Electric power system optimization scheduling method and device under hydroelectric visual angle

Abstract

The invention relates to the technical field of hydropower optimization scheduling, and discloses a power system optimization scheduling method and device under a hydropower perspective, and the method comprises the steps: carrying out the runoff set probability prediction based on the historical data of multi-energy power supply, and determining the hydroelectric generation prediction data based on the runoff set probability prediction result; predicting a wind and light power generation change trend based on the wind and light resource data and the station output data to obtain wind and light power generation prediction data; predicting regional power demands based on the historical power load data and the power load influence factor data to obtain regional power demands; establishing an electric quantity matching optimization objective function considering marginal cost under a hydroelectric perspective; and performing multi-objective optimization on the electric quantity matching optimization objective function considering the marginal cost under the hydroelectric perspective to obtain a multi-energy electric quantity optimization scheduling strategy of the electric power system. According to the method, the power generation efficiency and income of a hydropower enterprise are improved through hydroelectric power generation optimization scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower optimization scheduling, and in particular to a method and device for optimizing scheduling of a power system under a hydropower perspective. Background Art

[0002] Power system dispatching mostly aims at the energy security of the entire power system, pursuing a balanced match between multi-energy power supply and power demand. However, it lacks support for increasing profits through optimizing power generation dispatching. Hydropower, as a form of clean and renewable energy, has the characteristics of strong controllability and low marginal cost. It is an important energy structure and power regulator of the power system. The optimized dispatching of hydropower is of great significance to improving the overall operating efficiency, stability and economy of the power system.

[0003] Related hydropower optimization scheduling is mostly carried out by predicting or designing water inflow (runoff flow) scenarios, combining them with electricity load, with the goal of balancing the supply and demand of single hydropower generation and electricity consumption. Through this optimization method, power generation is scheduled, but it does not fully consider the actual operation and scheduling of the power market. As a form of energy supply, hydropower needs to cooperate with other energy sources to maintain the stability of the entire power system. How to regulate the proportion of hydropower participating in the power market to improve the power generation efficiency of hydropower companies is also a problem that needs to be urgently solved in the process of power system optimization scheduling. Summary of the Invention

[0004] In view of this, the present invention provides a method and device for optimizing the scheduling of a power system under the perspective of hydropower, so as to solve the problem of insufficient consideration of the coordinated regulation of hydropower and other energy sources.

[0005] In a first aspect, the present invention provides a method for optimizing and dispatching a power system under a water-voltage angle, the method comprising:

[0006] Obtaining historical data on multi-energy power supply in the target area, performing runoff aggregate probability prediction based on the multi-energy power supply historical data, and determining hydropower generation prediction data based on the runoff aggregate probability prediction results;

[0007] Based on the wind and solar resource data and station output data in the multi-energy power supply historical data, the wind and solar power generation trend is predicted to obtain wind and solar power generation prediction data;

[0008] Based on the historical power load data and power load influencing factor data in the multi-energy power supply historical data, the regional power demand is predicted to obtain the regional power demand;

[0009] Based on the hydropower generation forecast data, wind and solar power generation forecast data and regional electricity demand, an electricity matching optimization objective function considering marginal cost under the hydropower perspective is established;

[0010] Multi-objective optimization is performed on the electricity matching optimization objective function considering marginal cost under the hydropower angle, and the multi-energy electricity optimization dispatching strategy of the power system is obtained.

[0011] This embodiment provides a method for optimizing and dispatching a power system under a hydropower perspective. The method predicts the trends of hydropower generation, wind and solar power generation, and regional power demand, respectively, and establishes an electricity matching optimization objective function that takes marginal costs into consideration under a hydropower perspective based on the hydropower generation forecast data, wind and solar power generation forecast data, and regional power demand. The method determines an optimized dispatching strategy for multiple energy sources in the power system through multi-objective optimization, fully considers the coordinated dispatching between hydropower and other energy sources during the operation of the power system, and improves the power generation efficiency and revenue of hydropower enterprises through optimized dispatching of hydropower generation while ensuring regional power safety.

[0012] In an optional embodiment, performing runoff aggregate probability prediction based on multi-energy power supply historical data, and determining hydropower generation prediction data based on the runoff aggregate probability prediction result, includes:

[0013] Based on the meteorological historical data in the multi-energy power supply historical data, multiple models are used to predict short-, medium- and long-term runoff at characteristic points, and the multi-model runoff prediction results are obtained;

[0014] Based on the multi-model runoff prediction results, the runoff ensemble probability prediction is carried out to obtain the deterministic runoff prediction results of characteristic points and the upper and lower confidence intervals of the runoff prediction;

[0015] Based on the deterministic runoff forecast results of characteristic points and the upper and lower confidence intervals of the runoff forecast, the hydropower generation trend and hydropower generation fluctuation range are predicted using the cascade power station output equation to obtain hydropower generation forecast data.

[0016] This embodiment provides a method for optimizing the scheduling of a power system under a hydropower perspective. It establishes a runoff aggregate probability prediction method that takes into account the multi-source uncertainty of input data and physical models. Combined with the output equation of cascade power stations, it not only provides deterministic hydropower generation trends, but also provides uncertain hydropower generation fluctuation ranges, providing boundary constraints and upper and lower risk ranges for the marginal cost calculation of the power system under the hydropower perspective.

[0017] In an optional embodiment, a runoff ensemble probability prediction is performed based on the multi-model runoff prediction results to obtain deterministic runoff prediction results at characteristic points and upper and lower confidence intervals of the runoff prediction, including:

[0018] Obtain the measured runoff values ​​at the feature points, perform weighted average on the conditional probability distribution between the measured runoff values ​​at the feature points and the runoff prediction results of multiple models, and obtain the posterior distribution data of the measured runoff values;

[0019] The deterministic runoff forecast results and upper and lower confidence intervals of the runoff forecast at characteristic points are determined based on the posterior distribution data of the measured runoff values.

[0020] This embodiment provides a method for optimizing the dispatch of a power system under a hydropower perspective. By performing a weighted average on the conditional probability distribution between the measured runoff values ​​at feature points and the runoff prediction results from multiple models, the posterior distribution data of the measured runoff values ​​is obtained. This measures the importance of the multi-model runoff prediction results output by each model and quantifies the uncertainty of each model, making the deterministic runoff forecast results at the feature points and the upper and lower confidence intervals of the runoff forecast more accurate.

[0021] In an optional embodiment, based on the hydropower generation forecast data, the wind and solar power generation forecast data and the regional electricity demand, an electricity matching optimization objective function considering the marginal cost under the hydropower perspective is established, including:

[0022] Obtain the clearing electricity prices corresponding to the historical data of multi-energy power supply, perform multi-factor fitting on the historical data and clearing electricity prices, and obtain the total marginal cost of the power system and the marginal cost of hydropower generation;

[0023] Calculate thermal power load rate based on hydropower generation forecast data, wind and solar power generation forecast data and regional electricity demand;

[0024] Compare the thermal power load rate with the load rate threshold to determine the supply and demand relationship in the electricity market;

[0025] Based on the total marginal cost of the power system, the marginal cost of hydropower generation and the regional electricity demand, the supply and demand relationship of the electricity market is utilized to establish the electricity matching optimization objective function considering the marginal cost under the hydropower perspective.

[0026] This embodiment provides a method for optimizing the dispatch of a power system under a hydropower perspective. Based on the supply and demand relationship in the power market, an optimization objective function for electricity matching that considers marginal costs under a hydropower perspective is established. This provides an optimal electricity dispatching plan for hydropower to participate in power market transactions, thereby supporting the realization of optimal economic benefits for hydropower generation.

[0027] In an optional embodiment, based on the total marginal cost of the power system, the marginal cost of hydropower generation, and the regional electricity demand, the supply and demand relationship in the power market is utilized to establish an electricity matching optimization objective function that considers marginal costs from a hydropower perspective, including:

[0028] If the thermal power load rate is greater than the load rate threshold, the expression of the electricity matching optimization objective function considering the marginal cost under the hydropower angle is:

[0029]

[0030] Among them, Best[q water,t] represents the optimal hydroelectric power generation of the power system at time t under the current supply and demand relationship, ff(*) represents the multi-objective optimization function, eq t represents the total marginal cost of the power system at time t, hq t represents the marginal cost of hydropower generation at time t, PD t represents the regional electricity demand at time t, q water,t represents the hydroelectric power generation at time t, q max,t represents the maximum hydroelectric power generation at time t considering the reservoir water level and flow, q i,t represents the power generation of type i energy at time t, q other,t represents the power generation of other power sources except hydropower at time t, and n represents the number of power types.

[0031] In an optional embodiment, based on the total marginal cost of the power system, the marginal cost function of hydropower generation, and the regional electricity demand, the power market supply and demand relationship is utilized to establish an electricity matching optimization objective function that considers marginal costs from a hydropower perspective, further comprising:

[0032] If the thermal power load rate is less than the load rate threshold, the expression of the electricity matching optimization objective function considering the marginal cost under the hydropower angle is:

[0033]

[0034] Where g(*) represents the function of hydropower generation changing with the marginal cost of the power system; eq min Indicates the set minimum marginal cost threshold of the power system.

[0035] In a second aspect, the present invention provides a device for optimizing and dispatching a power system under a hydropower angle, the device comprising:

[0036] a determination module, configured to obtain historical data of multi-energy power supply in a target area, perform runoff aggregate probability prediction based on the multi-energy power supply historical data, and determine hydropower generation prediction data based on the runoff aggregate probability prediction result;

[0037] The first prediction module is used to predict the change trend of wind and solar power generation based on wind and solar resource data and station output data in the multi-energy power supply historical data to obtain wind and solar power generation prediction data;

[0038] The second prediction module is used to predict regional power demand based on historical power load data and power load influencing factor data in the multi-energy power supply historical data to obtain regional power demand;

[0039] Establish a module for establishing an electricity matching optimization objective function that considers marginal costs under the hydropower perspective based on hydropower generation forecast data, wind and solar power generation forecast data, and regional electricity demand;

[0040] The optimization module is used to perform multi-objective optimization on the electricity matching optimization objective function considering marginal cost under the hydropower perspective, and obtain the multi-energy electricity optimization dispatching strategy of the power system.

[0041] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to thereby execute the power system optimization and scheduling method under the water-voltage angle of the above-mentioned first aspect or any corresponding embodiment thereof.

[0042] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the power system optimization and scheduling method under the water-voltage angle of the above-mentioned first aspect or any corresponding embodiment thereof.

[0043] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for enabling a computer to execute the method for optimizing and dispatching an electric power system under the water-voltage angle of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 1 is a flow chart of a method for optimizing and dispatching a power system under a water-voltage perspective according to an embodiment of the present invention;

[0046] Figure 2 1 is a flow chart of another method for optimizing and dispatching a power system under a water-voltage perspective according to an embodiment of the present invention;

[0047] Figure 3 1 is a flow chart of another method for optimizing and dispatching a power system under a water-voltage perspective according to an embodiment of the present invention;

[0048] Figure 4 1 is a flow chart of a method for optimizing and dispatching a power system under a water-voltage perspective according to another embodiment of the present invention;

[0049] Figure 5 This is a structural block diagram of a power system optimization and scheduling device under a water-based television angle according to an embodiment of the present invention;

[0050] Figure 6 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0052] An embodiment of the present invention provides a method for optimizing and dispatching an electric power system under a water-based television angle. It should be noted that the execution subject of the method for optimizing and dispatching an electric power system under a water-based television angle provided by the embodiment of the present invention may be an electric power system optimizing and dispatching device under a water-based television angle. The electric power system optimizing and dispatching device under a water-based television angle may be implemented as part or all of an electronic device through software, hardware, or a combination of software and hardware. The electronic device may be a server or a terminal. The server in the embodiment of the present application may be a single server or a server cluster composed of multiple servers. The terminal in the embodiment of the present application may be a smart phone, a personal computer, a tablet computer, a wearable device, an intelligent robot, or other intelligent hardware devices. In the following method embodiments, the execution subject is an electronic device as an example for explanation.

[0053] According to an embodiment of the present invention, an embodiment of a method for optimizing and dispatching a power system under a water-voltage angle is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0054] In this embodiment, a method for optimizing and dispatching a power system under a water-voltage angle is provided, which can be used for the above-mentioned electronic equipment. Figure 1 is a flow chart of a method for optimizing and dispatching a power system under a water-voltage angle according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0055] Step S101: acquiring multi-energy power supply historical data of a target area, performing runoff aggregate probability prediction based on the multi-energy power supply historical data, and determining hydropower generation prediction data based on the runoff aggregate probability prediction result.

[0056] Step S102 : predicting the wind-solar power generation change trend based on the wind-solar resource data and the station output data in the multi-energy power supply historical data to obtain wind-solar power generation prediction data.

[0057] Specifically, wind and solar resource data and station output data are brought into the machine learning neural network model for model training to establish a nonlinear functional relationship between wind and solar resources and station output, namely the power prediction model; with reanalysis or model data as input, large meteorological models or physical models are used to predict regional wind and solar resources and generate meteorological forecast products; the meteorological forecast products are input into the power prediction model to predict the future trend of regional wind and solar power generation changes and obtain wind and solar power generation forecast data.

[0058] Step S103 : predicting the regional power demand based on the historical power load data and the power load influencing factor data in the multi-energy power supply historical data to obtain the regional power demand.

[0059] Specifically, the data on factors affecting power load include socio-economic information and meteorological information. The process of predicting regional electricity demand is as follows: historical power load information, socio-economic information and meteorological information are collected, and time sliced ​​according to seasons and holidays; principal component analysis is used to identify the main influencing factors of holiday and non-holiday power load in different seasons, and regression analysis is used to establish the functional relationship between the influencing factors and power load in each time period, that is, the power demand forecasting model; time series autoregressive analysis is used to establish a function of socio-economic factors changing over time, to make socio-economic development forecasts, and to combine meteorological forecast products, input the above-mentioned power demand forecasting model to predict regional power demand.

[0060] Step S104: establishing an electricity matching optimization objective function taking marginal cost into consideration under the hydropower perspective based on the hydropower generation forecast data, the wind and solar power generation forecast data and the regional electricity demand.

[0061] Step S105 , performing multi-objective optimization on the electricity matching optimization objective function considering marginal cost under the hydropower angle, and obtaining a multi-energy electricity optimization dispatching strategy for the power system.

[0062] Specifically, the multi-energy power optimization dispatching strategy of the power system is used to coordinate the control of hydropower and other new energy sources in the power system.

[0063] This embodiment provides a method for optimizing and dispatching a power system under a hydropower perspective. The method predicts the trends of hydropower generation, wind and solar power generation, and regional power demand, respectively, and establishes an electricity matching optimization objective function that takes marginal costs into consideration under a hydropower perspective based on the hydropower generation forecast data, wind and solar power generation forecast data, and regional power demand. The method determines an optimized dispatching strategy for multiple energy sources in the power system through multi-objective optimization, fully considers the coordinated dispatching between hydropower and other energy sources during the operation of the power system, and improves the power generation efficiency and revenue of hydropower enterprises through optimized dispatching of hydropower generation while ensuring regional power safety.

[0064] In this embodiment, a method for optimizing and dispatching a power system under a water-voltage angle is provided, which can be used for the above-mentioned electronic equipment. Figure 2 is a flow chart of a method for optimizing and dispatching a power system under a water-voltage angle according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0065] Step S201 : acquiring multi-energy power supply historical data of the target area, performing runoff aggregate probability prediction based on the multi-energy power supply historical data, and determining hydropower generation prediction data based on the runoff aggregate probability prediction result.

[0066] Specifically, the above step S201 includes:

[0067] Step S2011 , based on the meteorological historical data in the multi-energy power supply historical data, a multi-model is used to perform short-, medium-, and long-term runoff prediction on the characteristic points to obtain a multi-model runoff prediction result.

[0068] Specifically, meteorological reanalysis data and climate model data of the target area are collected, and deviation correction is performed using the measured meteorological data as a benchmark. The corrected data is used as input, and neural networks, lumped and distributed hydrological models are used as models to carry out short-, medium- and long-term runoff forecasts at characteristic points.

[0069] Step S2012: Perform runoff ensemble probability prediction based on the multi-model runoff prediction results to obtain deterministic runoff prediction results at characteristic points and upper and lower confidence intervals of the runoff prediction.

[0070] Specifically, based on the multi-model runoff prediction results, the Bayesian Model Averaging (BMA) method is used to carry out runoff ensemble probability prediction, and the deterministic runoff forecast results for different forecast periods and the upper and lower confidence intervals of the runoff forecast are obtained.

[0071] In some optional implementations, the above step S2012 includes:

[0072] Step a1: obtain the measured runoff values ​​of the feature points, perform weighted averaging on the conditional probability distributions between the measured runoff values ​​of the feature points and the runoff prediction results of multiple models, and obtain the posterior distribution data of the measured runoff values.

[0073] Specifically, the Bayesian model averaging method is essentially a weighted average of the conditional probability distributions of the measured values ​​and the ensemble forecast simulation values ​​(i.e., the multi-model runoff prediction results) to deduce the posterior distribution of the measured values. Its basic principle is: assuming that Q(t) represents the measured value at time t, f i (t) represents the forecast value of the i-th member at the t-th moment, F={F1,F2,…,F m} represents the set of m model simulations, then the ensemble forecast F i The BMA posterior distribution P(Q / F) of the measured value of (t) can be expressed as:

[0074]

[0075] Among them, P k (Q / f k ) is expressed as the probability density function of the simulated value of the k-th model under given measured data conditions; P(f k / F) represents the posterior probability density function of the kth model when given training data, reflecting the quality of the model simulation results.

[0076] Furthermore, the BMA method uses the posterior probability as the weight, performs a weighted average on the simulation values ​​of each model, and outputs a comprehensive simulation result. The model with higher accuracy has a larger weight value:

[0077]

[0078] Among them, ω k Represents the posterior probability distribution weight of the measured value corresponding to the k-th forecast member.

[0079] Step a2: Determine the deterministic runoff forecast results and upper and lower confidence intervals of the runoff forecast at the characteristic points based on the posterior distribution data of the measured runoff values.

[0080] Furthermore, the ensemble probability forecast based on the Bayesian model averaging method quantitatively describes and estimates the uncertainty of runoff forecast in the form of probability distribution, and the median or mean of the published probability forecast is used as the deterministic runoff forecast result (Q) of the characteristic point, and then the upper and lower confidence intervals of the runoff forecast (Q) are determined. 上 , Q 下 ).

[0081] Step S2013: Based on the deterministic runoff forecast results of the characteristic points and the upper and lower confidence intervals of the runoff forecast, the hydropower generation trend and the hydropower generation fluctuation range are predicted using the cascade power station output equation to obtain hydropower generation prediction data.

[0082] Specifically, the deterministic runoff forecast results of characteristic points and the upper and lower confidence intervals of the runoff forecast are respectively input into the output equation of the cascade power stations to predict and calculate the hydropower generation trend and its fluctuation range at different times or periods in the future.

[0083] Furthermore, the specific expression of the cascade power station output equation is:

[0084]

[0085] Among them, q op represents the power generation of the hydropower station in period o; ρ is the density of water; g is the acceleration of gravity; H op is the effective water head of the hydropower station p during period o; Q op It represents the turbine flow of hydropower station p during period o, and is calculated based on the free discharge according to the forecast flow; Q pmax is the maximum discharge flow of the turbine of the hydropower station; η is the power generation efficiency; t is the time.

[0086] Step S202: predict the wind and solar power generation trend based on the wind and solar resource data and station output data in the multi-energy power supply history data to obtain wind and solar power generation prediction data. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0087] Step S203: forecast the regional power demand based on the historical power load data and power load influencing factor data in the multi-energy power supply historical data to obtain the regional power demand. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0088] Step S204: Based on the hydropower generation forecast data, wind and solar power generation forecast data and regional electricity demand, establish the electricity matching optimization objective function considering the marginal cost under the hydropower perspective. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0089] Step S205: Perform multi-objective optimization on the electricity matching optimization objective function considering marginal cost under the hydropower angle to obtain the multi-energy electricity optimization dispatching strategy of the power system. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0090] This embodiment provides a method for optimizing the scheduling of a power system under a hydropower perspective. It establishes a runoff aggregate probability prediction method that takes into account the multi-source uncertainty of input data and physical models. Combined with the output equation of cascade power stations, it not only provides deterministic hydropower generation trends, but also provides uncertain hydropower generation fluctuation ranges, providing boundary constraints and upper and lower risk ranges for the marginal cost calculation of the power system under the hydropower perspective.

[0091] In this embodiment, a method for optimizing and dispatching a power system under a water-voltage angle is provided, which can be used for the above-mentioned electronic equipment. Figure 3 is a flow chart of a method for optimizing and dispatching a power system under a water-voltage angle according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0092] Step S301: Obtain the multi-energy power supply history data of the target area, perform runoff aggregate probability prediction based on the multi-energy power supply history data, and determine the hydropower generation prediction data based on the runoff aggregate probability prediction result. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0093] Step S302: predict the wind and solar power generation trend based on the wind and solar resource data and station output data in the multi-energy power supply history data to obtain wind and solar power generation prediction data. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0094] Step S303: forecast the regional power demand based on the historical power load data and power load influencing factor data in the multi-energy power supply historical data to obtain the regional power demand. Figure 2 Step S203 of the illustrated embodiment will not be described in detail here.

[0095] Step S304: Based on the hydropower generation forecast data, the wind and solar power generation forecast data and the regional electricity demand, an electricity matching optimization objective function considering the marginal cost under the hydropower perspective is established.

[0096] Specifically, the above step S304 includes:

[0097] Step S3041: Obtain the clearing electricity price corresponding to the multi-energy power supply historical data, perform multi-factor fitting on the multi-energy power supply historical data and the clearing electricity price, and obtain the total marginal cost of the power system and the marginal cost of hydropower generation.

[0098] Specifically, the comprehensive cost function of the power system is the functional relationship between the electricity price and the power generation of the power system, and its expression is:

[0099]

[0100] Where EP represents the comprehensive electricity price of the power system, f(*) represents the comprehensive cost function of the power system, HP(*), NE(*), TP(*) and ES(*) represent the cost functions of hydropower generation, other clean energy generation, thermal power generation and energy storage, respectively, which are obtained by fitting historical data, and q i represents the power generation of energy type i, where i=1, 2, 3, and 4 correspond to hydropower generation, other clean energy generation, thermal power generation, and energy storage, respectively. j represents the power demand at time j, q ij It represents the power generation of type i energy at time j.

[0101] Furthermore, the expression for the total marginal cost of the power system is as follows:

[0102]

[0103] Among them, eq represents the marginal cost function of multi-energy power generation, that is, the total marginal cost of the power system.

[0104] Furthermore, the marginal cost of hydropower generation is the partial derivative of the comprehensive electricity price with respect to hydropower generation. The expression of the marginal cost function of hydropower generation is as follows:

[0105]

[0106] Where hp represents the marginal cost function of hydropower generation.

[0107] Step S3042: Calculate the thermal power load rate based on the hydropower generation forecast data, wind and solar power generation forecast data, and regional electricity demand.

[0108] Step S3043: Compare the thermal power load rate with the load rate threshold to determine the supply and demand relationship in the electricity market.

[0109] Specifically, the thermal power load rate is calculated based on the forecast of short-, medium- and long-term hydro, wind and solar power generation and regional power demand. The supply and demand relationship of the power market in the corresponding period is judged based on whether the thermal power load rate exceeds the threshold as a conditional constraint. The expression is:

[0110]

[0111] Among them, tq j 、tq j上 、tq j下 They represent the load rate of the thermal power average and upper and lower confidence intervals in the predicted j period; PD j represents the regional electricity demand load during period j; qe j 、qe j上 、qe j下 , respectively represent the mean value and upper and lower confidence intervals of the predicted hydropower generation in period j; qw j 、qrj and qs j represent the wind power generation, solar power generation and energy storage capacity in period j respectively; B j represents the available thermal power installed capacity in the region during period j; tq max This is the thermal power load rate threshold, which is set based on daily regional electricity consumption and expert experience.

[0112] Step S3044: Based on the total marginal cost of the power system, the marginal cost of hydropower generation and the regional electricity demand, the electricity market supply and demand relationship is used to establish an electricity matching optimization objective function that considers marginal costs under the hydropower perspective.

[0113] Specifically, forecasting the medium and long term tq j上 >tq max That is, when the power system is in short supply, the marginal cost of hydropower generation is close to the comprehensive marginal cost of the power system, that is, the maximum hydropower generation, while ensuring that short-term transactions meet market demand is the optimization goal, multi-objective constraints are established, and multi-energy power scheduling is carried out.

[0114] Furthermore, if the thermal power load rate is greater than the load rate threshold, the expression of the electricity matching optimization objective function considering the marginal cost under the hydropower angle is:

[0115]

[0116] Among them, Best[q water,t ] represents the optimal hydroelectric power generation of the power system at time t under the current supply and demand relationship, ff(*) represents the multi-objective optimization function, eq t represents the total marginal cost of the power system at time t, hq t represents the marginal cost of hydropower generation at time t, PD t represents the regional electricity demand at time t, q water,t represents the hydroelectric power generation at time t, q max,t represents the maximum hydroelectric power generation at time t considering the reservoir water level and flow, q i,t represents the power generation of type i energy at time t, q other,t represents the power generation of other power sources except hydropower at time t, and n represents the number of power types.

[0117] Furthermore, we can predict the medium and long term tq j上 >tq max That is, when the power system is oversupplied, under the premise of ensuring minimum power abandonment, the hydropower generation capacity is determined according to the total marginal cost threshold of the power system, multi-objective constraints are established, and multi-energy power scheduling is carried out.

[0118] Furthermore, if the thermal power load rate is less than the load rate threshold, the expression of the electricity matching optimization objective function considering the marginal cost under the hydropower angle is:

[0119]

[0120] Step S305: Perform multi-objective optimization on the electricity matching optimization objective function considering marginal cost under the hydropower angle to obtain the multi-energy electricity optimization dispatching strategy of the power system. Figure 2 Step S205 of the illustrated embodiment will not be described in detail here.

[0121] This embodiment provides a method for optimizing the dispatch of a power system under a hydropower perspective. Based on the supply and demand relationship in the power market, an optimization objective function for electricity matching that considers marginal costs under a hydropower perspective is established. This provides an optimal electricity dispatching plan for hydropower to participate in power market transactions, thereby supporting the realization of optimal economic benefits for hydropower generation.

[0122] The following describes the specific steps of a method for optimizing and dispatching a power system under a water-based television perspective through a specific embodiment.

[0123] Example 1:

[0124] like Figure 4 As shown in FIG, the specific steps of the power system optimization dispatching method under the hydropower perspective include:

[0125] S1: Collect historical information on regional multi-energy power supply and its corresponding clearing electricity prices, perform multi-factor fitting, and obtain the comprehensive cost of the power system and the marginal cost function of hydropower generation;

[0126] S2: Utilize multi-source data and multiple models to conduct runoff probability forecasting based on the Bayesian model averaging method, and combine it with the output equation of cascade hydropower stations to predict future hydropower generation trends and fluctuation ranges;

[0127] S3: Based on the historical output information of regional wind and solar power stations, a wind and solar power prediction model is established using machine learning algorithms. Combined with meteorological forecast products, the model is used to predict future trends in wind and solar power generation.

[0128] S4: Collect power load information, combine it with regional development patterns and future climate change, and use time series and regression analysis methods to predict regional power demand;

[0129] S5: Calculate and evaluate the supply and demand relationship of the regional short-term, medium-term and long-term power system, establish the electricity matching optimization objective function considering the marginal cost under the hydropower perspective, and obtain the multi-energy electricity dispatch combination plan of the power system through multi-objective optimization.

[0130] In this embodiment, a power system optimization and scheduling device under a water-based TV angle is also provided. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the details that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0131] This embodiment provides a power system optimization and dispatching device under the water-TV angle, such as Figure 5 Shown, including:

[0132] Determination module 501, configured to obtain multi-energy power supply historical data of a target area, perform runoff aggregate probability prediction based on the multi-energy power supply historical data, and determine hydropower generation prediction data based on the runoff aggregate probability prediction result;

[0133] A first prediction module 502 is configured to predict a change trend of wind and solar power generation based on wind and solar resource data and station output data in the multi-energy power supply historical data, thereby obtaining wind and solar power generation prediction data;

[0134] The second prediction module 503 is used to predict the regional power demand based on the historical power load data and the power load influencing factor data in the multi-energy power supply historical data to obtain the regional power demand;

[0135] Establishing module 504, for establishing an electricity matching optimization objective function considering marginal cost under the hydropower perspective based on the hydropower generation forecast data, the wind and solar power generation forecast data and the regional electricity demand;

[0136] The optimization module 505 is used to perform multi-objective optimization on the power matching optimization objective function considering marginal cost under the hydropower perspective, and obtain a multi-energy power optimization dispatching strategy for the power system.

[0137] In some optional implementations, the determining module 501 includes:

[0138] The first prediction unit is used to use multiple models to perform short-, medium- and long-term runoff prediction on characteristic points based on meteorological historical data in the multi-energy power supply historical data, and obtain a multi-model runoff prediction result;

[0139] A determination unit is used to perform runoff ensemble probability prediction based on the multi-model runoff prediction results, and obtain the deterministic runoff prediction results of the characteristic points and the upper and lower confidence intervals of the runoff prediction;

[0140] The second prediction unit is used to predict the hydropower generation trend and hydropower generation fluctuation range based on the deterministic runoff forecast results of the characteristic points and the upper and lower confidence intervals of the runoff forecast using the cascade power station output equation to obtain hydropower generation prediction data.

[0141] In some optional implementations, the determining unit includes:

[0142] The weighted average subunit is used to obtain the measured runoff values ​​at the feature points, perform weighted average on the conditional probability distribution between the measured runoff values ​​at the feature points and the runoff prediction results of the multiple models, and obtain the posterior distribution data of the measured runoff values;

[0143] The determination subunit is used to determine the deterministic runoff forecast results and the upper and lower confidence intervals of the runoff forecast at the characteristic points based on the posterior distribution data of the runoff measured values.

[0144] In some optional implementations, the establishing module 504 includes:

[0145] A fitting unit is used to obtain the clearing electricity price corresponding to the historical data of multi-energy power supply, perform multi-factor fitting on the historical data of multi-energy power supply and the clearing electricity price, and obtain the total marginal cost of the power system and the marginal cost of hydropower generation;

[0146] a calculation unit, configured to calculate a thermal power load rate based on hydropower generation forecast data, wind and solar power generation forecast data, and regional electricity demand;

[0147] a comparison unit, for comparing the thermal power load rate with a load rate threshold to determine the supply and demand relationship in the power market;

[0148] A unit is established to establish an electricity matching optimization objective function considering marginal costs from a hydropower perspective based on the total marginal cost of the power system, the marginal cost of hydropower generation and the regional electricity demand, and by utilizing the supply and demand relationship in the electricity market.

[0149] In some optional implementations, if the thermal power load rate in the establishment unit is greater than the load rate threshold, the expression of the electricity matching optimization objective function considering the marginal cost under the hydropower angle is:

[0150]

[0151] Among them, Best[q water,t ] represents the optimal hydroelectric power generation of the power system at time t under the current supply and demand relationship, ff(*) represents the multi-objective optimization function, eq t represents the total marginal cost of the power system at time t, hq t represents the marginal cost of hydropower generation at time t, PD t represents the regional electricity demand at time t, q water,t represents the hydroelectric power generation at time t, q max,t represents the maximum hydroelectric power generation at time t considering the reservoir water level and flow, q i,t represents the power generation of type i energy at time t, q other,trepresents the power generation of other power sources except hydropower at time t, and n represents the number of power types.

[0152] In some optional implementations, if the thermal power load rate in the establishment unit is less than the load rate threshold, the expression of the electricity matching optimization objective function considering the marginal cost under the hydropower angle is:

[0153]

[0154] Where g(*) represents the function of hydropower generation changing with the marginal cost of the power system; eq min Indicates the set minimum marginal cost threshold of the power system.

[0155] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0156] In this embodiment, a power system optimization and scheduling device under a water-TV angle is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0157] The embodiment of the present invention also provides a computer device having the above Figure 5 A power system optimization and dispatching device under a water TV angle is shown.

[0158] See also Figure 6 , Figure 6 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 6 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of a GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0159] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0160] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0161] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0162] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0163] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 6 The bus connection is taken as an example.

[0164] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0165] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0166] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0167] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for optimizing and dispatching a power system under a water-voltage angle, characterized in that: The method comprises: Acquiring multi-energy power supply historical data for a target area, performing runoff aggregate probability prediction based on the multi-energy power supply historical data, and determining hydropower generation prediction data based on the runoff aggregate probability prediction result; Predicting the change trend of wind and solar power generation based on the wind and solar resource data and station output data in the multi-energy power supply historical data to obtain wind and solar power generation prediction data; Predicting regional power demand based on historical power load data and power load influencing factor data in the multi-energy power supply historical data to obtain regional power demand; Establishing an electricity matching optimization objective function considering marginal cost under the hydropower perspective based on the hydropower generation forecast data, the wind and solar power generation forecast data and the regional electricity demand; Multi-objective optimization is performed on the electricity matching optimization objective function considering marginal cost under the hydropower angle to obtain a multi-energy electricity optimization dispatching strategy for the power system.

2. The method according to claim 1, characterized in that The method of performing runoff aggregate probability prediction based on the multi-energy power supply historical data and determining hydropower generation prediction data based on the runoff aggregate probability prediction result includes: Based on the meteorological historical data in the multi-energy power supply historical data, a multi-model runoff prediction is performed on the characteristic points in the short, medium and long term to obtain a multi-model runoff prediction result; Based on the multi-model runoff prediction results, a runoff ensemble probability prediction is performed to obtain a deterministic runoff prediction result at a characteristic point and upper and lower confidence intervals of the runoff prediction; Based on the deterministic runoff forecast results of the characteristic points and the upper and lower confidence intervals of the runoff forecast, the hydropower generation trend and the hydropower generation fluctuation range are predicted using the cascade power station output equation to obtain the hydropower generation prediction data.

3. The method according to claim 2, characterized in that The runoff ensemble probability prediction based on the multi-model runoff prediction results is performed to obtain the deterministic runoff forecast results of the characteristic points and the upper and lower confidence intervals of the runoff forecast, including: Obtaining measured runoff values ​​at characteristic points, performing weighted averaging on the conditional probability distributions between the measured runoff values ​​at the characteristic points and the runoff prediction results of the multiple models, and obtaining posterior distribution data of the measured runoff values; The deterministic runoff forecast result of the characteristic point and the upper and lower confidence intervals of the runoff forecast are determined based on the posterior distribution data of the runoff measured value.

4. The method according to claim 1, wherein The establishing of an electricity matching optimization objective function considering marginal cost under a hydropower perspective based on the hydropower generation forecast data, the wind and solar power generation forecast data, and the regional electricity demand includes: Obtaining clearing electricity prices corresponding to historical data on multi-energy power supply, performing multi-factor fitting on the historical data on multi-energy power supply and the clearing electricity prices, and obtaining a total marginal cost of the power system and a marginal cost of hydropower generation; Calculating a thermal power load rate based on the hydropower generation forecast data, the wind and solar power generation forecast data, and the regional electricity demand; Comparing the thermal power load rate with a load rate threshold to determine the supply and demand relationship in the power market; Based on the total marginal cost of the power system, the marginal cost of hydropower generation and the regional electricity demand, the supply and demand relationship of the electricity market is utilized to establish an electricity matching optimization objective function that considers marginal costs under the hydropower perspective.

5. The method according to claim 4, characterized in that The method of establishing an electricity matching optimization objective function taking marginal costs into consideration under the hydropower perspective based on the total marginal cost of the power system, the marginal cost of hydropower generation, and the regional electricity demand and utilizing the supply and demand relationship of the electricity market comprises: If the thermal power load rate is greater than the load rate threshold, the expression of the electricity matching optimization objective function considering the marginal cost under the hydropower angle is: Among them, Best[q water,t ] represents the optimal hydroelectric power generation of the power system at time t under the current supply and demand relationship, ff(*) represents the multi-objective optimization function, eq t represents the total marginal cost of the power system at time t, hq t represents the marginal cost of hydropower generation at time t, PD t represents the regional electricity demand at time t, q water,t represents the hydroelectric power generation at time t, q max,t represents the maximum hydroelectric power generation at time t considering the reservoir water level and flow, q i,t represents the power generation of type i energy at time t, q other,t represents the power generation of other power sources except hydropower at time t, and n represents the number of power types.

6. The method according to claim 5, characterized in that The method of establishing an electricity matching optimization objective function that considers marginal costs under the hydropower perspective based on the total marginal cost of the power system, the hydropower marginal cost function, and the regional electricity demand and utilizing the power market supply and demand relationship further includes: If the thermal power load rate is less than the load rate threshold, the expression of the electricity matching optimization objective function considering the marginal cost under the hydropower angle is: Where g(*) represents the function of hydropower generation changing with the marginal cost of the power system; eq min Indicates the set minimum marginal cost threshold of the power system.

7. A power system optimization and dispatching device under the water-television angle, characterized in that: The device comprises: a determination module, configured to obtain historical data of multi-energy power supply in a target area, perform runoff aggregate probability prediction based on the historical data of multi-energy power supply, and determine hydropower generation prediction data based on the runoff aggregate probability prediction result; A first prediction module is configured to predict a change trend of wind and solar power generation based on wind and solar resource data and station output data in the multi-energy power supply historical data to obtain wind and solar power generation prediction data; A second prediction module is used to predict regional power demand based on historical power load data and power load influencing factor data in the multi-energy power supply historical data to obtain regional power demand; Establishing a module for establishing an electricity matching optimization objective function taking into account marginal cost under a hydropower perspective based on the hydropower generation forecast data, the wind and solar power generation forecast data, and the regional electricity demand; The optimization module is used to perform multi-objective optimization on the power matching optimization objective function considering marginal cost under the hydropower angle to obtain a multi-energy power optimization dispatching strategy for the power system.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the power system optimization scheduling method under the water-voltage angle according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the power system optimization scheduling method under the water-voltage angle according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for optimizing and dispatching a power system under a water-voltage angle according to any one of claims 1 to 6.

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