Wind-solar-storage microgrid optimal dispatching method and system containing small hydropower storage

Abstract

The application provides a wind-solar-storage micro-grid optimization scheduling method and system containing small hydropower storage, the method comprises the following steps: obtaining historical operation data, and obtaining a full-dimensional prediction data set according to the historical operation data; according to the full-dimensional prediction data set, a multi-objective optimization model is constructed, which takes the minimization of residual load, the minimization of abandoned electricity cost, the minimization of cascade abandoned water quantity, the maximization of green energy collaborative utilization efficiency and the minimization of time-weighted voltage-power comprehensive error as the target; a hybrid optimization algorithm of Hannibal Bacca optimization algorithm and improved sparrow search algorithm is used to solve the multi-objective optimization model, and finally output is obtained. The application can realize efficient consumption of green energy, reduce the phenomenon of abandoned electricity, reduce carbon emissions, and improve the stability, economy and environmental protection of the power system.

Description

Technical Field

[0001] This invention relates to the field of microgrid dispatching technology, and in particular to an optimized dispatching method and system for a wind-solar-storage microgrid with small hydropower storage. Background Technology

[0002] With increasing global focus on sustainable development, the gradual replacement of traditional fossil fuels and the increasing prevalence of green energy, especially the rapid development of renewable energy sources such as wind, solar, and hydropower, have become crucial to the global energy transition. However, the volatility and intermittency of these renewable energy sources pose significant challenges to their integration into the power system. Wind and solar power generation is affected by weather, climate, and diurnal cycles, often making it difficult to guarantee stable power output. This necessitates a high degree of flexibility in the power system to cope with fluctuations in load and power generation.

[0003] To address these issues, the coordinated dispatch of cascaded small hydropower and wind-solar-storage microgrids is gradually emerging as a solution. Cascaded small hydropower systems, due to their flexible regulation capabilities and minimal environmental impact, have become important green peak-shaving resources. Meanwhile, the grid integration of renewable energy sources such as wind and solar power can further improve energy utilization efficiency through abundant geographical and climatic resources. Energy storage systems (such as battery storage and pumped storage) provide the grid with stronger regulation capabilities, storing excess energy when green energy is abundant and releasing it during peak demand periods, thereby balancing system load fluctuations.

[0004] However, how to achieve effective coordination and optimized dispatch in wind, solar, and energy storage microgrids, fully utilize the advantages of various energy sources, maximize the absorption of green energy, and ensure the load balance and stable operation of the system remains a major challenge in current power system dispatch. Existing dispatch methods mostly rely on traditional dispatch rules and fail to fully consider the synergistic effects and dispatch efficiency between different energy sources. Therefore, a new method is urgently needed to optimize the coordinated dispatch of cascaded small hydropower, wind power, photovoltaic, and energy storage systems, improve the absorption rate of green energy, and ensure the economy and stability of the power system. Summary of the Invention

[0005] The purpose of this invention is to provide an optimized scheduling method and system for wind, solar and energy storage microgrids with small hydropower storage, aiming to solve the problem that traditional microgrid scheduling methods rely heavily on traditional scheduling rules and fail to fully consider the synergistic effect and scheduling efficiency between different energy sources.

[0006] In a first aspect, the present invention provides an optimized scheduling method for a wind-solar-storage microgrid containing small hydropower storage, the method comprising:

[0007] Historical operating data of wind and solar power stations, cascade small hydropower stations, pumped storage stations, and energy storage systems in the wind-solar-storage microgrid are obtained, and based on the historical operating data, wind and solar power output, cascade water inflow, and load demand are predicted in the future scheduling cycle to obtain a full-dimensional prediction dataset including the predicted total wind and solar power output, the inflow and net head of each level of cascade hydropower station, and the total load.

[0008] Based on the full-dimensional prediction dataset, a multi-objective optimization model is constructed with the objectives of minimizing residual load, minimizing the cost of curtailed electricity, minimizing the amount of cascaded water curtailment, maximizing the efficiency of green energy synergistic utilization, and minimizing the time-weighted voltage-power comprehensive error.

[0009] A hybrid optimization algorithm combining the Hannibal Barca optimization algorithm and the improved sparrow search algorithm is used to solve the multi-objective optimization model. Based on the solution results, the optimal power generation and power generation flow of the cascade small hydropower stations, the optimal operating conditions and power of the pumped storage station, the optimal charging and discharging power of the energy storage system, and the optimal output limit of the wind and solar power station are output in the future scheduling cycle.

[0010] Based on the optimal power generation, optimal operating conditions and power, optimal charging and discharging power, and optimal output limit adjustment instructions, dispatch instructions are generated and sent to the corresponding power stations and systems for execution.

[0011] Secondly, the present invention provides an optimized dispatching system for a wind-solar-storage microgrid containing small hydropower storage, the system comprising:

[0012] The prediction module is used to acquire historical operating data of wind and solar power stations, cascade small hydropower stations, pumped storage stations and energy storage systems in the wind-solar-storage microgrid, and to predict wind and solar power output, cascade water inflow and load demand in the future scheduling cycle based on the historical operating data, so as to obtain a full-dimensional prediction dataset including the predicted value of total wind and solar power output, the inflow and net head of each level of cascade hydropower station and the total load.

[0013] The objective function construction module is used to construct a multi-objective optimization model based on the full-dimensional prediction dataset, with the objectives of minimizing the remaining load, minimizing the cost of power curtailment, minimizing the amount of water curtailment in the cascade, maximizing the efficiency of green energy synergistic utilization, and minimizing the time-weighted voltage-power comprehensive error.

[0014] The solution module is used to solve the multi-objective optimization model by using a hybrid optimization algorithm that combines the Hannibal Barca optimization algorithm and the improved sparrow search algorithm. Based on the solution results, it outputs the optimal power generation and power generation flow of the cascade small hydropower stations, the optimal operating conditions and power of the pumped storage station, the optimal charging and discharging power of the energy storage system, and the optimal output limit of the wind and solar power station within the future scheduling cycle.

[0015] The control execution module is used to generate scheduling instructions based on the optimal power generation, optimal operating conditions and power, optimal charging and discharging power and optimal output limit adjustment instructions, and send them to the corresponding power station and system for execution.

[0016] Thirdly, the present invention provides a storage medium that stores one or more programs, which, when executed by a processor, implement the above-described optimized scheduling method for a wind-solar-storage microgrid containing small hydropower storage.

[0017] Fourthly, the present invention provides an electronic device, the electronic device comprising a memory and a processor, wherein:

[0018] The memory is used to store computer programs;

[0019] When the processor executes the computer program stored in the memory, it implements the above-mentioned optimized scheduling method for wind-solar-storage microgrids containing small hydropower storage.

[0020] Compared with the prior art, the present invention has the following advantages:

[0021] This scheme integrates historical and forecast data from wind and solar power, cascade hydropower, pumped storage, and energy storage systems to construct a multi-objective optimization model centered on minimizing surplus load, minimizing curtailment costs, minimizing cascade water curtailment, maximizing the efficiency of green energy synergistic utilization, and minimizing the time-weighted voltage-power comprehensive error. This model accurately characterizes the physical laws governing microgrid operation, such as water level, flow rate, reservoir capacity, output constraints, and multiple economic and environmental objectives. To solve this complex nonlinear problem with high dimensions, strong constraints, and multiple objectives, a hybrid optimization algorithm is innovatively adopted, combining the Hannibal Barca optimization algorithm and an improved sparrow search algorithm. This algorithm incorporates multiple strategies, including adaptive search, learning towards the optimum, random perturbation, and mode block exchange, effectively avoiding getting trapped in local optima and efficiently generating scheduling schemes that satisfy all constraints within the feasible region. This systematically generates the optimal power commands for each controllable resource (cascade hydropower stations, pumped storage stations, and energy storage systems) and the output ceiling for wind and solar power generation within future scheduling cycles. This not only achieves precise spatiotemporal matching between fluctuating wind and solar power and adjustable power of small hydropower and flexible power of pumped storage, thereby maximizing the local consumption of renewable energy and significantly reducing wind, solar and hydropower curtailment; but also, through multi-timescale coordination and optimization, it smooths out net load fluctuations, maintains the stability of system voltage and power, and ultimately, under the premise of ensuring the safe operation of the power grid, comprehensively improves the economy, environmental protection and power supply reliability of the microgrid, and achieves green power dispatch with optimal comprehensive benefits. Attached Figure Description

[0022] Figure 1 This is a flowchart of an optimized scheduling method for a wind-solar-storage microgrid with small hydropower storage proposed in an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of the structure of a wind-solar-storage microgrid optimization and dispatching system with small hydropower storage proposed in an embodiment of the present invention.

[0024] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art. The terms "comprising" and similar expressions used herein mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but does not exclude other elements or objects.

[0026] like Figure 1 As shown, an embodiment of the present invention proposes an optimized scheduling method for a wind-solar-storage microgrid containing small hydropower storage. This method includes steps S101 to S104, wherein:

[0027] Step S101: Obtain historical operating data of wind and solar power stations, cascade small hydropower stations, pumped storage stations, and energy storage systems in the wind-solar-storage microgrid, and predict wind and solar power output, cascade water inflow, and load demand in the future scheduling cycle based on the historical operating data to obtain a full-dimensional prediction dataset including the predicted total wind and solar power output, the inflow and net head of each level of cascade hydropower station, and the total load.

[0028] It should be noted that historical data includes, but is not limited to, actual power output curves of wind farms and photovoltaic power plants, measured inflow and water levels at each level of cascade hydropower stations, reservoir energy storage changes, operating records of pumped storage power plants, charging and discharging power and state of charge changes in energy storage systems, and microgrid load demand curves. This data typically originates from Supervisory Control and Data Acquisition (SCADA) systems, Energy Management Systems (EMS), or meteorological and hydrological databases, and the time resolution should meet dispatching requirements (e.g., 15-minute or 1-hour intervals). Based on historical data, mature forecasting methods should be employed to predict key variables for future dispatching cycles. Specifically, wind and solar power output forecasting requires combining numerical weather prediction data with machine learning methods (such as Long Short-Term Memory networks, LSTM) or physical models to generate wind speed and irradiance sequences, which are then converted into power prediction curves for wind farms and photovoltaic power plants. Cascade inflow forecasting requires constructing hydrological models (such as the Xin'anjiang model) or data-driven models based on historical hydrological data and basin rainfall forecasts to progressively deduce the inflow process of each reservoir and estimate net head changes by combining reservoir scheduling rules and water level-flow relationship. Load demand forecasting, based on historical load curves and factors such as meteorology and date type, uses time series analysis or regression models to generate future load demand curves. Finally, this step will output a structured, full-dimensional forecast dataset. This dataset should include time-series forecasts of total wind and solar power output, inflow and net head sequences for each cascade hydropower station, and total system load forecast sequences. Its time span and resolution should be consistent with the scheduling cycle, providing complete boundary conditions and input parameters for subsequent optimized scheduling.

[0029] Step S102: Based on the full-dimensional prediction dataset, construct a multi-objective optimization model with the objectives of minimizing residual load, minimizing power curtailment cost, minimizing cascade water curtailment, maximizing the efficiency of green energy synergistic utilization, and minimizing the time-weighted voltage-power comprehensive error.

[0030] It should be noted that, in some embodiments, a multi-objective optimization model is constructed based on the following formula: ;

[0031] Where F is the fitness function, A is the residual load, B is the cost of power curtailment, and C is the cascade water curtailment volume. , , , , These represent the theoretical maximum values ​​of remaining load, cost of power curtailment, cascade water curtailment, collaborative utilization efficiency, and time-weighted voltage-power comprehensive error, respectively. , , , , These are the first objective function, the second objective function, the third objective function, the fourth objective function, and the fifth objective function, respectively. , , , , All are weighting coefficients.

[0032] Five objectives with different dimensions and orders of magnitude are normalized using theoretical maximum values, transforming them into dimensionless relative deviation indices. This formula solves the problem of imbalanced optimization preferences caused by inconsistent dimensions in multi-objective optimization. Furthermore, by flexibly adjusting the priority of different objectives in scheduling through weight coefficients, the model can balance economy, stability, environmental friendliness, and efficiency, while also providing a mathematical foundation for the direct solution of subsequent single-objective intelligent optimization algorithms.

[0033] Furthermore, in some embodiments, a first objective function aimed at minimizing the residual load is constructed according to the following formula:

[0034] ;

[0035] Where T represents the scheduling time period, This represents the value of the remaining load at time t under the condition of green energy consumption. This represents the average value of the remaining load. This represents the load consumption at time t, and N represents the total number of cascade hydropower stations. Let represent the power generation of the nth cascade hydropower station at time t. This represents the power generation of the pumped storage power station at time t. This represents the sum of the power output of the wind and solar power station at time t. This represents the output of the energy storage system at time t.

[0036] Traditional dispatching often focuses only on total cost or total abandoned power, while ignoring the smoothness of the output curve. Based on this, the above formula designs the remaining load as the net load after deducting the output of cascade hydropower, pumped storage, wind and solar power, and energy storage from the total load demand. By minimizing the volatility of the remaining load, it actively guides the wind, solar, hydro, and energy storage coordinated output curve to fit the load curve as closely as possible, thereby significantly reducing the system's peak-shaving pressure and reducing the power support requirements for the external power grid.

[0037] Furthermore, in some embodiments, a second objective function aimed at minimizing the cost of curtailment is constructed according to the following formula:

[0038] ;

[0039] in, Let t be the actual wind power output. The charging power of the energy storage system at time t. Let t be the load demand. Let t be the pumping power of the pumped storage unit. To cover the cost of power curtailment penalties, As a comprehensive compensation factor, it is determined by the wind power absorption rate. Hydropower response factors and energy storage status With pumping capacity Joint decision, , , These are the weighting coefficients.

[0040] The above formula dynamically links the cost of curtailed electricity with the system's real-time regulation capability: when the wind and solar power absorption rate is high, the hydropower response capability is strong, and the energy storage or pumping potential is large, Increased power consumption leads to lower penalties for curtailment, thus incentivizing the dispatch system to prioritize the use of internal flexible resources for energy consumption. This mechanism guides the system to tap into the potential of multi-resource collaboration through economic signals, achieving a flexible interaction between curtailment costs and system regulation capacity, fundamentally improving the efficiency of local renewable energy consumption.

[0041] Furthermore, in some embodiments, a third objective function aimed at minimizing the cascade water discharge is constructed according to the following formula:

[0042] ;

[0043] in, The inflow rate of the i-th level hydropower station during time period t The power generation flow rate of the i-th level hydropower station during time period t. Ecological base flow of the i-th level hydropower station The maximum reservoir capacity of the i-th level hydropower station Let be the reservoir capacity of the i-th hydropower station during time period t. Scheduling duration, Water is being released from the upper-level reservoir. Water from a tributary This represents the net head of water used for power generation at the i-th level hydropower station during time period t. This indicates the energy conversion efficiency of a hydroelectric power station. Let be the power generation of the nth cascade hydropower station at time t.

[0044] The above formula establishes a hydraulic-electric coupling optimization mechanism based on "electricity-driven water use," directly converting hydropower generation decisions into flow demand, thereby achieving synergy between power generation and water resource utilization under reservoir water balance constraints. This model not only considers ecological baseflow and reservoir capacity regulation but also explicitly defines water wastage as overflow caused by insufficient power generation flow, thus guiding the optimization algorithm to maximize hydropower generation efficiency while meeting electricity demand. This solves the problem of traditional hydropower dispatching being disconnected from wind, solar, and storage optimization, which easily leads to water resource waste.

[0045] Furthermore, in some embodiments, a fourth objective function aimed at maximizing the efficiency of green energy synergistic utilization is constructed according to the following formula:

[0046] ;

[0047] Where D represents the collaborative utilization efficiency. Contribute to the glory of moment t. Contribute to the efficient use of wind and solar power. Let t be the power generation capacity of the pumped storage power station at time t. To improve the power generation efficiency of pumped storage power stations. Let be the power generation capacity of the nth-level hydropower station at time t. For the energy conversion efficiency of hydropower stations, Let be the discharge power of the energy storage system at time t. For the discharge efficiency of the energy storage system, Let be the inflow rate of the i-th hydropower station at time t. Let t be the net head of power generation for the i-th stage hydropower station. The charging power of the energy storage system at time t. To improve the charging efficiency of energy storage systems, Let t be the pumping power of the pumped storage power station at time t. To measure the pumping efficiency of pumped storage power stations. The additional losses for wind and solar grid connection at time t.

[0048] In summary, by maximizing the efficiency of synergistic utilization, the overall operational efficiency and energy conversion economy of a multi-energy system combining wind, solar, hydro, energy storage, and pumped storage can be systematically optimized. This function comprehensively considers the effective utilization of wind and solar power output, the power generation efficiency of hydropower stations, the charging and discharging efficiency of energy storage systems, and the regulation capacity of pumped storage stations. It also incorporates grid connection losses into the evaluation system. Therefore, while ensuring stable system operation, it effectively improves the absorption level of renewable energy, reduces wind and solar curtailment rates, and enhances the flexibility and economy of system operation, ultimately achieving efficient complementarity and synergistic optimization of multiple energy sources in the spatiotemporal dimensions.

[0049] Furthermore, in some embodiments, a fifth objective function is constructed based on the following formula, which aims to minimize the time-weighted voltage-power combined error:

[0050] ;

[0051] Where E is the time-weighted voltage-power combined error. For voltage and power combined normalized error, , These refer to the tracking errors at the energy storage and pumped storage equipment levels. , All of these are due to device response delay. For the real-time voltage of the microgrid, For dynamic reference voltage, For dynamic reference power, This represents the real-time total power of the microgrid. The load power consumption is k, and the weighting coefficient is k.

[0052] In summary, by minimizing the time-weighted voltage-power combined error, the power quality and power balance levels of a microgrid under dynamic operating conditions can be accurately assessed and optimized. This function not only considers the normalized combined effects of voltage and power deviations but also strengthens the suppression of persistent deviations by introducing a time-weighted factor. Furthermore, it incorporates the response delays and tracking errors of energy storage and pumped storage devices, thereby effectively guiding the system to quickly and accurately track dynamic reference values, enhancing system transient stability, suppressing voltage fluctuations, and improving adaptability to intermittent renewable energy output and load fluctuations, ultimately ensuring the safe, reliable, and high-quality operation of the microgrid.

[0053] In addition, in some embodiments, the water level constraints of the multi-objective optimization model need to be constructed according to the following formula:

[0054] ;

[0055] in, This represents the minimum bit limit for the i-th level hydropower station during time period t. This represents the maximum water level limit for the i-th level hydropower station during time period t. This represents the water level fluctuation constraint of the i-th stage hydropower station. , These are the water levels of the i-th hydropower station during time period t and time period t-1, respectively.

[0056] The dynamic load balance of a hydropower station can be constructed using the following formula:

[0057] ;

[0058] in, Let be the power output of the i-th stage hydropower station during time period t. The total power output of the power grid to the cascade hydropower stations during time period t;

[0059] The power output constraints for each level of power station are constructed based on the following formula:

[0060] ;

[0061] in, Let represent the minimum generating capacity of the i-th stage hydroelectric power station. This represents the maximum power output of the i-th stage hydroelectric power station. Let be the power output of the i-th stage hydropower station during time period t;

[0062] The outflow constraint of the hydropower station is constructed based on the following formula:

[0063] ;

[0064] in, , These are the minimum and maximum outflow rates of the i-th level hydropower station, respectively.

[0065] The reservoir capacity constraint for a hydropower station is constructed using the following formula:

[0066] ;

[0067] in, , These are the minimum and maximum reservoir capacity limits for the i-th level hydropower station, respectively.

[0068] The operating constraints of the energy storage system are constructed based on the following formula:

[0069] ;

[0070] in, , These represent the minimum and maximum charging power of the energy storage system at time t, respectively. , Let t be the minimum and maximum discharge power of the energy storage system at time t. Let t be the remaining power of the energy storage system at time t. For the rated capacity of the energy storage system, To schedule the time step, , These represent the remaining energy storage capacity at the beginning and end of the scheduling cycle, respectively.

[0071] The operating constraints for pumped storage power stations are constructed based on the following formula:

[0072] ;

[0073] in, , These represent the minimum and maximum generating capacities of the pumped storage power station at time t, respectively. , These represent the minimum and maximum pumping power of the pumped storage power station at time t, respectively. , These represent the water levels at time t in the upper and lower reservoirs of the pumped storage power station, respectively. For the upper limit of the reservoir water level, , These represent the upper and lower limits of the reservoir water level, respectively; g is the acceleration due to gravity; and ρ is the density of water. , These represent the pumping head and power generation head of the pumped storage power station at time t, respectively.

[0074] In summary, by constructing comprehensive physical constraints, including water level fluctuation constraints, power station output upper and lower limits constraints, outflow constraints, reservoir capacity constraints, and energy storage system operation constraints, the scheduling scheme is ensured to comply with the actual operating procedures and equipment safety limits of the hydropower station. For example, water level fluctuation constraints prevent the impact of drastic reservoir water level fluctuations on dam safety, while output upper and lower limits constraints ensure that the units operate within a safe and economical range. Without these constraints, the optimization result may be mathematically optimal but engineering infeasible, and could even lead to equipment damage or safety accidents. Therefore, these constraints are the foundation for the feasibility of the scheduling scheme. Furthermore, by setting strict upper and lower limits for equipment charging and discharging power and operating capacity, and establishing a dynamic balance relationship between these limits and system state variables, it is ensured that energy storage and pumped storage equipment always operate within safe and reliable boundaries during actual operation. This prevents the risks of equipment overload, excessive discharge, or operation beyond limits, providing a physically feasible domain that conforms to engineering realities for optimized scheduling. This ensures the safe and stable operation of the system and provides crucial structural support for achieving energy time-shifting and regulation capabilities over long-term scheduling cycles.

[0075] Step S103: The multi-objective optimization model is solved by a hybrid optimization algorithm that combines the Hannibal Barca optimization algorithm and the improved sparrow search algorithm. Based on the solution results, the optimal power generation and power generation flow of the cascade small hydropower stations, the optimal operating conditions and power of the pumped storage station, the optimal charging and discharging power of the energy storage system, and the optimal output limit of the wind and solar power station are output in the future scheduling cycle.

[0076] It should be noted that the specific solution process in this step is as follows:

[0077] The power generation of cascade small hydropower stations, the pumping / power generation of pumped storage stations, the charging and discharging power of energy storage systems, and the adjustment amount of the upper limit of output of wind and solar power stations in each period of the future scheduling cycle are encoded into a decision vector. A set of initial scheduling schemes that satisfy all constraints are randomly generated to form an initial solution group, and the fitness function value corresponding to each individual in the initial solution group is calculated.

[0078] For individuals whose fitness function value is greater than the first threshold, sort them in ascending order of fitness function value, and assign a decreasing adaptive search step size to update their position.

[0079] For individuals whose fitness function value is less than or equal to the first threshold, the learning is performed by learning from the position of the current best individual, while introducing random perturbation and crossover operation.

[0080] For individuals that still violate device constraints after location updates, a new, corrected feasible solution is generated based on the physical association rules of the system operation, and their fitness function value is recalculated.

[0081] Every preset number of iterations, a subpopulation is selected from the current solution group based on spatial distribution or fitness value differences, and the Hannibal Barca optimization algorithm is used to perform the following operations:

[0082] From different individuals in the subpopulation, the decision variable sequences of different consecutive time periods within the scheduling cycle are randomly extracted to form the output mode block;

[0083] Output pattern blocks with different time characteristics are randomly exchanged among different individuals;

[0084] In the new individual that has completed the pattern block exchange, one or more consecutive time periods within its scheduling cycle are randomly selected, and the power generation of the cascade hydropower station or the operating power of the pumped storage station within that time period is reset to a random value within its feasible range.

[0085] Generate new individuals that meet the constraints, re-inject them into the master solution group, and participate in the next round of iterative optimization until the preset maximum number of iterations is reached, and then output the solution results.

[0086] In summary, the above solution process mainly consists of four core stages. The first stage is encoding initialization, where the power generation of the cascade small hydropower stations at each time period, the pumping / generating power of the pumped storage stations, the charging and discharging power of the energy storage system, and the adjustment amount of the wind and solar power output upper limit are encoded into decision vectors. An initial scheduling scheme group satisfying the constraints is randomly generated, and the fitness value is calculated. The second stage is adaptive iterative search, where individuals are divided into two categories based on their fitness values: for poor individuals with fitness values ​​greater than a first threshold, they are sorted by fitness value from smallest to largest and given a decreasing adaptive step size for local fine-tuning search; for excellent individuals with fitness values ​​less than or equal to the first threshold, they are guided to learn from the current best individual's position, while random perturbations and crossover operations are introduced to avoid premature convergence. The third stage is the constraint repair mechanism, where individuals that still violate physical constraints after position updates are corrected according to the physical association rules of the system operation, feasible schemes are regenerated, and the fitness value is updated. The fourth stage involves global pattern exchange exploration. Every preset number of iterations, a subpopulation is selected, and the Hannibal Barca algorithm is used to perform the extraction, exchange, and time-period reset operations of output pattern blocks. Specifically, this includes: randomly extracting consecutive time-period decision variable sequences from different individuals to form output pattern blocks; exchanging output pattern blocks with different time characteristics among different individuals; and randomly selecting consecutive time periods in the new individuals to reset their hydropower station's generating power or pumped storage station's operating power to random values ​​within a feasible range. The new individuals generated through pattern exchange are reinjected into the principal solution group to participate in iterative optimization until the maximum number of iterations is reached, at which point the optimal solution is output.

[0087] Since the microgrid optimal scheduling problem is a typical high-dimensional, nonlinear, and strongly constrained complex optimization problem, and traditional single intelligent algorithms such as genetic algorithms or particle swarm optimization are prone to getting trapped in local optima and have slow convergence speeds, they are difficult to meet the real-time requirements of online scheduling. Therefore, the solution algorithm in this embodiment integrates the global pattern exchange capability of the Hannibal Barca algorithm and the local fine-grained search characteristics of the improved sparrow search algorithm. By alternately executing local iterations and global perturbations, a balance between breadth exploration and depth development is achieved, significantly improving the global optimality and convergence efficiency of the solution. Furthermore, the adaptive search strategy and constraint repair mechanism designed in the algorithm are specifically designed for the practical engineering needs of the scheduling problem: the adaptive step size is dynamically adjusted according to the individual fitness, enabling the search process to quickly approach the optimal region in the early stages of iteration and finely adjust it in the later stages; the constraint repair mechanism ensures that all solutions satisfy complex physical and operational constraints, avoiding the problem of lost feasible solutions that easily occurs when the traditional penalty function method handles constraints. Furthermore, the mode block swapping operation in the Hannibal Barca algorithm is essentially an innovative global reorganization of the scheduling curve structure. This breaks through the limitations of traditional point-by-point optimization, discovering entirely new scheduling patterns in the solution space. This is particularly crucial for solving the collaborative optimization problem involving multiple time scales of wind, solar, hydro, and storage. In summary, this hybrid optimization algorithm is not a simple aggregation of existing algorithms, but a customized design specifically addressing the unique complexity of the wind-solar-hydro-storage microgrid scheduling problem.

[0088] Step S104: Generate a dispatching instruction based on the optimal power generation, optimal operating condition and power, optimal charging and discharging power and optimal output limit adjustment instruction, and send it to the corresponding power station and system for execution.

[0089] In this step, start-up and shutdown instructions and load allocation commands for hydropower stations at all levels are generated; based on the optimal operating conditions and power curves of pumped storage stations, a pumping / generation mode switching schedule and power setpoints are formulated; based on the optimal charging and discharging power plan of the energy storage system, its charging and discharging status and power values ​​for each time period are determined; and combined with the optimal output limit of wind and solar power stations, power limit instructions for wind and solar power stations are formed. All instructions must be arranged according to the time resolution of the scheduling cycle and sent to the local controllers of the corresponding wind and solar power stations, cascade small hydropower stations, pumped storage stations, and energy storage systems through the microgrid energy management system or scheduling communication network, driving each unit to execute according to the plan, ultimately achieving coordinated and optimized operation of wind, solar, hydro, and energy storage.

[0090] like Figure 2 As shown, one embodiment of the present invention proposes an optimized dispatching system for a wind-solar-storage microgrid with small hydropower storage, the system comprising:

[0091] Prediction module 10 is used to acquire historical operating data of wind and solar power stations, cascade small hydropower stations, pumped storage stations and energy storage systems in the wind-solar-storage microgrid, and to predict wind and solar power output, cascade water inflow and load demand in future scheduling cycles based on the historical operating data, so as to obtain a full-dimensional prediction dataset including the predicted value of total wind and solar power output, the inflow and net head of each level of cascade hydropower station and the total load.

[0092] The objective function construction module 20 is used to construct a multi-objective optimization model based on the full-dimensional prediction dataset, with the objectives of minimizing the remaining load, minimizing the cost of power curtailment, minimizing the amount of water curtailment in the cascade, maximizing the efficiency of green energy synergistic utilization, and minimizing the time-weighted voltage-power comprehensive error.

[0093] The solution module 30 is used to solve the multi-objective optimization model by using a hybrid optimization algorithm that combines the Hannibal Barca optimization algorithm and the improved sparrow search algorithm, so as to output the optimal power generation and power generation flow of the cascade small hydropower station, the optimal operating condition and power of the pumped storage station, the optimal charging and discharging power of the energy storage system, and the optimal output limit of the wind and solar power station within the future scheduling cycle based on the solution results.

[0094] Evaluation module 40 is used to introduce the efficiency of green energy synergistic utilization and the time-weighted voltage-power comprehensive error integral as post-evaluation standards to quantitatively evaluate the effect of the optimal scheduling scheme and generate a post-evaluation report.

[0095] The control execution module 50 is used to generate scheduling instructions based on the optimal power generation, optimal operating conditions and power, optimal charging and discharging power and optimal output limit adjustment instructions, and send them to the corresponding power station and system for execution.

[0096] In another aspect, the present invention also proposes a storage medium on which one or more programs are stored, which, when executed by a processor, implement the above-described optimized scheduling method for a wind-solar-storage microgrid containing small hydropower storage.

[0097] In another aspect, the present invention also proposes an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to realize the above-mentioned optimized scheduling method for wind, solar and energy storage microgrids with small hydropower storage.

[0098] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain stored, communicated, propagated, or transmitted programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0099] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0100] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0101] While embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations fall within the scope and spirit of the invention as set forth in the claims. Furthermore, the invention described herein may have other embodiments and can be implemented or carried out in various ways.

Claims

1. A method for optimized scheduling of a wind-solar-storage microgrid with small hydropower storage, characterized in that, The method includes: Historical operating data of wind and solar power stations, cascade small hydropower stations, pumped storage stations, and energy storage systems in the wind-solar-storage microgrid are obtained, and based on the historical operating data, wind and solar power output, cascade water inflow, and load demand are predicted in the future scheduling cycle to obtain a full-dimensional prediction dataset including the predicted total wind and solar power output, the inflow and net head of each level of cascade hydropower station, and the total load. Based on the full-dimensional prediction dataset, a multi-objective optimization model is constructed with the objectives of minimizing residual load, minimizing the cost of curtailed electricity, minimizing the amount of cascaded water curtailment, maximizing the efficiency of green energy synergistic utilization, and minimizing the time-weighted voltage-power comprehensive error. A hybrid optimization algorithm combining the Hannibal Barca optimization algorithm and the improved sparrow search algorithm is used to solve the multi-objective optimization model. Based on the solution results, the optimal power generation and power generation flow of the cascade small hydropower stations, the optimal operating conditions and power of the pumped storage station, the optimal charging and discharging power of the energy storage system, and the optimal output limit of the wind and solar power station are output in the future scheduling cycle. The power generation of cascade small hydropower stations, the pumping / power generation of pumped storage stations, the charging and discharging power of energy storage systems, and the adjustment amount of the upper limit of output of wind and solar power stations in each period of the future scheduling cycle are encoded into a decision vector. A set of initial scheduling schemes that satisfy all constraints are randomly generated to form an initial solution group, and the fitness function value corresponding to each individual in the initial solution group is calculated. For individuals whose fitness function value is greater than the first threshold, sort them in ascending order of fitness function value, and assign a decreasing adaptive search step size to update their position. For individuals whose fitness function value is less than or equal to the first threshold, the learning is performed by learning from the position of the current best individual, while introducing random perturbation and crossover operation. For individuals that still violate device constraints after location updates, a new, corrected feasible solution is generated based on the physical association rules of the system operation, and their fitness function value is recalculated. Every preset number of iterations, a subpopulation is selected from the current solution group based on spatial distribution or fitness value differences, and the Hannibal Barca optimization algorithm is used to perform the following operations: From different individuals in the subpopulation, the decision variable sequences of different consecutive time periods within the scheduling cycle are randomly extracted to form the output mode block; Output pattern blocks with different time characteristics are randomly exchanged among different individuals; In the new individual that has completed the pattern block exchange, one or more consecutive time periods within its scheduling cycle are randomly selected, and the power generation of the cascade hydropower station or the operating power of the pumped storage station within that time period is reset to a random value within its feasible range. Generate new individuals that meet the constraints, re-inject them into the principal solution group, and participate in the next round of iterative optimization until the preset maximum number of iterations is reached, and then output the solution results. Based on the optimal power generation, optimal operating conditions and power, optimal charging and discharging power, and optimal output limit adjustment instructions, dispatch instructions are generated and sent to the corresponding power stations and systems for execution.

2. The optimized scheduling method for a wind-solar-storage microgrid with small hydropower storage as described in claim 1, characterized in that, Construct a multi-objective optimization model based on the following formula: ； Where F is the fitness function, A is the residual load, B is the cost of power curtailment, and C is the cascade water curtailment volume. , , , , These represent the theoretical maximum values ​​of remaining load, cost of power curtailment, cascade water curtailment, collaborative utilization efficiency, and time-weighted voltage-power comprehensive error, respectively. , , , , These are the first objective function, the second objective function, the third objective function, the fourth objective function, and the fifth objective function, respectively. , , , , All are weighting coefficients.

3. The optimized scheduling method for a wind-solar-storage microgrid with small hydropower storage as described in claim 2, characterized in that, Construct the first objective function with the goal of minimizing the residual load based on the following formula: ； Where T represents the scheduling time period, This represents the value of the remaining load at time t under the condition of green energy consumption. This represents the average value of the remaining load. This represents the load consumption at time t, and N represents the total number of cascade hydropower stations. Let represent the power generation of the nth cascade hydropower station at time t. This represents the power generation of the pumped storage power station at time t. This represents the sum of the power output of the wind and solar power station at time t. This represents the output of the energy storage system at time t.

4. The optimized scheduling method for a wind-solar-storage microgrid with small hydropower storage as described in claim 3, characterized in that, Construct a second objective function based on the following formula, aiming to minimize the cost of electricity curtailment: ； in, Let t be the actual wind power output. The charging power of the energy storage system at time t. Let t be the load demand. Let t be the pumping power of the pumped storage unit. To cover the cost of power curtailment penalties, As a comprehensive compensation factor, it is determined by the wind power absorption rate. Hydropower response factors and energy storage status With pumping capacity Joint decision, , , These are the weighting coefficients.

5. The optimized scheduling method for a wind-solar-storage microgrid with small hydropower storage as described in claim 4, characterized in that, Construct a third objective function based on the following formula, with the goal of minimizing the amount of water wasted in the cascade irrigation system: ； in, The inflow rate of the i-th level hydropower station during time period t The power generation flow rate of the i-th level hydropower station during time period t. Ecological base flow of the i-th level hydropower station The maximum reservoir capacity of the i-th level hydropower station Let be the reservoir capacity of the i-th hydropower station during time period t. Scheduling duration, Water is being released from the upper-level reservoir. Water from a tributary This represents the net head of water used for power generation at the i-th level hydropower station during time period t. This indicates the energy conversion efficiency of a hydroelectric power station. Let be the power generation of the nth cascade hydropower station at time t.

6. The optimized scheduling method for a wind-solar-storage microgrid with small hydropower storage as described in claim 5, characterized in that, Construct a fourth objective function based on the following formula, aiming to maximize the efficiency of synergistic utilization of green energy: ； Where D represents the collaborative utilization efficiency. Contribute to the efficient use of wind and solar power. Let t be the power generation capacity of the pumped storage power station at time t. To improve the power generation efficiency of pumped storage power stations. Let be the power generation capacity of the nth-level hydropower station at time t. For the energy conversion efficiency of hydropower stations, For the discharge efficiency of the energy storage system, Let be the inflow rate of the i-th hydropower station at time t. Let t be the net head of power generation for the i-th stage hydropower station. The charging power of the energy storage system at time t. To improve the charging efficiency of energy storage systems, Let t be the pumping power of the pumped storage power station at time t. To measure the pumping efficiency of pumped storage power stations. Additional losses for wind and solar grid connection at time t; The fifth objective function, which aims to minimize the time-weighted voltage-power combined error, is constructed based on the following formula: ； Where E is the time-weighted voltage-power combined error. For voltage and power combined normalized error, , These refer to the tracking errors at the energy storage and pumped storage equipment levels. , All of these are due to device response delay. For the real-time voltage of the microgrid, For dynamic reference voltage, For dynamic reference power, This represents the real-time total power of the microgrid. The load power consumption is k, and the weighting coefficient is k.

7. The optimized scheduling method for a wind-solar-storage microgrid with small hydropower storage as described in claim 6, characterized in that, The method further includes: Construct the water level constraints for the multi-objective optimization model based on the following formula: ； in, This represents the minimum bit limit for the i-th level hydropower station during time period t. This represents the maximum water level limit for the i-th level hydropower station during time period t. This represents the water level fluctuation constraint of the i-th stage hydropower station. , These are the water levels of the i-th hydropower station during time period t and time period t-1, respectively. The dynamic load balance of a hydropower station can be constructed using the following formula: ； in, Let be the power output of the i-th stage hydropower station during time period t. The total power output of the power grid to the cascade hydropower stations during time period t; The power output constraints for each level of power station are constructed based on the following formula: ； in, Let represent the minimum generating capacity of the i-th stage hydroelectric power station. This represents the maximum power output of the i-th stage hydroelectric power station. Let be the power output of the i-th stage hydropower station during time period t; The outflow constraint of the hydropower station is constructed based on the following formula: ； in, , These are the minimum and maximum outflow rates of the i-th level hydropower station, respectively. The reservoir capacity constraint for a hydropower station is constructed using the following formula: ； in, , These are the minimum and maximum reservoir capacity limits for the i-th level hydropower station, respectively. The operating constraints of the energy storage system are constructed based on the following formula: ； in, , These represent the minimum and maximum charging power of the energy storage system at time t, respectively. , Let t be the minimum and maximum discharge power of the energy storage system at time t. Let t be the remaining power of the energy storage system at time t. For the rated capacity of the energy storage system, To schedule the time step, , These represent the remaining energy storage capacity at the beginning and end of the scheduling cycle, respectively. The operating constraints for pumped storage power stations are constructed based on the following formula: ； in, , These represent the minimum and maximum generating capacities of the pumped storage power station at time t, respectively. , These represent the minimum and maximum pumping power of the pumped storage power station at time t, respectively. , These represent the water levels at time t in the upper and lower reservoirs of the pumped storage power station, respectively. For the upper limit of the reservoir water level, , These represent the upper and lower limits of the reservoir water level, respectively; g is the acceleration due to gravity; and ρ is the density of water. , These represent the pumping head and power generation head of the pumped storage power station at time t, respectively.

8. A wind-solar-storage microgrid optimization dispatching system incorporating small hydropower storage, used to implement the wind-solar-storage microgrid optimization dispatching method incorporating small hydropower storage as described in any one of claims 1-7, characterized in that, The system includes: The prediction module is used to acquire historical operating data of wind and solar power stations, cascade small hydropower stations, pumped storage stations and energy storage systems in the wind-solar-storage microgrid, and to predict wind and solar power output, cascade water inflow and load demand in the future scheduling cycle based on the historical operating data, so as to obtain a full-dimensional prediction dataset including the predicted value of total wind and solar power output, the inflow and net head of each level of cascade hydropower station and the total load. The objective function construction module is used to construct a multi-objective optimization model based on the full-dimensional prediction dataset, with the objectives of minimizing the remaining load, minimizing the cost of power curtailment, minimizing the amount of water curtailment in the cascade, maximizing the efficiency of green energy synergistic utilization, and minimizing the time-weighted voltage-power comprehensive error. The solution module is used to solve the multi-objective optimization model by using a hybrid optimization algorithm that combines the Hannibal Barca optimization algorithm and the improved sparrow search algorithm. Based on the solution results, it outputs the optimal power generation and power generation flow of the cascade small hydropower stations, the optimal operating conditions and power of the pumped storage station, the optimal charging and discharging power of the energy storage system, and the optimal output limit of the wind and solar power station within the future scheduling cycle. The control execution module is used to generate scheduling instructions based on the optimal power generation, optimal operating conditions and power, optimal charging and discharging power and optimal output limit adjustment instructions, and send them to the corresponding power station and system for execution.

9. A storage medium, characterized in that, The storage medium stores one or more programs, which, when executed by a processor, implement the optimized scheduling method for wind-solar-storage microgrids with small hydropower storage as described in any one of claims 1-7.

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