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

Abstract

The invention discloses a multi-resource coordination control method and system in a new energy high-proportion power grid, and the method comprises the steps: collecting power grid data which comprises new energy output data, meteorological data, power grid load data, power grid operation state data and energy storage system operation parameters; constructing a new energy output prediction model based on the power grid data; calculating quantitative cost based on the power grid data and the new energy output prediction model, wherein the quantitative cost comprises energy storage system cost, power grid operation cost and power grid stability cost; constructing 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; and constructing a multi-resource cooperative scheduling model based on the optimal energy storage charging and discharging strategy, and outputting a scheduling plan. According to the method, the energy storage depreciation, the power grid loss and the stability risk are quantified into specific cost indexes by calculating the quantitative cost, hidden loss can be avoided, and the operation cost of the system is minimized.

Description

Technical Field

[0001] This invention belongs to the field of power grid optimization technology, specifically relating to a multi-resource coordination and control method and system in power grids with a high proportion of new energy sources. Background Technology

[0002] In power grids with a high proportion of renewable energy, the coordinated control of large-scale energy storage systems faces numerous challenges. Traditional scheduling methods are often based on rules or simple optimization models, failing to fully consider the strong randomness and uncertainty of renewable energy output. For example, scheduling energy storage charging and discharging solely based on historical averages cannot accurately capture short-term fluctuations in renewable energy output. This leads to either overcharging and wasted capacity or under-discharging, which fails to effectively mitigate fluctuations, thereby increasing operating costs and hindering the maximization of overall benefits.

[0003] Therefore, there is an urgent need for a solution to the operating cost problem of a power grid with a high proportion of new energy sources. Summary of the Invention

[0004] The purpose of this invention is to construct a comprehensive quantitative cost by collecting power grid data and predicting new energy output, and to effectively reduce the overall operating cost of the power grid by combining the optimized generation of energy storage charging and discharging strategies with multi-resource collaborative scheduling.

[0005] To achieve the above objectives, this invention proposes a multi-resource coordinated control method for power grids with a high proportion of renewable energy, comprising: collecting power grid data, including renewable energy output data, meteorological data, power grid load data, power grid operating status data, and energy storage system operating parameters; constructing a renewable energy output prediction model based on the power grid data; calculating a quantified cost based on the power grid data and the renewable energy output prediction model, the quantified cost including energy storage system cost, power grid operating cost, and power grid stability cost; constructing an energy storage charging and discharging strategy model based on the quantified cost; obtaining an optimal energy storage charging and discharging strategy based on the energy storage charging and discharging strategy model; constructing a multi-resource collaborative scheduling model based on the optimal energy storage charging and discharging strategy, and outputting a scheduling plan, the scheduling plan including renewable energy station output plans and load demand response curves, the multi-resources including renewable energy resources, energy storage resources, and load resources.

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

[0007] In one optional implementation, the quantified cost includes energy storage system cost, grid operation cost, and grid stability cost; wherein, the energy storage system cost includes investment cost and operating cost, and the grid operation cost includes electricity purchase cost and curtailment cost; the investment cost... for: In the formula, Cost per unit of power; This refers to the rated power of the energy storage system. The unit capacity investment cost The rated capacity of the energy storage system; the operating cost for: In the formula, Cost per unit of charging For unit discharge cost, and They are time points The energy storage charging power and discharging power, The time interval; the electricity purchase cost for: In the formula, For a moment The power purchased from the grid, For a moment The grid electricity price; the cost of abandoned electricity for: In the formula, For renewable energy feed-in tariffs; To predict the total output of new energy sources; For a moment The grid load; the grid stability cost for: In the formula, and These are the weighting coefficients for voltage deviation and frequency deviation, respectively. and They are time points Voltage deviation and frequency deviation.

[0008] In one optional implementation, the energy storage charging and discharging strategy model includes an objective function and constraints, wherein the objective function... for: In the formula, , The total cost of power grid operation. For investment costs, For operating costs, For electricity purchase costs, For the cost of curtailing electricity, , , These are the weighting coefficients. Costs related to power grid stability; Curtailment rate: ; In the formula, , for time 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.

[0009] In one optional implementation, the constraints include power constraints, capacity constraints, grid operation constraints, and energy balance constraints; the power constraint is: ; 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; the capacity constraint is: In the formula, hour, For a moment State of charge of energy storage system: , 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. The power grid operation constraints are as follows: ; 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; the energy balance constraint is: In the formula, For a moment Power loss during power grid transmission.

[0010] In one optional implementation, the optimal energy storage charging and discharging strategy is obtained based on the energy storage charging and discharging strategy model, 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; 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.

[0011] In an optional implementation, the multi-resource coordinated control method in a power grid with a high proportion of new energy sources further includes: acquiring 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 coordinated scheduling model based on the comparison result.

[0012] In one optional implementation, 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 includes: comparing the real-time energy storage charging and discharging strategy with the optimal energy storage charging and discharging strategy to obtain a deviation cause analysis result; calculating the execution deviation based on the deviation cause analysis result to obtain the comparison result, wherein the execution deviation includes: energy storage power deviation, new energy consumption deviation, and load response deviation.

[0013] The present invention also provides a power grid multi-resource coordination control system based on new energy sources, comprising: a data acquisition module adapted to acquire power grid data, the power grid data including new energy output data, meteorological data, power grid load data, power grid operating status data and energy storage system operating parameters; an electronic device communicatively connected to the data acquisition module to acquire the power grid data, and the electronic device storing executable instructions, the instructions being executed to perform any of the multi-resource coordination control methods in a power grid with a high proportion of new energy sources.

[0014] The present invention also provides a medium storing a computer program, which, when executed by a processor, implements any one of the multi-resource coordination control methods in a power grid with a high proportion of new energy sources.

[0015] The beneficial effects of this invention are as follows: This invention calculates and quantifies costs based on grid data and new energy output prediction models, and quantifies energy storage depreciation, grid losses, and stability risks into specific cost indicators, which can avoid hidden losses, realize charging during off-peak hours and discharging during peak hours, coordinate the response of new energy, energy storage, and load side, and achieve the minimum system operating cost. Attached Figure Description

[0016] Figure 1 A flowchart of a multi-resource coordination control method in a power grid with a high proportion of new energy sources, provided in an embodiment of the present invention;

[0017] 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

[0018] 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.

[0019] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] 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:

[0021] 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.

[0022] Step S103: Construct a new energy output prediction model based on grid data.

[0023] 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.

[0024] Step S107: Construct an energy storage charging and discharging strategy model based on quantified costs;

[0025] Step S109: Obtain the optimal energy storage charging and discharging strategy based on the energy storage charging and discharging strategy model.

[0026] 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.

[0027] 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 power grid operation status data includes node voltage, system frequency, branch power flow, etc., and the energy storage system operation parameters include energy storage capacity, charging and discharging power limits, charging and discharging efficiency, etc.

[0028] Combining meteorological and operational status data can reduce errors in renewable energy output and load forecasting, and avoid strategy deviations caused by data gaps. Integrating multi-dimensional data can eliminate information silos. By fusing meteorological data, the forecasting error of renewable energy output can be reduced by 20%-30%, mitigating the impact of fluctuations on the power grid. Advance prediction of power generation capacity can reduce the spinning reserve capacity of traditional units, thereby reducing dependence on fossil fuels.

[0029] Quantifying energy storage depreciation, grid losses, and stability risks into specific cost indicators can avoid hidden losses, such as the potential cost of a primary frequency regulation failure. The logic of the energy storage charging and discharging strategy is as follows: when energy storage is charging and renewable energy output is at its peak, renewable energy power is prioritized for consumption. If output exceeds load demand, the energy storage charges to store excess electricity, reducing power curtailment. When energy storage is discharging and renewable energy output is at its low point, the energy storage discharges to supplement the power gap, reducing the grid's electricity purchase cost. By charging during off-peak hours and discharging during peak hours, annual returns can be increased by 15%-25%, while frequency deviations can be suppressed through rapid response, such as controlling frequency fluctuations within ±0.05Hz. Precise charging and discharging can reduce peak-load differences and lower the rigid demand on new transmission lines or transformers. By coordinating renewable energy, energy storage, and load-side response, the system's operating costs can be minimized. The load demand response curve guides users to stagger their electricity consumption during peak periods, automatically increasing the absorption rate during periods of high renewable energy generation.

[0030] Among them, the new energy output prediction model includes a wind power output prediction model and a photovoltaic output prediction model, which can respectively obtain wind power output prediction data and photovoltaic output prediction data.

[0031] 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:

[0032] ;

[0033] In the formula, To provide power for wind power, For wind speed, , , These are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively. Parameters a, b, c, and d are determined through fitting historical data. Input data include wind speed (m / s), rated turbine power (kW), and power curve parameters.

[0034] Photovoltaic power output forecast Including theoretical power models based on photovoltaic output prediction:

[0035] ;

[0036] In the formula, Let A be the light intensity and A be the array area. For optimal conversion efficiency, the default setting can be 15%~20%. The temperature coefficient is typically 0.004 / ℃, where T is the module temperature. Input data includes: illuminance (W / m²), temperature (℃), photovoltaic array area (m²), and conversion efficiency.

[0037] As can be seen, wind power prediction depends on wind speed segments, while photovoltaic power prediction depends on sunlight and temperature. High-precision prediction can be achieved by combining historical data with the prediction.

[0038] The quantified costs include: energy storage system costs, grid operation costs, and grid stability costs.

[0039] The cost of an energy storage system includes investment costs and operating costs.

[0040] Investment costs :

[0041] ;

[0042] In the formula, The unit cost per unit power is yuan / kW. Rated power of the energy storage system, in kW. The cost per unit capacity is expressed in yuan / kWh. This refers to the rated capacity of the energy storage system, expressed in kWh.

[0043] Operating costs : ;

[0044] In the formula, The unit charging cost is expressed in yuan / kWh. The unit cost of discharge is expressed in yuan / kWh. and They are time points The energy storage charging and discharging power, in kW. The time interval is expressed in hours (h).

[0045] The operating costs of a power grid include the cost of purchasing electricity and the cost of abandoning electricity.

[0046] Electricity purchase cost :

[0047] ;

[0048] In the formula, For a moment The power purchased from the grid is measured in kW. For a moment The electricity price is expressed in yuan / kWh.

[0049] Cost of curtailment :

[0050] ;

[0051] In the formula, The on-grid tariff for renewable energy is expressed in yuan / kWh.

[0052] Power grid stability costs : ;

[0053] In the formula, and These are the weighting coefficients for voltage deviation and frequency deviation, respectively. and They are time points Voltage deviation and frequency deviation.

[0054] Furthermore, with the primary goal of reducing the overall operating cost of the power grid, while also considering factors such as the renewable energy absorption rate and power grid stability, a comprehensive objective function is constructed.

[0055] The energy storage charging and discharging strategy model includes an objective function and constraints. The objective function... for:

[0056] ;

[0057] In the formula, , The total cost of power grid operation. For investment costs, For operating costs, For electricity purchase costs, For the cost of curtailing electricity, , , These are weighting coefficients, which are adjusted according to the actual situation to balance the relationship between the various factors. Costs related to power grid stability.

[0058] Curtailment rate:

[0059] ;

[0060] In the formula, , for 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.

[0061] 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.

[0062] The constraints include power constraints, capacity constraints, grid operation constraints, and energy balance constraints.

[0063] Power constraints:

[0064] ;

[0065] ;

[0066] 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.

[0067] Capacity constraints:

[0068] ;

[0069] 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.

[0070] 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:

[0071] ;

[0072] ;

[0073] 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;

[0074] Energy balance constraints:

[0075] ;

[0076] 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.

[0077] In this embodiment, power constraints lock the charging / discharging power within physical limits, preventing inverter overcurrent and battery over-power aging. Capacity constraints use SoC upper and lower limits plus efficiency correction to ensure that the battery will neither overcharge and bulge nor over-discharge and deposit lithium, extending cycle life by more than 10%. Voltage constraints clamp all node voltages within allowable bands, preventing insulation breakdown or equipment tripping caused by prolonged exceeding limits. Frequency constraints limit the system frequency to within 50 ± Δf, and with primary frequency regulation, the frequency compliance rate can be increased to over 99.9%. Energy balance constraints incorporate the entire "source-grid-load-storage-loss" equation, ensuring that any power deficit / surplus can be absorbed in real time by energy storage or the grid, eliminating frequency drops or overvoltages caused by power mismatch.

[0078] Step S109: Based on the energy storage charging and discharging strategy model, obtain the optimal energy storage charging and discharging strategy, which specifically includes the following steps:

[0079] Step S1091: Generate a preliminary charging and discharging strategy based on the energy storage charging and discharging strategy model and the model predictive control framework.

[0080] Step S1093: The improved adaptive genetic algorithm is used to optimize the initial charging and discharging strategy to obtain the optimal energy storage charging and discharging strategy.

[0081] Based on the energy storage charging and discharging strategy model, a model predictive control framework (MPC) is used to generate the initial charging and discharging strategy for the energy storage system. MPC is a rolling optimization control method that solves a finite-time optimization problem at each sampling time based on the current system state and future prediction information to obtain the control input for the current time. This process is then repeated at the next sampling time. In this invention, the prediction time domain is set to 24 hours, and the control time domain is set to 4 hours. That is, the energy storage operation strategy for the next 24 hours is optimized each time, but only the control commands for the first 4 hours are executed. Then, the model is updated based on new measurement data, and the optimization is repeated.

[0082] An improved adaptive genetic algorithm is used to solve the objective function under constraints to obtain the optimal energy storage charging and discharging strategy. The specific steps of the improved adaptive genetic algorithm are as follows:

[0083] Step S201: Initialize the algorithm based on the generated initial charge and discharge strategy to generate a population.

[0084] The specific details of the algorithm initialization are as follows:

[0085] Coding Design: The generated preliminary charge / discharge strategy is encoded into a gene sequence of length T as the control time domain, e.g., 4 hours, corresponding to 16 15-minute time intervals. Each gene position represents the charge / discharge power value at the corresponding time. Real-number encoding is used, directly using the power value as the gene value, e.g. ,in Indicates discharge power. This indicates the charging power. The absolute value represents the power level.

[0086] Population Generation: Randomly generate M individuals (e.g., M=10). These individuals represent the initial charge / discharge strategy and form the initial population. Each individual must satisfy the following power constraints: During charging , must meet During discharge , must meet .

[0087] Parameter settings:

[0088] Maximum number of iterations ;

[0089] Adaptive crossover probability initial value ;

[0090] Adaptive mutation probability initial value ;

[0091] Elite retention rate That is, the top 5% of the best individuals are retained in each generation.

[0092] Step S202: Calculate the fitness value for each individual based on the objective function and constraints.

[0093] The specific steps for calculating the fitness function are as follows:

[0094] Objective function transformation: Since the objective function is to minimize The objective function needs to be transformed into a fitness function that maximizes the problem. Define the fitness. Where X is an individual, For example, a very small positive number This can avoid the denominator being 0.

[0095] Constraint handling: A penalty function method is used to handle constraints, imposing penalties on individuals that violate the constraints. For example:

[0096] If the state of charge Out of range The penalty item is If the voltage or frequency exceeds the constraint range, the penalty term is: .

[0097] Final fitness is ,in, , These are the penalty coefficients, set according to the importance of the constraints. A crossover operation is performed on the selected individuals to generate new individuals.

[0098] Step S203: Perform selection operations based on the population to obtain superior individuals.

[0099] The specific details of the selected operation are as follows:

[0100] Fitness normalization: Calculates the sum of the fitness of all individuals in the population. And calculate the selection probability for each individual. .

[0101] Roulette selection: Selecting via the roulette mechanism Each individual, for example 95, ensures that individuals with high fitness have a greater probability of being selected.

[0102] Elite Preservation: Directly preserve the population with the highest fitness. From each individual, we obtain superior individuals, such as 5, and carry them to the next generation to avoid losing the optimal solution.

[0103] Step S204: Perform crossover operations on superior individuals to generate new individuals.

[0104] The specific details of the crossover operation are as follows:

[0105] Crossover probability update: For the selected individuals, calculate the adaptive crossover probability based on their fitness values. :

[0106] ;

[0107] in, For individual fitness, and These are the maximum fitness and average fitness of the current population, respectively. ≥ hour, Follow Increase and decrease; when hour, Take the maximum value .

[0108] Cross-processing: For each pair of selected individuals, with probability... Perform crossover. Use arithmetic crossover, such as for individuals... and Generate new individuals and :

[0109] ;

[0110] in, Cross coefficient, Randomly generated. Power constraints must be checked after crossover; if not met, the system should be regenerated. .

[0111] Step S205: Perform a mutation operation on the new individual to obtain a mutated individual.

[0112] The specific details of the mutation operation are as follows:

[0113] Mutation probability update: For the new individuals generated after crossover, calculate the adaptive mutation probability. :

[0114] ;

[0115] When individual fitness The higher, The smaller the value, the less the mutation damage to superior individuals; conversely, Take a larger value to increase search diversity.

[0116] Mutation execution: For each gene locus in each individual, with probability Initiate mutation. Use Gaussian mutation to generate new gene values:

[0117] ;

[0118] in, The variable asynchronous length decreases with the number of iterations, such as... , The initial step size is N(0,1), and N(0,1) is a random number distributed according to a standard normal distribution. After mutation, the power constraint and capacity constraint need to be checked. If they are not satisfied, the mutation is repeated.

[0119] Step S206: Iteratively calculate the state of charge for each individual. .

[0120] For each individual, i.e., the energy storage strategy, it needs to be based on the charge / discharge power sequence. Iterative calculation of each time step To verify the capacity constraint:

[0121] ;

[0122] Among them, when When <0, ,when hour, Initial state of charge The value is known, for example, 0.5. If... If the value exceeds the limit, a penalty function will be triggered.

[0123] Step S207: If the iteration meets the termination condition, terminate the iteration and output the optimal energy storage charging and discharging strategy.

[0124] Termination condition: Iteration stops when any of the following conditions are met:

[0125] The number of iterations has reached the preset number of iterations. ;

[0126] The algebra of continuous iteration is For example, after 20 generations, the rate of change of the optimal fitness of the population is less than a threshold. For example, 1%.

[0127] Optimal solution extraction: Select the individual with the highest fitness during the iteration process and decode it into an energy storage charging and discharging strategy. ,in >0 represents the discharge power. <0 represents the charging power. This strategy must satisfy all constraints and minimize the objective function F.

[0128] The final generated energy storage charging and discharging strategy will serve as the core input for the next stage of multi-resource collaborative scheduling, guiding the real-time operation of the energy storage system.

[0129] Through the above steps, the improved adaptive genetic algorithm can efficiently search for the optimal energy storage strategy under complex constraints, balance grid operating costs, new energy absorption rate and system stability, and achieve closed-loop control for multi-objective optimization.

[0130] The above steps can also be summarized as follows:

[0131] Population initialization: A certain number of individuals are randomly generated, each representing a set of energy storage charging and discharging strategies. That is, the individual's gene encoding is the charging and discharging power at each time point in the future control time domain.

[0132] Calculate fitness: Calculate the fitness value for each individual based on the objective function and constraints.

[0133] Selection operation: The roulette wheel selection method is used to select superior individuals from the population.

[0134] Crossover operation: Perform a crossover operation on the selected individuals to generate new individuals.

[0135] Mutation operation: Perform mutation operation on individuals after crossover to increase the diversity of the population.

[0136] Elite retention: The best individuals in the current population are retained for the next generation.

[0137] Termination criteria: If the termination condition is met, such as the maximum number of iterations or the fitness value converges, then stop the iteration and output the optimal solution; otherwise, return to step 2 to continue the iteration.

[0138] Among them, multi-resource collaborative scheduling based on the optimal energy storage charging and discharging strategy specifically includes the following:

[0139] Based on the generated energy storage charging and discharging strategy, a multi-resource collaborative scheduling model for new energy sources, energy storage, and loads is constructed. This model takes the energy storage strategy as its core and coordinates the output plan of new energy power plants with the demand response of loads.

[0140] Formulate a power output plan for new energy power plants.

[0141] Based on the predicted output curves of wind power / solar power and the energy storage charging and discharging strategy, the output plan of new energy power plants is dynamically adjusted. The wind power output range is determined by fitting the Weibull distribution of the predicted output curves of wind power / solar power. .

[0142] The power output plan for new energy power stations is as follows: when energy storage is in the charging period, that is... Furthermore, during peak periods of renewable energy output, priority is given to consuming renewable energy. If output exceeds load demand, energy storage is used to charge and store excess electricity, reducing power curtailment. When energy storage is in its discharge phase, that is... Furthermore, when new energy output is low, energy storage discharges can supplement the power gap and reduce the cost of purchasing electricity from the grid.

[0143] Adjusting load demand response includes the following processes:

[0144] Real-time electricity price In conjunction with new energy output and energy storage strategies, price signals are generated:

[0145] ;

[0146] in, To predict the total output of new energy sources, This is the average value. This is the adjustment coefficient. When the output of new energy sources is high, such as during energy storage charging periods, Lowering the load encourages increased electricity consumption; conversely, raising the load guides peak-shifting.

[0147] Based on user electricity consumption elasticity coefficient Calculate the load adjustment amount:

[0148] ;

[0149] In the formula, For a moment The grid load.

[0150] Calculate the load power after demand response:

[0151] For example, during periods of low electricity prices, the load can increase electricity consumption to match the peak output of energy storage charging and new energy sources.

[0152] Furthermore, such as Figure 2 As shown, the multi-resource coordination and control method in a power grid with a high proportion of new energy sources also includes the following steps:

[0153] Step S301: Obtain the real-time energy storage charging and discharging strategy.

[0154] Step S303: Compare the real-time energy storage charging and discharging strategy with the optimal energy storage charging and discharging strategy to obtain the comparison results.

[0155] Step S305: Dynamically adjust and update the multi-resource collaborative scheduling model based on the comparison results.

[0156] In this embodiment, data such as new energy output, energy storage operation status, load power consumption, and power grid operation parameters can be collected in real time through smart meters, sensors, and other devices, and the data can be transmitted to the dispatch center for real-time monitoring and analysis.

[0157] In this embodiment, data acquisition and output prediction are performed first, followed by the generation of an energy storage strategy. Then, multi-resource coordination and scheduling are conducted to formulate a new energy processing plan, adjust load demand response, execute the plan, and collect grid data. Deviation analysis is performed to determine if adjustments are needed. If adjustments are required, the process returns to the energy storage strategy generation step; otherwise, it returns to the execution and grid data collection step. By acquiring the charging and discharging strategy of the energy storage system in real time, the current operating status and dynamic response capability of energy storage resources can be promptly grasped, providing the most timely and accurate data support for subsequent optimization and decision-making, avoiding scheduling errors or resource waste caused by information lag. Comparing the real-time strategy with the "optimal" strategy allows for precise quantification of differences in multiple dimensions such as power, time period, cost, grid loss, and reserve capacity. This quickly identifies the source of deviation, such as prediction errors, equipment aging, and sudden changes in market electricity prices, providing a clear direction and quantitative basis for model correction and reducing blind trial and error. Based on the comparison results, the multi-resource collaborative scheduling model is dynamically adjusted and updated, enabling the model to have "self-learning and self-correction" capabilities. This improves the model's adaptability to future operating conditions, reduces the impact of wind and solar power fluctuations and load surges on the power grid, and corrects the scheduling weights of energy storage and other adjustable resources in real time, thereby increasing the renewable energy absorption rate and reducing system operating costs. Through rolling optimization closed-loop, the safe, economical, and low-carbon operation of the power grid in complex and uncertain environments is ensured, extending the lifespan of energy storage equipment and delaying new investment needs.

[0158] Based on step S303, the comparison results are obtained by comparing the real-time energy storage charging and discharging strategy and the optimal energy storage charging and discharging strategy, specifically including the following steps: Step S3031: Compare the real-time energy storage charging and discharging strategy with the optimal energy storage charging and discharging strategy to obtain the results of the deviation analysis.

[0159] Step S3033: Calculate the execution deviation based on the deviation cause analysis results and obtain the comparison results. The execution deviation includes: energy storage power deviation, new energy consumption deviation and load response deviation.

[0160] The actual energy storage charging and discharging strategies were compared with the optimized strategies to analyze the reasons for the deviations. These deviations may originate from errors in new energy output forecasting, load forecasting errors, abnormal operation of the energy storage system, or grid faults.

[0161] Calculate the execution deviation and complete the positioning.

[0162] The energy storage power deviation is as follows: If the deviation exceeds the threshold, for example This may be due to decreased efficiency of the energy storage device or abnormal power limitation.

[0163] In the formula, The energy storage power deviation at time t, in kW, represents the difference between the actual energy storage charging and discharging power and the optimal strategy. The actual energy storage charging and discharging power at time t, in kW; positive values ​​represent discharging, and negative values ​​represent charging. The optimal energy storage charging and discharging power at time t is expressed in kW. It is calculated from the energy storage charging and discharging strategy model. Positive values ​​represent discharging, and negative values ​​represent charging.

[0164] The deviation in the absorption of new energy sources is: If power is wasted, that is This may be due to errors in new energy forecasting or insufficient energy storage and charging capacity.

[0165] In the formula, The deviation of new energy consumption at time t, in kW, represents the difference between the theoretical consumption and the actual consumption. The target renewable energy consumption power at time t is expressed in kW, which is the renewable energy consumption power determined based on the prediction and scheduling model. The actual renewable energy consumption power at time t, in kW, is the renewable energy power actually consumed by the grid, load, or energy storage.

[0166] The load response deviation is: This may be due to a lag in electricity price signals or a delay in user response.

[0167] In the formula, The load response deviation at time t, in kW, represents the difference between the actual load adjustment and the optimization target. The actual load response power at time t, in kW, which is the actual power consumption adjusted by the user under the electricity price signal or dispatch instruction; The optimized target load response power at time t, in kW, is the optimal user power consumption calculated by the multi-resource collaborative scheduling model and matching new energy and energy storage strategies.

[0168] Based on step S305, the multi-resource collaborative scheduling model is dynamically adjusted and updated according to the comparison results. Jubi includes the following steps:

[0169] Step S3051: Short-term deviation correction:

[0170] If the predicted output of new energy sources deviates significantly, for example, if the actual wind speed is lower than the predicted value, the subsequent energy storage strategy should be adjusted immediately.

[0171] If the energy storage is currently charging, reduce the charging power or switch to discharging in advance to avoid the abandonment of renewable energy.

[0172] If the grid power is insufficient, the energy storage discharge power is increased to make up for the power gap.

[0173] If the load response is insufficient, the electricity price signal is updated in real time, for example, by increasing the peak-hour electricity price to enhance user adjustment incentives.

[0174] Step S3053: Long-term model parameter update:

[0175] The parameters of the wind power and photovoltaic forecasting models are updated daily based on the latest data to improve the accuracy of power output forecasting.

[0176] The efficiency parameters were adjusted based on actual charge and discharge data. The formula for calculating the state of charge is updated by analyzing the capacity decay curve and the state of charge calculation formula.

[0177] Monthly analysis of the genetic algorithm's performance and adjustment of crossover probabilities. Probability of mutation These parameters improve the efficiency of strategy generation.

[0178] In another aspect, the present invention also provides an electronic device, comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute any one of the multi-resource coordination control methods in a power grid with a high proportion of new energy sources.

[0179] On the other hand, the present invention also proposes a computer storage medium storing a computer program, which, when executed by a processor, implements a multi-resource coordination control method in a power grid with a high proportion of new energy sources.

[0180] Computer storage media may be simply referred to as media. Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Dual Data SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus Direct RAM (RDRAM), Direct Memory Bus Dynamic RAM (DRDRAM), and Memory Bus Dynamic RAM (RDRAM). The various embodiments described in this specification are presented in a progressive manner, with reference allowed to each other for similar or identical parts. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for apparatuses, devices, and non-volatile computer storage media are described simply because they are substantially similar to the method embodiments; relevant details can be found in the descriptions of the method embodiments.

[0181] Specific application examples are as follows.

[0182] I. Application Background and Data Acquisition

[0183] Taking a power grid with a high proportion of new energy sources as an example, wind power and photovoltaic installed capacity account for 45% of the total capacity in this region, but the curtailment rate is as high as 18%, resulting in significant pressure on the grid's peak-shaving capacity. A typical day in this region was selected as the research object, with a time granularity of 15 minutes, and the following data were collected:

[0184] New energy power output data: Historical power output data of photovoltaic power plants (Peak power 800kW), historical power output data of wind farms (Rated power 1000kW);

[0185] Meteorological data: light intensity (maximum 1000W / m²), wind speed (3-20m / s), temperature (25-35℃);

[0186] Power grid load data: (Peak: 1500kW, Valley: 800kW);

[0187] Energy storage system parameters: rated power 500kW, rated capacity 2000kWh, charging efficiency 90%, discharging efficiency 85%, initial state of charge. .

[0188] II. New Energy Output Forecast

[0189] (I) Wind power output forecast

[0190] Input parameters:

[0191] Wind speed forecast data: The predicted wind speed for 0-24 hours is 4-12 m / s;

[0192] The fan has a rated power of 1000kW;

[0193] Power curve parameters: cut-in wind speed 3m / s, rated wind speed 12m / s, cut-out wind speed 20m / s. The coefficients of the cubic polynomial obtained by fitting historical data are a=0.005, b=-0.06, c=0.25, and d=0.

[0194] Calculation process:

[0195] When the wind speed v = 8 m / s, and it is between the cut-in wind speed and the rated wind speed...

[0196] kW.

[0197] (II) Photovoltaic power output forecast

[0198] Input parameters:

[0199] Predicted light intensity: 600W / m²; Predicted temperature: 30℃.

[0200] The photovoltaic array has an area of ​​5000m² and a conversion efficiency of 18%.

[0201] Temperature coefficient α = 0.004 / ℃.

[0202] Calculation process:

[0203] W=531kW.

[0204] III. Cost Quantification and Construction of Multi-Objective Functions

[0205] (a) Cost Quantification

[0206] Energy storage system cost:

[0207] Investment costs: Yuan;

[0208] Operating costs: Assuming an average daily charging power of 100kW and an average daily discharging power of 150kW,

[0209] Yuan (Δt=0.25h).

[0210] Power grid operating costs:

[0211] Electricity purchase cost: Grid electricity price 0.6 yuan / kWh, average purchased power 200kW.

[0212] Yuan;

[0213] 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.

[0214] The calculated amount is 1200 yuan.

[0215] Power grid stability costs:

[0216] 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.

[0217] Yuan.

[0218] (II) Calculation of Multi-Objective Functions

[0219] Set the weights to a=0.5, b=0.3, and c=0.2.

[0220] Yuan,

[0221] curtailment rate In the calculation of the curtailment rate Rwaste, wind power output forecasts and photovoltaic power output forecasts are also applied.

[0222] objective function .

[0223] IV. Constraints and Strategy Generation

[0224] (a) Key Constraints

[0225] Power constraint: Charging power kW, discharge power kW;

[0226] Capacity constraints: For example, at t=1, because the predicted photovoltaic output of 531kW is relatively high, the strategy generates a "charging - 200kW" instruction. This charging power is substituted into the SOC formula:

[0227] ;

[0228] Power grid operation constraints: voltage 0.95-1.05 pu, frequency 49.5-50.5 Hz.

[0229] (II) Solving with an improved adaptive genetic algorithm

[0230] Parameter settings:

[0231] The control time domain was 4 hours, with 16 time points, and the population size M=50;

[0232] Maximum number of iterations: 200, adaptive crossover probability , ;

[0233] Mutation probability , The elite retention rate is 5%.

[0234] Example of optimal strategy:

[0235] 0-4 hours: Charging power -150kW (negative value indicates charging), discharging power 0;

[0236] 4-8 hours: Charging power -200kW, discharging power 0;

[0237] 8-12 hours: Discharge power 300kW, charging power 0 (during off-peak hours for new energy vehicles).

[0238] V. Multi-resource collaborative scheduling and feedback adjustment

[0239] (I) Implementation of Coordinated Scheduling

[0240] New Energy Power Output Plan:

[0241] When the photovoltaic output reaches 531kW (11:00-14:00) and the energy storage is charging, the photovoltaic power is given priority for consumption, and the excess power is charged by the energy storage at a power of 200kW, reducing the amount of abandoned power by 40%.

[0242] During periods of low wind power output (22:00-6:00), energy storage can discharge at a power of 300kW to supplement the grid gap.

[0243] Load demand response:

[0244] Real-time electricity price When photovoltaic output is high, the electricity price drops to 0.55 yuan / kWh, which will guide industrial load to increase electricity consumption by 100kW.

[0245] (II) Execution Feedback and Optimization

[0246] Real-time deviation analysis:

[0247] At 14:00, the measured photovoltaic output was 480kW (predicted 531kW), and the energy storage charging power was adjusted from -200kW to -150kW to avoid power curtailment.

[0248] If the load response deviation is 50kW, the electricity price will be increased to 0.65 yuan / kWh in real time to incentivize users to increase their electricity consumption.

[0249] Model update:

[0250] Daily updates of wind power / solar power prediction model parameters, such as wind speed-power curve coefficients;

[0251] The efficiency parameters are adjusted monthly based on the energy storage charging and discharging data. The current charging efficiency has been adjusted from 90% to 88% (due to equipment degradation).

[0252] VI. Implementation Results

[0253] Curtailment rate: decreased from 18% to 9.2%, while renewable energy consumption rate increased by 48%;

[0254] Power grid costs: Overall operating costs decreased by 15.3%, resulting in annual savings of approximately 1.2 million yuan;

[0255] Stability: Voltage deviation reduced to 0.015 pu, frequency deviation controlled within 0.008 Hz;

[0256] Energy storage utilization: The number of charge-discharge cycles increased from 3 times per day to 5 times per day, resulting in a 25% increase in capacity utilization.

[0257] This embodiment achieves coordinated optimization of new energy sources, energy storage, and load through a complete closed-loop control process, verifying the effectiveness and feasibility of the method in actual power grids.

[0258] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A multi-resource coordinated control method for power grids with a high proportion of new energy sources, characterized in that, include: Collect power grid data, including new energy output data, meteorological data, power grid load data, power grid operating status data, and energy storage system operating parameters; A new energy output prediction model is constructed based on the aforementioned power grid data; The quantitative cost is calculated based on the power grid data and the new energy output prediction model. The quantitative cost includes the energy storage system cost, power grid operation cost, and power grid stability cost. Based on the aforementioned quantified costs, an energy storage charging and discharging strategy model is constructed. The optimal energy storage charging and discharging strategy is obtained based on the energy storage charging and discharging strategy model. Based on the optimal energy storage charging and discharging strategy, a multi-resource collaborative scheduling model is constructed, and a scheduling plan is output. The scheduling plan includes the output plan of new energy stations and the load demand response curve. The multi-resources include new energy resources, energy storage resources and load resources.

2. The multi-resource coordination and control method in a power grid with a high proportion of new energy sources according to claim 1, characterized in that, The new energy output prediction model includes a wind power output prediction model and a photovoltaic power output prediction model.

3. The multi-resource coordination control method in a power grid with a high proportion of new energy sources according to claim 1, characterized in that, The quantified costs include energy storage system costs, grid operation costs, and grid stability costs; wherein, the energy storage system costs include investment costs and operating costs, and the grid operation costs include electricity purchase costs and curtailment costs. The investment cost for: ； In the formula, The unit power investment cost, Rated power of the energy storage system; The unit capacity investment cost This refers to the rated capacity of the energy storage system. The operating costs for: ; In the formula, Cost per unit of charging For unit discharge cost, and They are time points The energy storage charging power and discharging power, For time intervals; The cost of electricity purchase for: ； In the formula, For a moment The power purchased from the grid, For a moment The electricity price of the grid; The cost of abandoned electricity for: ； In the formula, For renewable energy feed-in tariffs; For a moment Total output of new energy sources; For a moment The grid load; The power grid stability cost for: ; In the formula, and These are the weighting coefficients for voltage deviation and frequency deviation, respectively. and They are time points Voltage deviation and frequency deviation.

4. The multi-resource coordination control method in a power grid with a high proportion of new energy sources according to any one of claims 1 to 3, characterized in that, The energy storage charging and discharging strategy model includes an objective function and constraints. The objective function... for: ； In the formula, , which is the total cost of power grid operation. For investment costs, For operating costs, For electricity purchase costs, For the cost of curtailing electricity, , , These are the weighting coefficients. Costs related to power grid stability; Curtailment 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.

5. The multi-resource coordinated control method in a power grid with a high proportion of new energy sources according to claim 4, characterized in that, The constraints include: Power constraints: ； ； 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. Capacity constraints: ； 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, For time intervals; Power grid operation constraints: ； ； 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; Energy balance constraints: ； In the formula, For a moment Power loss during power grid transmission.

6. The multi-resource coordinated control method in a power grid with a high proportion of new energy sources according to any one of claims 1 to 3, characterized in that, Based on the energy storage charging and discharging strategy model, the optimal energy storage charging and discharging strategy is obtained, specifically including: A preliminary charging and discharging strategy is generated based on the energy storage charging and discharging strategy model and the model prediction and control framework. An improved adaptive genetic algorithm is used to optimize the initial charging and discharging strategy to obtain the optimal energy storage charging and discharging strategy.

7. The multi-resource coordinated control method in a power grid with a high proportion of new energy sources according to claim 6, characterized in that, The energy storage charging and discharging strategy model includes an objective function and constraints. An improved adaptive genetic algorithm is used to optimize the initial charging and discharging strategy to obtain the optimal energy storage charging and discharging strategy, which specifically includes: Based on the aforementioned preliminary charge-discharge strategy, the algorithm is initialized to generate a population. The fitness value of each individual in the population is calculated based on the objective function and the constraints. Based on the population, a selection process is performed to obtain superior individuals; Based on the fitness values, crossover operations are performed on the superior individuals to generate new individuals; The state of charge is calculated iteratively for 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. The termination condition is that the number of iterations reaches a preset number of iterations or the number of consecutive iterations makes the rate of change of the optimal fitness of the population less than a threshold.

8. The multi-resource coordination control method in a power grid with a high proportion of new energy sources according to any one of claims 1 to 3, characterized in that, Also includes: Obtain real-time energy storage charging and discharging strategies; The comparison results are obtained by comparing the real-time energy storage charging and discharging strategy with the optimal energy storage charging and discharging strategy; The multi-resource collaborative scheduling model is dynamically adjusted and updated based on the comparison results.

9. The multi-resource coordinated control method in a power grid with a high proportion of new energy sources according to claim 8, characterized in that, The comparison results between the real-time energy storage charging and discharging strategy and the optimal energy storage charging and discharging strategy are as follows: The real-time energy storage charging and discharging strategy is compared with the optimal energy storage charging and discharging strategy to obtain the results of the deviation analysis. The execution deviation is calculated based on the deviation cause analysis results, and the comparison results are obtained. The execution deviation includes: energy storage power deviation, new energy consumption deviation, and load response deviation.

10. A multi-resource coordination control system for a power grid with a high proportion of new energy sources, characterized in that, include: The data acquisition module is suitable for collecting power grid data, which includes new energy output data, meteorological data, power grid load data, power grid operating status data, and energy storage system operating parameters. An electronic device is communicatively connected to the data acquisition module to acquire the power grid data, and the electronic device stores executable instructions, which are executed to perform the multi-resource coordination control method in a power grid with a high proportion of new energy sources as described in any one of claims 1 to 9.

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