Micro-grid optimization scheduling method and system

Abstract

The invention relates to the field of micro-grid optimal scheduling, in particular to a micro-grid optimal scheduling method and system. The method comprises the following steps: presetting a micro-grid dispatching model, and presetting an objective function and constraint conditions for the micro-grid dispatching model; constructing an optimization model based on the micro-grid dispatching model, wherein the optimization model comprises a robust optimization model and an opportunity optimization model; obtaining current operation data of the micro-grid, and selecting one optimization model from the optimization models as a target model according to the current operation data; obtaining optimal configuration parameters of the micro-grid based on the current operation data and the target model; and performing configuration on the micro-grid based on the configuration parameters, and performing optimization scheduling on the micro-grid based on the configured micro-grid and the current operation data of the micro-grid. The method is used for solving the problem of singleness of a micro-grid optimization scheduling method in the prior art, and the compatibility, practicability and generalization ability of the micro-grid optimization scheduling method are improved.

Description

Technical Field

[0001] This invention relates to the field of microgrid optimal scheduling, and more specifically, to a method and system for microgrid optimal scheduling. Background Technology

[0002] Currently, microgrids are small-scale power generation and distribution networks composed of distributed power sources, energy storage units, loads, and monitoring and protection units. They possess the dual capabilities of autonomous operation and grid-connected coordination, enabling local power supply and demand balance. Microgrid optimal scheduling uses optimization algorithms to dynamically coordinate various internal resources to maintain the safe and stable operation of the microgrid and improve its economic efficiency. Due to the significant weather-dependent impact of intermittent renewable energy sources such as wind or solar power, the strong randomness of user-side loads, the unpredictable probability of sudden equipment failures, and the added factor of future market electricity price fluctuations, microgrid optimal scheduling faces uncertainties due to multi-source heterogeneity. To address this, existing technologies have proposed mainstream approaches such as robust optimization, opportunity-constrained optimization, and Information Gap Decision Theory (IGDT). Robust optimization constructs a safety boundary based on the worst-case scenario defense concept, and uses robustness functions within the IGDT framework to expand the load uncertainty range with critical revenue thresholds as constraints, specifically designed for risk-averse decision-makers. However, while robust optimization effectively ensures the microgrid's resilience to disturbances, it can easily increase operating costs due to excessive conservatism. Opportunity-constrained optimization (OCE) adopts a proactive risk-embracing strategy, relying on the opportunistic function of the IGDT framework. Based on deterministic optimal cost, it compresses the uncertainty range through a risk tolerance coefficient, making it more suitable for risk-seeking decision-makers seeking excess returns. However, OCE may lead to microgrid instability by underestimating extreme operating conditions. Furthermore, in addition to the shortcomings of robust optimization and OCE, existing technologies lack a unified framework supporting bidirectional switching between different risk attitudes, and generally suffer from insufficient consideration of multi-objective optimization in microgrids. Moreover, setting key parameters within these frameworks often relies on manual experience, lacking a data-driven dynamic adjustment mechanism, resulting in weak generalization ability when facing large-scale heterogeneous loads and failing to effectively optimize microgrid scheduling. Summary of the Invention

[0003] This invention provides a method and system for optimal microgrid scheduling, which addresses the limitations of existing microgrid optimal scheduling methods and improves their compatibility, practicality, and generalization capabilities.

[0004] According to a first aspect of this application, a method for optimal scheduling of a microgrid is provided, the method comprising: A microgrid scheduling model is preset, and an objective function and constraints are preset for the microgrid scheduling model; An optimization model is constructed based on the microgrid scheduling model, wherein the optimization model includes a robust optimization model and a chance optimization model. Obtain the current operating data of the microgrid, and select one of the optimization models as the target model based on the current operating data; The optimal configuration parameters of the microgrid are obtained based on the current operating data and the target model; The microgrid is configured based on the configuration parameters, and the microgrid is optimized and scheduled based on the configured microgrid and the current operating data of the microgrid.

[0005] Understandably, by pre-setting an optimization model that includes both robust and opportunistic models, full-scenario coverage of uncertainty risks is achieved. The robust model ensures the stability of the microgrid under extreme loads with controllable costs in the worst-case scenario, while the opportunistic model explores the economic optimization space through acceptable risk thresholds. The two work together to solve the shortcomings of traditional single models that are overly conservative or ignore extreme risks. Ultimately, the microgrid can achieve both high stability and reliability under diverse operating requirements, while effectively reducing operating costs and improving computational optimization efficiency.

[0006] Optionally, the objective function of the microgrid scheduling model is: in, This represents the total operating cost of the microgrid. This represents the total cost of distributed power sources in the microgrid. This represents the operating cost of the energy storage units in the microgrid. This represents the interaction cost between the microgrid and the main grid.

[0007] Understandably, this method achieves full-factor coupled optimization of the multi-source costs of microgrids by constructing an objective function with the minimization of total operating costs as its core. This not only avoids resource misallocation caused by the simplification of traditional single cost items, but also improves the global adaptability of the scheduling scheme through automatic optimization guided by cost minimization. This enables microgrids to maintain low operating costs under complex load fluctuations and strengthen their synergy with the electricity market.

[0008] Optionally, the total cost of distributed power sources in the microgrid for: in, Representing a scene The expected function; Indicates in Time, Scene Below, the curtailment power of photovoltaic power generators, This represents the curtailment cost of the photovoltaic power generation unit; the scenario described is a pre-set scenario for the microgrid.

[0009] And / or, the operating cost of the energy storage units in the microgrid for: in, express The charging power of the energy storage unit at that time. express The energy storage loss cost of the energy storage unit at that time. This represents the charging efficiency coefficient of the energy storage unit; And / or, the interaction cost between the microgrid and the main grid. for: in, This represents the electricity purchase cost of the microgrid from the main grid. This represents the revenue generated from the sale of electricity from the microgrid to the main grid. Representing a scene The expected function; Indicates in Time, Scene The input power that the microgrid purchases from the main grid; Indicates in At any given time, the electricity price at which the microgrid purchases electricity from the main grid; exist Time, Scene The output power of the microgrid selling electricity to the main grid; Indicates in At any given time, the electricity price at which the microgrid sells electricity to the main grid.

[0010] Understandably, this solution achieves high-fidelity quantification and global collaborative optimization of the multi-source operating cost of a microgrid by constructing a function model corresponding to the total cost of distributed power sources in the microgrid, a function model corresponding to the operating cost of energy storage units in the microgrid, and a function model corresponding to the interaction cost between the microgrid and the main grid. It not only accurately captures the characteristics of photovoltaic curtailment loss, dynamic loss of energy storage system, and grid interaction, but also effectively solves the resource allocation deviation problem caused by neglecting randomness and physical details in traditional simplified models. This significantly improves the scenario adaptability and risk resistance capability of microgrid optimized scheduling under complex operating conditions.

[0011] Optionally, the constraints include: Main power grid interaction constraints: in, This represents the minimum input power that the main power grid can transmit to the microgrid. Indicates the power input status flag value, if , indicating in At any given time, the microgrid purchases electricity from the main grid, and the main grid transmits electricity to the microgrid; if , indicating in At any given time, the microgrid stops purchasing electricity from the main grid, and the main grid stops transmitting electricity to the microgrid; Indicating microgrids in Time, Scene The input power from which the microgrid purchases electricity from the main grid; This represents the maximum input power from the main power grid to the microgrid. This represents the minimum output power of the microgrid that it sells to the main grid. Indicates the power output status flag value; if This indicates that the microgrid sells electricity to the main grid and transmits electricity back to the main grid; if This indicates that the microgrid has stopped selling electricity to the main grid and has stopped transmitting electricity to the main grid. Indicating microgrids in Time, Scene Output power sold to the main power grid This represents the maximum output power that the microgrid sells to the main grid; And / or, energy balance constraints: in, express The basic load demand of microgrids at all times. Indicates energy storage unit Charging power at any time Indicating microgrids in Time, Scene Output power sold to the main power grid Indicates in Time, Scene Output power of the photovoltaic generator set; Indicating microgrids in Time, Scene The input power from which the microgrid purchases electricity from the main grid; Indicates energy storage unit Discharge power at any given moment; And / or, the energy dynamic constraints of the energy storage unit: in, express The energy storage capacity of the energy storage unit at all times. express The energy storage capacity of the energy storage unit at any time This indicates the preset interval time. This represents the discharge efficiency coefficient of the energy storage unit; This represents the charging efficiency coefficient of the energy storage unit. Indicates energy storage unit Charging power at any time Indicates energy storage unit Discharge power at any given moment; And / or, operating constraints of energy storage units: in, This indicates the minimum energy storage capacity of the energy storage unit. express The energy storage capacity of the energy storage unit at any time This indicates the maximum energy storage capacity of the energy storage unit. Indicates energy storage unit Charging power at any time This indicates the maximum power generation capacity of the energy storage unit. express The charging status flag is always on. This indicates that the energy storage unit is in a charging state. This indicates that the energy storage unit is in a stopped charging state. This indicates that the energy storage unit has stopped operating. Indicates energy storage unit Discharge power at any given time This indicates the maximum discharge power of the energy storage unit. express Discharge status flag bit at all times, if This indicates that the energy storage unit is in a discharging state. This indicates that the energy storage unit is in a stopped discharging state. This indicates that the energy storage unit has stopped operating; And / or, demand response constraints: in, express Actual load demand at any given time express Load transfer amount at any given time express The basic load requirement at any given time This indicates the maximum adjustment ratio.

[0012] Understandably, by constructing a comprehensive constraint system covering the main grid's interactive power range and state mutual exclusion, real-time energy balance conservation equations, dynamic constraints and operational boundary limits of energy storage units, and flexible demand response adjustment, a high-fidelity coupling of the physical laws and operational specifications of the microgrid is achieved. This not only strictly guarantees the safety boundaries of the units and the steady-state operation of the microgrid, but also provides a reliable feasible domain for the optimization process through refined boundary conditions. This significantly improves the physical consistency, engineering practicality, and execution stability of microgrid optimized scheduling in complex and uncertain environments, effectively reduces the risk of microgrid exceeding limits, and strengthens its adaptability to diverse operational demands.

[0013] Optionally, the constraints include: Constraints of photovoltaic power generation units: Temperature correction coefficient for: in, Indicates that the photovoltaic power generation unit is Time, Scene Output power at the following levels This indicates the rated power of a single photovoltaic panel under standard testing conditions. This indicates the total number of photovoltaic panels. Indicates that sunlight is at Time, Scene Direct irradiance below, This indicates the angle between sunlight and the normal to the photovoltaic panel. Indicates that sunlight is at Time, Scene Scattered irradiance under the following conditions Indicates the tilt angle of the photovoltaic panel. Indicates ground reflectivity, Indicates that all light rays are at Time, Scene Total horizontal irradiance below Indicates the temperature decay coefficient. Indicates in Time, Scene The ambient temperature below, Indicates in Time, Scene The actual temperature of the photovoltaic panel. Indicates effective irradiance, This indicates the nominal temperature of the photovoltaic panel.

[0014] Understandably, by constructing a photovoltaic output power constraint model that integrates multiple physical parameters, the nonlinear coupling relationship between direct / scattered solar irradiance, photovoltaic panel tilt angle, ground reflectivity, and temperature attenuation effect is accurately characterized. The temperature correction coefficient is calculated in real time based on ambient temperature and effective irradiance to achieve high-fidelity quantification of temperature and output sensitivity characteristics. This not only fully restores the real output behavior of photovoltaic generators under complex weather conditions, but also significantly reduces the power generation prediction deviation caused by neglecting temperature sensitivity in traditional simplified models. It provides high-precision photovoltaic generator boundary conditions for microgrid optimized scheduling, effectively enhancing the operational reliability and cost optimization adaptability of microgrids under fluctuating light conditions.

[0015] Optionally, an optimization model is constructed based on the microgrid scheduling model, including: A robust model for optimizing the microgrid dispatch model is constructed based on the aforementioned model, wherein the optimization expression of the robust model is: in, This represents the total time for microgrid scheduling. Indicates in The radius of the uncertainty range of the robust model at time t. This represents the mean of the radius of the uncertain range; The constraints of the robust model are: in, Represents the expectation function, This represents the optimal cost under a preset baseline scenario. This represents the risk tolerance coefficient, and , express The baseline value for basic load forecasting at any given time. Indicates in Time, Scene Basic load demand in microgrids.

[0016] Understandably, this solution constructs a robust model and designs an adaptive control mechanism for the radius of dynamic uncertainty. The objective function expands the coverage of uncertainty while suppressing parameter dispersion. Combined with hard constraints on scenario costs and boundary control of load fluctuations, it achieves highly elastic defense against extreme load demands. This not only completely avoids the cost losses caused by fixed conservatism in traditional robust optimization, but also significantly enhances the operational stability and reliability of microgrids in complex and uncertain environments.

[0017] Optionally, an optimization model is constructed based on the microgrid scheduling model, including: An opportunity model for optimizing the microgrid dispatch model is constructed based on the aforementioned model, wherein the optimization expression of the opportunity model is: in, This represents the total time for microgrid scheduling. Indicates in The radius of uncertainty in the opportunity model at a given time. Represents the expectation function, Indicates in Uncertainty radius of the time-chance model The microgrid loss function is characterized by the increase in the total operating cost of the microgrid. Denotes the infimum function. Represent real numbers, Indicates the confidence level; The constraints of the opportunity model are: in, This represents the risk tolerance coefficient, and , This represents the optimal cost under a preset baseline scenario. express The baseline value for basic load forecasting at any given time. Indicates in Time, Scene Basic load demand in microgrids.

[0018] Understandably, by constructing an opportunity model and integrating conditional value-at-risk theory to dynamically adjust the radius of uncertainty, its objective function smooths risk volatility while compressing the uncertainty range. Relying on elastic cost constraints and load fluctuation flexibility boundaries, it accurately captures cost optimization opportunities within acceptable risk thresholds. This not only solves the optimization blind spot problem caused by neglecting tail risks in traditional opportunity constraints, but also significantly reduces the cost of microgrids in non-extreme load fluctuation scenarios, achieving synergistic optimization of cost optimization and reliability.

[0019] According to a second aspect of this application, a system for optimized scheduling of a microgrid is provided, the system comprising: The preset scheduling model module is used to preset the microgrid scheduling model and to preset the objective function and constraints for the microgrid scheduling model. A preset optimization model module is used to construct an optimization model based on the microgrid scheduling model, wherein the optimization model includes a robust model and an opportunistic model. The selection module is used to acquire the current operating data of the microgrid and select a target model of the optimization model from the optimization models based on the current operating data. The configuration parameter acquisition module is used to obtain the optimal configuration parameters of the microgrid based on the current operating data and the target model; The configuration module is used to configure the microgrid based on the configuration parameters, and to optimize the scheduling of the microgrid based on the configured microgrid and the current operating data of the microgrid.

[0020] According to a third aspect of this application, an electronic device is provided, comprising: Memory, used to store one or more computer programs; A processor, when the one or more computer programs are executed by the processor, implements the microgrid optimization scheduling method described in the first aspect above.

[0021] According to a fourth aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the microgrid optimization scheduling method described in the first aspect above.

[0022] Based on any of the above aspects, the microgrid optimization scheduling method, system, electronic device, and storage medium provided in this application embodiment can achieve the following benefits: This approach considers both robustness and opportunism in scheduling to adapt to the diverse operational needs of microgrids. By constructing robust and opportunistic models, it achieves full-scenario coverage of microgrid operational risk preferences. The robust model, constrained by "worst-case cost controllability," maximizes the coverage of uncertainty through dynamic adjustment of the uncertainty radius, ensuring microgrid stability under extreme load fluctuations and avoiding resource waste caused by excessive conservatism in traditional robust optimization. The opportunistic model, on the other hand, uses "acceptable risk of cost overrun" as a threshold, minimizing the uncertainty radius to explore optimization space under load fluctuations, compensating for the insufficient adaptability of a single opportunistic constraint to extreme scenarios. Furthermore, a risk tolerance coefficient enables flexible switching between the robust and opportunistic models, fully adapting to the diverse needs of microgrids in different scenarios.

[0023] Precise photovoltaic (PV) power generation modeling and constraints improve the accuracy of PV power generation constraints and predictions: A temperature correction coefficient is introduced to address the characteristics of PV power generation. Through comprehensive calculations of ambient temperature, direct solar radiation, solar scattering, total horizontal irradiance, and the actual temperature of the PV panels, errors caused by neglecting temperature decay effects in traditional simplified models are corrected. Simultaneously, by incorporating physical parameters such as the tilt angle of the PV panels and ground reflectivity, the dynamic characteristics of PV output power are fully reconstructed. Furthermore, a cost penalty mechanism for PV curtailment power is clearly defined, coupled with constraints such as energy balance and equipment output boundaries, significantly improving the accuracy of PV power generation predictions and the feasibility of scheduling.

[0024] This paper employs a joint optimization framework based on stochastic information gap decision theory (IGDT) to optimize the scheduling of microgrids, thereby improving the stability, reliability, and efficiency of microgrid optimization scheduling. It addresses multi-source uncertainties through a scenario expectation function, avoiding the accumulation of errors from a single deterministic assumption. Leveraging the contractility and nesting of the information gap decision theory model, it quantifies risk using the radius of the uncertainty range, eliminating the need for precise probability distribution information and effectively addressing the pain point of insufficient data in real-world scenarios. Furthermore, it automatically iterates and optimizes the uncertainty range, replacing manual trial-and-error calculations and significantly improving solution efficiency while ensuring robustness. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a microgrid optimization scheduling method provided in this embodiment.

[0027] Figure 2 This embodiment provides a functional module diagram of a microgrid optimized scheduling system.

[0028] Figure 3 This embodiment provides a schematic diagram of the electronic device. Detailed Implementation

[0029] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this application. To better illustrate the following embodiments, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product; it is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0030] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Microgrid optimization and dispatch needs to address the challenges of multi-source heterogeneous uncertainty, such as output fluctuations of renewable energy, especially photovoltaic power generation, load randomness, and changes in electricity purchase prices, in order to balance safety and cost optimization. However, existing robust optimization is prone to over-conservatism that increases costs, while opportunity-constrained optimization may underestimate risks and cause instability. Furthermore, there is a lack of a unified framework to support two-way switching of risk attitudes and a data-driven dynamic parameter adjustment mechanism.

[0033] This embodiment provides a technical solution that can solve the above problems. The specific implementation of this application will be described in detail below with reference to the accompanying drawings.

[0034] like Figure 1 As shown, this embodiment provides a method for optimized scheduling of a microgrid, which can be further divided into the following steps: S100. Preset a microgrid scheduling model, and preset an objective function and constraints for the microgrid scheduling model; Specifically, the objective function of the microgrid scheduling model is: in, This represents the total operating cost of the microgrid. This represents the total cost of distributed power sources in the microgrid. This represents the operating cost of the energy storage units in the microgrid. This represents the interaction cost between the microgrid and the main grid.

[0035] In this embodiment, the core objective is to minimize the total operating cost of the microgrid. The costs of distributed power sources, energy storage units, and the interaction between the microgrid and the main grid are comprehensively considered, thereby achieving a significant reduction in the total operating cost and ensuring the safe and stable operation of the microgrid.

[0036] Specifically, the total cost of distributed power sources in the microgrid for: in, Representing a scene The expected function can take into account the average cost under multiple scenarios when microgrids are set up in multiple scenarios. Indicates in Time, Scene Below, the curtailment power of photovoltaic power generators, This represents the curtailment cost of the photovoltaic power generation unit; the scenario described is a pre-set scenario for the microgrid.

[0037] In this embodiment, the scenario can be a preset scenario that may occur or be encountered during the operation of the microgrid, including weather scenarios that can affect the operation of the microgrid, such as sunny, cloudy, or rainy scenarios, and scenarios related to the microgrid's operating load, such as high-load and low-load scenarios. The preset scenario can be adjusted according to the actual situation and historical experience so that the total cost of distributed power generation is more in line with the conditions and operating conditions of each scenario.

[0038] And / or, the operating cost of the energy storage units in the microgrid for: in, express The charging power of the energy storage unit at that time. express The energy storage loss cost of the energy storage unit at that time. This represents the charging efficiency coefficient of the energy storage unit; In this embodiment, the operating cost of the energy storage unit is mainly related to the charging efficiency of the energy storage unit. Therefore, the function of the operating cost of the energy storage unit needs to consider the charging power, energy storage loss cost, and charging efficiency coefficient of the energy storage unit in order to accurately obtain the operating cost of the energy storage unit in the microgrid.

[0039] And / or, the interaction cost between the microgrid and the main grid. for: in, This represents the electricity purchase cost of the microgrid from the main grid. This represents the revenue generated from the sale of electricity from the microgrid to the main grid. Representing a scene The expected function; Indicates in Time, Scene The input power that the microgrid purchases from the main grid; Indicates in At any given time, the electricity price at which the microgrid purchases electricity from the main grid; exist Time, Scene The output power of the microgrid selling electricity to the main grid; Indicates in At any given time, the electricity price at which the microgrid sells electricity to the main grid.

[0040] In this embodiment, the electricity purchase cost of the microgrid to the main grid can take into account the uncertainties such as load fluctuations caused by different scenarios in the operation of the microgrid. Therefore, when calculating the electricity purchase cost, it is necessary to calculate the overall expected situation in multiple scenarios. The electricity sales revenue of the microgrid to the main grid can also reflect the average revenue level of electricity sales in the operation of the microgrid. When calculating the electricity sales cost, it is necessary to calculate the overall expected situation in multiple scenarios.

[0041] Specifically, the constraints include: Main power grid interaction constraints: in, This represents the minimum input power that the main power grid can transmit to the microgrid. Indicates the power input status flag value, if , indicating in At any given time, the microgrid purchases electricity from the main grid, and the main grid transmits electricity to the microgrid; if , indicating in At any given time, the microgrid stops purchasing electricity from the main grid, and the main grid stops transmitting electricity to the microgrid; Indicating microgrids in Time, Scene The input power from which the microgrid purchases electricity from the main grid; This represents the maximum input power from the main power grid to the microgrid. This represents the minimum output power of the microgrid that it sells to the main grid. Indicates the power output status flag value; if This indicates that the microgrid sells electricity to the main grid and transmits electricity back to the main grid; if This indicates that the microgrid has stopped selling electricity to the main grid and has stopped transmitting electricity to the main grid. Indicating microgrids in Time, Scene Output power sold to the main power grid This represents the maximum output power that the microgrid sells to the main grid; In this embodiment, the main grid interaction constraint can take into account that the microgrid cannot purchase and sell electricity at the same time, and the power purchased and sold must be within a safe range, so as to ensure the reliability of the interaction between the microgrid and the main grid.

[0042] Understandably, the power input status flag value and power output status flag value The variable is a binary representation of the status, using "0" and "1" to represent the corresponding status flag values, making the status of electricity purchase and sale clear and unambiguous.

[0043] And / or, energy balance constraints: in, express The basic load demand of microgrids at all times. Indicates energy storage unit Charging power at any time Indicating microgrids in Time, Scene Output power sold to the main power grid Indicates in Time, Scene Output power of the photovoltaic generator set; Indicating microgrids in Time, Scene The input power from which the microgrid purchases electricity from the main grid; Indicates energy storage unit Discharge power at any given moment; In this embodiment, the energy balance constraint can ensure the supply and demand balance in the real-time operation of the microgrid, avoiding the operational pain points of supply exceeding demand or supply falling short of demand.

[0044] And / or, the energy dynamic constraints of the energy storage unit: in, express The energy storage capacity of the energy storage unit at any time express The energy storage capacity of the energy storage unit at any time This indicates the preset interval time. This represents the discharge efficiency coefficient of the energy storage unit; This represents the charging efficiency coefficient of the energy storage unit. Indicates energy storage unit Charging power at any time Indicates energy storage unit Discharge power at any given moment; And / or, operating constraints of energy storage units: in, This indicates the minimum energy storage capacity of the energy storage unit. express The energy storage capacity of the energy storage unit at any time This indicates the maximum energy storage capacity of the energy storage unit. Indicates energy storage unit Charging power at any time This indicates the maximum power generation capacity of the energy storage unit. express The charging status flag is always on. This indicates that the energy storage unit is in a charging state. This indicates that the energy storage unit is in a stopped charging state. This indicates that the energy storage unit has stopped operating. Indicates energy storage unit Discharge power at any given time This indicates the maximum discharge power of the energy storage unit. express Discharge status flag bit at all times, if This indicates that the energy storage unit is in a discharging state. This indicates that the energy storage unit is in a stopped discharging state. This indicates that the energy storage unit has stopped operating; In this embodiment, the energy dynamic constraints and operation constraints of the energy storage unit can be set to ensure the safe operation of the energy storage unit.

[0045] Understandably, the charging status flag bit and discharge status flag bit It is a Boolean variable, using "1", "0" and "-1" to represent the charging and discharging state, making the charging and discharging state of the energy storage unit clearer.

[0046] Preferably, the maximum energy storage capacity of the energy storage unit It mainly relates to the battery capacity of the energy storage unit; the maximum energy storage capacity can likely be determined by directly checking the battery properties. At the same time, the minimum energy storage capacity of the energy storage units must be limited. This is to prevent the batteries in the energy storage unit from being over-discharged, which could damage the batteries.

[0047] And / or, demand response constraints: in, express Actual load demand at any given time express Load transfer amount at any given time express The basic load requirement at any given time This indicates the maximum adjustment ratio.

[0048] Understandably, the base load demand can be the predicted power consumption of the microgrid that naturally occurs without any optimized scheduling; the actual load demand can be the total power consumption of the microgrid at a specific time after optimized scheduling is implemented.

[0049] Constraints of photovoltaic power generation units: Temperature correction coefficient for: in, Indicates that the photovoltaic power generation unit is Time, Scene Output power at the following levels This indicates the rated power of a single photovoltaic panel under standard testing conditions. This indicates the total number of photovoltaic panels. Indicates that sunlight is at Time, Scene Direct irradiance below, This indicates the angle between sunlight and the normal to the photovoltaic panel. Indicates that sunlight is at Time, Scene Scattered irradiance under the following conditions Indicates the tilt angle of the photovoltaic panel. Indicates ground reflectivity, Indicates that all light rays are at Time, Scene Total horizontal irradiance below Indicates the temperature decay coefficient. Indicates in Time, Scene The ambient temperature can be expressed in degrees (°C) and can be provided by relevant data from the meteorological bureau. Indicates in Time, Scene The actual temperature of the photovoltaic panel; It represents the effective irradiance, and its unit can be: watts per square meter (W / m²). Indicates the nominal temperature of the photovoltaic panel, preferably, 25 degrees (°C) can be used.

[0050] In this embodiment, the photovoltaic generator set constraint incorporates a temperature correction factor and considers the angle of sunlight incidence, enabling a more accurate calculation of the photovoltaic generator set's output power by fully taking into account multiple factors in photovoltaic power generation.

[0051] Preferably, all light rays are in Time, Scene Total horizontal irradiance The unit can be watts per square meter (W / m²), and it is related to sunlight. Time, Scene Direct irradiance and sunlight Time, Scene Scattered irradiance The following mathematical relationship exists: Preferably, effective irradiance and sunlight Time, Scene Direct irradiance and sunlight Time, Scene Scattered irradiance The following mathematical relationship exists: S200. Construct an optimization model based on the microgrid scheduling model, wherein the optimization model includes a robust optimization model and a chance optimization model; Understandably, the optimization model was built upon the foundation of stochastic uncertainty models and IGDT.

[0052] Preferably, in a stochastic uncertainty model, the input or output function is pre-defined. To describe the total operating cost of a microgrid, where The decision variable vector includes, but is not limited to, the distributed power generation output power, energy storage unit charging power, energy storage unit discharging power, microgrid and main grid power purchase power, and microgrid and main grid power sales power, as defined in the above constraints. This is an uncertain parameter vector, mainly referring to uncertain parameters such as load forecasting errors during the actual operation of the microgrid. The input or output function... The function quantifies the combined impact of the decision variable vector and the uncertain parameter vector on the operating cost of the microgrid, and serves as the basic mapping relationship for subsequent optimization.

[0053] Specifically, the expression for the stochastic uncertainty model can be: in, For the predicted value of the uncertain parameter vector, preferably, It can be the predicted value of an uncertain parameter load; For a moment The radius of the uncertainty range is the core parameter to be optimized. It is the infinite norm, used to measure the degree of deviation between the uncertain parameter vector and the predicted value.

[0054] The stochastic uncertainty model has contractibility, meaning the radius of the uncertainty range can continuously shrink, and the uncertainty range with small nesting is a subset of the large uncertainty range. This allows the solver to automatically determine the boundary based on the optimization objective during actual solving, ensuring that the deviation between the actual load value and the predicted value does not exceed the set range, thus ensuring controllable operating costs.

[0055] Specifically, an optimization model is constructed based on the microgrid scheduling model, including: A robust model for optimizing the microgrid dispatch model is constructed based on the aforementioned model, wherein the optimization expression of the robust model is: in, This represents the total time for microgrid scheduling. Indicates in The radius of the uncertainty range of the robust model at time t. This represents the mean of the radius of the uncertain range; The constraints of the robust model are: in, Represent the expected function; This represents the optimal cost under a preset benchmark scenario, which can be used as a benchmark value. This represents the risk tolerance coefficient, and This indicates that no risk of cost overruns is accepted; express The baseline value for basic load forecasting at any given time. Indicates in Time, Scene Basic load demand in microgrids.

[0056] In this embodiment, the robust model can prioritize microgrid stability by adjusting the output of distributed power sources, the charging and discharging strategies of energy storage units, and the electricity purchased from the main grid of the microgrid, while keeping the load fluctuation range as large as possible.

[0057] Specifically, an optimization model is constructed based on the microgrid scheduling model, including: An opportunity model for optimizing the microgrid dispatch model is constructed based on the aforementioned model, wherein the optimization expression of the opportunity model is: in, This represents the total time for microgrid scheduling. Indicates in The radius of uncertainty in the opportunity model at a given time. Represents the expectation function, Indicates in Uncertainty radius of the time-chance model The microgrid loss function is characterized by the increase in the total operating cost of the microgrid. Denotes the infimum function. Represent real numbers, Indicates the confidence level; The constraints of the opportunity model are: in, This represents the risk tolerance coefficient, and This indicates that a certain probability of cost overrun is allowed; This represents the optimal cost under a preset baseline scenario. express The baseline value for basic load forecasting at any given time. Indicates in Time, Scene Basic load demand in microgrids.

[0058] In this embodiment, the opportunity model is designed for risk-seeking decision-makers. By relaxing the uncertainty radius limit, that is, allowing load fluctuations to be within a small range, the model seeks to reduce the start-up and shutdown costs of distributed power sources and the electricity purchase costs of microgrids and the main grid by taking advantage of opportunities such as peak periods of photovoltaic power generation and the discharge of energy storage units, thereby seeking a balance between risk and return.

[0059] S300. Obtain the current operating data of the microgrid, and select one of the optimization models as the target model based on the current operating data. In this embodiment, the current operating data of the microgrid is obtained, including the charging and discharging power of the energy storage unit, weather conditions, and decision-maker preferences. Based on the current operating data, a robust model or an opportunistic model is selected as the target model in the optimization model, thereby avoiding overly conservative or risk runaway problems caused by a fixed model and ensuring the reliable and stable operation of the microgrid.

[0060] S400. Obtain the optimal configuration parameters of the microgrid based on the current operating data and the target model; In this embodiment, based on the current operating data and the selected target model, as well as the constraints and the function corresponding to the target model set above, the optimal configuration parameters of the microgrid are obtained. Taking into account the multi-factor influence of microgrid operation, the system can accurately balance safety and cost optimization under uncertain conditions in microgrid operation, and avoid the biased scheduling due to human experience.

[0061] S500. Configure the microgrid based on the configuration parameters, and optimize the scheduling of the microgrid based on the configured microgrid and the current operating data of the microgrid.

[0062] In this embodiment, the configuration parameters are configured to correspond to each unit of the microgrid, so that each unit has clear power generation, energy storage and regulation capabilities. Then, based on the configured microgrid and the current operating data of the microgrid, the microgrid is optimized and scheduled to achieve the optimal operation of the microgrid with the lowest total operating cost and the highest energy utilization efficiency.

[0063] like Figure 2 As shown in the illustration, this application also provides a system for optimized scheduling of microgrids. Optionally, the system includes: The system includes a preset scheduling model module 611, a preset optimization model module 612, a selection module 613, a configuration parameter acquisition module 614, and a configuration module 615, among which: The preset scheduling model module 611 is used to preset a microgrid scheduling model and preset an objective function and constraints for the microgrid scheduling model. In this embodiment, the preset scheduling model module 611 can be used to execute Figure 1For a detailed description of the preset scheduling model module 611, please refer to the description of step S100 shown.

[0064] The preset optimization model module 612 is used to construct an optimization model based on the microgrid scheduling model, wherein the optimization model includes a robust model and an opportunistic model. In this embodiment, the preset optimization model module 612 can be used to execute... Figure 1 For a detailed description of the preset optimization model module 612, please refer to the description of step S200 shown.

[0065] The selection module 613 is used to acquire the current operating data of the microgrid and select a target model of the optimization model from the optimization models based on the current operating data. In this embodiment, the selection module 613 can be used to perform... Figure 1 For a detailed description of the selection module 613, please refer to the description of step S300 shown.

[0066] The configuration parameter acquisition module 614 is used to acquire the optimal configuration parameters of the microgrid based on the current operating data and the target model; In this embodiment, the configuration parameter acquisition module 614 can be used to execute... Figure 1 For a detailed description of the configuration parameter acquisition module 614 shown in step S400, please refer to the description of step S400.

[0067] The configuration module 615 is used to configure the microgrid based on the configuration parameters and to optimize the scheduling of the microgrid based on the configured microgrid and the current operating data of the microgrid.

[0068] In this embodiment, the configuration module 615 can be used to execute... Figure 1 For a detailed description of the configuration module 615, please refer to the description of step S500 shown.

[0069] This application also provides an electronic device, the structure of which is as follows: Figure 3 As shown, the electronic device includes a memory 711, a processor 712, a communication module 713, and an input / output interface 714, etc. Optionally, the memory 711, the processor 712, the communication module 713, and the input / output interface 714 can be connected and communicate with each other through a bus 715.

[0070] The memory 711 is used to store one or more computer programs and to transfer the code of the computer programs to the processor 712; when the one or more computer programs are executed by the processor 712, a microgrid optimization scheduling method is implemented in the embodiments of this application.

[0071] Optionally, the electronic device can be connected to a network via the communication module 713 to communicate with other devices, such as terminals or servers, and to interact with data. The electronic device can be various forms of digital computers, exemplarily such as desktop computers, servers, workbenches, mainframes, or other types of computers. The electronic device can also be various forms of mobile terminals, exemplarily such as smartphones, tablets, wearable devices (such as helmets, glasses, watches, etc.), and other similar mobile terminals.

[0072] Optionally, the electronic device can connect to desired input / output devices, such as a keyboard or display device, via the input / output interface 714. The electronic device itself may have a display device, and other display devices can also be connected externally via the input / output interface 714. Optionally, a storage device, such as a hard disk, can also be connected via the input / output interface 714 to store data from the electronic device, read data from the storage device, or store data from the storage device in the memory 711. It is understood that the input / output interface 714 can be a wired interface or a wireless interface. Depending on the actual application scenario, the device connected to the input / output interface 714 can be a component of the electronic device or an external device connected to the electronic device when needed.

[0073] Optionally, the memory 711 may be a volatile memory and / or a non-volatile memory. The volatile memory may be a random access memory, etc., and the non-volatile memory may be a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, or a flash memory, etc.

[0074] Optionally, the computer program stored in the memory 711 can be divided into one or more modules, which are stored in the memory 711 and executed by the processor 712 to perform the method provided in this embodiment. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device.

[0075] Optionally, the processor 712 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 712 include, but are not limited to, a central processing unit, a graphics processing unit, a digital signal processor, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, and can also be any suitable controller, microcontroller, processor, etc. The processor 712 executes the various methods and processes of this embodiment, exemplified by a microgrid optimization scheduling method according to an embodiment of this application.

[0076] Optionally, the bus 715 may include a path for transmitting information. Depending on its function, the bus 715 may be classified as an address bus, a data bus, a control bus, etc.

[0077] In an optional implementation, this application embodiment also provides a computer storage medium storing a computer program thereon. When the computer program is executed by a computer, it enables the computer to perform the methods described in the above-described method embodiments. Part or all of the computer program can be loaded and / or installed on the memory 711 of an electronic device. When the computer program is executed by the processor 712, one or more steps of a microgrid optimization scheduling method according to an embodiment of this application can be performed.

[0078] Optionally, the computer-readable storage medium may be a random access memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, etc.

[0079] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A method for micro-grid optimal scheduling, characterized in that, The method comprises: presetting a micro-grid scheduling model, and presetting a target function and a constraint condition for the micro-grid scheduling model; constructing an optimization model based on the micro-grid scheduling model, wherein the optimization model comprises a robust optimization model and an opportunity optimization model; obtaining current operation data of the micro-grid, and selecting one of the optimization models as a target model in the optimization model according to the current operation data; obtaining optimal configuration parameters of the micro-grid based on the current operation data and the target model; configuring the micro-grid based on the configuration parameters, and optimizing scheduling of the micro-grid based on the configured micro-grid and the current operation data of the micro-grid.

2. The method of claim 1, wherein, The target function of the micro-grid scheduling model is: wherein, represents the total operation cost of the microgrid, represents the total cost of distributed power sources in the microgrid, represents the operation cost of energy storage units in the microgrid, represents the interaction cost of the microgrid with the main grid.

3. The method of claim 2, wherein, Total cost of distributed power sources in the microgrid Is: in, Representing a scene The expected function; Indicates in Time, Scene Below, the curtailment power of photovoltaic power generators, This represents the curtailment cost of the photovoltaic power generation unit; the scenario described is a pre-set configuration for the microgrid. And / or, the operation cost of the energy storage unit in the micro-grid Is: wherein, represents the charging power of the energy storage unit at the time instant, represents the energy storage loss cost of the energy storage unit at the time instant, represents the charging efficiency coefficient of the energy storage unit; And / or, the microgrid's interaction cost with the main grid Is: wherein, represents a purchase cost of electricity purchased by the microgrid from the main grid, represents a sale revenue of electricity sold by the microgrid to the main grid, represents a scenario ; represents an input power of the microgrid purchased from the main grid at time, scenario ; represents a purchase price of electricity purchased by the microgrid from the main grid at time; represents an output power of the microgrid sold to the main grid at time, scenario ; represents a sale price of electricity sold by the microgrid to the main grid at time.

4. The method of claim 1, wherein, The constraint condition comprises: a main grid interaction constraint: wherein, Pmin represents the minimum value of the input power of the microgrid purchasing power from the main grid at time t, Pmin represents the minimum value of the input power of the microgrid purchasing power from the main grid at time t, Pmin represents the minimum value of the input power of the microgrid purchasing power from the main grid at time t, Pmin represents the minimum value of the input power of the microgrid purchasing power from the main grid at time t, Pmin represents the minimum value of the input power of the microgrid purchasing power from the main grid at time t, Pmin represents the minimum value of the input power of the microgrid purchasing power from the main grid at time t, Pmin represents the minimum value of the input power of the microgrid purchasing power from the main grid at time t, Pmin represents the minimum value of the input power of the microgrid purchasing power from the main grid at time t, Pmin represents the minimum value of the input power of the microgrid purchasing power from the main grid at time t, Pmax represents the maximum value of the input power of the main grid transmitting power to the microgrid at time t, Pmin represents the minimum value of the output power of the microgrid selling power to the main grid at time t, Pmin represents the minimum value of the output power of the microgrid selling power to the main grid at time t, Pmin represents the minimum value of the output power of the microgrid selling power to the main grid at time t, Pmin represents the minimum value of the output power of the microgrid selling power to the main grid at time t, Pmin represents the minimum value of the output power of the microgrid selling power to the main grid at time t, Pmin represents the minimum value of the output power of the microgrid selling power to the main grid at time t, Pmin represents the minimum value of the output power of the microgrid selling power to the main grid at time t, Pmax represents the maximum value of the output power of the microgrid selling power to the main grid at time t, and / or, an energy balance constraint: wherein, denotes the base load demand of the microgrid at the time instant, denotes the charging power of the energy storage unit at the time instant, denotes the output power of the microgrid to the main grid at the time instant, denotes the scenario at the time instant, denotes the output power of the photovoltaic generator at the time instant, denotes the scenario at the time instant, denotes the input power of the microgrid from the main grid at the time instant, denotes the scenario at the time instant, denotes the discharging power of the energy storage unit at the time instant. and / or, an energy dynamic constraint of a storage unit: wherein, represents represents the energy storage capacity of the energy storage unit at the time point, represents represents the energy storage capacity of the energy storage unit at the time point, represents a preset interval time, represents a discharge efficiency coefficient of the energy storage unit; represents a charge efficiency coefficient of the energy storage unit, represents the charge power of the energy storage unit at the time point, represents the discharge power of the energy storage unit at the time point. and / or, an operation constraint of the storage unit: wherein, represents the minimum energy storage capacity of the energy storage unit, represents the energy storage capacity of the energy storage unit at the moment, represents the maximum energy storage capacity of the energy storage unit, represents the energy storage unit the charging power at the moment, represents the maximum power generation of the energy storage unit, represents the charging state flag at the moment, if , the energy storage unit is in the charging state, if , the energy storage unit is in the stop charging state, if , the energy storage unit stops running, represents the energy storage unit the discharging power at the moment, represents the maximum discharging power of the energy storage unit, represents the discharging state flag at the moment, if , the energy storage unit is in the discharging state, if , the energy storage unit is in the stop discharging state, if , the energy storage unit stops running; and / or, a demand response constraint: wherein represents actual load demand at the time, represents load transfer amount at the time, represents base load demand at the time, represents the maximum adjustment ratio.

5. The method of claim 1, wherein, The constraint condition comprises: a photovoltaic generator constraint: where the temperature correction factor is: wherein, Pout represents the output power of the photovoltaic generator set at the time instant, scene Pout represents the output power of the photovoltaic generator set at the time instant, scene Pout represents the output power of the photovoltaic generator set at the time instant, scene Pn represents the rated power of the monolithic photovoltaic panel under standard test, N represents the total number of photovoltaic panels, Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene Iin represents the direct irradiance of the solar rays at the time instant, scene 6. The method of claim 2, wherein, constructing an optimization model based on the micro-grid scheduling model comprises: constructing a robust model of the optimization model based on the micro-grid scheduling model, wherein an optimization expression of the robust model is: wherein, denotes the total time of microgrid dispatch, denotes the uncertainty range radius of the robust model at time instant, denotes the mean value of the uncertainty range radius; The constraint condition of the robust model is: wherein, represents a desired function, represents an optimal cost under a preset reference scenario, represents a risk tolerance coefficient, and , represents a reference value of the base load prediction at the moment, represents a base load demand in the micro-grid at the moment, under the scenario .

7. The method of claim 2, wherein, constructing an optimization model based on the micro-grid scheduling model comprises: constructing an opportunity model of the optimization model based on the micro-grid scheduling model, wherein an optimization expression of the opportunity model is: in, This represents the total time for microgrid scheduling. Indicates in The radius of uncertainty in the opportunity model at a given time. Represents the expectation function, Indicates in Uncertainty radius of the time-chance model The loss function of the microgrid under the following conditions Denotes the infimum function. Represent real numbers, Indicates the confidence level; The constraint condition of the opportunity model is: wherein, represents a risk tolerance coefficient, and , represents an optimal cost under a preset reference scenario, represents a reference value of the base load prediction at the moment, represents the base load demand in the micro-grid at the moment, scenario . ​ 8.A system for micro-grid optimal scheduling, characterized in that, The system comprises: a scheduling model presetting module configured to preset a micro-grid scheduling model, and preset a target function and a constraint condition for the micro-grid scheduling model; an optimization model presetting module configured to construct an optimization model based on the micro-grid scheduling model, wherein the optimization model comprises a robust model and an opportunity model; a selection module configured to obtain current operation data of the micro-grid, and select one of the optimization models as a target model in the optimization model according to the current operation data; a configuration parameter obtaining module configured to obtain optimal configuration parameters of the micro-grid based on the current operation data and the target model; a configuration module configured to configure the micro-grid based on the configuration parameters, and optimize scheduling of the micro-grid based on the configured micro-grid and the current operation data of the micro-grid.

9. An electronic device, comprising: comprise: a memory configured to store one or more computer programs; a processor configured to implement a micro-grid optimization scheduling method according to any one of claims 1-7 when the one or more computer programs are executed by the processor. 10.A computer readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to implement a micro-grid optimization scheduling method according to any one of claims 1-7 when the computer instructions are executed by the processor.

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