A low-communication-dependence microgrid multi-objective optimization scheduling method and system

Abstract

This invention provides a multi-objective optimization scheduling method and system for microgrids with low communication dependence. It belongs to the field of microgrid optimization scheduling technology. The method is executed by the local controller during the daytime operation phase according to a preset scheduling cycle. The method includes: acquiring real-time photovoltaic power data and real-time load demand data; calculating the matching cost of each candidate scheduling strategy in the strategy library under the current operating state of the microgrid based on the real-time photovoltaic power data and real-time load demand data; wherein the strategy library is a set of candidate scheduling strategies generated by the cloud optimization layer and distributed to the local controller before the daytime operation; selecting the scheduling strategy with the minimum matching cost from the candidate scheduling strategies and outputting the power control command corresponding to this scheduling strategy. This method can achieve autonomous operation and continuous scheduling of the microgrid under conditions of limited or unstable communication, while meeting energy storage safety constraints and balancing operational economy, photovoltaic absorption capacity, and load demand satisfaction levels.

Description

Technical Field

[0001] This invention belongs to the field of microgrid optimization scheduling technology, and in particular relates to a multi-objective optimization scheduling method and system for microgrids with low communication dependence. Background Technology

[0002] With the advancement of dual-carbon goals, the penetration rate of distributed renewable energy, represented by photovoltaic and wind power, in microgrids continues to increase. As the core unit coordinating the operation of power sources, grid, loads, and storage, the microgrid energy management system plays a crucial role in ensuring safety and stability, improving economic efficiency, and promoting the consumption of new energy sources.

[0003] Meanwhile, microgrids exhibit significant diversity and time-varying characteristics in their operating components: the output of renewable energy sources such as photovoltaics and wind power is affected by weather conditions, resulting in fluctuations and randomness; energy storage systems are constrained by factors such as state of charge, charging and discharging power, and efficiency; adjustable loads face boundary limitations in terms of response amplitude, duration, and user comfort; and the electricity purchase and sale behavior of grid connection points must also meet electricity pricing mechanisms and grid connection constraints. Therefore, microgrid energy management typically requires balancing multiple objectives, such as net operating costs, renewable energy absorption levels, and the degree to which load demand is met, and forming feasible dispatch strategies under multi-time period and multi-constraint coupling conditions. This places higher demands on data integrity, model accuracy, and the robustness of dispatch strategies in practical engineering.

[0004] Existing microgrid energy management often employs a centralized control architecture: a cloud-based master station or central controller collects operational data and forecast information from distributed units via communication networks, performs global optimization, and then issues scheduling commands to local execution units. While this model achieves good control performance under ideal communication conditions, in practical engineering, communication networks are often affected by bandwidth limitations, signal interference, network congestion, and even physical outages, making it difficult to sustain high-frequency data interaction and command issuance over long periods. This makes it difficult for local execution units to continuously schedule operations based on changes in operational status, resulting in insufficient continuity and feasibility of scheduling decisions. Especially in scenarios with significant fluctuations in renewable energy output, deviations between the scheduling plan and actual output or load demand can easily lead to accumulated operational deviations and scheduling mismatches. These manifest as mismatches between energy storage charging and discharging arrangements and actual supply and demand, widening discrepancies between power purchase and sales, and increased curtailment of solar power, all of which affect the overall reliability and stability of the microgrid. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-objective optimization scheduling method and system for microgrids with low communication dependence, which can improve the continuity and feasibility of microgrid scheduling decisions, improve the operational deviation and scheduling mismatch caused by fluctuations in renewable energy output, and enhance operational reliability in complex communication environments, thereby improving the overall operational reliability and stability of the microgrid.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a multi-objective optimization scheduling method for microgrids with low communication dependence. The method is executed by a local controller during the daytime operation phase according to a preset scheduling cycle, and includes: Obtain real-time photovoltaic power data and real-time load demand data of the microgrid; Based on real-time photovoltaic power data and real-time load demand data, the matching cost of each candidate scheduling strategy in the strategy library is calculated under the current operating state of the microgrid. The strategy library is a set of candidate scheduling strategies generated offline by the cloud optimization layer based on the uncertainty of photovoltaic output and the local operating constraints of the microgrid. The strategy library is distributed to the local controller by the cloud optimization layer a day-ahead. Select the scheduling strategy with the lowest matching cost from the candidate scheduling strategies, and output the power control command corresponding to the scheduling strategy.

[0007] Furthermore, the low-communication-dependent microgrid multi-objective optimization scheduling method also includes a policy library generation process executed by the cloud optimization layer, which includes: Acquire photovoltaic power output forecast data and microgrid local operation constraint data; A set of uncertain photovoltaic output scenarios is constructed based on photovoltaic output prediction data and prediction errors; Based on the uncertainty scenario set of photovoltaic output and the local operation constraints of microgrid, a multi-objective optimization model is constructed with the optimization objectives of minimizing the net operating cost of microgrid, minimizing the flexible load non-fulfillment rate, and minimizing the photovoltaic curtailment rate. Solve the multi-objective optimization model to obtain a policy library consisting of multiple sets of candidate scheduling policies.

[0008] Furthermore, the objective function of the multi-objective optimization model includes: The objective function for minimizing the net operating cost of a microgrid is expressed by the following formula:

[0009] in, and These are the power purchased from the grid and the power sold to the grid, respectively. and These are the time-of-use electricity purchase price and the electricity sales price, For the charging and discharging power of the energy storage system, This refers to the aging and depreciation coefficient of the energy storage battery. To optimize the total number of time periods within the time domain; The objective function for minimizing the flexible load non-fulfillment rate is expressed by the following formula:

[0010] in, To meet the actual flexible load power requirements, Power required for flexible loads, To optimize the total number of time periods within the time domain; The objective function for minimizing photovoltaic curtailment rate is expressed by the following formula:

[0011] in, For photovoltaic power that cannot be absorbed, To contribute to actual photovoltaic power, To optimize the total number of time periods within the time domain.

[0012] Furthermore, the constraints of the multi-objective optimization model include: The state of charge constraint is expressed by the following formula:

[0013] in, For time period The state of charge, and These are the lower and upper limits of the state of charge, respectively; The charge / discharge power constraint is expressed by the following formula:

[0014] in, For energy storage systems during time periods The charging and discharging power, Indicates discharge, Indicates charging. For time period The upper limit of discharge power, For time period The lower limit of charging power; and They are determined using the following formulas respectively:

[0015]

[0016] in, The rated power of the energy storage system, For the rated capacity of the energy storage system, For charging and discharging efficiency, For time step; The energy state recursion constraint is expressed by the following formula:

[0017] in, Determined by the following formula:

[0018] in, This indicates the state of charge for the next time period.

[0019] Furthermore, solving the multi-objective optimization model includes: The intensity Pareto evolutionary algorithm 2 is used to solve the multi-objective optimization model offline, and the Pareto non-dominated solution set is obtained. Each non-dominated solution in the Pareto non-dominated solution set corresponds to a set of candidate scheduling strategies. The candidate scheduling strategies include at least the value sequence of the charging and discharging power of the energy storage system in each time period in the optimization time domain. The multiple sets of non-dominated solutions in the Pareto non-dominated solution set are used as a set of candidate scheduling strategies to form a strategy library.

[0020] Furthermore, the steps for obtaining the photovoltaic output uncertainty scenario set include: The photovoltaic output scenario generation constraints are expressed by the following formula:

[0021] in, For the first Simulated photovoltaic power in various scenarios This represents the current day's predicted photovoltaic power output. A random error factor following a specific distribution is used to simulate prediction bias when communication updates are delayed. For the prediction period index, Number the scene; Number the same scene Optimize each time period within the time domain Generated photovoltaic simulated power values This constitutes a set of photovoltaic power output scenarios, and the different scenarios are numbered. The corresponding multiple photovoltaic output scenarios constitute a photovoltaic output uncertainty scenario set.

[0022] Furthermore, the calculation of the matching cost includes: at time... For the first in the strategy library The strategy first calculates the system's internal net power using the following formula. The gap between basic supply and demand :

[0023]

[0024] in, This refers to the real-time collected photovoltaic power. For strategy energy storage capacity, For strategy Internal net power, For strategy The basic supply and demand gap, For real-time base load; Based on the supply and demand gap Based on the local power balance logic, the unmet power of grid interaction and flexible load is determined, where: when At that time, the strategy is determined by the following formula. Power purchased by the power grid Flexible loads do not meet power requirements and the power sold by the power grid :

[0025]

[0026]

[0027] when At that time, the strategy is determined by the following formula. The flexible load did not meet the power requirements. Power sold by the power grid and the power purchased by the power grid :

[0028]

[0029]

[0030] The strategy is calculated using the following formula. At any moment Instant matching cost :

[0031] in, Power required for flexible loads, This indicates the off-peak electricity pricing period; 1 indicates off-peak hours, and 0 indicates otherwise. and These are the real-time electricity purchase price and the electricity sales price. The user satisfaction penalty weighting coefficient.

[0032] In a second aspect, the present invention provides a microgrid multi-objective optimization scheduling system with low communication dependency, comprising: a local controller and a cloud optimization layer; The cloud optimization layer is used to generate a strategy library offline before the day and distribute the strategy library to the local controller for use by the local controller during the daytime operation phase. The strategy library is a set of candidate scheduling strategies generated by the cloud optimization layer based on the uncertainty of photovoltaic output and the local operation constraints of the microgrid. The local controller includes: a data acquisition module, a matching module, and a policy output module; The data acquisition module is used to acquire real-time photovoltaic power data and real-time load demand data of the microgrid; The matching module is used to calculate the matching cost of each candidate scheduling strategy in the strategy library under the current operating state of the microgrid, based on real-time photovoltaic power data and real-time load demand data. The strategy output module is used to select the scheduling strategy with the lowest matching cost from the candidate scheduling strategies and output the power control command corresponding to the scheduling strategy.

[0033] In a third aspect, the present invention provides an electronic device including a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement a microgrid multi-objective optimization scheduling method with low communication dependency.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements a microgrid multi-objective optimization scheduling method with low communication dependency.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The microgrid multi-objective optimization scheduling method with low communication dependence provided by this invention divides the energy management process into two stages: cloud-based offline optimization and local online scheduling. This enables the microgrid to maintain continuous and stable autonomous operation even under non-ideal communication conditions such as limited communication, delays, or short-term interruptions. Compared to traditional centralized scheduling methods that rely on high-frequency real-time communication during the day, this invention's method allows the local controller to independently complete scheduling decisions and power command outputs based on real-time measurement data when the cloud is unreachable or forecast information cannot be updated in a timely manner, thus avoiding scheduling mismatch problems. While meeting the safety constraints of energy storage operation, it achieves comprehensive coordination of multiple objectives such as operational economy, load demand satisfaction, and renewable energy absorption levels. This improves the microgrid's adaptability to renewable energy output fluctuations and its operational reliability in complex communication environments at the system level, demonstrating strong engineering applicability.

[0036] 2. This invention constructs a candidate scheduling strategy set covering various operating conditions of photovoltaic output and load changes during the offline phase and pre-installs this strategy set in the local controller. This method transforms the complex optimization calculations that would otherwise need to be completed during operation into a matching and selection process within a finite strategy set, thereby significantly reducing the computational complexity of online scheduling. This approach allows the local controller to complete scheduling responses within minutes or even shorter timescales without requiring high-performance computing power, making it suitable for edge device deployments with limited computing power. Furthermore, since the candidate strategies fully consider various uncertainties during the offline phase, the local scheduling process possesses stronger determinism and feasibility, effectively reducing the risk of scheduling instability caused by prediction errors or sudden changes in operating conditions, and improving the overall robustness of the microgrid operation.

[0037] 3. The method of this invention distinguishes between base load and flexible load during the scheduling process. While meeting the rigid requirements of the base load, it coordinates the scheduling of flexible load demands, achieving a reasonable trade-off between system operational economy and user-side energy experience. Simultaneously, by continuously constraining the state of charge and charging / discharging power of the energy storage system during scheduling, it ensures that energy storage operation remains within a safe range, preventing adverse conditions such as overcharging and over-discharging. In practical applications, this method can improve the overall satisfaction level of flexible loads and promote the local consumption of new energy sources while ensuring the safe operation of energy storage, thereby achieving a comprehensive improvement in the safety, economy, and flexibility of the microgrid and enhancing the system's long-term operational capability in real-world engineering environments.

[0038] The microgrid multi-objective optimization scheduling system, electronic device, and computer-readable storage medium provided by this invention also solve the problems raised in the background section. Attached Figure Description

[0039] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of a microgrid multi-objective optimization scheduling method with low communication dependency according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the specific process of the low communication dependency microgrid multi-objective optimization scheduling method according to an embodiment of the present invention; Figure 3 This is a schematic diagram showing the distribution of photovoltaic predicted output and actual output in multiple scenarios according to an embodiment of the present invention; Figure 4 This is a schematic diagram comparing the predicted and actual values ​​of the basic load and flexible load in an embodiment of the present invention. Figure 5This is a schematic diagram comparing the planned power output of energy storage and the power grid before and after offline optimization in an embodiment of the present invention; Figure 6 This is a schematic diagram of the optimized energy storage candidate scheduling strategy set according to an embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the online operation results of energy storage and grid output under the conventional method in an embodiment of the present invention. Figure 8 This is a schematic diagram illustrating the online matching operation results of energy storage and grid output using the method of this invention in an embodiment of the invention; Figure 9 This is a schematic diagram illustrating the state of charge changes of the energy storage system according to an embodiment of the present invention; Figure 10 This is a schematic diagram of a microgrid multi-objective optimization scheduling system with low communication dependence according to an embodiment of the present invention; Figure 11 This is a schematic diagram of a microgrid system according to an embodiment of the present invention; Figure 12 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0040] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0041] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0042] Example 1 This invention provides a low-communication-dependent multi-objective optimization scheduling method for microgrids, employing a two-layer architecture combining offline optimization and online matching. The cloud-based optimization layer generates a strategy library offline during the day-ahead phase and distributes it to the local controller. During the day-ahead operation phase, the local controller independently completes strategy matching and power control command output according to a preset scheduling cycle. Therefore, even when communication delays or interruptions cause prediction update lags, the microgrid's economic efficiency, load satisfaction, and photovoltaic (PV) integration level can still be maintained under local closed-loop conditions.

[0043] like Figure 1 and Figure 2 As shown, the low communication dependency microgrid multi-objective optimization scheduling method provided by the present invention includes steps S1 to S3.

[0044] In step S1, real-time photovoltaic power data and real-time load demand data of the microgrid are acquired. Specifically, the local controller performs data acquisition according to a preset scheduling cycle during the daytime operation phase, at each moment... Obtain real-time photovoltaic power data And obtain real-time load demand data, which includes at least the base load power. and the power demand of flexible loads In this embodiment, the scheduling cycle can be updated on a minute-by-minute basis to adapt to fluctuations in photovoltaic output and load changes. Simultaneously, the local controller can also acquire time-of-use electricity price parameters (purchase price and sales price) and off-peak period indicators for subsequent matching cost calculations.

[0045] In step S2, based on real-time photovoltaic power data and real-time load demand data, the matching cost corresponding to each candidate scheduling strategy in the strategy library under the current operating state of the microgrid is calculated. The strategy library is a set of candidate scheduling strategies generated offline by the cloud optimization layer based on photovoltaic output uncertainty and local microgrid operating constraints. The strategy library is distributed from the cloud optimization layer to the local controller a day-ahead.

[0046] To provide the source and basis for the aforementioned strategy library, the cloud optimization layer first acquires photovoltaic power output forecast data and microgrid local operation constraint data in the day-ahead phase, and constructs a set of photovoltaic power output uncertainty scenarios based on the photovoltaic power output forecast data and forecast errors.

[0047] The process of generating a scene set includes at least the following steps: generating the first scene set using the formula below. The scene in time period Simulated photovoltaic power:

[0048] in, For the first Simulated photovoltaic power in various scenarios For photovoltaic power prediction, A random error factor that follows a specific distribution is used to simulate the prediction bias when communication updates are delayed. For the prediction period index, Assign scene numbers. The cloud optimization layer can assign the same scene number. Optimize each time period within the time domain The generated photovoltaic simulated power values ​​form a sequence for this scenario, thus obtaining a set of photovoltaic output uncertainty scenarios covering multiple scenarios.

[0049] After constructing the scenario set, the cloud optimization layer further establishes a multi-objective optimization model and uses the local operation constraints of the microgrid as the model constraints.

[0050] The constraints must include at least: The state of charge constraint is expressed by the following formula:

[0051] in, For time period The state of charge, and These are the upper and lower limits for the state of charge, respectively.

[0052] The charge / discharge power constraint is expressed by the following formula:

[0053] in, For energy storage systems during time periods The charging and discharging power, Indicates discharge, Indicates charging. For time period The upper limit of discharge power, For time period The lower limit of charging power.

[0054] and They are determined using the following formulas respectively:

[0055]

[0056] in, The rated power of the energy storage system, For the rated capacity of the energy storage system, For charging and discharging efficiency, For time step.

[0057] The energy state recursion constraint is expressed by the following formula:

[0058] in, Determined by the following formula:

[0059] in, This indicates the state of charge for the next time period.

[0060] Based on the above scenario set and constraints, the cloud optimization layer constructs a multi-objective optimization model with the optimization objectives of minimizing net operating cost, minimizing flexible load non-fulfillment rate, and minimizing photovoltaic curtailment rate.

[0061] The objective function of a multi-objective optimization model should at least include: The objective function for minimizing the net operating cost of a microgrid is expressed by the following formula:

[0062] in, and These are the power purchased from the grid and the power sold to the grid, respectively. and These are the time-of-use electricity purchase price and the electricity sales price, For the charging and discharging power of the energy storage system, This refers to the aging and depreciation coefficient of the energy storage battery. To optimize the total number of time periods within the time domain.

[0063] The objective function of the multi-objective optimization model also includes the objective function of minimizing the flexible load non-fulfillment rate, expressed by the following formula:

[0064] in, For photovoltaic power that cannot be absorbed, To contribute to actual photovoltaic power, To optimize the total number of time periods within the time domain.

[0065] The objective function of the multi-objective optimization model also includes the objective function for minimizing the photovoltaic curtailment rate, expressed by the following formula:

[0066] in, For photovoltaic power that cannot be absorbed, To contribute to actual photovoltaic power, To optimize the total number of time periods within the time domain.

[0067] The cloud-based optimization layer solves the multi-objective optimization model to obtain a policy library consisting of multiple sets of candidate scheduling strategies. The solution process includes at least using the Intensity Pareto Evolutionary Algorithm 2 (SPEA2) to solve the multi-objective optimization model, obtaining a Pareto non-dominated solution set, and extracting multiple sets of candidate scheduling strategies from the Pareto non-dominated solution set to form the policy library. Each candidate scheduling strategy must include at least one strategy from each time period within the optimization time domain. The sequence of charging and discharging power values ​​for the energy storage system. Therefore, after the strategy library is distributed to the local controller once a day, the local controller can perform online matching and selection from the strategy library based on real-time measurements during the day's operation without relying on real-time communication with the cloud.

[0068] Returning to step S2, the local controller at the current moment For each candidate scheduling policy in the policy library Read the energy storage power corresponding to the strategy at that moment. This is then combined with real-time photovoltaic power and base load to calculate the internal net power and the base supply-demand difference. Specifically, the internal net power of the system is first calculated using the following formula. The gap between basic supply and demand :

[0069]

[0070] in, This refers to the real-time collected photovoltaic power. For strategy energy storage capacity, For strategy Internal net power, For strategy The basic supply and demand gap, This is the real-time base load.

[0071] Based on the supply and demand gap Based on the local power balance logic, the unmet power of grid interaction and flexible load is determined, where: when At that time, the strategy is determined by the following formula. Power purchased by the power grid Flexible loads do not meet power requirements and the power sold by the power grid :

[0072]

[0073]

[0074] when At that time, the strategy is determined by the following formula. The flexible load did not meet the power requirements. Power sold by the power grid and the power purchased by the power grid :

[0075]

[0076]

[0077] The strategy is calculated using the following formula. At any moment Instant matching cost :

[0078] in, Power required for flexible loads, This indicates the off-peak electricity pricing period; 1 indicates off-peak hours, and 0 indicates otherwise. and These are the real-time electricity purchase price and the electricity sales price. The user satisfaction penalty weight coefficient is used to include the impact of unmet flexible load in the matching cost, so that the online strategy selection can take into account both economic cost and user experience.

[0079] By local side To standardize the online matching of candidate strategies, enabling real-time measurement ( Load demand, etc., can directly influence strategy selection without changing the strategy library structure, thus absorbing the uncertainty of inaccurate forecasts / lagging communication updates into the question of which strategy is more suitable. Simultaneously, matching costs couple the electricity purchase and sale cost item with the penalty item for unmet flexible load requirements, enabling an adjustable trade-off between economic efficiency and satisfaction (by...). This reflects the need to avoid either a deterioration in user experience due to solely pursuing cost reduction or a significant increase in operating costs due to solely pursuing satisfaction.

[0080] In step S3, the scheduling strategy with the lowest matching cost is selected from the candidate scheduling strategies, and the power control command corresponding to this scheduling strategy is output. The local controller at time... For each candidate scheduling policy in the policy library Calculate the corresponding instant matching cost Then, the candidate scheduling strategy with the lowest matching cost is selected as the current execution strategy, and the power control command corresponding to this strategy is output to indicate the charging and discharging power value of the energy storage system at that moment (i.e., the output of the selected strategy's power control command). The local controller at a subsequent time. Continue to repeat steps S1 to S3 according to the scheduling cycle to form a continuous local closed-loop control process during the daytime operation phase, and satisfy the aforementioned state of charge constraints, charging and discharging power constraints and energy state recursion constraints throughout the process.

[0081] Step S3 uses the minimum matching cost strategy as the online decision criterion, eliminating the need for the local controller to perform complex online multi-objective optimization and re-solution. It can output power commands simply by comparing costs, thus enabling stable operation within minutes or even shorter periods. Simultaneously, since candidate strategies are generated offline in the cloud based on multiple scenarios, multiple objectives, and local constraints, and have embedded feasibility boundaries (such as SOC and power boundaries), the local output commands are naturally constrained and protected, reducing the risk of infeasible scheduling due to field noise, communication gaps, or prediction biases. This enhances the system's feasibility and security in real-world engineering environments.

[0082] In an exemplary parameter configuration, the microgrid system parameters may include: rated photovoltaic power. The rated energy storage capacity is 35kW. The rated energy storage capacity is 500kW. For 500kWh, initially It is 40%. 10%, The charge / discharge efficiency is 90%. The success rate is 95%. Under this configuration, the cloud optimization layer constructs a multi-scenario set based on the photovoltaic prediction curve and its error, and uses SPEA2 to obtain a strategy library containing multiple candidate scheduling strategies. The local controller continuously collects real-time photovoltaic power and load data during the day and calculates online according to the above steps. Select the minimum cost strategy and output the result. This enables local adaptive scheduling of photovoltaic fluctuations and load changes.

[0083] like Figure 3 As shown, this figure illustrates the predicted power output and actual power output distribution of microgrid photovoltaic units under exemplary parameter configurations in various scenarios. This figure serves to explain the process of modeling photovoltaic power output uncertainty and constructing scenarios in the method of this invention. The horizontal axis represents time, and the vertical axis represents photovoltaic active power. This represents the photovoltaic power output curve obtained during the offline optimization phase. This represents the actual photovoltaic output simulated under different uncertainty scenarios, reflecting prediction deviations caused by factors such as irradiance fluctuations, cloud cover, and communication lags. By constructing a set of photovoltaic output scenarios covering various deviation conditions, it provides an input basis for the subsequent scheduling strategy generation process.

[0084] like Figure 4 As shown, this figure illustrates the predicted demand and actual power changes of the base load and flexible load on the load side of a microgrid under the same application scenario. This figure serves to explain the process of classifying and modeling load data as scheduling input in the method of this invention. and These represent the predicted power demand and actual operating power of the base load, respectively, used to characterize the energy consumption characteristics of non-reducible loads. and These figures represent the predicted and actual power demand of flexible loads, respectively, illustrating the changes in loads with adjustment capabilities during operation. This figure reflects the discrepancy between load-side forecasts and actual demand, providing a basis for introducing flexible load adjustment mechanisms in subsequent scheduling processes.

[0085] like Figure 5As shown in the figure, under exemplary parameter configurations, the comparison between the planned output of the energy storage system and the power grid before and after applying the multi-objective optimization method of this invention during the offline phase is illustrated. This figure serves to explain the process of generating a scheduling plan through multi-objective optimization in the method of this invention and its impact on the planning results. and This represents the planned energy storage and grid output results obtained without using the method of this invention. and This figure represents the planned results obtained after introducing uncertainties in photovoltaic output and completing multi-objective optimization. It reflects the adjustment effect of the offline optimization process on the overall system scheduling structure.

[0086] like Figure 6 The figure illustrates the set of candidate energy storage scheduling strategies generated during the offline optimization phase. This figure serves to explain the process of constructing and pre-setting multiple sets of candidate scheduling strategies in the method of this invention. The horizontal axis represents time, and the vertical axis represents the active power of energy storage. At the same time, multiple different energy storage power values ​​correspond to a set of candidate values ​​expanded over time (presented in the figure as multiple discrete points / value sets). Each set of discrete values ​​collectively represents the existence of multiple selectable energy storage charging and discharging power sequences in the strategy library across the entire time domain. By constructing a strategy set containing multiple sets of candidate strategies, the operation phase can select a suitable scheduling scheme based on the real-time operating status without resolving the optimization model.

[0087] like Figure 7 The figure illustrates the actual power output of the microgrid energy storage system and the power grid during daily operation under real-world conditions without employing the online matching mechanism of this invention. This figure serves to explain the system's operational response characteristics to photovoltaic and load fluctuations without incorporating the method of this invention, and provides a comparative operational result when the method of this invention is subsequently adopted. Figure 7 The parameters marked in the legend and their corresponding objects in the figure are as follows: Among them, the energy storage output corresponds to the figure shown in the legend. The power grid interaction corresponds to the diagram shown in the legend. Both show the actual power trajectory of their intraday operation over time.

[0088] like Figure 8 As shown, under the same operating conditions, the energy storage system and the actual power output of the power grid after adopting the online matching and scheduling method of this invention are compared. This figure illustrates the process by which the method of this invention selects and executes a scheduling scheme from the strategy set based on real-time data during the operation phase. Figure 8 The correspondence between the parameters in the diagram and the objects in the diagram is as follows: Figure 7 Consistent: Energy storage output corresponds to the figure marked on the diagram. The power grid interaction corresponds to the diagram marked with symbols. Through with Figure 7The comparison can be used to demonstrate the regulating effect of the method of the present invention on the interaction behavior between energy storage and grid power during actual operation.

[0089] like Figure 9 As shown, this figure illustrates the changes in the state of charge (SOC) of an energy storage system during daily operation in an application scenario. This figure serves to explain the effectiveness of the method of this invention in constraining and ensuring the operating state of the energy storage system during the execution of the scheduling strategy. In the figure, SOC represents the state of charge of the energy storage system. Figure 9 The top right corner is marked , (and their corresponding values) are the charge state safety boundary parameters used in this embodiment, where , It can be seen that the SOC remains within the aforementioned safe range throughout the day's operation, thus demonstrating that the method of the present invention meets the safety requirements for energy storage operation while achieving the scheduling objective.

[0090] Under the aforementioned exemplary parameter configuration and corresponding operating data conditions, the method of this invention was compared and verified with the traditional method. The resulting economic benefit index was 2474.12 yuan, an increase of approximately 3.81% compared to the 2383.30 yuan of the traditional method. Regarding supply and demand satisfaction, both photovoltaic output absorption and base load can be fully met. The flexible load satisfaction rate was 39.24%, an increase of 8.50 percentage points compared to the 30.74% of the traditional method. Simultaneously, the state of charge (SOC) of the energy storage system remained within the preset safe range of 10% to 90% throughout the entire operation process, specifically as follows... Figure 9 As shown, in this specific application scenario, the energy management optimization method of the present invention, while meeting the safety constraints of energy storage operation, achieves comprehensive coordination between new energy consumption and load satisfaction, and obtains better economic performance indicators.

[0091] Example 2 like Figure 10 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a microgrid multi-objective optimization scheduling system with low communication dependence, including: a local controller and a cloud optimization layer.

[0092] The cloud optimization layer is used to generate a strategy library offline before the day and distribute the strategy library to the local controller for use by the local controller during the daytime operation phase. The strategy library is a set of candidate scheduling strategies generated by the cloud optimization layer based on the uncertainty of photovoltaic output and the local operation constraints of the microgrid.

[0093] The local controller includes: a data acquisition module, a matching module, and a policy output module.

[0094] The data acquisition module is used to acquire real-time photovoltaic power data and real-time load demand data of the microgrid.

[0095] The matching module is used to calculate the matching cost of each candidate scheduling strategy in the strategy library under the current operating state of the microgrid, based on real-time photovoltaic power data and real-time load demand data.

[0096] The strategy output module is used to select the scheduling strategy with the lowest matching cost from the candidate scheduling strategies and output the power control command corresponding to the scheduling strategy.

[0097] like Figure 11 The diagram illustrates the overall architecture of the microgrid multi-objective optimization scheduling system with low communication dependency of the present invention. The system includes a cloud-based optimization layer (offline optimization) and a local adaptive multi-objective energy management unit for the microgrid, which interact via a communication network. Energy is transmitted between the energy-consuming and energy-supplying units within the microgrid through the power grid. This two-layer architecture transforms the dependency on communication links from "continuous real-time dependency within the day" to "one-time low-frequency delivery dependency before the day," enabling the local side to still complete strategy selection and power command output based on real-time measurements even under conditions of communication delay, packet loss, or short-term interruption. This avoids scheduling mismatches caused by cloud unavailability or untimely prediction updates. Simultaneously, the cloud side generates a candidate strategy set covering multiple scenarios through offline multi-objective solving, allowing the local side to select from a limited set of strategies for online matching, thereby reducing online computational complexity and improving the determinism and feasibility of scheduling.

[0098] The cloud-based optimization layer performs global analysis and optimization of microgrid operation during the offline phase and distributes a set of candidate scheduling strategies to the local microgrid side. The local adaptive multi-objective energy management unit (MAU) coordinates and controls photovoltaic (PV) power generation units, energy storage batteries, grid interaction, and the load side during operation. PV power generation units and energy storage batteries are connected to the microgrid through corresponding DC / AC interfaces, forming an energy interaction relationship with the grid. The load side includes base loads and flexible loads. Base loads meet residential electricity demand, while flexible loads represent power loads with adjustment capabilities, such as electric vehicles. Based on operational information such as PV output, load demand, and energy storage status, the MAU performs scheduling of energy storage charging and discharging power and grid interaction power to achieve comprehensive coordination of multiple objectives, including net operating cost, flexible load satisfaction, and renewable energy absorption rate. Solid lines in the diagram represent power network connections, and dashed lines represent communication network connections.

[0099] Example 3 like Figure 12 As shown, the present invention also provides an electronic device 100 for implementing a multi-objective optimization scheduling method for microgrids with low communication dependence; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0100] The memory 101 can be used to store the computer program 103. The processor 102 implements the low communication dependency microgrid multi-objective optimization scheduling method of Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0101] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0102] At least one processor 102 may 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. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0103] The memory 101 in the electronic device 100 stores multiple instructions to implement a multi-objective optimization scheduling method for a microgrid with low communication dependency, and the processor 102 can execute multiple instructions to achieve the following: Obtain real-time photovoltaic power data and real-time load demand data of the microgrid; Based on real-time photovoltaic power data and real-time load demand data, the matching cost of each candidate scheduling strategy in the strategy library is calculated under the current operating state of the microgrid. The strategy library is a set of candidate scheduling strategies generated offline by the cloud optimization layer based on the uncertainty of photovoltaic output and the local operating constraints of the microgrid. The strategy library is distributed to the local controller by the cloud optimization layer a day-ahead. Select the scheduling strategy with the lowest matching cost from the candidate scheduling strategies, and output the power control command corresponding to the scheduling strategy.

[0104] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of 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 computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0105] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0107] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0108] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0109] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-objective optimization scheduling method for microgrids with low communication dependency, characterized in that, The method is executed by the local controller during the daytime operation phase according to a preset scheduling cycle, and the method includes: Obtain real-time photovoltaic power data and real-time load demand data of the microgrid; Based on the real-time photovoltaic power data and real-time load demand data, the matching cost of each candidate scheduling strategy in the strategy library is calculated under the current operating state of the microgrid; wherein, the strategy library is a set of candidate scheduling strategies generated offline by the cloud optimization layer based on the uncertainty of photovoltaic output and the local operating constraints of the microgrid, and the strategy library is distributed to the local controller by the cloud optimization layer a day-ahead; Select the scheduling strategy with the lowest matching cost from the candidate scheduling strategies, and output the power control command corresponding to the scheduling strategy.

2. The low-communication-dependency microgrid multi-objective optimization scheduling method according to claim 1, characterized in that, It also includes a strategy library generation process executed by the cloud optimization layer, the strategy library generation process including: Acquire photovoltaic power output forecast data and microgrid local operation constraint data; A set of uncertain photovoltaic output scenarios is constructed based on the photovoltaic output prediction data and prediction errors. Based on the photovoltaic output uncertainty scenario set and the microgrid local operation constraints, a multi-objective optimization model is constructed with the optimization objectives of minimizing the microgrid net operating cost, minimizing the flexible load non-fulfillment rate, and minimizing the photovoltaic curtailment rate. Solve the multi-objective optimization model to obtain a policy library consisting of multiple sets of candidate scheduling policies.

3. The low-communication-dependency microgrid multi-objective optimization scheduling method according to claim 2, characterized in that, The objective function of the multi-objective optimization model includes: The objective function for minimizing the net operating cost of a microgrid is expressed by the following formula: in, and These are the power purchased from the grid and the power sold to the grid, respectively. and These are the time-of-use electricity purchase price and the electricity sales price, For the charging and discharging power of the energy storage system, This refers to the aging and depreciation coefficient of the energy storage battery. To optimize the total number of time periods within the time domain; The objective function for minimizing the flexible load non-fulfillment rate is expressed by the following formula: in, To meet the actual flexible load power requirements, Power required for flexible loads, To optimize the total number of time periods within the time domain; The objective function for minimizing photovoltaic curtailment rate is expressed by the following formula: in, For photovoltaic power that cannot be absorbed, To contribute to actual photovoltaic power, To optimize the total number of time periods within the time domain.

4. The low-communication-dependency microgrid multi-objective optimization scheduling method according to claim 3, characterized in that, The constraints of the multi-objective optimization model include: The state-of-charge constraint is expressed by the following formula: in, For time period The state of charge, and These are the lower and upper limits of the state of charge, respectively; The charge / discharge power constraint is expressed by the following formula: in, For energy storage systems during time periods The charging and discharging power, Indicates discharge, Indicates charging. For time period The upper limit of discharge power, For time period The lower limit of charging power; The and They are determined using the following formulas respectively: in, The rated power of the energy storage system, For the rated capacity of the energy storage system, For charging and discharging efficiency, For time step; The energy state recursion constraint is expressed by the following formula: in, Determined by the following formula: in, This indicates the state of charge for the next time period.

5. The low-communication-dependency microgrid multi-objective optimization scheduling method according to claim 4, characterized in that, Solving the multi-objective optimization model includes: The multi-objective optimization model is solved offline using the Intensity Pareto Evolutionary Algorithm II to obtain a Pareto non-dominated solution set. Each non-dominated solution in the Pareto non-dominated solution set corresponds to a set of candidate scheduling strategies. The candidate scheduling strategies include at least the value sequence of the energy storage system's charging and discharging power for each time period in the optimization time domain. Multiple sets of non-dominated solutions in the Pareto non-dominated solution set are used as the candidate scheduling strategy set to constitute the strategy library.

6. The low-communication-dependency microgrid multi-objective optimization scheduling method according to claim 5, characterized in that, The steps for obtaining the photovoltaic output uncertainty scenario set include: The photovoltaic output scenario generation constraints are expressed by the following formula: in, For the first Simulated photovoltaic power in various scenarios This represents the current day's predicted photovoltaic power output. A random error factor following a specific distribution is used to simulate prediction bias when communication updates are delayed. For the prediction period index, Number the scene; Number the same scene Optimize each time period within the time domain Generated photovoltaic simulated power values This constitutes a set of photovoltaic power output scenarios, and the different scenarios are numbered. The corresponding multiple photovoltaic output scenarios constitute the photovoltaic output uncertainty scenario set.

7. The low-communication-dependency microgrid multi-objective optimization scheduling method according to claim 5, characterized in that, The calculation of the matching cost includes: At any moment For the first in the strategy library The strategy first calculates the system's internal net power using the following formula. The gap between basic supply and demand : in, This refers to the real-time collected photovoltaic power. For strategy energy storage capacity, For strategy Internal net power, For strategy The basic supply and demand gap, For real-time base load; Based on the supply and demand gap Based on the local power balance logic, the unmet power of grid interaction and flexible load is determined, where: when At that time, the strategy is determined by the following formula. Power purchased by the power grid Flexible loads do not meet power requirements and the power sold by the power grid : when At that time, the strategy is determined by the following formula. The flexible load did not meet the power requirements. Power sold by the power grid and the power purchased by the power grid : The strategy is calculated using the following formula. At any moment Instant matching cost : in, Power required for flexible loads, This indicates the off-peak electricity pricing period; 1 indicates off-peak hours, and 0 indicates otherwise. and These are the real-time electricity purchase price and the electricity sales price. The user satisfaction penalty weighting coefficient.

8. A microgrid multi-objective optimization scheduling system with low communication dependence, characterized in that, include: Local controller and cloud optimization layer; The cloud optimization layer is used to generate a strategy library offline before the day and distribute the strategy library to the local controller for use by the local controller during the daytime operation phase. The strategy library is a set of candidate scheduling strategies generated by the cloud optimization layer based on the uncertainty of photovoltaic output and the local operation constraints of the microgrid. The local controller includes: a data acquisition module, a matching module, and a policy output module; The data acquisition module is used to acquire real-time photovoltaic power data and real-time load demand data of the microgrid. The matching module is used to calculate the matching cost of each candidate scheduling strategy in the strategy library under the current operating state of the microgrid, based on the real-time photovoltaic power data and real-time load demand data. The strategy output module is used to select the scheduling strategy with the lowest matching cost from the candidate scheduling strategies and output the power control command corresponding to the scheduling strategy.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the low communication dependency microgrid multi-objective optimization scheduling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the low-communication-dependency microgrid multi-objective optimization scheduling method as described in any one of claims 1 to 7.

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