Regional micro-grid-oriented multi-stage scheduling method, device, equipment and medium

Abstract

The invention discloses a regional micro-grid-oriented multi-stage scheduling method, device, equipment and medium, and belongs to the field of electric power systems.The method comprises the steps that a net load set is established, feasible solution judgment is conducted, if it is judged that a solution exists, a robust scheduling model is established with the minimum weighted operation cost, and a robust scheduling model is established; the method comprises the steps of establishing a robust scheduling model based on current operation data, solving the robust scheduling model to obtain an equipment state and a safety range, establishing a rolling scheduling model with minimum operation cost based on the current operation data, solving the rolling scheduling model to obtain a current scheduling result, and finally scheduling each equipment in the regional micro-grid according to the current scheduling result. By implementing the invention, the problem that the scheduling cost is reduced while the scheduling robustness is ensured in the prior art can be solved.

Description

Technical Field

[0001] This invention relates to the field of power systems, and in particular to a multi-stage dispatching method, apparatus, equipment and medium for regional microgrids. Background Technology

[0002] In modern power systems, microgrid technology can promote the deep integration of renewable energy, but the volatility and uncertainty of renewable energy pose significant challenges to the reliable operation of microgrids. Energy storage, as an important asset for mitigating the uncertainty of renewable energy, is developing rapidly. Since the energy storage capacity for renewable energy is typically limited, microgrids with energy storage are usually connected to the grid or equipped with other traditional power generation equipment to ensure power supply security and quality.

[0003] Existing technologies include various microgrid scheduling methods such as chance-constraint, scenario-based, two-stage robust optimization, and interval optimization. Chance-constraint methods use confidence intervals and probability distributions to describe uncertainty, ensuring that the probability of satisfying the constraints is above a certain level. Scenario-based methods simulate uncertainty through multiple scenarios. Two-stage robust optimization methods guarantee the robustness of scheduling results by ensuring feasibility in the worst-case scenario within a pre-defined uncertainty set. Interval optimization uses uncertainty sets and interval variables instead of point variables to solve optimization problems involving uncertainty. However, due to inherent limitations in these methods—chance-constraint cannot guarantee robustness across all scenarios, scenario-based methods are infeasible for uncovered uncertainties, two-stage robust optimization violates unpredictability and is computationally complex, interval optimization faces the curse of dimensionality, and most methods are not fully adapted to energy storage characteristics, even exhibiting problems such as unclear prohibitions on energy storage while charging and discharging, and limitations on energy storage utilization—it is difficult to balance robustness, computational efficiency, and scheduling cost in the optimization solution. Summary of the Invention

[0004] This invention provides a multi-stage scheduling method, apparatus, equipment, and medium for regional microgrids, which can solve the problem of difficulty in ensuring scheduling robustness in the prior art while reducing scheduling costs.

[0005] In a first aspect, embodiments of the present invention provide a multi-stage scheduling method for regional microgrids, including: A net load set is established based on each device in the regional microgrid, and a feasible solution is determined for the multi-stage scheduling of the regional microgrid based on the net load set to obtain the determination result; If the judgment result is that there is a solution, a robust scheduling model is established to minimize the weighted operating cost of each device in the regional microgrid, and the robust scheduling model is solved by a preset solver to obtain the start-stop state, upper limit of energy storage level, lower limit of energy storage level, upper limit of safety range and lower limit of safety range corresponding to each device. The current operating data of each device in the regional microgrid is obtained, and a rolling scheduling model is established based on the start / stop status of each device, the upper limit of the energy storage level, the lower limit of the energy storage level, the upper limit of the safety range, the lower limit of the safety range, and the current operating data to minimize the operating cost of each device in the regional microgrid. The rolling scheduling model is solved by a preset solver to obtain the current scheduling result, and the devices in the regional microgrid are scheduled according to the current scheduling result.

[0006] This application's embodiments first establish a net load set and perform multi-stage scheduling feasibility analysis to proactively assess the feasibility of scheduling schemes, avoiding ineffective scheduling efforts and laying the foundation for subsequent scheduling work. After determining that a solution exists, a robust scheduling model is constructed and solved by minimizing the weighted operating cost of the equipment. This optimizes the operating cost of the regional microgrid from a global perspective, while clarifying the start-up and shutdown status of each device, the energy storage level, and the upper and lower limits of the safety range, thus defining scientific constraints on equipment operation and ensuring scheduling stability. Subsequently, based on the current operating data of the equipment, a rolling scheduling model is established and solved by minimizing the operating cost. This enables dynamic scheduling adjustments based on real-time operating conditions, allowing the scheduling results to accurately adapt to actual operating needs. Ultimately, through multi-stage collaborative scheduling, the economy, stability, and adaptability of the regional microgrid operation are effectively improved, solving the problems of insufficient scheduling feasibility, poor cost control, and difficulty in matching real-time operating conditions that may exist in existing technologies.

[0007] As a preferred example of the first aspect, the establishment of a net load set based on each device in the regional microgrid specifically includes: Obtain equipment data information corresponding to renewable energy output equipment and equipment data information corresponding to load equipment in each of the aforementioned devices; Based on the equipment data information corresponding to the renewable energy output equipment in each of the aforementioned devices, a set of uncertainties for renewable energy output equipment is determined, and based on the equipment data information corresponding to the load equipment in each of the aforementioned devices, a set of uncertainties for load equipment is determined. The net load set is obtained based on the uncertainty set of the renewable energy output equipment and the uncertainty set of the load equipment.

[0008] In this preferred example, by selectively acquiring equipment data information of both renewable energy output devices and load devices in the regional microgrid, and then determining their uncertainty sets based on the data of the two types of devices, the natural fluctuations of renewable energy output and the dynamic changes in load demand can be effectively captured. This breaks the limitations of the idealized assumptions about equipment operating status in traditional net load calculation. Finally, the net load set is obtained by combining the two uncertainty sets, so that the net load set can truly and comprehensively reflect the actual fluctuation characteristics of energy supply and demand in the regional microgrid. This provides data that fits the actual operating conditions for subsequent feasibility solution judgment of multi-stage scheduling and construction of robust scheduling models.

[0009] As a preferred example of the first aspect, the step of determining the feasible solution for the multi-stage scheduling of the regional microgrid based on the net load set and obtaining the determination result is specifically as follows: Based on the net load set and the preset auxiliary function, the output safety range and energy storage level safety range of the load equipment in each of the devices are determined by the reverse derivation method. If all output safety ranges and energy storage level safety ranges are not empty, then the judgment result is output as having a solution; otherwise, it is output as having no solution.

[0010] In this preferred example, the feasible solution determination method combines the net load set with a preset auxiliary function and employs a reverse derivation approach to determine the output safety range of load equipment and the energy storage level safety range. This approach leverages the supply and demand fluctuation characteristics of the regional microgrid reflected by the net load set and uses reverse derivation to deduce the key constraint boundaries of equipment operation from the scheduling objective, ensuring that the determined safety range more closely matches the actual supply and demand conditions of the microgrid. Furthermore, using the fact that all output safety ranges and energy storage level safety ranges are non-empty as the criterion for determining a solution pre-screens scheduling directions that cannot be executed due to constraints conflicting with some equipment, avoiding ineffective investment in subsequent robust scheduling model construction and solving. This improves the efficiency of multi-stage scheduling planning and provides a reliable premise for equipment operation in the subsequent construction and solving of the scheduling model, ensuring the feasibility of the final scheduling scheme in practical implementation.

[0011] As a preferred example of the first aspect, the establishment of a robust scheduling model to minimize the weighted operating cost of each device in the regional microgrid specifically includes: A first objective function is established to minimize the weighted operating cost of each device in the regional microgrid. Obtain the device information corresponding to each device in the regional microgrid, and establish power balance constraints, upper and lower limits of power exchange constraints, safe range constraints of energy storage level, constraints of renewable energy curtailment and output range constraints of thermal power units based on the device information corresponding to each device in the regional microgrid. The robust scheduling model is established based on the first objective function, the power balance constraint, the upper and lower limits of the power exchange constraint, the safe range constraint of the energy storage level, the renewable energy curtailment constraint, and the output range constraint of the thermal power unit.

[0012] In this preferred example, the first objective function is established to minimize the weighted operating cost of each device, which can take into account the differences in device costs, control the overall operating cost of the microgrid from the top level, and avoid resource waste. Then, multi-dimensional constraints are established in combination with device information to ensure the feasibility of scheduling results. Finally, the model is constructed by combining the objective function and these constraints, which can both pursue the optimal cost and resist uncertainties such as supply and demand fluctuations.

[0013] As a preferred example of the first aspect, the establishment of a rolling scheduling model to minimize the operating costs of each device in the regional microgrid specifically includes: A second objective function is established to minimize the operating cost of each device in the regional microgrid; Each constraint condition is established based on the start / stop status of each device, the upper limit of the energy storage level, the lower limit of the energy storage level, the upper limit of the safety range, the lower limit of the safety range, and the current operating data; The rolling scheduling model is established based on the second objective function and each of the constraints.

[0014] In this preferred example, a second objective function is first established to minimize the operating cost of each device, avoiding unnecessary cost consumption. Then, constraints are established by combining the determined start-stop states of the devices, the upper and lower limits of energy storage and safety ranges, and the current operating data. These constraints rely on the scientific planning of robust scheduling in the early stage and are in line with the current actual operating conditions, avoiding disconnection from real-time operation. Finally, a model is constructed by combining the objective function and constraints, so that the output scheduling scheme can not only accurately pursue the optimal cost, but also adapt to the current device status and meet safety requirements, effectively improving the rationality of real-time scheduling and the stability of microgrid operation.

[0015] Secondly, the present invention provides a multi-stage scheduling device for regional microgrids, comprising: a judgment module, a first processing module, a second processing module, and a scheduling module; The judgment module is used to establish a net load set based on each device in the regional microgrid, and to judge the feasibility of the multi-stage scheduling of the regional microgrid based on the net load set, so as to obtain the judgment result. The first processing module is used to establish a robust scheduling model by minimizing the weighted operating cost of each device in the regional microgrid if the judgment result is that there is a solution, and solve the robust scheduling model by a preset solver to obtain the start-stop state, upper limit of energy storage level, lower limit of energy storage level, upper limit of safety range and lower limit of safety range corresponding to each device. The second processing module is used to obtain the current operating data of each device in the regional microgrid, and establish a rolling scheduling model based on the start / stop status of each device, the upper limit of the energy storage level, the lower limit of the energy storage level, the upper limit of the safety range, the lower limit of the safety range, and the current operating data to minimize the operating cost of each device in the regional microgrid. The scheduling module is used to solve the rolling scheduling model through a preset solver to obtain the current scheduling result, and to schedule each device in the regional microgrid according to the current scheduling result.

[0016] As a preferred example of the second aspect, the judgment module includes a first judgment unit, a second judgment unit, and a third judgment unit; The first determination unit is used to obtain equipment data information corresponding to the renewable energy output equipment in each of the devices and equipment data information corresponding to the load equipment in each of the devices; The second judgment unit is used to determine the uncertainty set of renewable energy output equipment based on the equipment data information corresponding to the renewable energy output equipment in each of the devices, and to determine the uncertainty set of load equipment based on the equipment data information corresponding to the load equipment in each of the devices; The third judgment unit is used to obtain the net load set based on the uncertainty set of renewable energy output equipment and the uncertainty set of load equipment.

[0017] As a preferred example of the second aspect, the judgment module further includes a fourth judgment unit and a fifth judgment unit; The fourth judgment unit is used to determine the output safety range and energy storage energy level safety range of the load equipment in each of the devices by using the reverse derivation idea based on the net load set and the preset auxiliary function. The fifth judgment unit is used to output the judgment result as having a solution if all output safety ranges and energy storage level safety ranges are not empty, otherwise it is not having a solution.

[0018] As a preferred example of the second aspect, the establishment of a robust scheduling model to minimize the weighted operating cost of each device in the regional microgrid specifically includes: A first objective function is established to minimize the weighted operating cost of each device in the regional microgrid. Obtain the device information corresponding to each device in the regional microgrid, and establish power balance constraints, upper and lower limits of power exchange constraints, safe range constraints of energy storage level, constraints of renewable energy curtailment and output range constraints of thermal power units based on the device information corresponding to each device in the regional microgrid. The robust scheduling model is established based on the first objective function, the power balance constraint, the upper and lower limits of the power exchange constraint, the safe range constraint of the energy storage level, the renewable energy curtailment constraint, and the output range constraint of the thermal power unit.

[0019] As a preferred example of the second aspect, the establishment of a rolling scheduling model to minimize the operating costs of each device in the regional microgrid specifically includes: A second objective function is established to minimize the operating cost of each device in the regional microgrid; Each constraint condition is established based on the start / stop status of each device, the upper limit of the energy storage level, the lower limit of the energy storage level, the upper limit of the safety range, the lower limit of the safety range, and the current operating data; The rolling scheduling model is established based on the second objective function and each of the constraints.

[0020] In summary, this application's embodiments, by first establishing a net load set and performing multi-stage scheduling feasibility analysis, can proactively assess the feasibility of scheduling schemes, avoid ineffective scheduling efforts, and lay the foundation for subsequent scheduling work. After determining that a solution exists, a robust scheduling model is constructed and solved by minimizing the weighted operating cost of the equipment. This optimizes the operating cost of the regional microgrid from a global perspective, while clarifying the start-up and shutdown status of each device, the energy storage level, and the upper and lower limits of the safety range, thus defining scientific constraints on equipment operation and ensuring scheduling stability. Subsequently, by combining the current operating data of the equipment, a rolling scheduling model is established and solved by minimizing the operating cost. This enables dynamic scheduling adjustments based on real-time operating conditions, allowing the scheduling results to accurately adapt to actual operating needs. Ultimately, through multi-stage collaborative scheduling, the economy, stability, and adaptability of the regional microgrid operation are effectively improved, solving the problems of insufficient scheduling feasibility, poor cost control, and difficulty in matching real-time operating conditions that may exist in existing technologies.

[0021] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the multi-stage scheduling method for regional microgrids of the present invention.

[0022] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform steps of the multi-stage scheduling method for regional microgrids of the present invention. Attached Figure Description

[0023] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating an embodiment of a multi-stage scheduling method for regional microgrids provided by the present invention; Figure 2 This is a module structure diagram of one embodiment of a multi-stage dispatching device for regional microgrids provided by the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0027] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0030] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0031] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0032] Example 1 See Figure 1 To address the challenges of ensuring scheduling robustness and reducing scheduling costs in existing technologies, an embodiment of the present invention provides a multi-stage scheduling method for regional microgrids, comprising: S1. Establish a net load set based on each device in the regional microgrid, and determine the feasibility of the multi-stage scheduling of the regional microgrid based on the net load set to obtain the determination result; In some embodiments of this application, the step of establishing a net load set based on each device in the regional microgrid specifically includes: Obtain equipment data information corresponding to renewable energy output equipment and equipment data information corresponding to load equipment in each of the aforementioned devices; Based on the equipment data information corresponding to the renewable energy output equipment in each of the aforementioned devices, a set of uncertainties for renewable energy output equipment is determined, and based on the equipment data information corresponding to the load equipment in each of the aforementioned devices, a set of uncertainties for load equipment is determined. The net load set is obtained based on the uncertainty set of the renewable energy output equipment and the uncertainty set of the load equipment.

[0033] Specifically, the establishment of a net load set based on each device in the regional microgrid can be implemented through the following preferred methods: First, based on the equipment data information corresponding to the renewable energy output equipment in each of the aforementioned devices, the uncertainty set of the renewable energy output equipment is determined. Taking wind turbines as an example, the actual grid-connectable output of wind turbine k at time t is... Due to the influence of wind speed prediction errors, a conservative prediction range can be determined based on historical prediction data. The specific formula is as follows: Then, to quantify the uncertainty contribution of each unit, a normalized deviation variable is defined. The overall uncertainty level is limited by the meteorological system scale, and usually only some units deviate significantly at the same time. Therefore, the upper limit of the deviation variable should not be higher than [the limit should be specified in the original text]. The relevant formulas are shown below: Next, combining the above formula, we obtain the set of uncertainties in renewable energy output. As shown below: in, Let be the output of wind turbine k at time t. , These are the upper and lower limits for wind turbine units. It is a non-negative real number.

[0034] Next, based on the equipment data information corresponding to the load equipment in each of the aforementioned devices, the uncertainty set of the load equipment is determined, as shown below: in, Let be the value of the load at time t. , These are the upper and lower limits of the load, respectively. It is a non-negative real number.

[0035] Finally, based on the uncertainty set of the renewable energy output equipment and the uncertainty set of the load equipment, the net load set is obtained as follows: in, For the net load at time t, the set of uncertainties in renewable energy output .

[0036] In this way, by using the net load set, representative scenarios including the maximum and minimum net load scenarios can be selected to simulate the uncertainties of trading prices, renewable energy output, and load, so as to ensure the economic efficiency of the dispatch results.

[0037] In some embodiments of this application, the step of determining the feasible solution for multi-stage scheduling of the regional microgrid based on the net load set and obtaining the determination result specifically involves: Based on the net load set and the preset auxiliary function, the output safety range and energy storage level safety range of the load equipment in each of the devices are determined by the reverse derivation method. If all output safety ranges and energy storage level safety ranges are not empty, then the judgment result is output as having a solution; otherwise, it is output as having no solution.

[0038] Specifically, the step of determining the feasible solution for multi-stage scheduling of the regional microgrid based on the net load set and obtaining the determination result can be implemented in the following preferred manner: First, define the auxiliary function, as shown in the following formula: in, and These represent the charging and discharging efficiencies of energy storage, respectively. The length of each time period.

[0039] Then, a feasible solution is determined. Let the output of the i-th thermal power plant at each time be assuming , It can satisfy all constraints of the uncertainty set of renewable energy output equipment and the uncertainty set of load equipment. If the output of the thermal power unit is within a safe range... The safe range for energy storage SOC levels is not empty, so for... All The power generation scheduling problem must have a feasible solution; otherwise, no feasible solution exists.

[0040] The safe range for energy storage is shown in the following formula: in, and These are the lower and upper bounds of the safe range for the energy storage energy level at time t-1, respectively, and are used in the safety range constraints of the energy storage SOC level. and These are the upper limits for energy storage charging power and discharging power, respectively. The time interval is (h). and These are the upper limits of the physical energy storage capacity and the upper limit of the physical energy storage capacity at time t-1, respectively. It is the inverse function of the energy storage-to-power conversion function; and These are the upper and lower limits of the power required for interaction with the main network, respectively. and These represent the upper and lower bounds of the safe output range of the thermal power unit at time t, respectively. and These represent the lower bound of renewable energy output and the upper bound of load demand at time t, respectively.

[0041] S2. If the judgment result is that there is a solution, a robust scheduling model is established to minimize the weighted operating cost of each device in the regional microgrid, and the robust scheduling model is solved by a preset solver to obtain the start-stop state, upper limit of energy storage level, lower limit of energy storage level, upper limit of safety range and lower limit of safety range corresponding to each device. In some embodiments of this application, establishing a robust scheduling model to minimize the weighted operating cost of each device in the regional microgrid specifically involves: A first objective function is established to minimize the weighted operating cost of each device in the regional microgrid. Obtain the device information corresponding to each device in the regional microgrid, and establish power balance constraints, upper and lower limits of power exchange constraints, safe range constraints of energy storage level, constraints of renewable energy curtailment and output range constraints of thermal power units based on the device information corresponding to each device in the regional microgrid. The robust scheduling model is established based on the first objective function, the power balance constraint, the upper and lower limits of the power exchange constraint, the safe range constraint of the energy storage level, the renewable energy curtailment constraint, and the output range constraint of the thermal power unit.

[0042] Specifically, the establishment of a robust scheduling model to maximize the cluster response reliability of the distributed resource clusters in the power grid and minimize the weighted operating cost of the distributed resource clusters in the power grid can be implemented through the following preferred methods: The robust scheduling model includes an objective function and various constraints. The objective function is to maximize the cluster response reliability and minimize the weighted operating cost of the distributed resource cluster.

[0043] Taking thermal power units as an example, the specific constraints of the robust scheduling model are as follows: ① Power balance constraints: in, For the output of the i-th thermal power plant at time t in the s-th scenario, This represents the change in energy storage from time t-1 to time t in the s-th scenario. Since the unit time period is 1 hour, it can be considered as the energy storage output. Let the grid-connected power of the k-th renewable energy source be the power available for connection at time t in the s-th scenario. For the s-th scenario at time t, the amount of electricity wasted by the k-th renewable energy source. For the s-th scenario at time t, the m-th load, Let M be the power purchased at time t in the s-th scenario, and M be the total load. S represents the total scheduling period, and S represents the set of scenarios.

[0044] ② Upper and lower limits of power exchange with the main grid: in, , These represent the upper and lower limits for power exchange with the main network, respectively.

[0045] ③ Constraints on the safe range of energy storage SOC level: in, Let be the energy storage power at time t in the s-th scenario. and These are the upper limit and lower limit of energy storage capacity, respectively.

[0046] ④ Renewable energy curtailment constraints: ⑤ Output range and ramping constraints of thermal power units: in, These represent the upper and lower limits of the output of the i-th thermal power plant at time t in the s-th scenario.

[0047] S3. Obtain the current operating data of each device in the regional microgrid, and establish a rolling scheduling model based on the start / stop status of each device, the upper limit of the energy storage level, the lower limit of the energy storage level, the upper limit of the safety range, the lower limit of the safety range, and the current operating data to minimize the operating cost of each device in the regional microgrid. In some embodiments of this application, establishing a rolling scheduling model to minimize the operating costs of each device in the regional microgrid specifically involves: A second objective function is established to minimize the operating cost of each device in the regional microgrid; Each constraint condition is established based on the start / stop status of each device, the upper limit of the energy storage level, the lower limit of the energy storage level, the upper limit of the safety range, the lower limit of the safety range, and the current operating data; The rolling scheduling model is established based on the second objective function and each of the constraints.

[0048] Specifically, to fully explain the content of step S3 above, the following scheme will be used as an example: Based on the start-up and shutdown decisions of thermal power units obtained from the robust scheduling model described above, as well as the energy storage level and the safe range of thermal power output, a rolling scheduling model is established in the real-time stage. The objective function includes the fuel cost of thermal power units, start-up cost, main grid transaction cost, and load shedding cost.

[0049] In addition to the power balance and main grid exchange power constraints in step S2, the constraints also take into account the internal network structure of the microgrid, reactive power and node phase voltage, such as decoupled linearization power flow constraints, node voltage upper and lower limit constraints, and line transmission capacity limitations.

[0050] At each time t, the decision at time t is made by utilizing the uncertainty of time t and the uncertainty of future times, ensuring that the decision depends only on current and past information and is feasible for the realization of future uncertainties.

[0051] The specific constraints of the rolling scheduling model are as follows: ① Active power flow constraints in, This is the generator-node correlation matrix; The active power output of power source u during time period v; The load of node l during time period v; , These are the real part and the imaginary part of the admittance matrix, respectively, after removing the splitting elements; , These represent the voltage magnitude and phase angle of node j during time period v, respectively. For all power sources, It is a set of nodes.

[0052] ②Reactive power flow constraints in, The reactive power output of power source u during time period v; The reactive load of node l during time period v; This represents the imaginary part of the admittance matrix.

[0053] ③ Power exchange constraints with the main grid in, and These represent the lower and upper limits for power exchange with the main network, respectively.

[0054] ④ Energy storage energy level constraints in, and These represent the lower and upper limits of the safe energy level range for energy storage during time period v, obtained from the day-ahead scheduling model.

[0055] ⑤ Energy storage charging and discharging power constraints in, and These are the rated charging and discharging power of the energy storage, respectively. and These represent the energy levels stored during time periods v and v-1, respectively.

[0056] ⑥ Renewable energy consumption constraints in, Let k be the power of renewable energy consumed during time period v; The actual output of renewable energy k during time period v; It is a collection of renewable energy sources.

[0057] ⑦ Load shedding constraint in, For load sets.

[0058] ⑧ Output constraints of thermal power units in, and These are the lower and upper limits of the safe output range of thermal power unit i during time period v, obtained from the day-ahead dispatch model. This refers to a collection of thermal power units.

[0059] ⑨ Reactive power and power factor constraints in, and These represent the lower and upper limits of the reactive power output of power source u during time period v, respectively. and These are the lower and upper limits of the power factor angle, respectively.

[0060] ⑩ Node voltage constraints in, and These represent the lower and upper limits of the voltage amplitude of node l during time period v, respectively.

[0061] ⑪ Line transmission capacity constraints in, and These are the line parameters; and These represent the lower and upper limits of the transmission capacity of line lj, respectively.

[0062] The above formula achieves a closed loop in rolling decision-making, where historical information constrains current decisions and current decisions ensure future feasibility. At the same time, it ensures minimal operating costs and maximizes cluster response reliability when the response of flexible loads is uncertain.

[0063] S4. Solve the rolling scheduling model using a preset solver to obtain the current scheduling result, and schedule each device in the regional microgrid according to the current scheduling result.

[0064] In summary, this application's embodiments, by first establishing a net load set and performing multi-stage scheduling feasibility analysis, can proactively assess the feasibility of scheduling schemes, avoid ineffective scheduling efforts, and lay the foundation for subsequent scheduling work. After determining that a solution exists, a robust scheduling model is constructed and solved by minimizing the weighted operating cost of the equipment. This optimizes the operating cost of the regional microgrid from a global perspective, while clarifying the start-up and shutdown status of each device, the energy storage level, and the upper and lower limits of the safety range, thus defining scientific constraints on equipment operation and ensuring scheduling stability. Subsequently, by combining the current operating data of the equipment, a rolling scheduling model is established and solved by minimizing the operating cost. This enables dynamic scheduling adjustments based on real-time operating conditions, allowing the scheduling results to accurately adapt to actual operating needs. Ultimately, through multi-stage collaborative scheduling, the economy, stability, and adaptability of the regional microgrid operation are effectively improved, solving the problems of insufficient scheduling feasibility, poor cost control, and difficulty in matching real-time operating conditions that may exist in existing technologies.

[0065] Example 2 like Figure 2 As shown, based on the above method embodiments, corresponding device embodiments are provided; An embodiment of the present invention provides a multi-stage scheduling device for regional microgrids, comprising: a judgment module 21, a first processing module 22, a second processing module 23, and a scheduling module 24; The judgment module 21 is used to establish a net load set based on each device in the regional microgrid, and to judge the feasibility of the multi-stage scheduling of the regional microgrid based on the net load set, so as to obtain the judgment result. The first processing module 22 is used to establish a robust scheduling model by minimizing the weighted operating cost of each device in the regional microgrid if the judgment result is that there is a solution, and solve the robust scheduling model by a preset solver to obtain the start-stop state, upper limit of energy storage level, lower limit of energy storage level, upper limit of safety range and lower limit of safety range corresponding to each device. The second processing module 23 is used to obtain the current operating data of each device in the regional microgrid, and establish a rolling scheduling model based on the start / stop status of each device, the upper limit of the energy storage level, the lower limit of the energy storage level, the upper limit of the safety range, the lower limit of the safety range, and the current operating data to minimize the operating cost of each device in the regional microgrid. The scheduling module 24 is used to solve the rolling scheduling model through a preset solver to obtain the current scheduling result, and to schedule each device in the regional microgrid according to the current scheduling result.

[0066] In some embodiments of this application, the determination module 21 includes a first determination unit, a second determination unit, and a third determination unit; The first determination unit is used to obtain equipment data information corresponding to the renewable energy output equipment in each of the devices and equipment data information corresponding to the load equipment in each of the devices; The second judgment unit is used to determine the uncertainty set of renewable energy output equipment based on the equipment data information corresponding to the renewable energy output equipment in each of the devices, and to determine the uncertainty set of load equipment based on the equipment data information corresponding to the load equipment in each of the devices; The third judgment unit is used to obtain the net load set based on the uncertainty set of renewable energy output equipment and the uncertainty set of load equipment.

[0067] In some embodiments of this application, the determination module 21 further includes a fourth determination unit and a fifth determination unit; The fourth judgment unit is used to determine the output safety range and energy storage energy level safety range of the load equipment in each of the devices by using the reverse derivation idea based on the net load set and the preset auxiliary function. The fifth judgment unit is used to output the judgment result as having a solution if all output safety ranges and energy storage level safety ranges are not empty, otherwise it is not having a solution.

[0068] In some embodiments of this application, establishing a robust scheduling model to minimize the weighted operating cost of each device in the regional microgrid specifically involves: A first objective function is established to minimize the weighted operating cost of each device in the regional microgrid. Obtain the device information corresponding to each device in the regional microgrid, and establish power balance constraints, upper and lower limits of power exchange constraints, safe range constraints of energy storage level, constraints of renewable energy curtailment and output range constraints of thermal power units based on the device information corresponding to each device in the regional microgrid. The robust scheduling model is established based on the first objective function, the power balance constraint, the upper and lower limits of the power exchange constraint, the safe range constraint of the energy storage level, the renewable energy curtailment constraint, and the output range constraint of the thermal power unit.

[0069] In some embodiments of this application, establishing a rolling scheduling model to minimize the operating costs of each device in the regional microgrid specifically involves: A second objective function is established to minimize the operating cost of each device in the regional microgrid; Each constraint condition is established based on the start / stop status of each device, the upper limit of the energy storage level, the lower limit of the energy storage level, the upper limit of the safety range, the lower limit of the safety range, and the current operating data; The rolling scheduling model is established based on the second objective function and each of the constraints.

[0070] For more detailed steps and working principles of this embodiment, please refer to the relevant description in Embodiment 1, but not limited to these descriptions.

[0071] In summary, this application's embodiments, by first establishing a net load set and performing multi-stage scheduling feasibility analysis, can proactively assess the feasibility of scheduling schemes, avoid ineffective scheduling efforts, and lay the foundation for subsequent scheduling work. After determining that a solution exists, a robust scheduling model is constructed and solved by minimizing the weighted operating cost of the equipment. This optimizes the operating cost of the regional microgrid from a global perspective, while clarifying the start-up and shutdown status of each device, the energy storage level, and the upper and lower limits of the safety range, thus defining scientific constraints on equipment operation and ensuring scheduling stability. Subsequently, by combining the current operating data of the equipment, a rolling scheduling model is established and solved by minimizing the operating cost. This enables dynamic scheduling adjustments based on real-time operating conditions, allowing the scheduling results to accurately adapt to actual operating needs. Ultimately, through multi-stage collaborative scheduling, the economy, stability, and adaptability of the regional microgrid operation are effectively improved, solving the problems of insufficient scheduling feasibility, poor cost control, and difficulty in matching real-time operating conditions that may exist in existing technologies.

[0072] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can realize the multi-stage scheduling method for regional microgrids provided by any of the above-described method embodiments of the present invention.

[0073] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0074] Based on the above embodiments of the multi-stage scheduling method for regional microgrids, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the multi-stage scheduling method for regional microgrids of any embodiment of the present invention.

[0075] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0076] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0077] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0078] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the multi-stage scheduling method for regional microgrids described in any of the above-described method embodiments of the present invention.

[0079] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0080] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A multi-stage scheduling method for regional microgrid, characterized in that, The method comprises the following steps: According to the establishment of the net load set of each device in the regional micro-grid, the feasibility of the multi-stage scheduling of the regional micro-grid is judged according to the net load set, and a judgment result is obtained; If the judgment result is a solution, a robust scheduling model is established by minimizing the weighted operation cost of each device in the regional micro-grid, and the robust scheduling model is solved by a preset solver to obtain the corresponding start-stop state, upper limit of energy level, lower limit of energy level, upper limit of safety range and lower limit of safety range of each device; The current operation data of each device in the regional micro-grid is obtained, and a rolling scheduling model is established by minimizing the operation cost of each device in the regional micro-grid according to the corresponding start-stop state, upper limit of energy level, lower limit of energy level, upper limit of safety range, lower limit of safety range and current operation data of each device; The rolling scheduling model is solved by a preset solver to obtain a current scheduling result, and each device in the regional micro-grid is scheduled according to the current scheduling result.

2. The multi-stage scheduling method for regional microgrid of claim 1, wherein, The net load set is established according to each device in the regional micro-grid, and the specific method is as follows: The device data information corresponding to the renewable energy output device in each device and the device data information corresponding to the load device in each device are obtained; According to the device data information corresponding to the renewable energy output device in each device, a renewable energy output device uncertainty set is determined, and according to the device data information corresponding to the load device in each device, a load device uncertainty set is determined; According to the renewable energy output device uncertainty set and the load device uncertainty set, the net load set is obtained.

3. The multi-stage scheduling method for regional microgrid of claim 2, wherein, The feasibility of the multi-stage scheduling of the regional micro-grid is judged according to the net load set, and a judgment result is obtained, and the specific method is as follows: According to the net load set and a preset auxiliary function, the output safety range of the load device in each device and the energy level safety range of the energy storage are determined by adopting the reverse deduction idea; If all the output safety ranges and the energy level safety ranges are not empty, the judgment result is a solution, otherwise it is no solution.

4. The method of claim 1, wherein, The robust scheduling model is established by minimizing the weighted operation cost of each device in the regional micro-grid, and the specific method is as follows: A first objective function is established by minimizing the weighted operation cost of each device in the regional micro-grid; The device information corresponding to each device in the regional micro-grid is obtained, and power balance constraints, power exchange upper and lower limit constraints, energy level safety range constraints, renewable energy curtailment constraints and thermal power unit output range constraints are established according to the device information corresponding to each device in the regional micro-grid; The robust scheduling model is established according to the first objective function, the power balance constraints, the power exchange upper and lower limit constraints, the energy level safety range constraints, the renewable energy curtailment constraints and the thermal power unit output range constraints.

5. The method of claim 1, wherein, The rolling scheduling model is established by minimizing the operation cost of each device in the regional micro-grid, and the specific method is as follows: establishing a second objective function for minimizing operation cost of each device in the regional micro-grid; establishing each constraint condition according to the start-stop state corresponding to each device, the upper limit of the energy level of the energy storage, the lower limit of the energy level of the energy storage, the upper limit of the safety range, the lower limit of the safety range and current operation data; establishing the rolling scheduling model according to the second objective function and each constraint condition.

6. A multi-stage scheduling device for regional microgrids, characterized in that, comprise: a judgment module, a first processing module, a second processing module and a scheduling module; the judgment module is configured to establish a net load set according to each device in the regional micro-grid, and to make a feasible solution judgment on multi-stage scheduling of the regional micro-grid according to the net load set, to obtain a judgment result; the first processing module is configured to, if the judgment result is a solution, establish a robust scheduling model for minimizing weighted operation cost of each device in the regional micro-grid, and to solve the robust scheduling model through a preset solver to obtain the start-stop state corresponding to each device, the upper limit of the energy level of the energy storage, the lower limit of the energy level of the energy storage, the upper limit of the safety range and the lower limit of the safety range; the second processing module is configured to obtain current operation data of each device in the regional micro-grid, and to establish a rolling scheduling model for minimizing operation cost of each device in the regional micro-grid according to the start-stop state corresponding to each device, the upper limit of the energy level of the energy storage, the lower limit of the energy level of the energy storage, the upper limit of the safety range, the lower limit of the safety range and the current operation data; the scheduling module is configured to solve the rolling scheduling model through a preset solver to obtain a current scheduling result, and to schedule each device in the regional micro-grid according to the current scheduling result.

7. The apparatus for multi-stage scheduling of regional microgrid of claim 6, wherein, the judgment module comprises a first judgment unit, a second judgment unit and a third judgment unit; the first judgment unit is configured to obtain device data information corresponding to a renewable energy output device among each device and device data information corresponding to a load device among each device; the second judgment unit is configured to determine a renewable energy output device uncertainty set according to the device data information corresponding to the renewable energy output device among each device, and to determine a load device uncertainty set according to the device data information corresponding to the load device among each device; the third judgment unit is configured to obtain the net load set according to the renewable energy output device uncertainty set and the load device uncertainty set.

8. The apparatus for multi-stage scheduling of regional microgrid of claim 7, wherein, the judgment module further comprises a fourth judgment unit and a fifth judgment unit; the fourth judgment unit is configured to determine an output safety range corresponding to the load device among each device and an energy level safety range of the energy storage according to the net load set and a preset auxiliary function by using a reverse deduction idea; the fifth judgment unit is configured to output the judgment result as a solution if all the output safety ranges and the energy level safety ranges of the energy storage are not empty, and as no solution otherwise.

9. A terminal device, comprising: The computer readable storage medium stores a computer program, and the computer program is configured to be executed by the processor, and the processor executes the computer program to implement the multi-stage scheduling method for regional micro-grid according to any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is configured to be executed by the processor, and the processor executes the computer program to implement the multi-stage scheduling method for regional micro-grid according to any one of claims 1-5. The computer readable storage medium stores a computer program, and the computer program is configured to be executed by the processor, and the processor executes the computer program to implement the multi-stage scheduling method for regional micro-grid according to any one of claims 1-5.

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