Micro-grid scheduling method and system in grid-connected and isolated island operation modes

Abstract

The invention relates to the field of micro-grid dispatching, in particular to a micro-grid dispatching method and system in grid-connected and isolated island operation modes. The method comprises the following steps: presetting scheduling models, wherein the scheduling models comprise a grid-connected scheduling model, an island scheduling model and a grid-connected island hybrid scheduling model; presetting a corresponding objective function and a constraint condition for each scheduling model; obtaining current operation data of the micro-grid, and selecting one scheduling model from the scheduling models as a target scheduling model according to the current operation data; obtaining optimal configuration parameters of the micro-grid based on the current operation data of the micro-grid, the target scheduling model and the target function and the constraint condition corresponding to the target scheduling model; and configuring the micro-grid based on the configuration parameters to complete the scheduling of the micro-grid. The method is used for solving the problems of unsmooth separation and switching of a grid-connected mode and an island mode and inflexible optimization target in the prior art, improving the scheduling flexibility of the micro-grid in a dynamic environment and reducing the operation cost.

Description

Technical Field

[0001] This invention relates to the field of microgrid dispatching, and more specifically, to a microgrid dispatching method and system for grid-connected and islanded operation modes. Background Technology

[0002] Currently, microgrids are small-scale power generation and distribution networks composed of distributed power sources, energy storage units, interface equipment, and diverse loads. They possess autonomous control and management capabilities, and can operate both connected to the main grid and independently in islanded mode. Microgrid dispatch, as its core control component, coordinates the output allocation of various energy resources, energy storage charging and discharging strategies, and supply-demand balance to achieve economic operation, reliable power supply, and multi-objective optimization. In practical applications, existing technologies mainly adopt dispatch modes based on specific operating scenarios: in grid-connected mode, the focus is on utilizing external grid support, reducing overall costs by optimizing the combination of different types of generator sets, the charging and discharging of energy storage units, and the microgrid's electricity purchase and sale behavior; in islanded mode, the focus is on the reliable supply of local microgrid resources, relying on fuel consumption management, energy storage unit capacity configuration, and load regulation to maintain balanced microgrid operation.

[0003] However, with the continuous advancement of renewable power generation and energy storage technologies, both operating modes have revealed significant limitations: In grid-connected mode, the high proportion of renewable energy integration and the frequent, flexible regulation of energy storage units exacerbate power fluctuations in the microgrid, thus placing higher demands on the stability of interactions between different operating components within the microgrid; while islanded mode possesses sufficient autonomous adjustment capabilities, it struggles to achieve rapid and flexible resource reorganization by relying solely on preset rules when facing sudden load disturbances or energy shortages caused by extreme weather. Furthermore, current microgrid dispatch strategies are still limited by single-mode optimization thinking, generally designing dispatch strategies only for grid-connected or islanded modes, without establishing cross-mode collaborative optimization mechanisms, resulting in low efficiency during mode switching in actual operation. Simultaneously, existing dispatch schemes lack dynamic adaptability; traditional static dispatch strategies struggle to effectively capture the dynamic characteristics of microgrids, such as random load fluctuations, real-time market price changes, and uncertainties in renewable energy generation. Especially with the continuously increasing proportion of wind and solar renewable energy generation, existing dispatch strategies show a significant lack of flexibility in dealing with complex operating conditions across multiple time scales, hindering the overall improvement of microgrid efficiency. Summary of the Invention

[0004] This invention provides a microgrid scheduling method and system for both grid-connected and islanded operation modes, which addresses the problems of fragmentation and unsmooth switching between grid-connected and islanded modes and the overall inflexibility of optimization objectives in existing technologies. It improves the scheduling flexibility of microgrids in dynamic environments and reduces the operating costs of microgrids.

[0005] According to a first aspect of this application, a microgrid dispatching method is provided for grid-connected and islanded operation modes, the method comprising: A preset scheduling model is provided, which includes a grid-connected scheduling model, an islanded scheduling model, and a hybrid grid-connected and islanded scheduling model. Pre-define the corresponding objective function and constraints for each scheduling model; Obtain the current operating data of the microgrid, and select one of the scheduling models as the target scheduling model based on the current operating data. Based on the current operating data of the microgrid, the target scheduling model, and the objective function and constraints corresponding to the target scheduling model, the optimal configuration parameters of the microgrid are obtained. The microgrid is configured based on the configuration parameters to complete the scheduling of the microgrid.

[0006] Understandably, by constructing a multi-dimensional scheduling framework covering grid-connected, islanded, and hybrid operations, and combining it with a model selection mechanism driven by current operational data, precise control and optimized allocation of microgrid resources are achieved. Differentiated objective functions and constraints are preset for different operating scenarios. Based on real-time collected current operational data, the optimal scheduling model is intelligently matched, and configuration parameters that meet reliability and equipment safety constraints are solved through mathematical modeling. Ultimately, smooth switching and coordinated operation of the microgrid between grid-connected and islanded scheduling are achieved, significantly improving the microgrid's adaptive capability to changes in state. This effectively balances renewable energy consumption, load supply and demand matching, and energy storage health management, ensuring continuous power supply to critical loads while reducing overall operating costs. It provides a flexible and efficient solution for the stable operation of microgrids under complex operating conditions.

[0007] Optionally, the step of pre-setting corresponding objective functions and constraints for each scheduling model includes: The grid-connected scheduling model is pre-defined with a grid-connected objective function and grid-connected constraints; The island scheduling model is pre-defined with an island objective function and island constraints; The grid-connected islanding hybrid scheduling model is pre-set with a grid-connected islanding hybrid objective function and grid-connected islanding hybrid constraints.

[0008] Understandably, by designing dedicated objective functions and constraints for grid-connected, islanded, and hybrid dispatch models respectively, an optimization system that accurately adapts to different operating scenarios has been constructed. This differentiated modeling approach enables microgrids to automatically match the optimal dispatch strategy based on real-time operating conditions, significantly improving the robustness and adaptability of microgrids under different dispatch operations while ensuring the safety boundaries of the generating units.

[0009] Optionally, the grid connection objective function is: in, Indicates grid-connected operating costs, This indicates the total duration of microgrid dispatching. Indicates the first Taiwan fuel generator set, This represents a collection of fuel generator sets. Indicates the first Fuel cost coefficient of fuel generator set Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first The power generation efficiency of the fuel-powered generator set. It represents a collection of energy storage units. Indicates the first Group energy storage units, Indicates the first The operation and maintenance cost coefficient of the energy storage unit Indicates the first Group of energy storage units in Discharge power during the period Indicates the first Group of energy storage units in Charging power during the period Represent a The duration of the period , express Total number Indicates in The price at which a microgrid purchases electricity from the main grid during a given time period. Indicates in The amount of electricity purchased by the microgrid from the main grid during a given time period. Indicates in The price at which a microgrid sells electricity to the main grid during a given time period. Indicated as in The amount of electricity sold by the microgrid to the main grid during a given period. Indicates the billing cycle. Indicates the total number of billing cycles. Indicates the first Each billing cycle, Indicates the first Electricity price per billing cycle Indicates the first Maximum demand within a billing cycle.

[0010] Understandably, the mathematical expression of the grid-connected objective function encompasses fuel cost, energy storage operation and maintenance cost, net grid interaction expenditure, and demand charge. By accumulating time-period costs and capturing peak values ​​within the billing cycle, it accurately quantifies the comprehensive operating cost of the microgrid under grid-connected scheduling. It dynamically coordinates the output of distributed power sources, the charging and discharging behavior of energy storage, and the grid interaction plan in the microgrid, effectively reducing fuel substitution costs, energy storage cycle losses, and demand charge expenditure, and significantly improving the operating efficiency of the microgrid.

[0011] Optionally, preset grid connection constraints include: Preset power balance constraints: in, Indicates in Predicted power output of photovoltaic power generation units during the time period Indicates in The predicted power output of wind turbine generators during a given time period, wherein the photovoltaic and wind turbine generators are renewable energy generators. Indicates in Total load demand in a microgrid during a given time period; Preset operating constraints for fuel generator sets: in, Indicates the first The minimum output power of the fuel generator set, Indicates the first The maximum output power of the fuel generator set, Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first The ramp rate limit power of the fuel generator set; Preset constraints for energy storage units: in, Indicates the first Group of energy storage units in State of charge during a period of time Indicates the first Group of energy storage units in State of charge during a period of time Indicates the first The charging efficiency of the energy storage unit Indicates the first The discharge efficiency of the energy storage unit Indicates the first The rated capacity of the energy storage unit Indicates the first Minimum state of charge threshold for energy storage units Indicates the first The maximum state-of-charge threshold of the energy storage unit. Indicates the first Minimum charging power threshold for energy storage units Indicates the first The maximum charging power threshold of the energy storage unit. Indicates the first The minimum discharge power threshold of the energy storage unit Indicates the first The maximum discharge power threshold of the energy storage unit; Preset main power grid interaction constraints: in, This represents the maximum power purchase threshold that a microgrid can acquire from the main grid. Indicates the first The aforementioned renewable energy generator sets, This represents the sum of the renewable energy generator sets. Indicates the first The winning bid power of the renewable energy generator sets, The winning bid power for all renewable energy generator sets. Preferably, the renewable energy generator sets include photovoltaic generator sets and wind power generator sets.

[0012] Understandably, strict grid connection constraints establish the physical boundaries and operational norms for the safe and stable operation of microgrids. Power balance constraints, centered on dynamic energy conservation, precisely coordinate the real-time supply and demand relationships between fuel cell generators, energy storage units, renewable energy output and load demand, and grid interaction. Fuel cell generator operation constraints, by setting upper and lower limits on output power and ramp rate restrictions, ensure the safe operation of each unit while avoiding equipment damage caused by frequent and significant adjustments. Energy storage unit constraints, based on state of charge evolution and combined with charging and discharging efficiency, capacity limitations, and power thresholds, achieve refined management of energy storage units. Main grid interaction constraints, by limiting the range of purchased and sold power, ensure that energy exchange between the microgrid and the main grid remains within a safe and controllable range. These multi-dimensional constraints work together to effectively maintain the physical feasibility and equipment safety of microgrid operation, providing a reliable mathematical basis for optimized microgrid scheduling. Simultaneously, they prevent equipment damage or microgrid instability caused by exceeding limits, significantly improving the reliability and sustainability of microgrid operation.

[0013] Optionally, the objective function of the isolated island is: in, This represents the comprehensive index of isolated islands. This represents the fuel efficiency weighting coefficient. Indicates the first Taiwan fuel generator set, This represents a collection of fuel generator sets. Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first The power generation efficiency of the fuel-powered generator set. This represents the weighting coefficient for energy storage losses. Indicates the first Group energy storage units, It represents a collection of energy storage units. Indicates the first The maximum discharge power threshold of the energy storage unit Indicates the first The rated capacity of the energy storage unit Indicates the first Group of energy storage units in Remaining capacity for the time period This represents the power supply reliability weighting coefficient. Indicates in Total load deficit for the period.

[0014] Understandably, the islanding objective function takes into account the effective output of fuel generator sets, energy storage unit losses, and power supply reliability in a weighted summation form. By matching the weighting coefficients of fuel efficiency, energy storage loss, and power supply reliability, it incentivizes efficient operation of fuel generator sets while suppressing excessive discharge of energy storage units to extend their lifespan. This enables microgrid dispatch to achieve a globally optimal balance between energy utilization efficiency, equipment lifespan, and power supply service quality under limited resources, significantly enhancing the survivability and robustness of microgrid islanding operation.

[0015] Optionally, preset island constraints include: Preset survival constraints: in, Indicates the first Group of energy storage units in Discharge power during the period Indicates in Demand for critical loads during specific time periods; Preset fuel reserve constraints: in, This indicates the required time for the microgrid to operate autonomously. Indicates the first Fuel consumption rate of fuel generator sets Represent a The duration of the period Indicates the first Taiwan fuel generator set Output power over time Indicates the total fuel reserves; Preset dynamic adjustment constraint for energy storage loss weighting coefficient: in, Indicates in Energy storage loss weighting coefficient for different time periods The benchmark value representing the weighting coefficient for energy storage losses; This indicates that among all groups of energy storage units, in Minimum state of charge during a time period , The state of charge threshold that indicates the safety of an energy storage unit.

[0016] Understandably, a safety barrier for microgrid islanded operation is constructed by setting multi-level islanding constraints. Among them, the survival constraint focuses on critical load demand, mandating that the sum of the discharge power of fuel generators and energy storage units is not less than the critical load demand minus the total load shortfall, ensuring basic power supply safety in extreme situations. The fuel reserve constraint verifies the total fuel reserve by calculating the cumulative fuel consumption within the cycle and combining it with the fuel generator consumption rate parameter, thus avoiding the risk of shutdown due to insufficient fuel from the source. The dynamic adjustment constraint of energy storage loss weight is based on the real-time monitored minimum state of charge, proportionally reducing the energy storage loss weight coefficient. This avoids excessive discharge in low-power ranges that could damage battery life, and maintains a dynamic balance among multiple objectives through weight normalization design. This allows the microgrid to adhere to the power supply safety red line under islanded dispatch while achieving refined management of fuel and energy storage resources, significantly improving the reliability and sustainability of islanded operation.

[0017] Optionally, the objective function for the grid-connected islanding hybrid system includes: in, This indicates the comprehensive index of grid-connected islands. This represents the grid connection cost weighting coefficient. Indicates the first Taiwan fuel generator set, This represents a collection of fuel generator sets. Indicates the first Fuel cost coefficient of fuel generator set Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first The power generation efficiency of the fuel-powered generator set. This represents the power generation efficiency weighting coefficient. This represents the weighting coefficient for the health of energy storage. Indicates the first The maximum discharge power threshold of the energy storage unit Indicates the first The rated capacity of the energy storage unit Indicates the first Group of energy storage units in Remaining capacity for the time period.

[0018] Understandably, the hybrid objective function of grid-connected and islanded systems simultaneously considers fuel cost, power generation efficiency, and energy storage health in a weighted combination, dynamically adjusting the weighting of grid connection cost, power generation efficiency, and energy storage health. This incentivizes efficient operation of fuel generators to reduce energy costs while suppressing excessive discharge of energy storage units to extend equipment lifespan. It also takes into account the economic needs of grid interaction, enabling microgrid dispatch to achieve a globally optimal balance of energy utilization efficiency, unit lifespan, and operating costs during the switching between grid-connected and islanded dispatch. This effectively improves the resource adaptability and long-term operational stability of microgrids under different dispatch operations.

[0019] Optionally, the preset grid-connected islanding hybrid constraints include: Preset model switching constraints: in, Indicates in The amount of electricity purchased by the microgrid from the main grid during a given time period. Represents a maximum constant. Indicates the module flag bit, and , and when When, it indicates that the microgrid uses an islanded scheduling model, when When this occurs, it indicates that the microgrid is using a grid-connected dispatch model; Indicates in Total load deficit for the period.

[0020] Understandably, by designing model switching constraints based on module flags, a logical criterion for intelligent switching between grid-connected and islanded scheduling was constructed, where a maximum constant is used. The system of inequalities with boundary conditions incorporates the microgrid's power purchase from the main grid and the total load deficit into the model switching decision. When a change in the microgrid's state is detected, this constraint drives the microgrid to automatically select the optimal operating model based on the real-time load deficit and power purchase capacity. This ensures priority power supply to critical loads under the islanded dispatch model while allowing the main grid to supplement the power deficit under the grid-connected dispatch model. This effectively achieves a smooth transition and dynamic adaptation between the two operating dispatch models, significantly reducing the transient impact during dispatch model switching and improving the microgrid's response speed and operational stability to state changes.

[0021] Optionally, preset hybrid constraints for grid-connected islanding include: Pre-set preventive scheduling constraints: in, This indicates the minimum reserved capacity of the energy storage unit. Indicates the safety factor. This indicates a preset time window. Indicates in Demand for critical loads during specific time periods.

[0022] Understandably, the preventive scheduling constraint constructs a forward-looking security mechanism by requiring that the remaining capacity of energy storage units must not be lower than the weighted sum of the minimum reserved capacity and the critical load demand in the future forecast period. It strengthens the predictive protection of critical loads with a safety factor, dynamically linking the reserved capacity of energy storage base stations with the critical load demand in the future time window. This ensures that energy storage units retain necessary emergency margins and, through predictive calculations, locks in resource reserves to cope with potential risks in advance. This enables the microgrid to respond quickly to sudden scenarios such as load changes and equipment failures, relying on sufficient energy storage backup capacity. This effectively improves the microgrid's ability to withstand uncertainties and provides physical resource guarantees for preventive maintenance and fault-tolerant control, significantly enhancing the microgrid's operational resilience and power supply reliability under complex operating conditions.

[0023] According to a second aspect of this application, a microgrid dispatching system is provided for grid-connected and islanded operation modes, the system comprising: A preset scheduling model module is used to preset scheduling models, which include grid-connected scheduling models, islanded scheduling models, and hybrid grid-connected and islanded scheduling models. The module for pre-setting objective functions and constraints is used to pre-set corresponding objective functions and constraints for each scheduling model. The selection module is used to obtain the current operating data of the microgrid and select one of the scheduling models as the target scheduling model based on the current operating data. The parameter acquisition module is used to acquire the optimal configuration parameters of the microgrid based on the current operating data of the microgrid, the target scheduling model, and the objective function and constraints corresponding to the target scheduling model. The configuration module is used to configure the microgrid based on the configuration parameters and to complete the scheduling of the microgrid.

[0024] Based on any of the above aspects, the microgrid dispatching method and system for grid-connected and islanded operation modes provided in this application embodiment can achieve the following beneficial effects: A unified microgrid dispatch framework: By constructing a unified framework covering grid-connected, islanded, and hybrid dispatch models, the operational fragmentation caused by traditional discrete models is overcome. Through pre-setting multi-dimensional dispatch models and corresponding objective functions and constraints, the microgrid can dynamically select the optimal dispatch strategy based on real-time operational data. Model switching constraints enable a smooth transition between grid-connected and islanded dispatch models, significantly reducing transient impacts during model switching and enhancing the microgrid's adaptability to state changes. A unified system of power balance, unit operation, and main grid interaction constraints ensures global coordination among the generation, energy storage, and load sides, enabling the microgrid to maintain efficient and stable operation under complex conditions and providing a reliable underlying architecture for subsequent dynamic optimization.

[0025] The robustness of dispatching is improved by combining dynamic weighting, preventive dispatching, and energy storage health considerations: Dynamic weighting can automatically adjust the priority of the objective function according to real-time operating conditions, effectively balancing the operating cost and power supply reliability of microgrids in scenarios with a high proportion of renewable energy access; Preventive dispatching optimizes fuel reserves and energy storage unit configuration by calculating critical load demand in advance, significantly enhancing the microgrid's ability to cope with sudden failures or load fluctuations; Incorporating energy storage health into the objective function reduces battery aging rate by suppressing deep charge and discharge behavior, achieving synergistic optimization of long-term operation and maintenance costs and technical performance; The synergistic effect of these three factors enables the microgrid to maintain critical load power supply under extreme operating conditions and has stronger anti-disturbance capabilities. Attached Figure Description

[0026] 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.

[0027] Figure 1 This is a flowchart of a microgrid scheduling method for grid-connected and islanded operation modes provided in this embodiment.

[0028] Figure 2This is a schematic diagram of a preset objective function and constraints provided in this embodiment.

[0029] Figure 3 This embodiment provides a functional module diagram of a microgrid dispatching system with grid-connected and islanded operation modes. Detailed Implementation

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Currently, microgrid dispatching is limited by its reliance on a single dispatching model optimization, insufficient dynamic adaptability, and lack of cross-model coordination mechanisms. This makes it difficult to cope with power fluctuations, extreme operating conditions, and dynamic characteristics across multiple time scales under the high penetration of new energy sources, thus limiting the flexibility and operational efficiency of microgrid dispatching.

[0034] 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.

[0035] like Figure 1 As shown, this embodiment provides a microgrid dispatching method for grid-connected and islanded operation modes, which can be further divided into the following steps: S100, Preset scheduling model, the scheduling model includes grid-connected scheduling model, islanded scheduling model and grid-connected-island hybrid scheduling model; In this embodiment, by pre-setting multiple scheduling models, each containing a different optimization objective function, the microgrid can select a suitable scheduling model to guide the scheduling work based on the current operating conditions and status in subsequent scheduling work, thereby improving the flexibility of microgrid scheduling.

[0036] S200. Pre-set the corresponding objective function and constraints for each scheduling model; In this embodiment, each scheduling model needs to have a corresponding preset objective function and constraints. This allows the microgrid to be scheduled using the corresponding scheduling model, with the objective function optimized and the optimal configuration parameters obtained under the constraints. This enables the microgrid to maintain stability and improve operational efficiency during operation.

[0037] Specifically, such as Figure 2 As shown, the preset objective function and constraints for each scheduling model may include: The grid-connected scheduling model is pre-defined with a grid-connected objective function and grid-connected constraints; Specifically, the grid connection objective function is: in, Indicates grid-connected operating costs, This indicates the total duration of microgrid dispatching. Indicates the first Taiwan fuel generator set, Represents a set of fuel generator sets, in which ; Indicates the first The fuel cost coefficient for a fuel-powered generator set can be expressed in yuan per megawatt-hour (yuan / MWh). Indicates the first Taiwan fuel generator set The output power during a time period can be measured in megawatts (MW). Indicates the first The power generation efficiency of a fuel-powered generator set can be expressed as a percentage (%). It represents a collection of energy storage units. Indicates the first A group of energy storage units, of which ; Indicates the first The operation and maintenance cost coefficient of the energy storage unit can be expressed in RMB per megawatt (RMB / MW). Indicates the first Group of energy storage units in The discharge power during a period can be expressed in megawatts (MW). Indicates the first Group of energy storage units in The charging power during a given period can be expressed in megawatts (MW). Represent a The duration of a time period can be measured in hours (h). , express Total number Indicates in The price at which a microgrid purchases electricity from the main grid during a given time period can be expressed in yuan per megawatt-hour (yuan / MWh). Indicates in The power that a microgrid purchases from the main grid during a given time period can be expressed in megawatts (MW). Indicates in The price at which a microgrid sells electricity to the main grid during a given time period can be expressed in yuan per megawatt-hour (yuan / MWh). Indicated as in The power sold by a microgrid to the main grid during a given time period can be expressed in megawatts (MW). Indicates the billing cycle, preferably, You can withdraw for 720 hours; Indicates the total number of billing cycles. Indicates the first Each billing cycle, Indicates the first The electricity cost per billing cycle can be expressed in RMB per megawatt (RMB / MW). Indicates the first The maximum demand within a billing cycle can be expressed in megawatts (MW).

[0038] In this embodiment, the core objective of the grid-connected dispatch model is to reduce costs and improve operational efficiency. Understandably, the grid-connected objective function of the dispatch model comprehensively considers the fuel cost of fuel cell generators, the operation and maintenance costs of energy storage units, and the electricity purchase and sale costs between the microgrid and the main grid. That is, it minimizes the operating cost of the microgrid within the billing cycle while ensuring power balance within the microgrid, satisfying the constraints of the operating characteristics of each unit in the microgrid and the charging and discharging behavior of the energy storage units. The grid-connected dispatch model enables the microgrid to effectively interact with the main grid in a grid-connected state, maximizing operational benefits and reducing costs.

[0039] Specifically, the preset grid connection constraints may include: Preset power balance constraints: in, Indicates in The predicted power of photovoltaic generators during the time period can be expressed in megawatts (MW). Indicates in The predicted power output of wind turbine generators during a given time period can be expressed in megawatts (MW); both photovoltaic and wind turbine generators are renewable energy generators. Indicates in The total load demand in a microgrid during a given time period can be expressed in megawatts (MW).

[0040] Understandably, under power balance constraints, it is necessary to ensure that the power generation of renewable energy generators and fuel generators, the charging and discharging of energy storage units, the power interaction with the main grid, and the total load demand are balanced within the microgrid.

[0041] Preset operating constraints for fuel generator sets: in, Indicates the first The minimum output power of a fuel generator set can be expressed in megawatts (MW). Indicates the first The maximum output power of a fuel generator set can be expressed in megawatts (MW). Indicates the first Taiwan fuel generator set The output power during a time period can be measured in megawatts (MW). Indicates the first The ramp rate limit power of a fuel generator set, which can be expressed in megawatts (MW). Understandably, the operating constraints of the fuel generator set are used to limit the output power and ramp rate of the fuel generator set, so as to ensure the safe and stable operation of the fuel generator set.

[0042] Preset constraints for energy storage units: in, Indicates the first Group of energy storage units in The state of charge during a time period can be expressed as a percentage (%). Indicates the first Group of energy storage units in The state of charge during a time period can be expressed as a percentage (%). Indicates the first The charging efficiency of a group of energy storage units can be expressed as a percentage (%). Indicates the first The discharge efficiency of a group of energy storage units can be expressed as a percentage (%). Indicates the first The rated capacity of the energy storage unit can be expressed in megawatt-hours (MWh). Indicates the first The minimum state of charge threshold of a group of energy storage units can be expressed as a percentage (%). Indicates the first The maximum state of charge threshold of a group of energy storage units can be expressed as a percentage (%). Indicates the first The minimum charging power threshold for a group of energy storage units can be expressed in megawatts (MW). Indicates the first The maximum charging power threshold of a group of energy storage units can be expressed in megawatts (MW). Indicates the first The minimum discharge power threshold of a group of energy storage units can be expressed in megawatts (MW). Indicates the first The maximum discharge power threshold of a group of energy storage units can be expressed in megawatts (MW). Understandably, the constraints on the energy storage unit can limit the state of charge and charging / discharging power of the energy storage unit, thereby ensuring the safe operation of the energy storage unit and slowing down its aging process, and avoiding performance degradation caused by overcharging / discharging or operation beyond the power limit.

[0043] Preset main power grid interaction constraints: in, This represents the maximum power purchase threshold that a microgrid can acquire from the main grid. Indicates the first The aforementioned renewable energy generator sets, This represents the sum of the renewable energy generator sets. Indicates the first The winning bid power of the renewable energy generator sets, The winning bid power for all renewable energy generator sets.

[0044] Understandably, the energy storage unit constraints are used to limit the maximum interaction power between the microgrid and the main grid, to prevent the microgrid from operating under overload, and to enhance the safety and controllability of the microgrid operation.

[0045] The island scheduling model is pre-defined with an island objective function and island constraints; Specifically, the objective function for the isolated island is: in, This represents the comprehensive index of isolated islands. This represents the fuel efficiency weighting coefficient, preferably. It can take values ​​in the range of 0.6 to 0.8; This represents the weighting coefficient for energy storage losses, preferably... It can take values ​​in the range of 0.2 to 0.4; Indicates the first Group of energy storage units in The remaining capacity for a given time period can be expressed in megawatt-hours (MWh). This represents the power supply reliability weighting coefficient. Indicates in The total load deficit for a given period can be expressed in megawatts (MW).

[0046] In this embodiment, the islanding objective function comprehensively considers fuel efficiency, energy storage loss, and power supply reliability to maximize the islanding comprehensive index and ensure the reliability of microgrid operation. Understandably, the islanding dispatch model primarily focuses on the survivability and power supply reliability of the microgrid, ensuring continuous power supply to critical loads. Under the islanding dispatch model, the microgrid's autonomy is required to a higher degree, especially in optimizing fuel reserves and energy storage strategies. This islanding dispatch model will balance fuel efficiency, energy storage loss, and power supply reliability through dynamic weight adjustments.

[0047] It is understood that the critical load refers to core electrical equipment or areas with extremely high requirements for power supply reliability (such as medical equipment, emergency lighting, communication base stations, etc.). Its characteristic is that power outages may lead to significant losses and it needs to be prioritized in microgrid dispatch. The total load refers to the total power demand of all electrical equipment in the microgrid at the current moment, including critical loads and non-critical loads (such as ordinary commercial / residential electricity), reflecting the overall power supply and demand relationship of the microgrid.

[0048] Specifically, the pre-defined island constraints include: Preset survival constraints: in, Indicates in The demand for critical loads during a given period can be expressed in megawatts (MW). Understandably, the survival constraints are used to ensure that critical loads receive priority power supply in the microgrid under the islanded dispatch model, so as to avoid interruption of critical loads due to insufficient power and avoid major losses.

[0049] Preset fuel reserve constraints: in, The required time for autonomous operation of a microgrid can be expressed in hours (h). Indicates the first The fuel consumption rate of a fuel generator set can be expressed in cubic meters per megawatt-hour (m³ / MWh). Indicates the first Taiwan fuel generator set The output power over a period of time can be measured in megawatts (MW). This indicates the total fuel reserves, and the unit can be cubic meters (m³). Understandably, the fuel reserve constraint is used to ensure that the microgrid can continue to operate under the islanded dispatch model. The fuel reserve must be sufficient to support the time required for the microgrid to operate autonomously, so as to ensure that after the microgrid is disconnected from the main grid, the fuel generator can continue to provide stable power to the critical loads, avoid the microgrid from collapsing due to fuel depletion, and thus improve the reliability and survivability of the microgrid under the islanded dispatch model.

[0050] Preset dynamic adjustment constraint for energy storage loss weighting coefficient: in, Indicates in Energy storage loss weighting coefficient for different time periods The benchmark value representing the weighting coefficient for energy storage losses, preferably, It can take values ​​between 0.2 and 0.4; This indicates that among all groups of energy storage units, in Minimum state of charge during a time period , The state-of-charge threshold representing the safety of an energy storage unit, preferably, You can take 30%.

[0051] Understandably, the dynamic adjustment constraint of the energy storage loss weight coefficient is used to adaptively adjust the energy storage loss weight coefficient according to the state of charge of the energy storage unit, thereby optimizing the energy storage efficiency of the energy storage unit and extending its service life.

[0052] The grid-connected islanding hybrid scheduling model is pre-set with a grid-connected islanding hybrid objective function and grid-connected islanding hybrid constraints.

[0053] Specifically, the objective function for the grid-connected islanding hybrid system includes: in, This indicates the comprehensive index of grid-connected islands. This represents the grid connection cost weighting coefficient, preferably, , Represents the power generation efficiency weighting coefficient, preferably, , The weighting coefficient for energy storage health is preferably represented by the following: .

[0054] In this embodiment, a hybrid grid-connected and islanded dispatch model is proposed by integrating the requirements of grid-connected dispatch and islanded dispatch. In this model, the microgrid can not only smoothly switch between grid-connected and islanded dispatch, but also cope with sudden load fluctuations and energy shortages through a preventative dispatch mechanism. The objective function of the hybrid grid-connected and islanded dispatch model comprehensively considers the cost of grid-connected dispatch, the microgrid's power generation efficiency, and the energy storage health of the energy storage units, ensuring that the various objectives of the hybrid grid-connected and islanded dispatch model can be effectively balanced under different operating scenarios.

[0055] Specifically, the preset constraints for grid-connected islanding hybrid systems include: Preset model switching constraints: in, Represents a maximum constant. Indicates the module flag bit, and , and when When, it indicates that the microgrid uses an islanded scheduling model, when When this occurs, it indicates that the microgrid uses a grid-connected scheduling model.

[0056] Understandably, the model switching constraints can ensure a smooth transition between grid-connected scheduling and islanded scheduling, avoiding transient shocks during switching.

[0057] Pre-set preventive scheduling constraints: in, This indicates the minimum reserved capacity of the energy storage unit. Indicates the safety factor, preferably, It can take values ​​in the range of 1.2 to 1.8. This indicates a preset time window, preferably. Values ​​can be retrieved within 4 to 8 hours.

[0058] In this embodiment, the preventive scheduling constraint can optimize the reserved capacity of energy storage units based on the prediction of critical loads, ensuring that the microgrid can cope with emergencies and improving the stability and reliability of microgrid operation.

[0059] S300. Obtain the current operating data of the microgrid, and select one of the scheduling models as the target scheduling model based on the current operating data. In this embodiment, the current operating data in the microgrid may include the operating data of each unit and the total load in the microgrid, such as total load demand, output power of fuel generators, output power of renewable energy generators, state of charge, input power, and output power of energy storage units. Preferably, the current operating scenario of the microgrid can also be obtained. If the microgrid is currently unable to connect to the main grid, the microgrid can use an islanded scheduling model to guide scheduling; if the microgrid is currently connected to the main grid, it can select one of the scheduling models as the target scheduling model based on the current operating data to guide scheduling, making the microgrid scheduling more reliable and flexible, and improving the operating efficiency of the microgrid.

[0060] S400. Based on the current operating data of the microgrid, the target scheduling model, and the objective function and constraints corresponding to the target scheduling model, obtain the optimal configuration parameters of the microgrid. S500. Configure the microgrid based on the configuration parameters to complete the scheduling of the microgrid.

[0061] In this embodiment, by optimizing the objective function in the target scheduling model while satisfying relevant constraints, the optimal configuration parameters of the microgrid are obtained. The corresponding generating units within the microgrid are then configured according to these optimal parameters to ensure stable and efficient operation of the microgrid, thereby completing the microgrid scheduling. It is understood that the optimal configuration parameters of the microgrid can adjust the output power of fuel cell generators, the charging and discharging power of energy storage units, and the interaction power with the main grid, etc.

[0062] like Figure 3 As shown in the embodiments of this application, a microgrid dispatching system for grid-connected and islanded operation modes is also provided. Optionally, the system includes: The system includes a preset scheduling model module 611, a preset objective function and constraint condition module 612, a selection module 613, a parameter acquisition module 614, and a configuration module 615, wherein: The preset scheduling model module 611 is used to preset a scheduling model, which includes a grid-connected scheduling model, an islanded scheduling model, and a hybrid grid-connected and islanded scheduling model. In this embodiment, the preset scheduling model module 611 can be used to execute... Figure 1 For a detailed description of the preset scheduling model module 611, please refer to the description of step S100 shown.

[0063] The module 612 for presetting objective functions and constraints is used to preset corresponding objective functions and constraints for each scheduling model. In this embodiment, the preset objective function and constraint module 612 can be used to execute... Figure 1 For a detailed description of step S200, the preset objective function and constraint module 612 can be found in the description of step S200.

[0064] The selection module 613 is used to obtain the current operating data of the microgrid and select one of the scheduling models as the target scheduling model 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.

[0065] The parameter acquisition module 614 is used to acquire the optimal configuration parameters of the microgrid based on the current operating data of the microgrid, the target scheduling model, and the objective function and constraints corresponding to the target scheduling model. In this embodiment, the parameter acquisition module 614 can be used to perform... Figure 1 For a detailed description of the parameter acquisition module 614 shown in step S400, please refer to the description of step S400.

[0066] The configuration module 615 is used to configure the microgrid based on the configuration parameters and to complete the scheduling of the microgrid.

[0067] 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.

[0068] 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 micro-grid scheduling method for grid-connected and islanded operation, characterized in that, The method comprises: presetting scheduling models, wherein the scheduling models comprise a grid-connected scheduling model, an island scheduling model and a grid-connected island hybrid scheduling model; presetting corresponding objective functions and constraint conditions for each scheduling model; obtaining current operation data of a microgrid, and selecting one of the scheduling models as a target scheduling model according to the current operation data; obtaining optimal configuration parameters of the microgrid based on the current operation data of the microgrid, the target scheduling model and the corresponding objective function and constraint condition of the target scheduling model; configuring the microgrid based on the configuration parameters to complete scheduling of the microgrid.

2. The method of claim 1, wherein, The presetting of the corresponding objective functions and constraint conditions for each scheduling model comprises: presetting a grid-connected objective function and a grid-connected constraint condition for the grid-connected scheduling model; presetting an island objective function and an island constraint condition for the island scheduling model; presetting a grid-connected island hybrid objective function and a grid-connected island hybrid constraint condition for the grid-connected island hybrid scheduling model.

3. The method of claim 2, wherein, The grid-connected objective function is: wherein, represents the cost of grid-connected operation, represents the total length of microgrid dispatch, represents the th fuel generator unit, represents the set of fuel generator units, represents the th fuel generator unit, represents the th fuel generator unit, represents the output power of the th fuel generator unit in the th time period, represents the set of energy storage units, represents the th energy storage unit, represents the th energy storage unit, represents the operation and maintenance cost coefficient of the th energy storage unit, represents the discharge power of the th energy storage unit in the th time period, represents the charge power of the th energy storage unit in the th time period, represents the duration of a th time period, represents the total number of th time periods, represents the price of electricity purchased by the microgrid from the main grid in the th time period, represents the power of electricity purchased by the microgrid from the main grid in the th time period, represents the price of electricity sold by the microgrid to the main grid in the th time period, represents the power of electricity sold by the microgrid to the main grid in the th time period, represents the total number of billing periods, represents the th billing period, represents the demand charge price of the th billing period, represents the maximum demand in the th billing period.

4. The method of claim 3, wherein, The presetting of the grid-connected constraint condition comprises: presetting a power balance constraint; wherein, represents the predicted power of the photovoltaic power plant at represents the predicted power of the wind power plant at represents the predicted power of the photovoltaic power plant at represents the predicted power of the wind power plant at represents the total load demand in the microgrid at represents the total load demand in the microgrid at presetting a fuel generator set operation constraint; in, Indicates the first The minimum output power of the fuel generator set, Indicates the first The maximum output power of the fuel generator set, Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first The ramp rate limit power of the fuel generator set; presetting a storage unit constraint; wherein, represents the state of charge of the group of energy storage units at the time period, represents the state of charge of the group of energy storage units at the time period, represents the charging efficiency of the group of energy storage units, represents the discharging efficiency of the group of energy storage units, represents the rated capacity of the group of energy storage units, represents the minimum state of charge threshold of the group of energy storage units, represents the maximum state of charge threshold of the group of energy storage units, represents the minimum charging power threshold of the group of energy storage units, represents the maximum charging power threshold of the group of energy storage units, represents the minimum discharging power threshold of the group of energy storage units, represents the maximum discharging power threshold of the group of energy storage units. presetting a main grid interaction constraint; wherein, represents a maximum power purchase threshold of the microgrid from the main grid, represents the i-th renewable energy generator, represents the sum of the renewable energy generators, represents the i-th renewable energy generator, represents the i-th renewable energy generator, is the sum of the winning power of all renewable energy generators.​ 5. The method of claim 2, wherein, The island objective function is: wherein, represents an island comprehensive index, represents a fuel efficiency weight coefficient, represents the th fuel generator set, represents a fuel generator set collection, represents the th fuel generator set output power in the time period, represents the th fuel generator set power generation efficiency, represents an energy storage loss weight coefficient, represents the th energy storage set, represents an energy storage set collection, represents the th energy storage set maximum discharge power threshold, represents the th energy storage set rated capacity, represents the th energy storage set residual capacity in the time period, represents a power supply reliability weight coefficient, represents a total load shortage in the time period.

6. The method of claim 5, wherein, The presetting of the island constraint condition comprises: presetting a survival constraint; wherein, represents the group energy storage units in the discharge power of the energy storage units in the time period, represents the demand of the critical load in the time period. presetting a fuel reserve constraint; wherein, denotes the required time for autonomous operation of the microgrid, denotes the fuel consumption rate of the nth fuel generator unit, denotes the duration of the time period, denotes the output power of the nth fuel generator unit over the time period, denotes the total fuel reserve quantity; presetting a storage unit loss weight coefficient dynamic adjustment constraint; wherein, represents the energy storage loss weight coefficient of the time period, represents the reference value of the energy storage loss weight coefficient; represents the energy storage loss weight coefficient of the time period, represents the minimum state of charge of the time period among all groups of energy storage units, represents the minimum state of charge of the time period among all groups of energy storage units, , represents the state of charge threshold value for the safety of the energy storage unit.

7. The method of claim 2, wherein, The grid-connected island hybrid objective function comprises: wherein, represents a grid-connected island comprehensive index, represents a grid-connected cost weight coefficient, represents the th fuel generator set, represents a fuel generator set collection, represents the th fuel generator set, represents the th fuel generator set, represents the output power of the th fuel generator set in the time period, represents the power generation efficiency of the th fuel generator set, represents a power generation efficiency weight coefficient, represents an energy storage health degree weight coefficient, represents the maximum discharge power threshold of the th energy storage set, represents the rated capacity of the th energy storage set, represents the remaining capacity of the th energy storage set in the time period.

8. The method of claim 7, wherein, The presetting of the grid-connected island hybrid constraint condition comprises: presetting a model switching constraint; wherein, represents the power purchased by the microgrid from the main grid at the time period, represents a maximum constant, represents a module flag bit, and represents that the microgrid uses an island scheduling model when and represents that the microgrid uses a grid-connected scheduling model when ; represents the total load deficit at the time period.

9. The method of claim 7, wherein, The presetting of the grid-connected island hybrid constraint condition comprises: presetting a preventive scheduling constraint. wherein, represents the minimum reserved capacity of the energy storage unit, represents a safety factor, represents a preset time window, represents the demand of the critical load at the time period.

10. A micro-grid dispatching system of grid-connected and islanded operation mode, characterized in that, The system comprises: a scheduling model presetting module configured to preset scheduling models, wherein the scheduling models comprise a grid-connected scheduling model, an island scheduling model and a grid-connected island hybrid scheduling model; an objective function and constraint condition presetting module configured to preset corresponding objective functions and constraint conditions for each scheduling model; a selection module configured to obtain current operation data of a microgrid, and select one of the scheduling models as a target scheduling model according to the current operation data; a parameter obtaining module configured to obtain optimal configuration parameters of the microgrid based on the current operation data of the microgrid, the target scheduling model and the corresponding objective function and constraint condition of the target scheduling model; a configuration module configured to configure the microgrid based on the configuration parameters to complete scheduling of the microgrid.

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