Multi-resource coordination control method and system in new energy high proportion power grid

Abstract

The application discloses a kind of new energy high proportion power grid in multi-resource coordination control method and system, coordination control method includes: collection power grid data, power grid data includes new energy output data, meteorological data, power grid load data, power grid operating state data and energy storage system operating parameter;Based on power grid data, new energy output prediction model is constructed;Based on power grid data and new energy output prediction model, quantitative cost is calculated, and quantitative cost includes energy storage system cost, power grid operating cost and power grid stability cost;Based on quantitative cost, energy storage charging and discharging strategy model is constructed;Based on energy storage charging and discharging strategy model, optimal energy storage charging and discharging strategy is obtained;Based on optimal energy storage charging and discharging strategy, multi-resource collaborative scheduling model is constructed, and scheduling plan is output.The application quantifies energy storage depreciation, power grid loss and stability risk into specific cost indicators by calculating quantitative cost, can avoid implicit loss, and realizes system operating cost minimization.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power grid optimization, and particularly relates to a multi-resource coordinated control method and system in a new energy high-occupancy power grid. BACKGROUND

[0002] In a new energy high-occupancy power grid, the coordinated control of a large-scale energy storage system faces many challenges. Traditional dispatching methods are often based on rules or simple optimization models, and do not fully consider the strong randomness and uncertainty of new energy output. For example, only arranging energy storage charging and discharging according to historical average values, this method cannot accurately capture the short-term fluctuations of new energy output, resulting in overcharging of the energy storage system, causing capacity waste, or insufficient discharging to effectively smooth fluctuations, thereby increasing the operating cost and making it difficult to maximize the comprehensive benefit.

[0003] Therefore, there is an urgent need for a method that can solve the operating cost problem in a new energy high-occupancy power grid. SUMMARY

[0004] The purpose of the present application is to build a comprehensive quantitative cost through the collected power grid data and new energy output prediction, and to generate a multi-resource coordinated dispatching combined with the optimization of the energy storage charging and discharging strategy, which can effectively reduce the comprehensive operating cost of the power grid.

[0005] In order to achieve the above purpose, the present application proposes a multi-resource coordinated control method in a new energy high-occupancy power grid, comprising: collecting power grid data, the power grid data including new energy output data, meteorological data, power grid load data, power grid operating state data and energy storage system operating parameters; building a new energy output prediction model based on the power grid data; calculating a quantitative cost based on the power grid data and the new energy output prediction model, the quantitative cost including energy storage system cost, power grid operating cost and power grid stability cost; building an energy storage charging and discharging strategy model based on the quantitative cost; obtaining an optimal energy storage charging and discharging strategy based on the energy storage charging and discharging strategy model; building a multi-resource coordinated dispatching model based on the optimal energy storage charging and discharging strategy, and outputting a dispatching plan, the dispatching plan including a new energy station output plan and a load demand response curve, the multi-resource including new energy resources, energy storage resources and load resources.

[0006] In an optional embodiment, the new energy output prediction model includes a wind power output prediction model and a photovoltaic output prediction model.

[0007] In an optional embodiment, the quantitative cost includes energy storage system cost, power grid operating cost and power grid stability cost; wherein the energy storage system cost includes investment cost and operating cost, the power grid operating cost includes power purchase cost and power abandonment cost; the investment cost is: ; in the formula, is the unit power investment cost; is the rated power of the energy storage system; is the unit capacity investment cost, is the rated capacity of the energy storage system; the operation cost is: ; in which, is the unit charging cost, is the unit discharging cost, and are the charging power and discharging power of the energy storage at time , respectively, is the time interval; the power purchase cost is: ; in which, is the power purchased from the grid at time , is the grid price at time ; the curtailment cost is: ; in which, is the on-grid price of new energy, is the total output of new energy, is the grid load at time ; the grid stability cost is: ; in which, and are the weight coefficients of voltage deviation and frequency deviation, respectively, and are the voltage deviation and frequency deviation at time , respectively.

[0008] In an optional embodiment, the energy storage charging and discharging strategy model comprises an objective function and a constraint condition, and the objective function is: ; in which, , is the total cost of comprehensive operation of the grid, is the investment cost, is the operation cost, is the power purchase cost, is the curtailment cost, , , is the weight coefficient, is the grid stability cost; is the curtailment rate: ;

[0009] in which, is the total output of new energy at time , and are the photovoltaic and wind power outputs at time , is the power purchased from the grid at time , is the grid load at time , is the discharging power at time , is the time interval.

[0010] In an alternative embodiment, the constraints include power constraints, capacity constraints, grid operation constraints and energy balance constraints; the power constraints are: ; ; where and are the charging and discharging power of the energy storage at time , and are the maximum charging and discharging power of the energy storage system; the capacity constraints are: ; where , is the state of charge of the energy storage system at time , , is the initial state of charge of the energy storage system, is the minimum state of charge of the energy storage system, is the maximum state of charge of the energy storage system, is the rated capacity of the energy storage system, is the charging efficiency, is the discharging efficiency is the time interval. The grid operation constraints are: ; ; where is the node voltage at time , is the system frequency at time , is the minimum node voltage, is the maximum node voltage, is the minimum system frequency, is the maximum system frequency; the energy balance constraints are: ; where is the grid transmission loss power at time .

[0011] In an alternative embodiment, based on the energy storage charging and discharging strategy model, an optimal energy storage charging and discharging strategy is obtained, specifically comprising: generating a preliminary charging and discharging strategy based on the energy storage charging and discharging strategy model and a model predictive control framework; and using an improved adaptive genetic algorithm to optimize and solve the preliminary charging and discharging strategy to obtain the optimal energy storage charging and discharging strategy.

[0012] In an alternative embodiment, the new energy high proportion power grid multi-resource coordinated control method further comprises: obtaining a real-time energy storage charging and discharging strategy; comparing the real-time energy storage charging and discharging strategy with the optimal energy storage charging and discharging strategy to obtain a comparison result; and dynamically adjusting and updating the multi-resource collaborative scheduling model according to the comparison result.

[0013] In an alternative embodiment, comparing the real-time energy storage charging and discharging strategy with the optimal energy storage charging and discharging strategy to obtain a comparison result specifically comprises: comparing the real-time energy storage charging and discharging strategy with the optimal energy storage charging and discharging strategy to obtain a deviation reason analysis result; and calculating an execution deviation based on the deviation reason analysis result to obtain the comparison result, wherein the execution deviation comprises an energy storage power deviation, a new energy consumption deviation and a load response deviation.

[0014] The application also provides a new energy based power grid multi-resource coordinated control system, comprising: a data acquisition module adapted to acquire power grid data, wherein the power grid data comprises new energy output data, weather data, power grid load data, power grid operation state data and energy storage system operation parameters; and an electronic device in communication connection with the data acquisition module to acquire the power grid data, wherein the electronic device stores executable instructions, and the instructions are executed to be able to execute any one of the new energy high proportion power grid multi-resource coordinated control methods.

[0015] The application also provides a medium storing a computer program, wherein the computer program is executed by a processor to implement any one of the new energy high proportion power grid multi-resource coordinated control methods.

[0016] The application has the following beneficial effects: the application quantifies the energy storage depreciation, power grid loss and stability risk into specific cost indicators by calculating and quantifying the cost based on the power grid data and the new energy output prediction model, thereby avoiding implicit losses and achieving charging at a low price valley and discharging at a peak, overall planning of new energy, energy storage and load side response, and minimization of system operation cost. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of the new energy high proportion power grid multi-resource coordinated control method provided by an embodiment of the application is shown in the figure;

[0018] Figure 2A flowchart of a multi-resource coordination control method in a power grid with a high proportion of new energy sources, provided as another embodiment of the present invention. Detailed Implementation

[0019] Currently, most existing technologies have not built a complete closed-loop control process and lack full-chain optimization from data acquisition to execution feedback. They cannot adapt to the complex operating scenarios brought about by the increasing proportion of new energy sources, which will also lead to high overall operating costs of the power grid.

[0020] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] like Figure 1 As shown, according to an embodiment of the present invention, in one aspect, a multi-resource coordinated control method for a power grid with a high proportion of new energy sources is provided, comprising the following steps:

[0022] Step S101: Collect power grid data, including new energy output data, meteorological data, power grid load data, power grid operation status data, and energy storage system operation parameters.

[0023] Step S103: Construct a new energy output prediction model based on grid data.

[0024] Step S105: Calculate the quantitative cost based on grid data and new energy output prediction model. The quantitative cost includes energy storage system cost, grid operation cost and grid stability cost.

[0025] Step S107: Construct an energy storage charging and discharging strategy model based on quantified costs;

[0026] Step S109: Obtain the optimal energy storage charging and discharging strategy based on the energy storage charging and discharging strategy model.

[0027] Step S111: Construct a multi-resource collaborative scheduling model based on the optimal energy storage charging and discharging strategy, and output a scheduling plan. The scheduling plan includes the output plan of the new energy station and the load demand response curve. The multiple resources include new energy resources, energy storage resources and load resources.

[0028] In this embodiment, the new energy power stations include photovoltaic power stations and wind power stations. The new energy output data are historical output data, with a time granularity set to 15 minutes. The new energy output data includes photovoltaic output data sequences. Wind power output data sequence ,in, and Representing time respectively The output of photovoltaic power and wind power, This represents the number of time points corresponding to the total data duration. Meteorological data refers to the meteorological data corresponding to new energy power plants, such as solar irradiance, wind speed, wind direction, and temperature. Power grid load data is represented as follows: The grid operation state data includes node voltage, system frequency, branch power flow, etc., and the operation parameters of the energy storage system, including energy storage capacity, charge and discharge power limit, charge and discharge efficiency, etc.

[0029] The combination of meteorological and operation state data can reduce the error of new energy output and load prediction, avoid strategy deviation caused by data loss, integrate multi-dimensional data, and thus eliminate information islands. By fusing meteorological data, the prediction error of new energy output can be reduced by 20%-30%, which can alleviate the impact of volatility on the power grid. Early prediction of power generation capacity can reduce the spinning reserve capacity of traditional units, thereby reducing dependence on fossil energy.

[0030] Quantifying energy storage depreciation, grid loss, and stability risk as specific cost indicators can avoid implicit losses, such as the potential cost of primary frequency failure. The logic of energy storage charge and discharge strategy is: when the energy storage is in the charging period and the new energy output is at the peak, it is preferred to absorb new energy power, and if the output exceeds the load demand, the energy storage charges to store excess power, reducing power waste; when the energy storage is in the discharging period and the new energy output is at the trough, the energy storage discharges to supplement the power gap, reducing the cost of grid power purchase. By charging at the low price trough and discharging at the peak, annual income can be increased by 15%-25%, while suppressing frequency deviation through rapid response, such as controlling frequency fluctuation within ±0.05Hz. Precise charging and discharging can reduce peak load difference and reduce the rigid demand for new transmission lines or transformers. Coordinating new energy, energy storage, and load side response can achieve the lowest system operation cost. The load demand response curve guides users to stagger peak electricity consumption and automatically increases the absorption rate during the new energy generation period.

[0031] The new energy output prediction model includes a wind power output prediction model and a photovoltaic output prediction model, which can obtain wind power output prediction data and photovoltaic output prediction data, respectively.

[0032] The wind power output prediction model includes a power curve based on wind power output prediction, which uses a simplified cubic polynomial to fit the wind speed-power relationship:

[0033] ;

[0034] In the formula, is the wind power output, is the wind speed, , , are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively, and parameters a, b, c, d are determined by historical data fitting. The input data are wind speed (m / s), wind turbine rated power (kW), and power curve parameters.

[0035] Photovoltaic output prediction The theoretical power model based on photovoltaic output prediction is included:

[0036] ;

[0037] wherein, is the light intensity, A is the array area, is the conversion efficiency, which can be defaulted as 15%-20%, is the temperature coefficient, which is usually 0.004 / ℃, and T is the component temperature. The input data are: light intensity (W / m²), temperature (℃), photovoltaic array area (m²), and conversion efficiency.

[0038] It can be seen that the wind power prediction power is segmented according to the wind speed, and the photovoltaic prediction power is segmented according to the light and temperature, and high-precision prediction can be realized by combining historical data fitting.

[0039] Among them, the quantitative cost includes: energy storage system cost, power grid operation cost and power grid stability cost.

[0040] The energy storage system cost includes investment cost and operation cost.

[0041] Investment cost :

[0042] ;

[0043] wherein, is the unit power investment cost, with the unit of yuan / kW, is the energy storage system rated power, with the unit of kW, is the unit capacity investment cost, with the unit of yuan / kWh, is the energy storage system rated capacity, with the unit of kWh.

[0044] Operation cost : ;

[0045] wherein, is the unit charging cost, with the unit of yuan / kWh, is the unit discharging cost, with the unit of yuan / kWh, and are the energy storage charging power and discharging power at time t, respectively, with the unit of kW, is the time interval, with the unit of h.

[0046] The power grid operation cost includes power purchase cost and abandoned power cost.

[0047] Power purchase cost :

[0048] ;​

[0049] wherein, is the time is the power purchased from the power grid, in kW, is the time is the price of the power grid at the time

[0050] abandoned power cost :

[0051] ;

[0052] wherein, is the on-grid price of new energy, in yuan / kWh.

[0053] grid stability cost : ;

[0054] wherein, and are the weight coefficients of the voltage deviation and the frequency deviation, respectively, and are the voltage deviation and the frequency deviation at the time , respectively.

[0055] Further, in order to reduce the comprehensive operation cost of the power grid as the main target, while considering the new energy consumption rate, the power grid stability and other factors, a comprehensive objective function is constructed.

[0056] The energy storage charging and discharging strategy model includes an objective function and constraint conditions, and the objective function is:

[0057] ;

[0058] wherein, , is the total cost of the comprehensive operation of the power grid, is the investment cost, is the operation cost, is the power purchase cost, is the abandoned power cost, , , is the weight coefficient, which is adjusted according to the actual situation to balance the relationship between various factors, is the grid stability cost.

[0059] is the abandoned power rate:

[0060] ;

[0061] wherein, , is the time Total output of new energy sources and They are time points The output of photovoltaic power and wind power, Let t be the power purchased from the grid. For a moment The power grid load, Let be the discharge power at time t. For time intervals.

[0062] By quantifying and uniformly optimizing all explicit costs such as investment, operation and maintenance, power purchase, and curtailment, as well as implicit costs such as frequency / voltage penalties, the annual operating costs of the power grid can be reduced by 8%–15% in typical scenarios. Specifically, by using curtailment as an independent penalty term in the objective function, driving energy storage to "charge low and discharge high," the curtailment rate of wind and solar power can typically be reduced from over 5% to below 1%, directly increasing the absorption of new energy sources. Furthermore, by monetizing the risks of frequency and voltage exceeding limits through stability cost terms, the optimization results can reduce the primary frequency regulation deficit by 20%–30% and decrease the number of node voltage exceeding limits by over 40%, thus reducing stability risks. The weighting coefficients a, b, and c can be dynamically adjusted according to real-time electricity prices, policy guidance, or dispatch needs, enabling one-click switching between multiple modes such as "economic priority," "absorption priority," or "safety priority," improving the business adaptability of dispatch strategies.

[0063] The constraints include power constraints, capacity constraints, grid operation constraints, and energy balance constraints.

[0064] Power constraints:

[0065] ;

[0066] ;

[0067] In the formula, and They are time points The energy storage charging power and discharging power, and These are the maximum charging power and maximum discharging power of the energy storage system, respectively. This constraint ensures that the energy storage system operates within a safe power range, preventing damage to equipment or safety accidents caused by excessive power.

[0068] Capacity constraints:

[0069] ;

[0070] In the formula, For a moment State of charge of energy storage system: , hour, This represents the initial state of charge of the energy storage system. For the minimum charge of the energy storage system, For the maximum charge of the energy storage system, For the rated capacity of the energy storage system, For charging efficiency, For discharge efficiency The time interval is specified. Capacity constraints require that the remaining capacity of the energy storage system be kept within a reasonable range to avoid overcharging or over-discharging. This constraint ensures the lifespan and safety of the energy storage system and prevents irreversible damage to the energy storage equipment caused by overcharging and discharging.

[0071] The integration of energy storage systems must not affect the normal operation of the power grid; for example, node voltage and frequency must meet relevant standards. Power grid operation constraints:

[0072] ;

[0073] ;

[0074] In the formula, For a moment node voltage, For a moment The system frequency, Minimum voltage at the node The maximum voltage at the node. For the minimum system frequency, The maximum system frequency;

[0075] Energy balance constraints:

[0076] ;

[0077] In the formula, For a moment The power grid load, For a moment The power transmission loss in the power grid can be calculated based on grid parameters and operating conditions. This constraint ensures the balance between energy supply and demand in the grid, maintaining its normal operation. It also ensures that the operation of the energy storage system will not negatively impact grid stability and reliability, guaranteeing the safe and stable operation of the grid.

[0078] In this embodiment, the charging / discharging power is locked within the physical limit by power constraint, which can avoid inverter overcurrent and battery over-power aging. The capacity constraint ensures that the battery will neither overcharge nor over-discharge, and the cycle life can be extended by more than 10% by using the upper and lower limits of SoC and efficiency correction. The voltage constraint limits all node voltages within the allowed range to avoid insulation breakdown or device tripping caused by long-time over-limit; the frequency constraint limits the system frequency within 50 ± Δf range, and the frequency qualification rate can be improved to more than 99.9% by cooperating with primary frequency modulation. The energy balance constraint includes all the "source-grid-load-storage-loss" into the equation, and any power shortage / surplus at any time can be digested by energy storage or power grid in real time, eliminating the frequency drop or overvoltage caused by power mismatch.

[0079] Step S109, based on the energy storage charging and discharging strategy model, the optimal energy storage charging and discharging strategy is obtained, which specifically includes the following steps:

[0080] Step S1091: based on the energy storage charging and discharging strategy model and the model predictive control framework, a preliminary charging and discharging strategy is generated.

[0081] Step S1093: the improved adaptive genetic algorithm is used to optimize and solve the preliminary charging and discharging strategy, and the optimal energy storage charging and discharging strategy is obtained.

[0082] Based on the energy storage charging and discharging strategy model, a preliminary charging and discharging strategy of the energy storage system is generated by using a model predictive control framework (MPC for short). MPC is a rolling optimization control method, which, at each sampling time, solves a finite-time optimization problem based on the current system state and future prediction information, obtains the control input at the current time, and then repeats the process at the next sampling time. In this application, the prediction time domain is set to 24 hours, and the control time domain is set to 4 hours, i.e. the future 24-hour energy storage operation strategy is optimized each time, but only the control instructions for the first 4 hours are executed, and then the model is updated according to the new measurement data and re-optimized.

[0083] The improved adaptive genetic algorithm is used to solve the objective function under the constraint condition, and the optimal energy storage charging and discharging strategy is obtained. The specific steps of the improved adaptive genetic algorithm are as follows:

[0084] Step S201: based on the generated preliminary charging and discharging strategy, the algorithm is initialized to generate a population.

[0085] The specific content of algorithm initialization is as follows:

[0086] Encoding design: the length of the gene sequence of the generated preliminary charging and discharging strategy is T, which is the control time domain, such as 4 hours, corresponding to 16 15-minute time intervals, and each gene bit represents the charging and discharging power value at the corresponding time. Real number coding method is adopted, and the power value is directly used as the gene value, such as wherein represents the discharging power, represents the charging power. The absolute value is the power size.

[0087] Population generation: randomly generate M individuals, for example, M = 10, and the individuals are preliminary charging and discharging strategies, constituting the initial population. Each individual needs to meet the power constraint: , when charging , needs to meet ; when discharging , needs to meet .

[0088] Parameter setting:

[0089] Maximum number of iterations ;

[0090] Adaptive crossover probability initial value ;

[0091] Adaptive mutation probability initial value ;

[0092] Elite retention ratio , that is, the top 5% optimal individuals are reserved in each generation.

[0093] Step S202: Adaptive degree calculation based on the objective function and the constraint condition, and the adaptive value of each individual is calculated.

[0094] The specific content of the adaptive function calculation is as follows:

[0095] Objective function conversion: since the objective function is to minimize , the objective function needs to be converted into the adaptive function of the maximization problem. Define the adaptive , wherein X is the individual, is a very small positive number, for example , which can avoid the denominator being 0.

[0096] Constraint processing: the constraint condition is processed by using the penalty function method, and a penalty term is applied to the individual that violates the constraint. For example:

[0097] If the state of charge exceeds the range , the penalty term is ; if the voltage or frequency exceeds the constraint range, the penalty term is .

[0098] The final adaptive degree is , wherein , are the penalty coefficients, which are set according to the importance of the constraint. The selected individuals are subjected to crossover operation to generate new individuals.

[0099] Step S203: Selection operation based on population, to get elite individuals.

[0100] The specific content of the selection operation is as follows:

[0101] Fitness normalization: calculate the sum of the fitness of all individuals in the population , and calculate the selection probability of each individual .

[0102] roulette selection: select individuals, for example 95, to ensure that individuals with high fitness have a higher probability of being selected.

[0103] elite preservation: directly preserve the individuals with the highest fitness in the current population , to get elite individuals, for example 5, to the next generation, to avoid loss of optimal solution.

[0104] Step S204: Cross operation on elite individuals to generate new individuals.

[0105] The specific content of the cross operation is as follows:

[0106] Cross probability update: for the selected individuals, calculate the adaptive cross probability according to their fitness values:

[0107] ;

[0108] wherein, is the fitness of the individual, and are the maximum fitness and the average fitness of the current population, respectively. When ≥ , decreases as increases; when , take the maximum value .

[0109] Cross execution: for each pair of selected individuals, cross with probability . Arithmetic crossover method is adopted, such as for individuals and , to generate new individuals and :

[0110] ;

[0111] wherein, is the cross coefficient, , randomly generated. After crossover, check power constraints, if not satisfied, re-generate .

[0112] Step S205: Perform mutation operation on the new individual to obtain a mutated individual.

[0113] The specific content of the mutation operation is as follows:

[0114] Mutation probability update: For the new individual generated after crossover, calculate the adaptive mutation probability :

[0115] ;

[0116] When the individual fitness is higher, the mutation probability is smaller, to reduce the mutation damage of good individuals; on the contrary, , take a larger value, increase the search diversity.

[0117] Mutation execution: For each gene bit of each individual, mutate with a probability . Adopt Gaussian mutation method to generate new gene value:

[0118] ;

[0119] Wherein, is the mutation step size, which decreases with the number of iterations, such as , is the initial step size, and N(0, 1) is a standard normal distribution random number. After mutation, check the power constraint and capacity constraint, if not satisfied, re-mutate.

[0120] Step S206: Iteratively calculate the state of charge of each individual.

[0121] For each individual, that is, the energy storage strategy, the state of charge at each time point needs to be iteratively calculated according to the charging and discharging power sequence to verify the capacity constraint:

[0122] ;

[0123] Wherein, when <0, when , , the initial state of charge is a known value, for example, 0.5. If is out of range, trigger the penalty function.

[0124] Step S207: If the iteration satisfies the termination condition, terminate the iteration, and output the optimal energy storage charging and discharging strategy.

[0125] Termination condition: stop iteration when any of the following conditions is met:

[0126] The number of iterations reaches the preset number of iterations ;

[0127] The number of consecutive iterations is , for example, 20 generations, the rate of change of the optimal fitness of the population is less than a threshold value , for example, 1%.

[0128] Optimal solution extraction: select the individual with the highest fitness during the iteration process, and decode it into the energy storage charging and discharging strategy , where > 0 is the discharging power, < 0 is the charging power. The strategy must satisfy all the constraints and minimize the objective function F.

[0129] The final generated energy storage charging and discharging strategy will be used as the core input for the next stage of multi-resource collaborative scheduling, guiding the real-time operation of the energy storage system.

[0130] Through the above steps, the improved adaptive genetic algorithm can efficiently search for the optimal energy storage strategy under complex constraints, balance the grid operation cost, new energy consumption rate and system stability, and achieve closed-loop control of multi-objective optimization.

[0131] The above steps can also be summarized as follows:

[0132] Initialization of population: randomly generate a certain number of individuals, each individual represents a set of energy storage charging and discharging strategies, that is, the individual's gene code is the charging and discharging power at each time in the future control time domain.

[0133] Calculate fitness: calculate the fitness value of each individual according to the objective function and constraints.

[0134] Selection operation: use roulette wheel selection method to select good individuals from the population.

[0135] Cross operation: cross the selected individuals to generate new individuals.

[0136] Mutation operation: mutate the individuals after crossing to increase the diversity of the population.

[0137] Elite preservation: preserve the optimal individual in the current population to the next generation.

[0138] Termination judgment: if the termination condition is met, such as the maximum number of iterations or the convergence of the fitness value, stop iteration and output the optimal solution; otherwise, return to step 2 and continue iteration.

[0139] Wherein, based on the optimal energy storage charging and discharging strategy, multi-resource collaborative scheduling is carried out, which specifically includes the following contents:

[0140] Based on the generated energy storage charging and discharging strategy, a new energy, energy storage, load multi-resource collaborative scheduling model is constructed. The model takes the energy storage strategy as the core, coordinates the output plan of the new energy station and the demand response of the load.

[0141] Formulate the output plan of the new energy station.

[0142] According to the predicted output curve of wind power / photovoltaic and the energy storage charging and discharging strategy, the output plan of the new energy station is dynamically adjusted. According to the wind power output range fitted by the Weibull distribution of the predicted output curve of wind power / photovoltaic .

[0143] The output plan of the new energy station is: when the energy storage is in the charging period, that is , and the new energy output peak, the new energy power is preferentially consumed, and if the output exceeds the load demand, the energy storage charges to store the excess power, reducing the power abandonment; when the energy storage is in the discharging period, that is , and the new energy output trough, the energy storage discharges to supplement the power gap, reducing the power purchase cost of the power grid.

[0144] The load demand response includes the following processes:

[0145] Real-time electricity price Linkage with new energy output and energy storage strategy to generate price signal:

[0146] ;

[0147] Wherein, is the predicted total new energy output, is the average value, is the adjustment coefficient. When the new energy output is high, for example, the energy storage charging period, is reduced, encouraging the load to use electricity; on the contrary, it is increased, leading the load to move the peak.

[0148] Based on the user electricity elasticity coefficient , the load adjustment amount is calculated:

[0149] ;

[0150] In the formula, is the power grid load at time .

[0151] The load power after demand response is obtained:

[0152] For example, during the low valley period of electricity price, the load can increase electricity consumption to match the energy storage charging and new energy output peak.

[0153] Further, as shown in Figure 2 the new energy high proportion power grid multi-resource coordinated control method further includes the following steps:

[0154] Step S301: Obtain real-time energy storage charging and discharging strategy.

[0155] Step S303: Compare the real-time energy storage charging and discharging strategy with the optimal energy storage charging and discharging strategy to obtain a comparison result.

[0156] Step S305: Dynamically adjust and update the multi-resource collaborative scheduling model according to the comparison result.

[0157] In this embodiment, the new energy output, energy storage operating state, load power consumption, power grid operating parameters and other data can be collected in real time by intelligent meters, sensors and other devices, and transmitted to the dispatching center for real-time monitoring and analysis.

[0158] In this embodiment, data collection and output prediction are performed first, then energy storage strategy is generated, and then multi-resource coordinated scheduling is performed, so as to formulate a new energy processing plan, adjust load demand response, execute and collect power grid data, perform deviation analysis, and determine whether adjustment is needed. If adjustment is needed, return to the step of generating energy storage strategy, otherwise, return to the step of executing and collecting power grid data. By obtaining the charging and discharging strategy of the energy storage system in real time, the current operating state and dynamic response capability of the energy storage resource can be grasped in time, providing the most timely and accurate data support for subsequent optimization and decision making, and avoiding scheduling errors or resource waste caused by information lag. Comparing the real-time strategy with the "optimal" strategy can accurately quantify the differences between the two in power, time period, cost, network loss, backup capacity and other multi-dimensional indicators, quickly locate the deviation source, such as prediction error, equipment aging, market price mutation, etc., which can provide a clear direction and quantitative basis for model correction, reducing blind trial and error. According to the comparison result, the multi-resource collaborative scheduling model is dynamically adjusted and updated, so that the model has the ability of "self-learning and self-correction", which can improve the adaptability of the model to future working conditions, reduce the impact of wind and light fluctuations and load mutations on the power grid, real-time correct the scheduling weight of energy storage and other adjustable resources, improve the new energy consumption rate and reduce the system operation cost. Through rolling optimization closed loop, the safety, economy and low carbon operation of the power grid in complex and uncertain environment are guaranteed, the service life of the energy storage equipment is prolonged, and the demand for additional investment is delayed.

[0159] Based on step S303, the comparison result of comparing the real-time energy storage charging and discharging strategy with the optimal energy storage charging and discharging strategy includes the following steps:

[0160] Step S3031: Compare the real-time energy storage charging and discharging strategy with the optimal energy storage charging and discharging strategy to obtain a deviation reason analysis result.

[0161] Step S3033: Calculate the execution deviation based on the deviation cause analysis result, and obtain the comparison result, the execution deviation including: energy storage power deviation, new energy consumption deviation, and load response deviation.

[0162] Compare the actually executed energy storage charging and discharging strategy with the optimization generated strategy, and analyze the cause of the execution deviation. The deviation may be caused by new energy output prediction error, load prediction error, energy storage system operation abnormality, and power grid fault.

[0163] Calculate the execution deviation, and complete positioning.

[0164] The energy storage power deviation is: If the deviation exceeds the threshold value, for example It may be caused by energy storage device efficiency decline or power limitation abnormality.

[0165] In the formula, is the energy storage power deviation at time t, unit: kW, indicating the difference between the actual energy storage charging and discharging power and the optimal strategy, is the actual energy storage charging and discharging power at time t, unit: kW, positive value for discharging, negative value for charging, is the optimal energy storage charging and discharging power at time t, unit: kW, calculated by the energy storage charging and discharging strategy model, positive value for discharging, negative value for charging.

[0166] The new energy consumption deviation is: If there is power curtailment, that is It may be caused by new energy prediction error or insufficient energy storage charging capacity.

[0167] In the formula, is the new energy consumption deviation at time t, unit: kW, indicating the difference between the theoretical consumption and the actual consumption; is the optimal target new energy consumption power at time t, unit: kW, that is, the new energy power that should be consumed based on the prediction and dispatching model; is the actual new energy consumption power at time t, unit: kW, that is, the new energy power actually consumed by the power grid, load or energy storage.

[0168] The load response deviation is: It may be caused by price signal lag or user response delay.

[0169] In the formula, is the load response deviation at time t, unit: kW, indicating the difference between the actual load adjustment and the optimal target; is the actual load response power at time t, unit: kW, that is, the actual adjusted power consumption of the user under the price signal or dispatching instruction; The optimization target load response power at time t, unit: kW, is the optimal user power calculated by the multi-resource collaborative scheduling model, which matches the new energy and energy storage strategy.

[0170] Based on step S305, the multi-resource collaborative scheduling model is dynamically adjusted and updated according to the comparison result, including the following steps:

[0171] Step S3051: Short-term deviation correction

[0172] If the new energy output prediction deviation is large, for example, the actual wind speed is lower than the predicted value, the subsequent energy storage strategy is immediately adjusted:

[0173] If the current energy storage is in the charging state, the charging power is reduced or the discharging is switched in advance to avoid new energy curtailment;

[0174] If the power grid power is insufficient, the energy storage discharging power is increased to supplement the power gap.

[0175] If the load response is insufficient, the electricity price signal is updated in real time, for example, the peak period electricity price is increased, to enhance the user adjustment incentive.

[0176] Step S3053: Long-term model parameter update

[0177] The wind power and photovoltaic prediction model parameters are updated based on the latest data every day to improve the output prediction accuracy.

[0178] According to the actual charging and discharging data, the efficiency parameter (P ) and the capacity attenuation curve are corrected, and the state of charge calculation formula is updated.

[0179] The genetic algorithm solving effect is analyzed every month, and the crossover probability , mutation probability and other parameters are adjusted to improve the strategy generation efficiency.

[0180] In another aspect, the present application also provides an electronic device, comprising: at least one processor; a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform any one of the multi-resource coordination control methods in the new energy high proportion power grid.

[0181] In another aspect, the present application also provides a computer storage medium storing a computer program, and the computer program is executed by a processor to implement any one of the multi-resource coordination control methods in the new energy high proportion power grid.

[0182] The computer storage medium can be referred to as a medium simply. Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiments can be included. Any reference to memory, storage, database or other medium used in each embodiment provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM). Each embodiment in the specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments. Especially, for device, equipment, non-volatile computer storage medium embodiments, because they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0183] Specific application examples are as follows.

[0184] I. Application background and data collection

[0185] For example, in a new energy high proportion power grid, the installed capacity of wind power and photovoltaic power in the region accounts for 45%, but the curtailment rate is as high as 18%, and the power grid peak regulation pressure is significant. A typical day in the region is selected as the research object, and the time granularity is 15 minutes. The following data is collected:

[0186] New energy output data: historical output data of photovoltaic power station (maximum peak power 800kW), historical output data of wind power station (rated power 1000kW);

[0187] Meteorological data: light intensity (maximum 1000W / m²), wind speed (3-20m / s), temperature (25-35℃);

[0188] Grid load data: (peak 1500kW, valley 800kW);

[0189] Energy storage system parameters: rated power 500kW, rated capacity 2000kWh, charging efficiency 90%, discharging efficiency 85%, initial state of charge .

[0190] Second, new energy output prediction

[0191] (I) wind power output prediction

[0192] Input parameters:

[0193] Wind speed prediction data: 0-24 hour predicted wind speed 4-12m / s;

[0194] Fan rated power 1000kW;

[0195] Power curve parameters: cut-in wind speed 3m / s, rated wind speed 12m / s, cut-out wind speed 20m / s, cubic polynomial coefficients a=0.005, b=-0.06, c=0.25, d=0 are obtained by fitting historical data.

[0196] Calculation process:

[0197] When the wind speed v=8m / s, between the cut-in and rated wind speed,

[0198] kW.

[0199] (II) photovoltaic output prediction

[0200] Input parameters:

[0201] Illuminance prediction 600W / m², temperature prediction 30℃;

[0202] Photovoltaic array area 5000m², conversion efficiency 18%;

[0203] Temperature coefficient α=0.004 / ℃.

[0204] Calculation process:

[0205] W=531kW.

[0206] Three, cost quantification and multi-objective function construction

[0207] (I) cost quantification

[0208] Energy storage system cost:

[0209] Investment cost: Yuan;

[0210] Operating costs: Assuming an average daily charging power of 100kW and an average daily discharging power of 150kW,

[0211] Yuan (Δt=0.25h).

[0212] Power grid operating costs:

[0213] Electricity purchase cost: Grid electricity price 0.6 yuan / kWh, average purchased power 200kW.

[0214] Yuan;

[0215] Curtailment cost: The portion of the total output of new energy sources exceeding the load and the discharge of energy storage is treated as curtailment. The on-grid price of new energy is 0.4 yuan / kWh. Pre(t) is the total output of new energy sources, which is equal to the sum of the predicted wind power output and photovoltaic power output.

[0216] The calculated amount is 1200 yuan.

[0217] Power grid stability costs:

[0218] Voltage deviation weight Frequency deviation weight The average voltage deviation throughout the day was 0.02 pu, and the average frequency deviation was 0.01 Hz.

[0219] Yuan.

[0220] (II) Calculation of Multi-Objective Functions

[0221] Set the weights to a=0.5, b=0.3, and c=0.2.

[0222] Yuan,

[0223] curtailment rate In the calculation of the curtailment rate Rwaste, wind power output forecasts and photovoltaic power output forecasts are also applied.

[0224] objective function .

[0225] IV. Constraints and Strategy Generation

[0226] (a) Key Constraints

[0227] Power constraint: Charging power kW, discharge power kW;

[0228] Capacity constraints: For example, when t = 1, the photovoltaic predicted output is high at 531 kW, the strategy generates a "charge - 200 kW" instruction, and the charging power is substituted into the SOC formula:

[0229] ;

[0230] Grid operation constraints: voltage 0.95-1.05pu, frequency 49.5-50.5Hz.

[0231] (2) Improved adaptive genetic algorithm for solution

[0232] Parameter settings:

[0233] Control time domain 4 hours, 16 time points, population size M = 50;

[0234] Maximum number of iterations 200, adaptive crossover probability , ;

[0235] Mutation probability , , elite retention ratio 5%.

[0236] Optimal strategy example:

[0237] 0-4 hours: charging power -150kW (negative value indicates charging), discharge power 0;

[0238] 4-8 hours: charging power -200kW, discharge power 0;

[0239] 8-12 hours: discharge power 300kW, charging power 0 (new energy low valley period).

[0240] Five, multi-resource coordinated scheduling and feedback regulation

[0241] (1) Coordinated scheduling implementation

[0242] New energy output plan:

[0243] When the photovoltaic output reaches 531 kW (11:00-14:00) and the energy storage is charging, the photovoltaic power is preferentially consumed, the excess power is charged by the energy storage at a power of 200 kW, and the abandoned power is reduced by 40%;

[0244] When the wind power output is low (22:00-6:00), the energy storage discharges at a power of 300 kW to supplement the grid gap.

[0245] Load demand response:

[0246] Real-time electricity price When the photovoltaic output is high, the electricity price drops to 0.55 yuan / kWh, guiding the industrial load to increase electricity consumption by 100 kW.

[0247] (II) Execution of feedback and optimization

[0248] Real-time deviation analysis:

[0249] 14:00 Real-time photovoltaic output 480kW (predicted 531kW), energy storage charging power from -200kW to -150kW, to avoid power waste;

[0250] Load response deviation 50kW, real-time electricity price to 0.65 yuan / kWh, to encourage users to increase electricity consumption.

[0251] Model update:

[0252] Daily update of wind power / photovoltaic prediction model parameters, such as wind speed-power curve coefficient;

[0253] Monthly correction of efficiency parameters according to energy storage charging and discharging data, current charging efficiency from 90% to 88% (equipment degradation).

[0254] Six, implementation effect

[0255] Power waste rate: from 18% to 9.2%, new energy consumption rate increased by 48%;

[0256] Grid cost: comprehensive operation cost reduced by 15.3%, saving about 1.2 million yuan per year;

[0257] Stability: voltage deviation reduced to 0.015pu, frequency deviation controlled within 0.008Hz;

[0258] Energy storage utilization rate: charging and discharging cycle from 3 times a day to 5 times a day, capacity utilization rate increased by 25%.

[0259] This embodiment realizes the collaborative optimization of new energy, energy storage and load through a complete closed-loop control process, and verifies the effectiveness and feasibility of the method in the actual power grid.

[0260] The above examples are only examples for clear illustration, and do not limit the embodiments. For ordinary skilled persons in the art, other different forms of changes or variations can be made on the basis of the above description. Here, it is not necessary and impossible to exhaust all embodiments. The obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A method for multi-resource coordinated control in a new energy high-occupancy power grid, characterized in that, The method comprises the following steps: Collecting power grid data, including new energy output data, meteorological data, power grid load data, power grid operation state data and energy storage system operation parameters; Building a new energy output prediction model based on the power grid data; Calculating the quantitative cost based on the power grid data and the new energy output prediction model, the quantitative cost including energy storage system cost, power grid operation cost and power grid stability cost; Building an energy storage charging and discharging strategy model based on the quantitative cost; Obtaining the optimal energy storage charging and discharging strategy based on the energy storage charging and discharging strategy model; Building a multi-resource coordinated scheduling model based on the optimal energy storage charging and discharging strategy, and outputting a scheduling plan, the scheduling plan including new energy station output plan and load demand response curve, and the multi-resource including new energy resource, energy storage resource and load resource; The energy storage charging and discharging strategy model includes an objective function and a constraint condition, and the objective function is: ； In the formula, is the total cost of the comprehensive operation of the power grid, is the investment cost, is the operation cost, is the electricity purchase cost, is the abandoned electricity cost, , , is the weight coefficient, is the stability cost of the power grid; For the power rejection rate: ； In the formula, For a moment Total output of new energy sources and They are time points The output of photovoltaic power and wind power, For a moment The power purchased from the grid, For a moment The power grid load, For a moment The discharge power, For time intervals; The constraint condition includes: Power constraint: ； ； In the formula, and are the charging power and discharging power of the energy storage at time and are the maximum charging power and maximum discharging power of the energy storage system, respectively.​ Capacity constraint: ； wherein So is the time State of charge of the energy storage system: , So is the time So is the initial state of charge of the energy storage system, So is the minimum state of charge of the energy storage system, So is the maximum state of charge of the energy storage system, So is the rated capacity of the energy storage system, So is the charging efficiency, So is the discharging efficiency, So is the time interval; Power grid operation constraint: ； ； In the formula, For a moment node voltage, For a moment The system frequency, The minimum voltage at the node. The maximum voltage at the node. For the minimum system frequency, The maximum system frequency; Energy balance constraint: ； In the formula, is the power loss of the power grid at the moment of time. 2.The method of claim 1, wherein, The new energy output prediction model includes a wind power output prediction model and a photovoltaic output prediction model. 3.The method of claim 1, wherein, The quantitative cost includes energy storage system cost, power grid operation cost and power grid stability cost; wherein the energy storage system cost includes investment cost and operation cost, and the power grid operation cost includes power purchase cost and power abandonment cost; The investment cost is: ； wherein P is the unit power investment cost, P is the rated power of the energy storage system; C is the unit capacity investment cost, C is the rated capacity of the energy storage system; The operating cost is: ; wherein is the unit charging cost, is the unit discharging cost, and are the charging and discharging power of the energy storage at time is the time interval;​ The power purchase cost is: ； In the formula, is the time the power purchased from the grid, is the time the grid price; The power abandonment cost is: ； In the formula, is the new energy on-grid electricity price; is the predicted total new energy output; is the time is the grid load at the time The power grid stability cost is: ; wherein, and are weight coefficients of voltage deviation and frequency deviation, respectively, and are voltage deviation and frequency deviation at time , respectively.

4. The method of claim 1 to 3, wherein, Based on the energy storage charging and discharging strategy model, the optimal energy storage charging and discharging strategy is obtained, specifically including: Generating a preliminary charging and discharging strategy based on the energy storage charging and discharging strategy model and the model predictive control framework; Optimizing and solving the preliminary charging and discharging strategy by using an improved adaptive genetic algorithm to obtain the optimal energy storage charging and discharging strategy.

5. The method of claim 4, wherein, The energy storage charging and discharging strategy model includes an objective function and a constraint condition, and the improved adaptive genetic algorithm is used to optimize and solve the preliminary charging and discharging strategy to obtain the optimal energy storage charging and discharging strategy, specifically including: Initializing the algorithm based on the preliminary charging and discharging strategy to generate a population; Calculating the fitness value of each individual in the population based on the objective function and the constraint condition; Performing selection operation based on the population to obtain excellent individuals; Performing crossover operation on the excellent individuals based on the fitness value to generate new individuals; Iteratively calculating the state of charge of each new individual; If the iteration meets the termination condition, the iteration is terminated, and the optimal energy storage charging and discharging strategy is output, and the termination condition is that the number of iterations reaches a preset number of iterations or the number of consecutive iterations makes the optimal fitness of the population change rate less than a threshold.

6. The method of claim 1 to 3, wherein, Further comprising: Obtaining a real-time energy storage charging and discharging strategy; Comparing the real-time energy storage charging and discharging strategy with the optimal energy storage charging and discharging strategy to obtain a comparison result; According to the comparison result, dynamically adjusting and updating the multi-resource coordinated scheduling model.

7. The method of claim 6, wherein, Comparing the real-time energy storage charging and discharging strategy with the optimal energy storage charging and discharging strategy to obtain a comparison result, specifically including: Comparing the real-time energy storage charging and discharging strategy with the optimal energy storage charging and discharging strategy to obtain a deviation reason analysis result; The execution deviation includes a storage energy power deviation, a new energy consumption deviation, and a load response deviation.

8. A multi-resource coordinated control system in a new energy high-occupancy power grid, characterized in that, Comprise: A data acquisition module adapted to acquire power grid data, the power grid data comprising new energy output data, weather data, power grid load data, power grid operation state data, and storage energy system operation parameters; An electronic device in communication connection with the data acquisition module to acquire the power grid data, and the electronic device storing executable instructions, the instructions being executed to enable the execution of the multi-resource coordination control method in a new energy high-occupancy power grid according to any one of claims 1 to 7.

Images