Micro-grid multi-target scheduling method and system
By constructing a multi-objective scheduling model and solving fuzzy satisfaction problems, the uncertainty of wind and solar power generation and the conflict of multiple objectives in microgrid scheduling are solved, and efficient, reliable and environmentally friendly scheduling optimization of microgrids is achieved.
Patent Information
- Application Number
- CN202511694080.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-18
- Publication Date
- 2026-02-24
AI Technical Summary
In existing technologies, microgrid scheduling algorithms struggle to achieve flexibility, anti-interference capabilities, and reliability when facing uncertainties and multi-objective conflicts in wind and solar power generation. Traditional methods are rigid, while heuristic algorithms are prone to getting trapped in local optima and lack the ability to adapt to uncertainties, leading to scheduling failures.
By constructing a multi-objective scheduling model that integrates cost optimization and emission optimization, introducing fuzzy satisfaction solution, and combining robust constraints and opportunity constraints, the uncertainty characteristics of wind and solar power generation are optimized, and the regulation capability of energy storage units is utilized to achieve multi-objective collaborative optimization of microgrids.
It significantly improves the resource utilization efficiency and reliability of microgrids, achieves high reliability, low operating cost and optimized carbon emission scheduling under complex operating conditions, and enhances anti-interference capability and stability.
Smart Images
Figure CN121566481A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of microgrid dispatching, and more specifically, to a method and system for multi-objective dispatching of microgrids. Background Technology
[0002] A microgrid is a small-scale power generation and distribution system consisting of distributed power sources, energy storage units, energy conversion equipment, and loads. It can achieve efficient energy utilization and reliable power supply through self-control. Microgrid dispatch refers to coordinating the output allocation of various internal resources through optimization algorithms to meet load demand and achieve multiple goals such as cost optimization and environmental protection. Currently, traditional mathematical methods and heuristic optimization algorithms are the two core approaches to solving microgrid scheduling problems, but both have significant limitations. Traditional mathematical methods rely heavily on accurate deterministic prediction models. Because they do not integrate uncertainty quantification analysis and multi-objective collaborative mechanisms, the single-objective optimization framework they construct cannot effectively cope with the random disturbances caused by the fluctuations in wind and solar power output, nor can they balance the contradictory relationship between minimizing fuel costs, carbon emission constraints, and the absorption rate of wind and solar power output. Although traditional mathematical methods usually use fixed weights for trade-offs, they essentially sacrifice the ability to explore the space of the global optimal solution. Although heuristic optimization algorithms have the potential to handle nonlinear and nonconvex problems, the strong nonconvex solution space formed by the unit ramp-up rate limit, the dynamic characteristics of energy storage charging and discharging, and the penalty terms for wind and solar power curtailment can easily cause heuristic optimization algorithms to fall into local optimum traps. The explosive growth of the decision variable dimension with the expansion of the time scale leads to a sharp drop in convergence speed and a long time for a single solution, which is completely lagging behind the actual needs of real-time scheduling. Furthermore, both traditional mathematical methods and heuristic optimization algorithms lack a sensitive prediction mechanism for uncertain parameters. When the actual wind and solar power outputs deviate from the predicted values, the so-called optimal solution generated by the model will quickly become invalid, forcing the algorithm to repeatedly restart the solution process, further exacerbating the ineffective consumption of computing resources. Summary of the Invention
[0003] This invention provides a method and system for multi-objective scheduling of microgrids, which addresses the problem of poor performance of multi-objective optimization in existing microgrid scheduling algorithms, thereby improving the flexibility, anti-interference ability, robustness and reliability of multi-objective scheduling of microgrids.
[0004] According to a first aspect of this application, a method for multi-objective scheduling of a microgrid is provided, the method comprising: A microgrid cost optimization scheduling model is preset, and a cost optimization objective function is preset for the microgrid cost optimization scheduling model; A microgrid emission optimization scheduling model is preset, and an emission optimization objective function is preset for the microgrid emission optimization scheduling model; A multi-objective optimization scheduling model for microgrids is constructed based on the microgrid cost optimization scheduling model and the microgrid emission optimization scheduling model, and a multi-objective optimization objective function and constraints are preset for the multi-objective optimization model of microgrids. The current operating data of the microgrid is obtained, and based on the current operating data and the multi-objective optimization scheduling model, the optimal configuration parameters of the microgrid are obtained by solving according to the fuzzy satisfaction method. The microgrid is configured based on the configuration parameters, and multi-objective scheduling is performed on the microgrid based on the current operating data of the microgrid and the configured microgrid.
[0005] Understandably, by integrating cost optimization objective functions and emission optimization objective functions to form a multi-objective scheduling framework, the requirements of cost optimization, environmental protection and microgrid stability are comprehensively coordinated. Furthermore, fuzzy satisfaction is introduced for intelligent solution, which significantly improves the efficiency and accuracy of obtaining optimal configuration parameters, ultimately achieving high reliability, low operating cost and optimized carbon emission scheduling of microgrids under complex operating conditions.
[0006] Optionally, the cost optimization objective function is: in, This indicates the operating cost of a microgrid. This represents the function for calculating the operating cost of a microgrid. Indicates the first Taiwan fuel generator set Output power during the time period This represents the total time period for microgrid scheduling. This indicates the total number of fuel generator sets. , This represents the fuel cost coefficient. This represents the unit curtailment penalty cost of a photovoltaic power generation unit. express The amount of electricity wasted by photovoltaic power generation units during certain periods This represents the unit cost of wind curtailment penalty for wind power generators. express The amount of electricity wasted by wind turbine generators during certain periods.
[0007] Understandably, the cost optimization objective function, by integrating a quadratic operating cost model of fuel-powered generators with a renewable energy curtailment penalty mechanism included in photovoltaic and wind power generation, accurately characterizes the optimality of fuel consumption costs and wind and solar power absorption losses in microgrid operation. This not only achieves the global minimization of total operating costs but also effectively suppresses the waste of renewable energy by dynamically balancing fuel unit output and curtailment penalties, significantly improving the resource utilization efficiency and reliability of microgrids and providing a solid decision-making foundation for multi-objective scheduling of microgrids.
[0008] Optionally, the emission optimization objective function is: in, This indicates the emissions from the microgrid's combustion generator set. Indicates the first Taiwan fuel generator set Emissions over a period of time , , Indicates the first The emission coefficient of the combustion generator set, Indicates the first Taiwan fuel generator set Output power during a given time period.
[0009] Understandably, the emission optimization objective function accurately characterizes the nonlinear dynamic relationship between the emissions and output power of fuel generator sets through a quadratic polynomial form, thereby achieving the global minimization of the total carbon emissions of the microgrid. This provides a key environmental constraint benchmark for multi-objective scheduling, thus significantly reducing pollution emission levels while ensuring the safe and stable operation of the microgrid, and promoting the efficient transformation of the microgrid towards a green and low-carbon direction.
[0010] Optionally, the multi-objective optimization objective function is: in, This represents the total time period for microgrid scheduling. This indicates the total number of fuel generator sets. Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first Taiwan fuel generator set Operating costs for a given period of time Indicates the first Taiwan fuel generator set Emissions during a given period.
[0011] Understandably, this multi-objective optimization objective function effectively constructs a synergistic optimization framework for cost optimization and environmental protection by jointly minimizing total operating costs and total carbon emissions. This not only avoids the decision-making biases in traditional single-objective scheduling, which may lead to an overemphasis on cost reduction while neglecting environmental impact or vice versa, but also accurately captures the comprehensive benefit boundary of the fuel power generation link by relying on refined modeling of all time periods and all units. Thus, it significantly improves resource utilization efficiency and sustainable development level while ensuring the safe and stable operation of the microgrid. This provides a high-fidelity dual-objective benchmark for subsequent intelligent solutions based on fuzzy satisfaction, and ultimately achieves the global optimal trade-off of microgrid scheduling under complex operating conditions.
[0012] Optionally, the constraints include: Robust constraints: in, Let be a probability function. Indicates the first Taiwan fuel generator set Output power during the time period express The actual output power of renewable energy generator sets during a given period, including photovoltaic generator sets and wind power generator sets. express The load demand of the microgrid during different time periods This indicates the probability of a violation of the opportunity constraint. express The actual output power of the photovoltaic generator set during the time period express The actual output power of the wind turbine generator set during the specified time period.
[0013] Understandably, robust constraints, through probabilistic functions and aggregated expressions of actual renewable energy output, accurately quantify the dynamic impact of random fluctuations in wind and solar power output on load demand. This not only ensures that the microgrid meets power supply reliability requirements with controllable risk probability under high uncertainty scenarios, but also effectively suppresses power imbalance caused by sudden changes in renewable energy by integrating probabilistic boundaries and real-time operating data. This significantly enhances the anti-interference capability and stability of microgrid dispatch, providing a solid foundation for safe operation for multi-objective optimization.
[0014] Optionally, the Actual output power of photovoltaic generator set during the period for: in, express The predicted baseline power of the photovoltaic power generation unit during the time period Indicates the photovoltaic fluctuation coefficient. Let represent a random variable that follows a first standard normal distribution, and .
[0015] Understandably, the actual output power model of photovoltaic generators accurately characterizes the random fluctuation characteristics of photovoltaic power generation by integrating the predicted baseline power, photovoltaic fluctuation coefficient, and random disturbance term based on the standard normal distribution. This not only achieves high-fidelity simulation of sudden changes in irradiance and weather uncertainties, but also significantly enhances the model's adaptability by dynamically adjusting the ratio of fluctuation amplitude to the predicted baseline power. This provides reliable wind and solar power output boundary conditions for multi-objective scheduling of microgrids, effectively supports cost optimization decisions for minimizing operating costs and suppressing curtailment penalties, and strengthens the microgrid's robust handling capability against photovoltaic power generation uncertainties.
[0016] Optionally, the Actual output power of wind turbine generators during certain periods for: in, express The predicted baseline power of wind turbine generators during the time period Indicates the wind energy fluctuation coefficient. Let represent a random variable that follows a second standard normal distribution, and .
[0017] Understandably, the actual wind power output model accurately characterizes the stochastic fluctuation characteristics of wind power output by integrating the predicted baseline power, wind power fluctuation coefficient, and random disturbance term based on the standard normal distribution. It not only achieves high-fidelity simulation of sudden wind speed changes and meteorological uncertainties, but also significantly enhances the model's adaptability by dynamically adjusting the ratio of fluctuation amplitude to predicted power. This provides reliable wind power output boundary conditions for multi-objective scheduling of microgrids, effectively supports cost optimization decisions for minimizing operating costs and suppressing wind curtailment penalties, and strengthens the microgrid's robust handling capability against wind power generation uncertainties.
[0018] Optionally, the constraints include: Power balance constraints: in, This indicates the total number of fuel generator sets. Indicates the first Taiwan fuel generator set Output power during the time period express The predicted baseline power of renewable energy generating units during the specified time period, including photovoltaic generating units and wind power generating units. express The load demand of the microgrid during different time periods; And / or, fuel generator set output power constraints: in, Indicates the first The minimum output power of the fuel generator set, Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first The maximum output power of the fuel generator set; And / or, power ramp rate constraint: in, Indicates the first The rate of power degradation of the fuel-powered generator set. Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first The power ramp-up rate of the fuel generator set; And / or, state constraints of the energy storage unit: in, Indicates energy storage unit State of charge during a period of time Indicates energy storage unit State of charge during a period of time This indicates the charging efficiency of the energy storage unit. express Charging power of time-limited energy storage units express Discharge power of the time-limited energy storage unit This indicates the discharge efficiency of the energy storage unit. Indicates a unit of time period; And / or, operating constraints of energy storage units: in, This indicates the minimum charging power of the energy storage unit. express Charging power of time-limited energy storage units This indicates the maximum charging power of the energy storage unit. This indicates the minimum discharge power of the energy storage unit. express Discharge power of the time-limited energy storage unit This indicates the maximum discharge power of the energy storage unit. This indicates the safe state of charge limit for the energy storage unit. Indicates energy storage unit State of charge during a period of time This indicates the upper limit of the safe state of charge for the energy storage unit.
[0019] Understandably, the solution ensures accurate real-time supply and demand matching through integrated power balance constraints, prevents fuel generator output power constraints from exceeding limits, suppresses sudden power surges in fuel generator output through power ramp-up constraints, and coordinates the state and operation constraints of energy storage units to regulate their charging and discharging processes. This comprehensively constructs a safe operation protection system for the microgrid, effectively avoiding risks such as power imbalance, unit damage, and overcharging / over-discharging of energy storage. It provides a highly reliable physical constraint framework for multi-objective optimized scheduling, strongly supporting the stable, efficient, and sustainable operation of the microgrid under complex operating conditions.
[0020] Optionally, the step of obtaining the optimal configuration parameters of the microgrid based on the current operating data and the multi-objective optimization scheduling model, according to the fuzzy satisfaction method, includes: Constructing the membership function of fuzzy satisfaction: in, Indicate target Satisfaction indicators Indicate target The actual value, Indicate target The maximum value of the Pareto front. Indicate target The Pareto front minimum; when hour, This represents the operating cost of a microgrid, when... hour, This indicates the emissions from the microgrid's combustion generator set; Preset solution constraints: in, , Indicates the preset preference weights; Indicates satisfaction with cost optimization; This indicates satisfaction with emissions optimization. This represents the minimum satisfaction threshold for cost optimization. This represents the minimum satisfaction threshold for emissions optimization. The optimal configuration parameters of the microgrid are obtained by solving the membership function of the fuzzy satisfaction level, the solution constraints, the current operating data, and the multi-objective optimization scheduling model.
[0021] Understandably, by constructing a fuzzy satisfaction membership function, the multi-objective optimization problem is transformed into a single-objective maximization problem. With the addition of a minimum satisfaction threshold constraint, not only is intelligent screening and dynamic trade-offs achieved on the Pareto front for non-dominated solution sets, significantly improving the efficiency and convergence speed of obtaining optimal configuration parameters, but also the preset preference weights flexibly adapt to the cost optimization and environmental protection requirements under different operating scenarios. At the same time, it ensures that each objective reaches the acceptable minimum performance boundary, thereby significantly reducing computational complexity while ensuring the global optimality of the microgrid scheduling scheme. This provides a highly reliable and adaptable solution framework for real-time decision-making under complex operating conditions.
[0022] According to a second aspect of this application, a system for multi-objective scheduling of a microgrid is provided, the system comprising: The module for setting a pre-defined cost optimization scheduling model is used to pre-define a microgrid cost optimization scheduling model and to pre-define a cost optimization objective function for the microgrid cost optimization scheduling model. A preset emission optimization scheduling model module is used to preset a microgrid emission optimization scheduling model and to preset an emission optimization objective function for the microgrid emission optimization scheduling model. A pre-defined multi-objective optimization scheduling model module is used to construct a microgrid multi-objective optimization scheduling model based on the microgrid cost optimization scheduling model and the microgrid emission optimization scheduling model, and to pre-define multi-objective optimization objective functions and constraints for the microgrid multi-objective optimization model. The solution module is used to acquire the current operating data of the microgrid, and based on the current operating data and the multi-objective optimization scheduling model, solves the problem according to the fuzzy satisfaction level to obtain the optimal configuration parameters of the microgrid. The configuration module is used to configure the microgrid based on the configuration parameters and to perform multi-objective scheduling of the microgrid based on the current operating data of the microgrid and the configured microgrid.
[0023] Based on any of the above aspects, the microgrid multi-objective scheduling method and system provided in this application embodiment can achieve the following benefits: Effectively addressing the uncertainties of wind and solar power generation in microgrid operation: By constructing actual output power models for photovoltaic and wind power generation, the predicted baseline power of renewable energy sources is combined with stochastic fluctuation terms to accurately characterize the uncertainty characteristics of wind and solar power output. Based on this, robust constraints and opportunity constraints are introduced, combined with the regulation capabilities of energy storage units, significantly enhancing the microgrid dispatch's ability to withstand random fluctuations in wind and solar power. This overcomes the limitations of traditional simplified models, dynamically matching actual operating scenarios through a data-driven approach, providing a highly reliable input foundation for subsequent optimization.
[0024] Multi-objective collaborative optimization to ensure stable operation of microgrids: By establishing a dual-objective optimization system that includes cost optimization objective function and emission optimization objective function, it comprehensively covers core indicators such as fuel unit operating cost, curtailment penalty cost and carbon emissions. At the same time, by integrating physical boundary conditions such as power balance constraints, fuel generator output power constraints, power ramp rate constraints, energy storage unit state constraints, and energy storage unit operation constraints, it achieves a balance between cost optimization, environmental protection and safety.
[0025] A robust solution method based on fuzzy satisfaction improves solution accuracy: Addressing the problem of large non-dominated solution sets in multi-objective optimization, an intelligent optimal solution selection mechanism based on fuzzy satisfaction is proposed. By presetting preference weights and a minimum satisfaction threshold, multi-dimensional decision-making is transformed into a single-objective maximization problem. Simultaneously, by fully utilizing the distribution characteristics of the Pareto front, the method can preserve the information integrity of key candidate solutions and quickly converge to a compromise solution that meets practical needs through dynamic weight adjustment, significantly improving the efficiency and accuracy of obtaining optimal configuration parameters. Attached Figure Description
[0026] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0027] Figure 1 This is a flowchart of a microgrid multi-objective scheduling method provided in this embodiment.
[0028] Figure 2 This is a flowchart for obtaining optimal configuration parameters provided in this embodiment.
[0029] Figure 3 This embodiment provides a functional module diagram of a microgrid multi-objective scheduling system. Detailed Implementation
[0030] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this application. To better illustrate the following embodiments, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product; it is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.
[0031] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.
[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0033] Both traditional mathematical methods and heuristic algorithms for microgrid dispatching have fundamental flaws: traditional mathematical methods, due to their rigid models, cannot effectively coordinate uncertainties and multi-objective conflicts; heuristic algorithms, due to their computational inefficiency and susceptibility to local optima, struggle to meet real-time dispatching requirements. Both lack the ability to proactively adapt to uncertainties, leading to rapid failures in microgrid dispatching practices.
[0034] This embodiment provides a technical solution that can solve the above problems. The specific implementation of this application will be described in detail below with reference to the accompanying drawings.
[0035] like Figure 1 As shown, this embodiment provides a method for multi-objective scheduling of microgrids, which can be further divided into the following steps: S100. Pre-set a microgrid cost optimization scheduling model, and pre-set a cost optimization objective function for the microgrid cost optimization scheduling model; Specifically, the cost optimization objective function is: in, This indicates the operating cost of a microgrid. This function represents the calculation function for microgrid operating costs. Indicates the first Taiwan fuel generator set The output power during a time period can be measured in megawatts (MW). This represents the total time period for microgrid scheduling. Indicates the total number of fuel generator sets; , This represents the fuel cost coefficient, where The unit can be the first specific unit: $ / MW²h, The unit can be a second specific unit: $ / MWh, It can be a third specific unit: $ / h; The unit curtailment penalty cost of a photovoltaic power generation unit can be expressed as: $ / MW; express The amount of electricity abandoned by photovoltaic power generation units during a given period can be expressed in megawatts (MW). This represents the unit cost of wind curtailment penalty for wind power generators, and its unit can be: $ / MW; express The amount of abandoned power generated by wind turbines during a given period can be expressed in megawatts (MW).
[0036] In this embodiment, the core objective of cost-optimized scheduling of the microgrid system is to minimize the sum of the operating costs of fuel generators and the curtailment penalties for renewable energy sources, including solar and wind power, while meeting load demand. A quadratic function is used to accurately simulate the fuel cost characteristics of fuel generators, and a linear penalty term is introduced to quantify the economic losses from wind and solar curtailment. This provides a comprehensive and accurate cost-optimization metric for microgrid scheduling.
[0037] S200. Pre-set a microgrid emission optimization scheduling model, and pre-set an emission optimization objective function for the microgrid emission optimization scheduling model; Specifically, the emission optimization objective function is: in, This indicates the emissions from the microgrid's combustion generator set. Indicates the first Taiwan fuel generator set Emissions during a given period; , , Indicates the first The emission coefficients of the combustion generator sets, among which The unit can be a fourth specific unit: kg / MW²h. The unit can be a fifth specific unit: kg / MWh. The unit can be a sixth specific unit: kg / h; Indicates the first Taiwan fuel generator set Output power during a given time period.
[0038] In this embodiment, to reduce carbon emissions from the microgrid, the pollutant emissions from the combustion-powered generator sets within the microgrid need to be optimized simultaneously. A quadratic function is used to characterize the nonlinear relationship between the emissions and output power of the combustion-powered generator sets. Preferably, the pollutants involved in the carbon emissions cover major pollutants such as carbon dioxide (CO2) and sulfur dioxide (SO2).
[0039] S300. Construct a multi-objective optimization scheduling model for microgrids based on the microgrid cost optimization scheduling model and the microgrid emission optimization scheduling model, and preset multi-objective optimization objective functions and constraints for the multi-objective optimization model of microgrids. Specifically, the multi-objective optimization objective function is: in, This represents the total time period for microgrid scheduling. This indicates the total number of fuel generator sets. Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first Taiwan fuel generator set Operating costs for a given period of time Indicates the first Taiwan fuel generator set Emissions during a given period.
[0040] In this embodiment, a dual-objective optimization framework of cost optimization and emission optimization is constructed to coordinate the conflict between cost optimization and environmental protection. Furthermore, the Pareto front solution set of the generated multi-objective optimization objective function is used for selection by the subsequent fuzzy satisfaction algorithm to obtain the optimal configuration parameters.
[0041] Understandably, the Pareto front solution set refers to the set of all optimal trade-off solutions in a multi-objective optimization problem. In this embodiment, the two objectives of the multi-objective optimization objective function are conflicting. For example, minimizing operating costs may mean using more low-cost diesel fuel, leading to increased carbon emissions; minimizing carbon emissions may mean requiring a larger proportion of renewable energy generation such as wind or solar power. However, renewable energy generation such as wind or solar power is severely affected by weather, resulting in large fluctuations in power generation and requiring frequent charging and discharging or starting and stopping of expensive energy storage units, thus increasing operating costs. Therefore, based on the multi-objective optimization objective function, it is necessary to find multiple sets of candidate optimal solutions that can balance the power supply and demand of the microgrid while reasonably minimizing operating costs and carbon emissions, forming the Pareto front solution set. This allows the optimal solution to be found among multiple candidate solutions in the Pareto front solution set, thereby enabling the microgrid to operate more healthily and stably.
[0042] Specifically, the constraints include: Robust constraints: in, Let be a probability function. Indicates the first Taiwan fuel generator set Output power during the time period express The actual output power of renewable energy generator sets during a given period, including photovoltaic generator sets and wind power generator sets. express The load demand of the microgrid during different time periods; Represents the probability of violation of the opportunity constraint, preferably, It can be taken as 0.05; express The actual output power of a photovoltaic generator during a given period can be expressed in megawatts (MW). express The actual output power of a wind turbine generator during a given period can be expressed in megawatts (MW).
[0043] In this embodiment, based on the probability of violation of chance constraints, it can be ensured that the microgrid meets the reliability requirements of power supply with a 95% confidence level. This makes the microgrid dispatch take into account both the robustness of operation and scientifically manage the randomness risk of microgrid operation.
[0044] Specifically, the Actual output power of photovoltaic generator set during the period for: in, express The predicted baseline power of the photovoltaic power generation unit during the time period can be expressed in megawatts (MW). Represents the photovoltaic fluctuation coefficient, preferably, 0.7 can be taken; Let represent a random variable that follows a first standard normal distribution, and ,in It follows a standard normal distribution.
[0045] Specifically, the Actual output power of wind turbine generators during certain periods for: in, express The predicted baseline power of wind turbine generators for a given period can be expressed in megawatts (MW). Represents the wind energy fluctuation coefficient, preferably, 0.8 can be taken; Let represent a random variable that follows a second standard normal distribution, and .
[0046] In this embodiment, the randomness of the prediction error of wind power generators and photovoltaic generators needs to be considered. A geometric Brownian motion model is used to describe the actual output fluctuation of wind power generators and photovoltaic generators, so that the actual output power of wind power generators and photovoltaic generators can be transformed into a solvable mathematical programming problem, thereby accurately optimizing the calculation.
[0047] Specifically, power balance constraints: in, This indicates the total number of fuel generator sets. Indicates the first Taiwan fuel generator set Output power during the time period; express The predicted baseline power of renewable energy generating units for a given period, expressed in megawatts (MW); these renewable energy generating units include photovoltaic generating units and wind power generating units. express The load demand of a microgrid during a given time period can be expressed in megawatts (MW). Output power constraints of fuel generator sets: in, Indicates the first The minimum output power of a fuel generator set can be expressed in megawatts (MW). Indicates the first Taiwan fuel generator set Output power during the time period; Indicates the first The maximum output power of a fuel generator set can be expressed in megawatts (MW). Power ramp rate constraint: in, Indicates the first The power ramp-up rate of a fuel generator set, which can be expressed in megawatt-hours (MW / h). Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first The power ramp-up rate of a fuel generator set, which can be expressed in megawatt-hours (MW / h). And / or, state constraints of the energy storage unit: in, Indicates energy storage unit The state of charge during a time period can be expressed as a percentage (%). Indicates energy storage unit The state of charge during a time period can be expressed as a percentage (%). Indicates the charging efficiency of the energy storage unit; express The charging power of the time-of-use energy storage unit can be expressed in megawatts (MW). express The discharge power of the time-of-use energy storage unit can be expressed in megawatts (MW). This indicates the discharge efficiency of the energy storage unit. Indicates a unit of time period; And / or, operating constraints of energy storage units: in, This indicates the minimum charging power of the energy storage unit, and its unit can be megawatts (MW). express Charging power of time-limited energy storage units This indicates the maximum charging power of the energy storage unit, and its unit can be megawatts (MW). This indicates the minimum discharge power of the energy storage unit. express Discharge power of the time-limited energy storage unit This indicates the maximum discharge power of the energy storage unit. This indicates the safe state of charge limit of the energy storage unit, which can be expressed as a percentage (%). Indicates energy storage unit State of charge during a period of time This indicates the upper limit of the safe state of charge of the energy storage unit, which can be expressed as a percentage (%).
[0048] In this embodiment, by pre-setting the above constraints, the physical implementation and operational limitations of the microgrid can be transformed into mathematical language that the optimization algorithm can understand, thereby ensuring that microgrid scheduling not only pursues the optimal cost in mathematics, but is also safe and feasible in actual operation.
[0049] S400. Obtain the current operating data of the microgrid, and based on the current operating data and the multi-objective optimization scheduling model, solve the problem according to the fuzzy satisfaction level to obtain the optimal configuration parameters of the microgrid. In this embodiment, in order to obtain the optimal configuration parameters for microgrid scheduling, fuzzy satisfaction can be used to solve the multiple optimization objectives in this embodiment, which simplifies the solution process and improves the accuracy of the solution.
[0050] Understandably, fuzzy satisfaction is a mathematical tool used to solve optimization problems with imprecise, non-rigorous, or subjectively biased objectives and constraints. It does not pursue the optimal solution under strict constraints, but rather seeks a satisfactory solution that satisfies the decision-maker. In this embodiment, different satisfaction values can be calculated for each objective function based on different optimal solutions. By using multiple satisfaction values and preset weights, the degree of satisfaction for the decision-maker with different optimal solutions is measured. This helps the decision-maker more clearly select a satisfactory solution that satisfies them sufficiently, enabling the microgrid to be optimally scheduled under this satisfactory solution, thus meeting the decision-maker's management and scheduling philosophy.
[0051] Specifically, such as Figure 2 As shown, the process of obtaining the optimal configuration parameters of the microgrid based on the current operating data and the multi-objective optimization scheduling model, according to the fuzzy satisfaction method, may include the following steps: S410. Construct the membership function of fuzzy satisfaction level: in, Indicate target The satisfaction index, preferably, ; Indicate target The actual value, Indicate target The maximum value of the Pareto front. Indicate target The Pareto front minimum; when hour, This represents the operating cost of a microgrid, when... hour, This indicates the emissions from the microgrid's combustion generator set; Understandably, in order to quantify the satisfaction level of each optimization objective function, this embodiment uses a linear membership function to transform the multi-objective optimization into a satisfaction maximization problem; where, when hour, This indicates the operating cost of a microgrid. = This represents the maximum value of the Pareto front among multiple optimal solutions in the operating cost. This represents the minimum Pareto front among multiple optimal solutions in the operating cost, which can ultimately be calculated. = This indicates the satisfaction level with the operating costs of the microgrid.
[0052] Similarly, when hour, This indicates the emissions from the microgrid's combustion generator set. = This represents the maximum Pareto front among multiple optimal solutions in the emissions data. This represents the minimum Pareto front among multiple optimal solutions in the emissions, which can ultimately be calculated. = This indicates the satisfaction level with the emissions from the microgrid's combustion generator set.
[0053] S420, Preset solution constraints: in, , Indicates the preset preference weights; Indicates satisfaction with cost optimization; This indicates satisfaction with emissions optimization. This represents the minimum satisfaction threshold for cost optimization. This represents the minimum satisfaction threshold for emissions optimization. In this embodiment, preference weight It may be set according to the decision-maker's preference for minimizing operating costs; similarly, preference weights. The thresholds may be set based on the decision-maker's preference for minimizing emissions, to fully consider their intentions. Furthermore, this embodiment also includes minimum satisfaction thresholds for cost optimization and emissions optimization. These ensure that the final optimal solution obtained from the optimization process reaches a basic level acceptable to the decision-maker in terms of both operating cost and emissions, avoiding the excessive pursuit of one objective at the expense of the other, thereby ensuring the overall feasibility of microgrid dispatch.
[0054] S430. The optimal configuration parameters of the microgrid are obtained by solving the membership function of the fuzzy satisfaction, the solution constraints, the current operating data, and the multi-objective optimization scheduling model.
[0055] In this embodiment, by setting preference weights and minimum satisfaction thresholds as constraints, and by solving the optimal configuration parameters of the microgrid based on the membership function of fuzzy satisfaction, current operating data, and the multi-objective optimization scheduling model, a compromise balance can be achieved in solving the multi-objective optimization function. This ensures that the final microgrid scheduling can clearly reflect the decision-maker's subjective preferences, such as whether the decision-maker values cost optimization or environmental protection more, while also ensuring that all optimized objectives meet a basic, inviolable performance baseline. Thus, a satisfactory solution that is both biased and takes into account the overall situation can be found in the complex trade-offs.
[0056] S500. Configure the microgrid based on the configuration parameters, and perform multi-objective scheduling on the microgrid based on the current operating data of the microgrid and the configured microgrid.
[0057] In this embodiment, the hardware scale and capability boundary of the microgrid are first determined based on the configuration parameters. Then, based on the current operating data, a multi-objective optimization function that integrates decision-maker preferences is constructed and solved under the physical constraints of robustness constraints, power balance constraints, fuel generator output power constraints, power ramp rate constraints, energy storage unit state constraints, and operation constraints. Furthermore, by setting preference weights and minimum satisfaction thresholds, intelligent trade-offs are achieved between conflicting objectives such as operating costs and environmental emissions. Finally, an optimal microgrid scheduling that combines cost optimization, environmental friendliness, and high reliability under a given microgrid configuration is generated.
[0058] like Figure 3 As shown in the illustration, this application also provides a system for multi-objective scheduling of microgrids. Optionally, the system includes: The system includes a preset cost optimization scheduling model module 611, a preset emission optimization scheduling model module 612, a preset multi-objective optimization scheduling model module 613, a solution module 614, and a configuration module 615, wherein: The preset cost optimization scheduling model module 611 is used to preset the microgrid cost optimization scheduling model and to preset the cost optimization objective function for the microgrid cost optimization scheduling model. In this embodiment, the preset cost optimization scheduling model module 611 can be used to execute... Figure 1 For a detailed description of the preset cost optimization scheduling model module 611, please refer to the description of step S100.
[0059] The preset emission optimization scheduling model module 612 is used to preset the microgrid emission optimization scheduling model and preset the emission optimization objective function for the microgrid emission optimization scheduling model. In this embodiment, the preset emission optimization scheduling model module 612 can be used to execute... Figure 1 For a detailed description of the preset emission optimization scheduling model module 612, see step S200 shown below. For a detailed description of step S200, please refer to the description of step S200.
[0060] The preset multi-objective optimization scheduling model module 613 is used to construct a microgrid multi-objective optimization scheduling model based on the microgrid cost optimization scheduling model and the microgrid emission optimization scheduling model, and to preset multi-objective optimization objective functions and constraints for the microgrid multi-objective optimization model. In this embodiment, the preset multi-objective optimization scheduling model module 613 can be used to execute... Figure 1 For a detailed description of the preset multi-objective optimization scheduling model module 613, see step S300 shown below. For a detailed description of step S300, please refer to the description of step S300.
[0061] The solution module 614 is used to acquire the current operating data of the microgrid, and based on the current operating data and the multi-objective optimization scheduling model, to solve the problem according to the fuzzy satisfaction level to obtain the optimal configuration parameters of the microgrid. In this embodiment, the solver module 614 can be used to perform... Figure 1 For a detailed description of the solution module 614, please refer to the description of step S400 shown.
[0062] The configuration module 615 is used to configure the microgrid based on the configuration parameters and to perform multi-objective scheduling of the microgrid based on the current operating data of the microgrid and the configured microgrid.
[0063] In this embodiment, the configuration module 615 can be used to execute... Figure 1 For a detailed description of the configuration module 615, please refer to the description of step S500 shown.
[0064] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention should be included within the protection scope of the claims of the present invention.
Claims
1. A method for multi-objective scheduling of microgrids, characterized in that, The method includes: A microgrid cost optimization scheduling model is preset, and a cost optimization objective function is preset for the microgrid cost optimization scheduling model; A microgrid emission optimization scheduling model is preset, and an emission optimization objective function is preset for the microgrid emission optimization scheduling model; A multi-objective optimization scheduling model for microgrids is constructed based on the microgrid cost optimization scheduling model and the microgrid emission optimization scheduling model, and a multi-objective optimization objective function and constraints are preset for the multi-objective optimization model of microgrids. The current operating data of the microgrid is obtained, and based on the current operating data and the multi-objective optimization scheduling model, the optimal configuration parameters of the microgrid are obtained by solving according to the fuzzy satisfaction method. The microgrid is configured based on the configuration parameters, and multi-objective scheduling is performed on the microgrid based on the current operating data of the microgrid and the configured microgrid.
2. The method according to claim 1, characterized in that, The cost optimization objective function is: in, This indicates the operating cost of a microgrid. This represents the function for calculating the operating cost of a microgrid. Indicates the first Taiwan fuel generator set Output power during the time period This represents the total time period for microgrid scheduling. This indicates the total number of fuel generator sets. , This represents the fuel cost coefficient. This represents the unit curtailment penalty cost of a photovoltaic power generation unit. express The amount of electricity wasted by photovoltaic power generation units during certain periods This represents the unit cost of wind curtailment penalty for wind power generators. express The amount of electricity wasted by wind turbine generators during certain periods.
3. The method according to claim 1, characterized in that, The emission optimization objective function is: in, This indicates the emissions from the microgrid's combustion generator set. Indicates the first Taiwan fuel generator set Emissions over a period of time , , Indicates the first The emission coefficient of the combustion generator set, Indicates the first Taiwan fuel generator set Output power during a given time period.
4. The method according to claim 1, characterized in that, The multi-objective optimization objective function is: in, This represents the total time period for microgrid scheduling. This indicates the total number of fuel generator sets. Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first Taiwan fuel generator set Operating costs for a given period of time Indicates the first Taiwan fuel generator set Emissions during a given period.
5. The method according to claim 1, characterized in that, The constraints include: Robust constraints: in, Let be a probability function. Indicates the first Taiwan fuel generator set Output power during the time period express The actual output power of renewable energy generator sets during a given period, including photovoltaic generator sets and wind power generator sets. express The load demand of the microgrid during different time periods This indicates the probability of a violation of the opportunity constraint. express The actual output power of the photovoltaic generator set during the time period express The actual output power of the wind turbine generator set during the specified time period.
6. The method according to claim 5, characterized in that, The Actual output power of photovoltaic generator set during the period for: in, express The predicted baseline power of photovoltaic power generation units during the time period Indicates the photovoltaic fluctuation coefficient. Let represent a random variable that follows a first standard normal distribution, and .
7. The method according to claim 5, characterized in that, The Actual output power of wind turbine generators during certain periods for: in, express The predicted baseline power of wind turbine generators during the time period Indicates the wind energy fluctuation coefficient. Let represent a random variable that follows a second standard normal distribution, and .
8. The method according to claim 1, characterized in that, The constraints include: Power balance constraints: in, This indicates the total number of fuel generator sets. Indicates the first Taiwan fuel generator set Output power during the time period express The predicted baseline power of renewable energy generating units during the specified time period, including photovoltaic generating units and wind power generating units. express The load demand of the microgrid during different time periods; And / or, fuel generator set output power constraints: in, Indicates the first The minimum output power of the fuel generator set, Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first The maximum output power of the fuel generator set; And / or, power ramp rate constraint: in, Indicates the first The rate of power degradation of the fuel-powered generator set. Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first Taiwan fuel generator set Output power during the time period Indicates the first The power ramp-up rate of the fuel generator set; And / or, state constraints of the energy storage unit: in, Indicates energy storage unit State of charge during a period of time Indicates energy storage unit State of charge during a period of time This indicates the charging efficiency of the energy storage unit. express Charging power of time-limited energy storage units express Discharge power of the time-limited energy storage unit This indicates the discharge efficiency of the energy storage unit. Indicates a unit of time period; And / or, operating constraints of energy storage units: in, This indicates the minimum charging power of the energy storage unit. express Charging power of time-limited energy storage units This indicates the maximum charging power of the energy storage unit. This indicates the minimum discharge power of the energy storage unit. express Discharge power of the time-limited energy storage unit This indicates the maximum discharge power of the energy storage unit. This indicates the safe state of charge limit for the energy storage unit. Indicates energy storage unit State of charge during a period of time This indicates the upper limit of the safe state of charge for the energy storage unit.
9. The method according to any one of claims 1 to 8, characterized in that, The optimal configuration parameters of the microgrid are obtained by solving the problem based on the current operating data and the multi-objective optimization scheduling model according to the fuzzy satisfaction method, including: Constructing the membership function of fuzzy satisfaction: in, Indicate target Satisfaction indicators Indicate target The actual value, Indicate target The maximum value of the Pareto front. Indicate target The Pareto front minimum; when hour, This represents the operating cost of a microgrid, when... hour, This indicates the emissions from the microgrid's combustion generator set; Preset solution constraints: in, , Indicates the preset preference weights; Indicates satisfaction with cost optimization; This indicates satisfaction with emissions optimization. This represents the minimum satisfaction threshold for cost optimization. This represents the minimum satisfaction threshold for emissions optimization. The optimal configuration parameters of the microgrid are obtained by solving the membership function of the fuzzy satisfaction level, the solution constraints, the current operating data, and the multi-objective optimization scheduling model.
10. A system for multi-objective scheduling of microgrids, characterized in that, The system includes: The module for setting a pre-defined cost optimization scheduling model is used to pre-define a microgrid cost optimization scheduling model and to pre-define a cost optimization objective function for the microgrid cost optimization scheduling model. A preset emission optimization scheduling model module is used to preset a microgrid emission optimization scheduling model and to preset an emission optimization objective function for the microgrid emission optimization scheduling model. A pre-defined multi-objective optimization scheduling model module is used to construct a microgrid multi-objective optimization scheduling model based on the microgrid cost optimization scheduling model and the microgrid emission optimization scheduling model, and to pre-define multi-objective optimization objective functions and constraints for the microgrid multi-objective optimization model. The solution module is used to acquire the current operating data of the microgrid, and based on the current operating data and the multi-objective optimization scheduling model, solves the problem according to the fuzzy satisfaction level to obtain the optimal configuration parameters of the microgrid. The configuration module is used to configure the microgrid based on the configuration parameters and to perform multi-objective scheduling of the microgrid based on the current operating data of the microgrid and the configured microgrid.