall not exceed 20% of its initial capacity divided by the battery system life (LY), plus a small tolerance This is a practical limit on how much the battery capacity will decay over its operational lifetime, with a tolerance that allows for slight deviations. Together, these constraints form a comprehensive battery capacity management model that includes the effects of decay over time and sets a limit to ensure that the battery remains useful over its expected lifetime.

[0104] Additionally, the day-ahead optimization scheduling model and the intraday optimization scheduling model in the embodiments of the present application can also be used to evaluate the actual power generation capacity of the photovoltaic system and the degradation rate of the hybrid energy storage system.

[0105] For example, after pre-establishing a photovoltaic output model to evaluate the actual power generation capacity of the photovoltaic system, this application can add a multi-time scale data update mechanism. On the basis of long-term evaluation at the hour level, environmental monitoring and data acquisition at the minute level are added, allowing the model to be frequently updated based on short-term real-time data, so that the microgrid can be flexibly dispatched in small particles within the day, improving the accuracy of photovoltaic prediction.

[0106] In addition, when using the hybrid energy storage system degradation dynamics model to evaluate the degradation rate, multiple time scale effects are considered, and battery usage and power dispatch strategies are dynamically adjusted through hourly and minute-level data collection and analysis. This refined granularity enables the evaluation to keep up with real conditions, timely predict and respond to changes in the state of the energy storage system, improve efficiency and extend service life.

[0107] The embodiments of the present application can generate an optimal configuration plan for the microgrid by taking into account various constraints, combining the actual power generation capacity of the photovoltaic system, the degradation rate of the hybrid energy storage battery, and minimizing the total cost of the microgrid. Through reasonable planning and capacity configuration, the system operating cost can be minimized, the economy and overall performance of the microgrid system operation can be improved, and the microgrid can be kept stable in the face of various constraints to maximize the scope of application of the present application.

[0108] Optionally, in one embodiment of the present application, a multi-time-scale optimization configuration scheme for a target microgrid is generated in combination with a preset day-ahead optimization scheduling model and a preset intraday optimization scheduling model, including: obtaining at least one system parameter of the target microgrid; determining at least one operating constraint of the target microgrid based on at least one system parameter, so as to formulate a day-ahead optimization scheduling target based on at least one operating constraint and the day-ahead optimization scheduling strategy; solving the preset day-ahead optimization scheduling model based on the day-ahead optimization scheduling target to obtain an output plan of the target microgrid; obtaining an intraday optimization scheduling target according to the preset intraday optimization scheduling model, so as to optimize the output plan according to the intraday optimization scheduling target and at least one operating constraint, and obtain a multi-time-scale optimization configuration scheme for the target microgrid.

[0109] It is understandable to those skilled in the art that the optimization dispatch of a single time scale on the day before will often lead to deviations from the actual dispatch results due to the low accuracy of source-load prediction. In order to improve the flexibility and adaptability of dispatch, a multi-time scale optimization dispatch method is proposed, which combines the forecast data of different time scales on the day before and within the day to perform rolling optimization, thereby obtaining a more reasonable dispatch plan. At the same time, considering the potential of demand-side response in improving system flexibility and promoting the consumption of new energy, incorporating it into the optimization dispatch process can further improve the economic and reliability of microgrid operation.

[0110] Based on this, when optimizing the flexibility resource configuration of the microgrid, in addition to considering the various costs of photovoltaic and hybrid energy storage systems, this application can also consider the characteristics of the microgrid's own resource configuration, and perform rolling optimization scheduling of the source-load-storage microgrid based on multiple time scales, which can effectively improve the flexibility of wind and light distributed power sources in the microgrid, promote the consumption of renewable energy, and ensure the economical, efficient, reliable and stable operation of the microgrid. The specific process can be expressed as follows:

[0111] (1) Obtain at least one system parameter of the target microgrid: including but not limited to wind power, photovoltaic power, load including thermal load and electrical load hourly level forecast data, gas turbine equipment parameters, electricity price parameters, heat price, heat pump equipment parameters, waste heat boiler parameters, heat storage tank parameters, and battery parameters.

[0112] Figure 2 This is a schematic diagram of a microgrid structure according to an embodiment of the present application. Figure 2 As shown, the microgrid system in the embodiment of the present application mainly includes but is not limited to wind turbines, photovoltaic panels, micro steam turbines (GT), waste heat boilers (WHB), heat pumps, heat storage tanks, storage batteries (SOC), loads (heat, electricity) and other devices, and its overall architecture is as follows Figure 2 Among them, the flexibility resources mainly include but are not limited to heat pumps, heat storage tanks, micro turbines, waste heat boilers and batteries.

[0113] Furthermore, if Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown in FIG. 1 , this is a schematic diagram of wind power, photovoltaic, electric load and thermal load forecast data obtained in the embodiment of the present application. Specifically, Figure 3 A schematic diagram of wind power prediction data according to an embodiment of the present application; Figure 4 A schematic diagram of photovoltaic prediction data according to an embodiment of the present application; Figure 5 This is a schematic diagram of electric load prediction data according to an embodiment of the present application; Figure 6 This is a schematic diagram of heat load prediction data according to an embodiment of the present application.

[0114] And, Table 1 is the system parameters of the microgrid, Table 2 is the load power supply time-of-use electricity price and heat price, and Table 3 is the purchase and sale electricity price of the upper power grid, which are as follows:

[0115] Table 1

[0116]

[0117]

[0118] Table 2

[0119]

[0120]

[0121] Table 3

[0122] Time Electricity purchase price / (yuan / kWh) Electricity sales price / (yuan / kWh) 0:00～7:00,22:00～24:00 0.35 0.22 7:00～9:00,12:00～17:00,21:00～22:00 0.68 0.42 9:00～12:00,17:00～21:00 1.09 0.65

[0123] (ii) Determine at least one operating constraint of the target microgrid based on at least one system parameter, and formulate a day-ahead optimization scheduling target based on at least one operating constraint and the day-ahead optimization scheduling strategy: Determine the operating constraints of each part of the target microgrid based on at least one system parameter, and optimize them according to the day-ahead optimization scheduling strategy, so as to formulate a day-ahead optimization scheduling target.

[0124] First, the embodiment of the present application can determine the device constraints of the target microgrid to model each device, wherein the device model involved in the embodiment of the present application can include but is not limited to the following:

[0125] 1. The general mathematical model of the micro gas turbine can be, but is not limited to, as follows:

[0126]

[0127] Among them, P GT (t) is the power generation of the micro gas turbine during time period t; Q GT is the thermal power of the gas turbine during period t; η power Represents power generation efficiency; η hot is the thermal efficiency coefficient; V GT Indicates the volume of gas consumed by the micro gas turbine for power generation; L gas It is the low calorific value of natural gas, generally a fixed value of 9.7kWh / m 3 .

[0128] Normally, the efficiency of micro gas turbine power generation is power There is a nonlinear relationship between its power generation, and its mathematical expression can be, but is not limited to, expressed as:

[0129] ηpower =aP GT (t) 3 +bP GT (t) 2 +cP GT (t)+d (32)

[0130] 2. General mathematical model of waste heat boiler:

[0131] When the gas turbine is running, the high-temperature flue gas discharged is collected by the heat boiler and supplied to the heat load. The mathematical model of its heating can be expressed as follows but is not limited to:

[0132] H GT_t =P GT_t / δ GT (33)

[0133] H WHB_t =λ WHB ·H GT_t (34)

[0134] Among them, H GT_t is the thermal power output of the micro gas turbine; H WHB_t is the heat output power of the waste heat boiler; GT , WHB are the electric-to-heat power ratio and the heat recovery efficiency, respectively.

[0135] 3. Mathematical model of heat pump:

[0136] The function of the heat pump is to convert electrical energy into thermal energy to supply the heat load demand in the microgrid. Its working mathematical model and constraints can be expressed as, but not limited to:

[0137]

[0138] Among them, P Hp Represents the electrical power input to the heat pump; η Hp is the heating efficiency of the heat pump; Q Hp is the heat pump output thermal power; Q Hp_max The upper limit of the heat pump output thermal power.

[0139] 4. Electric energy storage system model:

[0140] In the embodiment of the present application, the energy storage device is taken as a battery as an example, and its charge and discharge characteristic mathematical model can be but is not limited to the following:

[0141]

[0142] Among them, S Bat (t) is the remaining capacity of the battery at time t (KW·h); S Bat(t-1) is the remaining capacity of the battery at time t-1 (KW·h), P Bat (t) represents the charging and discharging power of the battery during period t (kW)(P Bat (t)>0 discharge, P Bat (t) <0 charging); η dis , η cha Respectively represent the charging efficiency and discharging efficiency of the battery (%), σ Bat It represents the self-discharge rate of the battery (%), and Δt represents the time interval.

[0143] 5. Thermal energy storage system model:

[0144] The operation mode of the heat storage tank (HS) is similar to that of the power storage group, that is, it absorbs heat when there is excess heat energy and releases heat when the output of the heating device cannot meet the heat load demand. It needs to meet capacity constraints and power constraints. Its model can be, but is not limited to, expressed as follows:

[0145]

[0146] Among them, W HS_t H is the heat energy stored / released by the heat storage tank during period t; HS_chr_t , H HS_dis_t are the heat storage / release power of HS in period t respectively; η HS_chr , η HS_dis are heat storage / release efficiency; W HS_min , W HS_max are the upper and lower limits of HS endothermic / exothermic properties; H HS_dis_min , H HS_dis_max are the upper and lower limits of HS heat release power respectively; H HS_chr_min , H HS_chr_max are the upper and lower limits of HS heat absorption power respectively.

[0147] Next, based on the above equipment model, the day-ahead optimization scheduling target in the embodiment of the present application can be set, but not limited to, to minimize the total daily scheduling cost. The mathematical model can be, but not limited to, expressed as follows:

[0148] C Total =min(C Oper +C Pollu +C Gird +C DR )(39)

[0149] Among them, C Total represents the total scheduling cost on the day before; C Oper , C Pollu Respectively represent the total operating cost of the microgrid system and the cost of pollutant discharge control; C Grid is the interaction cost between microgrid and large grid; C DR Compensation for interrupted load.

[0150] Specifically:

[0151] 1. Daily operation and maintenance costs C Oper It includes the fuel cost consumed by the micro gas turbine and its startup and shutdown costs, as well as the energy storage life loss costs caused by the battery charging and discharging process, which can be expressed as follows but not limited to:

[0152]

[0153] Among them, P Bat (t) is the charging and discharging power of the battery during period t (P Bat (t)>0, when discharging P Bat (t)<0); K Bat is the life loss cost coefficient caused by battery charge and discharge; γ GT is the fuel cost function of the micro gas turbine; are the start-up and shutdown costs of the gas turbine respectively; They are respectively the startup and shutdown behaviors of the micro gas turbine in period t (0-1 variables and different values ​​are 1). When the value is 1, it means that startup or shutdown behavior occurs in period t. The fuel cost function of the micro gas turbine can be expressed as follows, but is not limited to:

[0154]

[0155] Among them, a, b, c, and d are the cost coefficients of the micro gas turbine.

[0156] 2. Pollution emission cost C Pollu The pollution emission cost of micro gas turbine can be expressed as but not limited to:

[0157]

[0158] Among them, P GT (t) is the output of the gas turbine during period t; It represents the pollution cost coefficient per unit power generation of micro gas turbine.

[0159] 3. Interaction cost with the power grid C Gird It can be expressed as, but not limited to:

[0160]

[0161] Among them, P Gird (t) represents the absolute value of the interaction power between the microgrid and the grid during period t; σ Gird Represents the cost coefficient of interaction with the power grid.

[0162] 4. Demand response load compensation fee C DR It can be expressed as, but not limited to:

[0163]

[0164] Among them, λ e , h are the unit compensation coefficients of electrical load and thermal load respectively; ΔL e，cut_t , ΔL h，cut_t They are the interrupted electrical load and thermal load respectively.

[0165] Furthermore, the constraints involved in the day-ahead optimization scheduling target set in the embodiment of the present application may be, but are not limited to, expressed as follows:

[0166] 1. Power balance constraints can be expressed as follows but are not limited to:

[0167] P Load (t)+P Bat_Dis (t)+P Buy (t) = P Wt (t)+P Pv (t)+P Bat_Cha (t)+P GT (t)+P Sell (t)(45)

[0168] H WHB_t +Q Hp_t +H HS_dis_t =L h_t +H HS_chr_t (46)

[0169] Among them, P Load (t) is the load demand power of the microgrid during period t; P Bat_Dis (t) is the discharge power of the battery pack; P Buy (t) is the power purchased by the microgrid from the large power grid during period t; P Wt (t), P Pv (t) are wind power output and photovoltaic power generation power in period t. Bat_Cha (t) is the charging power of the battery pack during period t; P GT (t) is the output power of the micro gas turbine during period t; P Sell (t) is the power sold by the microgrid to the large grid during period t; L h_t is the thermal power required by the system during period t.

[0170] 2. The transmission power constraint of the tie line can be expressed as follows but is not limited to:

[0171]

[0172] in, is the minimum transmission power of the interconnection line between the microgrid and the large power grid; It is the maximum transmission power of the interconnection line between the microgrid and the large power grid.

[0173] 3. The output and start-stop constraints of the micro gas turbine can be expressed as follows but are not limited to:

[0174]

[0175] Among them, O GT (t+1) is the operation status of the next period of t; GT (t) is the operating status of the period t, which is a 0-1 variable. A value of 1 indicates that the micro gas turbine is running.

[0176]

[0177] in, They represent the minimum operating time of the gas turbine unit and the minimum shutdown time of the micro gas turbine respectively.

[0178] P GT_min <P GT (t) <P GT_max (51)

[0179] Among them, P GT_min Indicates the minimum output value of the micro gas turbine, P GT_max Indicates the maximum output value of the micro gas turbine.

[0180] 4. Energy storage operation constraints

[0181] Battery constraints are divided into power constraints and capacity constraints (energy storage SOC constraints), which can be expressed as follows but are not limited to:

[0182]

[0183] Among them, P Bat_Cha (t), P Bat_Dis (t) is the charging power and discharging power of the battery during period t; P Bat_Cha,max , P Bat_Dis,max are the maximum charging and discharging power of the battery in period t; C Bat Indicates the rated capacity of the battery; P Bat_E Rated power of meter type battery.

[0184]

[0185] Among them, SOC t is the capacity during period t, SOC min , SOC maxThe upper and lower limits of the battery capacity. To ensure the continuity of the scheduling cycle, the initial and final storage capacity of the battery is kept consistent, that is, the initial capacity SOC star and SOC end Consistent.

[0186] 5. Wind power and photovoltaic power output constraints can be expressed as follows, but are not limited to:

[0187] 0≤P Pv (t)≤N Pv P Pv0 (t)(54)

[0188] 0≤P Wt (t)≤N Wt P Wt0 (t)(55)

[0189] Among them, P Pv (t), P Wt (t) represent the output power of wind farm and photovoltaic power station respectively; N Pv 、N Wt Respectively represent the number of wind turbines and the number of photovoltaic panels; P Pv0 (t), P Wt0 (t) represent the output power of a single wind turbine generator and a single photovoltaic panel during period t, respectively.

[0190] And, the price demand response model involved in the day-ahead optimization scheduling in the embodiment of the present application can be, but is not limited to, expressed as follows:

[0191] 1. Interruptible load demand response model

[0192] Because different loads react differently to the same electricity price, the price-based demand response load is divided into interruptible load (CL) and transferable load (SL) in the embodiment of the present application. The interruptible load determines whether to interrupt its own load by judging the change of electricity price before and after DR. The price demand elasticity matrix is ​​generally used to describe the DR characteristics. The element e in the mth row and nth column of the elasticity matrix E(m,n) is m,n The elasticity coefficient of electricity price at time m and time n can be expressed as follows:

[0193]

[0194] Where ΔP L,m_e is the load change before and after the response at time m; ΔP L,m_e0 is the original load; Δρ n is the change in electricity price before and after demand response at time n; ρ n_0 is the original electricity price at time n. The change in interruptible load after demand response ΔP CL,m_eIt can be expressed as, but not limited to:

[0195]

[0196] Among them, P CL,m_e0 E is the original interruptible load at time m; CL (m,n) is the interruptible load price demand elasticity matrix; ρ n is the electricity price at time n.

[0197] 2. Transferable load demand response model

[0198] The principle of transferable load is that users flexibly adjust energy consumption time according to their own needs and real-time electricity / heat price information, so that the load under high electricity price is transferred to the low or flat electricity price period. Then the mathematical model of the transferable change after DR can be expressed as but not limited to:

[0199]

[0200] Among them, P SL,m_e0 is the original transferable load at time m; E SL (m,n) is the transferable load price demand elasticity matrix; ρ n is the electricity price at time n.

[0201] (III) Solving the day-ahead optimization scheduling model based on the day-ahead optimization scheduling target to obtain the output plan of the target microgrid: The embodiment of the present application may, but is not limited to, use an improved particle swarm algorithm to solve the day-ahead optimization scheduling model, thereby obtaining a 24-hour optimal output plan of the microgrid, wherein the time granularity may, but is not limited to, be set to one hour.

[0202] Specifically, the improved particle swarm algorithm process is as follows:

[0203] 1. Population initialization based on chaotic mapping

[0204] Since the flexible resource model of the distribution network has many variables and complex constraints, the randomness of the initialization population will cause the individuals in the population to be far away from the optimal value, which will weaken the algorithm's search ability and thus fall into the local optimum. Therefore, this application embodiment designs a method for initializing the population based on chaotic mapping. Among them, the basic idea of ​​chaotic population initialization can be understood as utilizing the randomness and unpredictability in chaos theory to distribute the individuals of the initial population in multiple positions in the search space to increase the search range of the solution space, thereby having a greater probability of finding the global optimal solution. As a basic chaotic mapping, the Logistic mapping has the characteristics of good convergence performance and easy implementation. Its mathematical expression is as follows:

[0205] x n+1 =μx n (1-xn )(n=0,1,2,...)(59)

[0206] Where μ is the control parameter. When the value of μ is 4, the generated chaotic sequence is most evenly distributed

[59] . When x0∈(0,1), the chaotic variable x n ∈(0,1).

[0207] Based on this, the embodiment of the present application designs a population initialization method based on Logistic chaotic mapping, which can initialize the position of particles. Assume that the particle dimension D,x max,j and x min,j are the maximum and minimum values ​​of the particle in the dimension search position, respectively. m0 and n0 are two arbitrary initialized chaotic variables. Then, we can first generate two chaotic variables m k,j and n k,j ; Then, the generated chaotic variables are used to update the particle positions; Finally, the fitness of x(D) and p(D) is compared and the smaller particle is selected. The formula can be, but is not limited to, expressed as follows:

[0208] m k,j =μm k-1,j (1-m k-1,j ) (60)

[0209] n k,j =μn k-1,j (1-n k-1,j )(61)

[0210] x i,j =x min,j +m k,j (x max,j -x min,j )(62)

[0211] p i,j =p min,j +n k,j (p max,j -p min,j )(63)

[0212] 2. Calculate the objective function value according to the objective function of the flexible resource optimization configuration, and update the historical best position and global optimal position of the particle.

[0213] 3. Adaptively update the inertia weight ω.

[0214] This step mainly defines the difference between the particle position vector and the global optimal solution of the population as the difference between the particle itself and the optimal particle of the population. In the embodiment of the present application, the difference degree can be used as a guide for the value of ω. As the difference degree is adaptively adjusted, the difference value X between the i-th particle at time t and the global optimal solution of the population can be calculated. i (t), the formula can be but is not limited to:

[0215]

[0216] ω i (t) = ω start -(ω start -ω end )(X i (t)-1) 2 (65)

[0217] Where D represents the dimension of the solution space, and ω i (t) represents the inertia weight of the i-th particle at time t. In addition, the starting and ending values ​​of ω are respectively given by ω start ,ω end The upper and lower limits of the particle position variable are represented by x max and x min OK. Difference X i The dynamic adjustment of (t) enables particles to flexibly adjust their behavior during the search process to more effectively explore the solution space. This adaptive inertia weight mechanism based on the degree of gap helps particles gradually approach the global optimal solution, thereby improving the convergence speed and search efficiency of the algorithm.

[0218] 4. Update the velocity and position components of the particle group. The formula can be, but is not limited to, expressed as:

[0219] v id (t+1)=ωv id (t)+c1rand1(p id (t)-x id (t))+c2rand2(g d (t)-x id (t)) (66)

[0220] x id (t+1)=x id (t)+v id (t+1) (67)

[0221] 5. Recalculate the particle's objective function value and update the particle's optimal solution. Update the particle's individual optimal solution by comparing the newly calculated objective function value with the historical optimal solution.

[0222] 6. Determine whether the improved particle swarm algorithm has reached the set number of iterations. If so, terminate the operation.

[0223] (iv) obtaining an intraday optimization scheduling target according to the intraday optimization scheduling model, optimizing the output plan according to the intraday optimization scheduling target and at least one operation constraint condition, and obtaining a multi-time scale optimization configuration plan for the target microgrid.

[0224] In order to prevent the problem of poor adaptability of scheduling results due to large long-term forecast data in the previous day, the embodiment of the present application can perform rolling optimization through an intraday optimization scheduling model. Figure 7 This is a schematic diagram of the optimized scheduling relationship between the day before and the day after according to an embodiment of the present application. Figure 7 As shown, the day-ahead optimization scheduling in the embodiment of the present application is mainly an output optimization plan within 24 hours, with a resolution of 1 hour. During intra-day optimization scheduling, the embodiment of the present application can use more accurate short-time scale wind power, photovoltaic, and load data, which are updated every 15 minutes, and 4 hours of forecast data are issued each time to roll forward and correct the day-ahead scheduling plan.

[0225] The intraday optimization scheduling in the embodiment of the present application can be, but is not limited to, set to minimize the sum of the adjustment amount penalties within the rolling scheduling period. Its mathematical expression can be, but is not limited to, expressed as follows:

[0226] C Total_0 =min(C punish_g +C punish_h )(68)

[0227] Among them, C punish_g , C punish_h They are the penalty fees for the installation quantity of each part of the power grid and the penalty fees for the adjustment quantity of each part of the heat network.

[0228] C punish_g =min[ΔP Bat λ bat +ΔP GT λ gt +ΔP Gird (t)λ gird ] (69)

[0229]

[0230] Where ΔP Bat , ΔP GT , ΔP Gird They are the total amount of battery, micro gas turbine and grid interaction electricity adjusted compared with the day-ahead dispatch plan. bat , gt , girdare the penalty adjustment coefficients for battery, micro gas turbine and grid interaction respectively. Bat_0 (t), P GT_0 (t), P Gird_0 (t) are the daily electric power of the battery, micro gas turbine and grid interaction.

[0231] C punish_h =min[ΔP HS λ hs +ΔP WHB λ whb +ΔP HP (t)λ hp ] (71)

[0232]

[0233] Among them, ΔP HS , ΔP WHB , ΔP HP They are the total amount of adjustments made to the heat storage tank, waste heat boiler, and heat pump compared to the day-ahead dispatch plan. hs , whb , hp are the penalty adjustment coefficients for the heat storage tank, waste heat boiler, and heat pump, respectively. HS_0 (t), P WHB_0 (t), P HP_0 (t) are the daily electrical power of the heat storage tank, waste heat boiler and heat pump respectively.

[0234] Next, the embodiment of the present application can be optimized according to the optimization goal within the day and the previous operating constraints, and the improved particle swarm algorithm is also used for solving. Only 4 hours are optimized each time, and the optimization is performed every 15 minutes, and then rolling forward, and finally the purpose of correcting the day-ahead scheduling plan is achieved.

[0235] Among them, when performing rolling optimization scheduling, the embodiment of the present application can, but is not limited to, use the data predicted for the next day for optimization, with 24 hours as a scheduling cycle and the time scale of the predicted data being 1 hour. At the same time, in order to enhance the two-way interaction between the energy supply side and the energy consumption side, a demand response (DR) mechanism is added. However, the prediction error at the hour level the day before is still higher than the prediction error at the minute level, so the optimization scheduling of a single time scale the day before is difficult to fully utilize the advantages of multiple time scales. According to the principle that the accuracy of wind, solar power generation and load demand forecasts increases with the decrease of the time scale, a multi-time scale optimization method can be used to adjust the optimized scheduling plan the day before. The scheduling cycle used within the day is 4 hours and the time scale is 15 minutes. The rolling optimization completes the correction of the day-ahead scheduling plan.

[0236] Figure 8This is a flow chart of the day-ahead and day-intraday optimization scheduling of one embodiment of the present application. Figure 8 As shown, the embodiment of the present application can first obtain wind power, photovoltaic, load including heat load and electric load hourly level forecast data, gas turbine equipment parameters, electricity price parameters, heat price, heat pump equipment parameters, waste heat boiler parameters, heat storage tank parameters, battery parameters; then determine the operating constraints of each part of the microgrid and model them, optimize according to the day-ahead optimization scheduling strategy, formulate the day-ahead optimization target, then select the improved particle swarm algorithm for solving, which is used to solve the day-ahead optimization scheduling model, and obtain the 24-hour microgrid optimal output plan, with a time granularity of one hour; further, in order to prevent the problem of poor adaptability of the scheduling result due to the large long-term prediction data a day ago, the intraday optimization scheduling model adopts more accurate short-term wind power, photovoltaic, and load data, which are updated every 15 minutes, and 4 hours of forecast data are issued each time; finally, according to the intraday optimization target and constraint conditions, optimization is performed, and the improved particle swarm algorithm is also used for solving, only 4 hours are optimized each time, and optimization is performed every 15 minutes, and then rolling forward, and finally the purpose of correcting the day-ahead scheduling plan is achieved.

[0237] The present application is described and verified in detail below with a specific embodiment.

[0238] Fig. 9 This is a flow chart of a method for optimizing configuration of a microgrid based on photovoltaic and hybrid energy storage according to an embodiment of the present application. Fig. 9 As shown:

[0239] Step 901: When constructing the photovoltaic output model, add dynamic environmental monitoring at multiple time scales, and update the evaluation results of photovoltaic power generation capacity by collecting minute-level data in real time, so as to achieve flexible scheduling within a short period of time within the day. According to the ambient temperature and light intensity of the installation site, establish the output model of the photovoltaic system to accurately evaluate the actual power generation capacity of the photovoltaic system.

[0240] Step 902: Construct a degradation dynamics model for the hybrid energy storage system. Evaluate the degradation of the battery according to the Ah throughput model. At the same time, consider the effect of the integration of supercapacitors on extending the battery life. When evaluating the degradation rate of the hybrid energy storage system, multi-time scale monitoring is used to calculate the degradation rate of the battery at different time scales to ensure that the capacity configuration optimization strategy is adjusted during the operation of the microgrid, so as to respond more accurately to short-term load demand.

[0241] Step 903: Construct a mixed integer linear programming model. With the goal of minimizing the total cost of the microgrid, a mixed integer linear programming model including components such as photovoltaic systems, hybrid energy storage systems, and diesel generators is constructed. The model considers multiple factors such as initial investment costs, long-term operating costs, and energy supply interruption costs. While constructing the mixed integer linear programming model, a dynamic cost balance of multiple time scales is added, combined with real-time electricity prices and market fluctuations, and short-term interaction strategies are adjusted in real time to optimize the overall system cost structure, thereby improving operating efficiency.

[0242] Step 904: Set constraints. Set investment budget constraints, equipment installation constraints, power balance constraints, energy storage system operation constraints, etc. to ensure the reliable operation and cost-effectiveness of the microgrid. When setting constraints, use multi-time scale dynamic budget and space constraint management to ensure that the microgrid configuration can achieve optimal functions at different time scales while maintaining high reliability and economic benefits.

[0243] Specifically, parameter setting may be performed first.

[0244] The embodiment of the present application can evaluate the proposed microgrid configuration method through two different embodiments. For example, the situation of the embodiment of the present application under three different photovoltaic (PV) module technologies and two popular battery technologies can be simulated. In addition, the embodiment of the present application can also evaluate the supercapacitor (SC) integration in each technology and case study.

[0245] Among them, Table 4 and Table 5 are related parameter tables commonly used in the simulation of Scheme 1 and Scheme 2 in the embodiments of the present application. They can be expressed as follows:

[0246] Table 4

[0247]

[0248]

[0249] Table 5

[0250]

[0251] Further, Fig.10 Schematic diagram of parameter curve of one embodiment of the present application. Fig.10 As shown, (a), (b), and (c) are respectively the wind speed, solar irradiance, and temperature curves commonly used in Scheme 1 and Scheme 2 of the present application. In addition, Can be set to, but not limited to, 1200 kW. can be set to 72 kW, the rate can be set to 6%, C budget It can be set, but is not limited to, $25,000.

[0252] Scenario 1:

[0253] The input parameters can be set as follows: A av is 3000 meters, is 15 kilowatts, is $45 / kWh, To reflect the higher implicit cost in a specific period (such as 10:00 to 14:00), the ENS cost can be adjusted by multiplying it by 20 and 10, respectively, so that two cases are formed in Scheme 1 for comparison, namely Case 1A and Case 1B, which can represent At different levels of , the microgrid optimal configuration schemes generated under Case 1A and Case 1B are Scheme 1A and Scheme 1B respectively.

[0254] Among them, in case 1B, the higher It will lead to a larger objective function value, a smaller PV array size and a larger ESS capacity, so the robustness of the solution can be enhanced by increasing the storage capacity. Specifically, the PV generation in Scenario 1A consists of two sub-arrays with different technologies, while the ESS includes 6 Li-ion + SC units, which will remain unchanged throughout the life of the project.

[0255] Comparing economic indicators, we can find that When the capacity is higher, it can minimize ENS during operation and improve reliability, but it will reduce the energy output to the grid, thus reducing economic benefits. Therefore, the total cost of the microgrid project will increase. The value is crucial for planners to balance reliability and economic benefits, and a trade-off can be achieved based on the planner's preferences.

[0256] Table 6 is a capacity allocation result table of an embodiment of the present application, and Table 7 is an economic index table of an embodiment of the present application, which can be expressed as follows:

[0257] Table 6

[0258]

[0259] Table 7

[0260] Case Case 1A Case 1B Photovoltaic power generation investment 10788.00 20048.00 Investing in the Slovenian Employment Service 11022.72 4324.95 ESS maintenance costs 0.00 47.88 Grid energy costs 47928.91 51263.52 Energy Costs of Backup Generators 2819.97 4784.64 Cost of unsupplied energy 0.00 14845.60 The cost of selling energy to the grid -20708.05 -47522.91 Objective Value 51851.55 47791.68

[0261] Scenario 2:

[0262] You can enter the same parameter A av is 3000 meters, is 15 kilowatts, is $45 / kWh, the difference is that The setting is based on the load changes throughout the day, and a comparison is made with the previous case. Similarly, the ENS cost can be adjusted by multiplying it by 20 and 10 respectively, thus forming two cases for comparison. These cases are called Case 2A and Case 2B, representing Different levels of load. During the peak load period from 6:00 to 18:00, the ENS cost coefficient and marginal However, during other off-peak hours, when economic losses from energy service interruptions are much smaller, the ENS cost is set at one-tenth of the marginal cost.

[0263] The solutions generated in these two cases are called Solution 2A and Solution 2B, respectively. The embodiment of the present application compares the two solutions: in Solution 2A, the capacity configuration model recommended by the microgrid optimization configuration method based on photovoltaic and hybrid energy storage in the embodiment of the present application is used, while Solution 2B assumes that the battery energy storage system (BESS) has not degraded. This means that the addition of supercapacitors (SC) to the HESS to extend the battery life is not considered, the linear battery attenuation model is not considered, and the storage replacement unit is not estimated based on the equipment attenuation. Therefore, the objective function (5) does not contain the term Constraints (24) and (25) are omitted, and constraint (23) is replaced by Thus, battery degradation and replacement units in capacity configuration are not modeled.

[0264] Table 8 is the capacity configuration result table of Scheme 2A and Scheme 2B, and Table 9 is the economic index table of Scheme 2A and Scheme 2B. They can be expressed as follows:

[0265] Table 8

[0266]

[0267] Table 9

[0268] Case Case 2A Case 2B Photovoltaic power generation investment 22912.00 22400.00 Investing in the Slovenian Employment Service 4324.95 6130.00 ESS maintenance costs 0.00 0.00 Grid energy costs 35144.75 25693.01 Energy Costs of Backup Generators 0.00 0.00 Cost of unsupplied energy 30676.91 15811.63 The cost of selling energy to the grid -42934.61 -31219.84 Objective Value 50123.99 38814.80

[0269] In solution 2A, a HESS including two single crystal photovoltaic modules and five VRLA (Valve-Regulated Lead-Acid Battery) + SC units may be provided but is not limited thereto. Since the cost of ENS (Energy Not Supplied) is relatively low, the present embodiment does not consider the situation of replacing the battery.

[0270] Comparing Tables 8 and 9, in scenario 2B, where battery aging and replacement are not considered, the number of storage units increases and their technical composition changes, excluding SC coupling in BESS (Battery Energy Storage System). However, the increase in BESS operation in scenario 2B does not affect the battery life, so SC is not included in the capacity configuration results. Ignoring the degradation of BESS underestimates the benefits of SC coupling, resulting in increased operating costs when implementing a microgrid. Therefore, excluding the BESS degradation model in scenario 2B leads to optimistic estimates of the expected ENS and microgrid interactions with the main grid, thereby underestimating the benefits of SC coupling.

[0271] Comparing the results of scenario 2B in Table 8 and Table 9, it can be seen that ignoring battery aging and replacement has a critical impact on the design and operation of the battery energy storage system (BESS) in the microgrid. In scenario 2B, where these factors are not considered, the number of storage units increases, the technology mix changes, and in particular, the integration of supercapacitors (SC) with the BESS is excluded. This oversight underestimates the advantages of incorporating SC into the energy storage system, resulting in increased operating costs in practical applications. In fact, SC has the potential to reduce battery replacement frequency and maintenance requirements, thereby reducing overall operating expenses. In addition, the evaluation of expected unsupplied energy (ENS) and the interaction between the microgrid and the main grid without considering BESS degradation in scenario 2B does not take into account the challenges that may arise from ignoring battery aging, such as increased instability and reduced energy availability during peak demand periods. It can be shown that SC coupling has certain advantages in improving microgrid performance, reliability, and cost-effectiveness.

[0272] Furthermore, the change in component cost plays an important role in forming a capacity configuration plan, so the embodiment of the present application can also perform a sensitivity analysis, that is, analyze the impact of the change in energy storage system (ESS) cost on capacity configuration and economic indicators when the cost of batteries and supercapacitors (SC) is reduced by the same percentage. Table 10 is a planning result and economic index table of different ESS investment costs for an embodiment of the present application, that is, a summary table of sensitivity analysis results, which can be expressed as follows:

[0273] Table 10

[0274]

[0275] From the results in Table 10, it can be seen that the reduction in ESS investment cost affects the configuration of storage units in the MG of Scenario 2, although no battery replacement is required in both cases. This shows that although the configuration of battery energy storage helps to support load supply in a specific period, the main focus is still on exporting energy to the grid, as shown by the reduction in the objective function value.

[0276] A closer look at the two scenarios reveals that the microgrid (MG) responds differently to a decrease in the unit price of the energy storage system (ESS). In scenario 1, a decrease in the ESS price leads to a proportional increase in the number of photovoltaic (PV) modules, with a total of 48 modules added when the price drops to 90%, 80%, and 70% of the base price. At the same time, the number of ESS units remains unchanged. In contrast, the number of PV modules in scenario 2 remains unchanged despite the decrease in ESS prices. However, when the price drops to 70% of the original cost, a strategic shift occurs to introduce new storage technologies to enhance energy management in the MG. The economic analysis highlights the importance of intermittent energy service costs in optimizing MG capacity, balancing project costs, and minimizing unserved energy. The potential to export energy to the main grid also plays a crucial role, generating revenue to offset costs and influencing the capacity configuration and overall goals of the MG. Restrictions on exports prompt the MG to prioritize local load needs and promote the use of energy storage units to improve reliability and resilience.

[0277] According to the microgrid optimization configuration method proposed in the embodiment of the present application, the optimization configuration scheme of the microgrid can be generated by combining the actual power generation capacity of the photovoltaic system, the degradation rate of the hybrid energy storage battery, and the minimization of the total cost of the microgrid under various constraints. Thus, the operation economy of the power grid system is improved by considering the battery attenuation cost and the effect of the supercapacitor on the long-term operation of the microgrid, and the capacity configuration of the microgrid is optimized through the mixed integer linear programming model to improve the reliability and operation efficiency of the microgrid system. The optimization planning and capacity configuration of the microgrid are realized under the condition of considering the battery aging, thereby improving the operation and overall performance of the microgrid system; further, the present application can also combine the forecast data of different time scales of the day before and within the day to carry out rolling optimization, so as to obtain a more reasonable scheduling plan. At the same time, considering the potential of demand-side response in improving system flexibility and promoting the consumption of new energy, it is incorporated into the optimization scheduling process, which can improve the flexibility of wind and light distributed power sources in the microgrid, promote the consumption of renewable energy, and ensure the economical, efficient, reliable and stable operation of the microgrid. . This solves the problem that the microgrid optimization configuration methods in related technologies often fail to fully consider the large replacement and maintenance costs of batteries during long-term use, as well as energy prices affected by various factors, which affects the stability of the power grid system operation and increases the difficulty and cost of independent operation of the microgrid. In addition, the optimization strategy of a single time scale often leads to deviations between the scheduling results and the actual situation due to the low accuracy of source and load prediction, showing great limitations and making it difficult to meet the optimization needs of the microgrid operation configuration.

[0278] Next, a microgrid optimization configuration device proposed according to an embodiment of the present application is described with reference to the accompanying drawings.

[0279] Fig.11 It is a structural diagram of a microgrid optimization configuration device according to an embodiment of the present application.

[0280] like Fig.11 As shown, the microgrid optimization configuration device 10 includes: a first evaluation module 100 , a second evaluation module 200 , a first calculation module 300 and a generation module 400 .

[0281] The first evaluation module 100 is used to evaluate the actual power generation capacity of the photovoltaic system based on a pre-established photovoltaic output model.

[0282] The second evaluation module 200 is used to evaluate the degradation rate of the hybrid energy storage system based on a pre-established hybrid energy storage system degradation dynamics model.

[0283] The first calculation module 300 is used to calculate the minimized microgrid total cost of the mixed integer linear programming model based on a pre-established mixed integer linear programming model.

[0284] The generation module 400 is used to generate a multi-time scale optimization configuration plan for the target microgrid based on the actual power generation capacity of the photovoltaic system, the degradation rate of the hybrid energy storage battery, the minimized total cost of the microgrid and at least one preset constraint condition, combined with a preset day-ahead optimization scheduling model and a preset intra-day optimization scheduling model.

[0285] Optionally, in one embodiment of the present application, it also includes: an acquisition module and a first construction module.

[0286] The acquisition module is used to obtain the ambient temperature and light intensity of the target location of the photovoltaic system before evaluating the actual power generation capacity of the photovoltaic system.

[0287] The first building module is used to train a model using ambient temperature and light intensity to build a photovoltaic output model.

[0288] Optionally, in one embodiment of the present application, it also includes: a second calculation module and a second construction module.

[0289] The second calculation module is used to calculate the total energy availability of the hybrid energy storage system before evaluating the degradation rate of the hybrid energy storage system, and to evaluate the battery attenuation rate per unit discharge power of the hybrid energy storage system.

[0290] The second building module is used to construct a degradation dynamics model of the hybrid energy storage system based on the total energy availability and the battery degradation rate.

[0291] Optionally, in one embodiment of the present application, the objective function of minimizing the total cost of the microgrid can be expressed as, but not limited to:

[0292]

[0293] where Z is the total cost to be minimized, The sum is in the set Ω PV The relevant cost of installing PV systems on all nodes i in is, represents the initial capital cost of the ith PV system, and represents any installation-specific costs, is a binary variable indicating whether the i-th PV system is installed, The sum is in the set Ω B The cost of installing batteries on all members j in is the cost of the jth battery, is similar to But for the binary variable of battery, It is the discount of future costs over the project life (LY) at a discount rate of (1+r) y , is the battery cost, is its average lifetime energy output, represents the coefficient of degradation or efficiency loss, and the other terms in brackets reflect the associated costs of interacting with the grid over all time periods t in the set T. Specifically, Indicates the grid purchase cost, including the general grid purchase cost and the cost of purchasing electricity from the grid specifically for charging the batteries Indicates the calculation of backup power cost, represents the penalty cost of not supplying energy to meet demand, Represents the revenue from selling photovoltaic power to the grid.

[0294] Optionally, in one embodiment of the present application, the generation module 400 includes: an acquisition unit, a formulation unit, a solution unit and an optimization unit.

[0295] Wherein, the acquisition unit is used to acquire at least one system parameter of the target microgrid;

[0296] A formulation unit, configured to determine at least one operation constraint of the target microgrid based on at least one system parameter, so as to formulate a day-ahead optimization scheduling target according to the at least one operation constraint and the day-ahead optimization scheduling strategy;

[0297] A solving unit, used for solving a preset day-ahead optimization dispatching model based on the day-ahead optimization dispatching target to obtain an output plan of a target microgrid;

[0298] The optimization unit is used to obtain the intraday optimization scheduling target according to the preset intraday optimization scheduling model, optimize the output plan according to the intraday optimization scheduling target and at least one operation constraint condition, and obtain the multi-time scale optimization configuration scheme of the target microgrid.

[0299] It should be noted that the aforementioned explanation of the embodiment of the microgrid optimization configuration method is also applicable to the microgrid optimization configuration device of this embodiment, and will not be repeated here.

[0300] According to the microgrid optimization configuration device proposed in the embodiment of the present application, a multi-time scale optimization configuration scheme for the target microgrid can be generated by combining the actual power generation capacity of the photovoltaic system, the degradation rate of the hybrid energy storage battery, the minimization of the total cost of the microgrid, the day-ahead optimization scheduling model and the intraday optimization scheduling model under various constraints. Thus, the economic efficiency of the operation of the power grid system is improved by considering the battery attenuation cost and the influence of the supercapacitor on the long-term operation of the microgrid, and the capacity configuration of the microgrid is optimized through the mixed integer linear programming model to improve the reliability and operation efficiency of the microgrid system, and the optimization planning and capacity configuration of the microgrid are realized under the condition of considering the battery aging, thereby improving the operation and overall performance of the microgrid system; further, the present application can also combine the forecast data of different time scales of the day-ahead and the day, and perform rolling optimization to obtain a more reasonable scheduling plan. At the same time, considering the potential of demand-side response in improving system flexibility and promoting the consumption of new energy, it is incorporated into the optimization scheduling process, which can improve the flexibility of wind and light distributed power sources in the microgrid, promote the consumption of renewable energy, and ensure the economical, efficient, reliable and stable operation of the microgrid. This solves the problem that the microgrid optimization configuration methods in related technologies often fail to fully consider the large replacement costs and maintenance costs of batteries during long-term use, as well as energy prices affected by various factors, which affects the stability of the power grid system operation and increases the difficulty and cost of independent operation of the microgrid. In addition, the optimization strategy of a single time scale often leads to deviations between the scheduling results and the actual situation due to the low accuracy of source and load prediction, showing great limitations and making it difficult to meet the optimization needs of the microgrid operation configuration.

[0301] Fig.12 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0302] A memory 1201 , a processor 1202 , and a computer program stored in the memory 1201 and executable on the processor 1202 .

[0303] When the processor 1202 executes the program, the microgrid optimization configuration method provided in the above embodiment is implemented.

[0304] Furthermore, the electronic device further comprises:

[0305] The communication interface 1203 is used for communication between the memory 1201 and the processor 1202 .

[0306] The memory 1201 is used to store computer programs that can be executed on the processor 1202 .

[0307] The memory 1201 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0308] If the memory 1201, the processor 1202 and the communication interface 1203 are implemented independently, the communication interface 1203, the memory 1201 and the processor 1202 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.12 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0309] Optionally, in a specific implementation, if the memory 1201, the processor 1202 and the communication interface 1203 are integrated on a chip, the memory 1201, the processor 1202 and the communication interface 1203 can communicate with each other through an internal interface.

[0310] The processor 1202 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0311] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned microgrid optimization configuration method.

[0312] An embodiment of the present application also provides a computer program product, including a computer program, which can run computer instructions. When the computer instructions are executed by a processor, the microgrid optimization configuration method provided in the embodiment of the present application is implemented.

[0313] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0314] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0315] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0316] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0317] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0318] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0319] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0320] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A microgrid optimization configuration method, characterized in that: The following steps are involved: Evaluate the actual power generation capacity of the PV system based on the pre-established PV output model; Based on the pre-established degradation dynamics model of the hybrid energy storage system, the degradation rate of the hybrid energy storage system is evaluated; Based on a pre-established mixed integer linear programming model, calculating a minimized total microgrid cost of the mixed integer linear programming model; Based on the actual power generation capacity of the photovoltaic system, the degradation rate of the hybrid energy storage battery, the minimized total cost of the microgrid and at least one preset constraint condition, a multi-time scale optimization configuration plan for the target microgrid is generated in combination with a preset day-ahead optimization scheduling model and a preset intraday optimization scheduling model.

2. The method according to claim 1, characterized in that Before evaluating the actual power generation capacity of the photovoltaic system, it also includes: Obtaining the ambient temperature and light intensity at the target location of the photovoltaic system; The ambient temperature and the light intensity are used to train a model to construct the photovoltaic output model.

3. The method according to claim 1, characterized in that Before evaluating the degradation rate of the hybrid energy storage system, it also includes: Calculating the total energy availability of the hybrid energy storage system and evaluating the battery degradation rate per unit discharge power of the hybrid energy storage system; A degradation dynamics model of the hybrid energy storage system is constructed according to the total energy availability and the battery attenuation rate.

4. The method according to claim 1, characterized in that: The objective function of minimizing the total cost of the microgrid is: where Z is the total cost to be minimized, The sum is in the set Ω PV The relevant cost of installing PV systems on all nodes i in is, represents the initial capital cost of the ith PV system, and represents any installation-specific costs, is a binary variable indicating whether the i-th PV system is installed, The sum is in the set Ω B The cost of installing batteries on all members j in is the cost of the jth battery, is similar to But for the binary variable of battery, It is the discount of future costs over the project life (LY) at a discount rate of (1+r) y , is the battery cost, is its average lifetime energy output, represents the coefficient of degradation or efficiency loss, and the other terms in brackets reflect the costs associated with interacting with the grid over all time periods t in the set T, Indicates the grid purchase cost, including the general grid purchase cost and the cost of purchasing electricity from the grid specifically for charging the batteries Indicates the calculation of backup power cost, represents the penalty cost of not supplying energy to meet demand, Represents the revenue from selling photovoltaic power to the grid.

5. The method according to claim 1, characterized in that The multi-time scale optimization configuration scheme of the target microgrid is generated by combining the preset day-ahead optimization dispatch model and the preset intraday optimization dispatch model, including: Acquire at least one system parameter of the target microgrid; Determine at least one operation constraint of the target microgrid based on the at least one system parameter, so as to formulate a day-ahead optimization scheduling target according to the at least one operation constraint and the day-ahead optimization scheduling strategy; Solving the preset day-ahead optimization scheduling model based on the day-ahead optimization scheduling target to obtain the output plan of the target microgrid; An intraday optimization scheduling target is obtained according to the preset intraday optimization scheduling model, so as to optimize the output plan according to the intraday optimization scheduling target and the at least one operating constraint condition, and obtain a multi-time scale optimization configuration scheme for the target microgrid.

6. A microgrid optimization configuration device, characterized in that: include: A first evaluation module is used to evaluate the actual power generation capacity of the photovoltaic system based on a pre-established photovoltaic output model; A second evaluation module is used to evaluate the degradation rate of the hybrid energy storage system based on a pre-established degradation dynamics model of the hybrid energy storage system; A calculation module, configured to calculate a minimized microgrid total cost of the mixed integer linear programming model based on a pre-established mixed integer linear programming model; A generation module is used to generate a multi-time scale optimization configuration scheme for a target microgrid based on the actual power generation capacity of the photovoltaic system, the degradation rate of the hybrid energy storage battery, the minimized total cost of the microgrid and at least one preset constraint condition, in combination with a preset day-ahead optimization scheduling model and a preset intra-day optimization scheduling model.

7. The device according to claim 6, characterized in that Also includes: An acquisition module, used for acquiring the ambient temperature and light intensity of a target location of the photovoltaic system before evaluating the actual power generation capacity of the photovoltaic system; A construction module is used to train a model using the ambient temperature and the light intensity to construct the photovoltaic output model.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the microgrid optimization configuration method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the microgrid optimization configuration method as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed, it is used to implement the microgrid optimization configuration method as described in any one of claims 1-5.

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