Day-ahead peak regulation scheduling method and device based on distribution robustness, equipment and medium

Abstract

The invention relates to the technical field of optimal scheduling of a multi-energy complementary power generation system, in particular to a day-ahead peak regulation scheduling method, device and equipment based on distributed robustness and a medium, and the method comprises the steps: building a photovoltaic-hybrid pumped storage day-ahead peak regulation scheduling model by taking the minimum peak-valley difference of the residual load of a receiving-end power grid as an objective function; establishing a hybrid pumping and storage constraint condition considering complex hydraulic coupling and an operation state; the objective function and the constraint conditions are linearized; based on a day-ahead peak regulation scheduling model, establishing an opportunity constraint model for photovoltaic output, introducing a distribution robust model based on a Wasserstein distance to convert the opportunity constraint model into a distribution robust opportunity constraint model, and linearizing the distribution robust opportunity constraint model based on conditional value-at-risk approximation; and solving the linearized day-ahead peak regulation scheduling model to obtain the day-ahead peak regulation scheduling method. According to the method, the uncertainty of photovoltaic output can be fully considered, and the peak load shifting effect of the system and photovoltaic absorption are considered.

Description

Technical Field

[0001] The present invention relates to the technical field of optimization scheduling of multi-energy complementary power generation systems, and in particular to a method, device, equipment and medium for day-ahead peak-shaving scheduling based on distributed blue bars. Background Art

[0002] With the development of social economy and the continuous adjustment of national electricity consumption structure, the power grid is facing more and more serious peak load regulation problems. With the continuous increase in the installed capacity of new energy sources such as wind power and photovoltaic power, the randomness, volatility and uncertainty of new energy output have brought challenges to the safe and stable operation of the power grid. Hybrid pumped storage power stations can adjust their output according to the load curve due to their flexible start and stop characteristics, and can arrange power generation plans according to the randomness and volatility of new energy output, becoming an effective way to promote the consumption of new energy and smooth the load curve.

[0003] The randomness and volatility of photovoltaic processing seriously affect the operational reliability and peak-shaving effect of photovoltaic-hybrid pumped storage systems. In the prior art, photovoltaic processing prediction methods have been widely studied, but most scenario-based optimization methods have high data requirements and often fail to consider the impact of extreme weather on system operation, reducing the system's operating effect. In addition, most studies in the prior art do not simultaneously consider the confidence level of constraints and the robust variability of photovoltaic output, and cannot adjust parameters according to demand, resulting in a single operating mode.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0005] The present invention provides a method, device, equipment and medium for day-ahead peak-shaving scheduling based on distributed robustness, thereby effectively solving the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: a day-ahead peak-shaving scheduling method based on distributed robustness, comprising the following steps:

[0007] Taking the minimum peak-to-valley difference of the residual load of the receiving grid as the objective function, a photovoltaic-hybrid pumped storage day-ahead peak-shaving dispatching model is constructed;

[0008] Establishing hybrid pumped storage constraints that take into account complex hydraulic coupling and operating conditions; linearizing the objective function and constraints;

[0009] Based on the day-ahead peak-shaving scheduling model, an opportunity constraint model is established for the photovoltaic output, a distributed robust model based on Wasserstein distance is introduced to transform it into a distributed robust opportunity constraint model, and the distributed robust opportunity constraint model is linearized based on conditional value at risk approximation;

[0010] The linearized day-ahead peak-shaving scheduling model is solved to obtain a day-ahead peak-shaving scheduling method.

[0011] Furthermore, the objective function includes:

[0012] ;

[0013] ;

[0014] In the formula, The peak-to-valley difference of the residual load of the receiving power grid; is the number of time periods in a scheduling cycle; and They are The original load and residual load of the receiving power grid during the period; for Output of photovoltaic power station during the time period; The number of hybrid pumped storage power stations participating in the dispatch; is the number of hybrid pumped-storage power stations with pumped-storage units; Hybrid pumped storage power station The total number of conventional hydropower units in; Hybrid pumped storage power station Total number of medium pumped storage units; for Time period power station Conventional hydropower units The power generation capacity; for Time period power station Medium Pumped Storage Unit The power generation capacity; for Time period power station Medium Pumped Storage Unit of pumping power.

[0015] Furthermore, the objective function is linearized, including:

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] In the formula, and is the maximum and minimum value of the residual load of the receiving power grid; It is the remaining load after deducting the output of the hybrid pumped-storage power station from the receiving-end power grid load.

[0021] Furthermore, the establishment includes hybrid pumped storage constraints that take into account complex hydraulic coupling and operating conditions, including:

[0022] Water balance constraints of each power station reservoir:

[0023] ;

[0024] ;

[0025] In the formula, For power station exist The reservoir capacity at the end of the time period; The step length of the scheduling calculation period is 1h; For power station exist Inbound flow during the time period; For power station exist The discharge flow in the time period is composed of the power generation flow of conventional hydropower units and the power generation flow and pumping flow of pumped storage units; is the water flow lag between hybrid pumped storage power stations, which is taken as 1h; For power station Conventional hydropower units exist The power generation flow during the period; For power station Medium Pumped Storage Unit exist The power generation flow during the period; For power station Medium Pumped Storage Unit exist Pumping flow rate during the period;

[0026] Constraints on the relationship between water level and reservoir capacity before the dam:

[0027] ;

[0028] in, For power station exist The water level at the end of the period; For power station The functional relationship between the water level in front of the dam and the reservoir capacity;

[0029] Power constraints of conventional hydropower units:

[0030] ;

[0031] ;

[0032] in, It is the functional relationship between the power generation of conventional hydropower units and the water head and downstream flow rate; For power station exist The water head of the period; For power station Conventional hydropower units exist The operating status of the time period, if it is in power generation state, it is 1, otherwise it is 0; and Power Station Conventional hydropower units The upper and lower limits of power generation;

[0033] Unit operating status constraints:

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] In the formula, and Respectively represent power stations Conventional hydropower units exist Whether the power-on and power-off operations are performed during the time period. If so, the value is 1; otherwise, the value is 0.

[0039] Furthermore, the constraint condition is linearized, including:

[0040] Linearization of the relationship between the water level in front of the dam and the reservoir capacity function:

[0041] ;

[0042] ;

[0043] ;

[0044] in, and is the correlation coefficient between the water level in front of the dam and the reservoir capacity;

[0045] Linearization of the functional relationship between power generation, water head and downstream flow rate:

[0046] ;

[0047] ;

[0048] ;

[0049] ;

[0050] ;

[0051] ;

[0052] ;

[0053] In the formula, For power station No. Water head segmentation points; is the total number of sections of water head; For power station exist Is the time period in A 0-1 variable at each water head segmentation point, if it is, it is 1, otherwise it is 0; For power station exist The water head of the period; For power station In the The specific functional relationship curve between the discharge flow and the power generation at each water head segment point; For power station exist Is the time period in A 0-1 variable for each downstream flow segmentation point, if it is, it is 1, otherwise it is 0; is the total number of sections for the downstream flow; Indicates The first curve of the relationship between the discharge flow and the power generation function segmentation points; is a piecewise linearization function.

[0054] Furthermore, the opportunity constraint model is established for the photovoltaic output, and the distributed robust model based on Wasserstein distance is introduced to transform it into a distributed robust opportunity constraint model, including the following steps:

[0055] The photovoltaic output in the objective function is transformed into an opportunity constraint model:

[0056] ;

[0057] ;

[0058] in, is the confidence level of the PV output opportunity constraint;

[0059] Establishing photovoltaic output fuzzy set based on Wasserstein distance:

[0060] ;

[0061] In the formula, is the photovoltaic output fuzzy set based on Wasserstein distance; For the support set The set of all probability distributions in , the support set is taken as ; is the empirical distribution of PV output samples; is any probability distribution of the fuzzy set of photovoltaic output; is the Wasserstein distance between the empirical distribution and any probability distribution of the fuzzy set; is the radius of the Wasserstein sphere;

[0062] Constructing a photovoltaic output distribution robust opportunity constraint model based on Wasserstein distance:

[0063] ;

[0064] ;

[0065] in, A random variable representing the photovoltaic output.

[0066] Furthermore, the conditional value at risk approximation linearizes the distributed robust opportunity constraint model, including:

[0067] The distributed robust opportunity constraint model of photovoltaic output is transformed into a conditional risk value expression:

[0068] ;

[0069] ;

[0070] In the formula, is a random variable; Indicates that in a random distribution expectations;

[0071] Linearize the conditional risk value expression:

[0072] ;

[0073] ;

[0074] In the formula, and is a random variable; , , , , and is the dual variable generated in the linearization process; Represents the infinite norm.

[0075] Furthermore, solving the linearized day-ahead peak-shaving scheduling model includes:

[0076] The day-ahead peak-shaving scheduling model is solved by calling the Cplex solver using Yalmip, and the conventional hydropower output and pumped storage output of each level of power stations in the hybrid pumped-storage power station are obtained, as well as the peak-shaving results under different confidence levels and Wasserstein sphere radii are compared.

[0077] The present invention also includes a day-ahead peak-shaving scheduling device based on distributed robustness, using the above method, the device comprising:

[0078] A modeling unit is used to construct a photovoltaic-hybrid pumped storage day-ahead peak regulation dispatching model with the minimum peak-to-valley difference of the residual load of the receiving power grid as the objective function;

[0079] A linearization unit is used to establish hybrid pumped storage constraint conditions including consideration of complex hydraulic coupling and operating conditions; linearize the objective function and constraint conditions;

[0080] A distributed robust unit is used to establish an opportunity constraint model for the photovoltaic output based on the day-ahead peak-shaving scheduling model, introduce a distributed robust model based on Wasserstein distance to convert it into a distributed robust opportunity constraint model, and linearize the distributed robust opportunity constraint model based on conditional value at risk approximation;

[0081] A solving unit is used to solve the linearized day-ahead peak-shaving scheduling model to obtain a day-ahead peak-shaving scheduling method.

[0082] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0083] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0084] The beneficial effects of the present invention are as follows: the present invention takes minimizing the peak-to-valley difference of the residual load of the receiving power grid as the objective function, takes the hybrid pumped storage constraint considering complex hydraulic coupling as the constraint condition, establishes a photovoltaic-hybrid pumped storage day-ahead peak-shaving scheduling model based on distributed blue-rod opportunity constraints, and finally solves the model to obtain the optimized scheduling result, which can fully consider the uncertainty of photovoltaic output, take into account the system peak shaving and valley filling effect and photovoltaic absorption, and can formulate more effective and complete power generation plans compared with deterministic models. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0086] Figure 1 Schematic diagram of the process of the method in the embodiment of the present invention;

[0087] Figure 2 This is a diagram showing the results of the day-ahead peak load regulation in an embodiment of the present invention;

[0088] Figure 3 This is a total output result diagram of conventional hydropower generating units of No. 1 hybrid pumped-storage power station in an embodiment of the present invention;

[0089] Figure 4 This is a total output result diagram of conventional hydropower generating units of No. 2 hybrid pumped-storage power station in an embodiment of the present invention;

[0090] Figure 5 This is a total output result diagram of conventional hydropower generating units of No. 3 hybrid pumped-storage power station in an embodiment of the present invention;

[0091] Figure 6 This is a diagram showing the total output result of the pumped storage unit in an embodiment of the present invention;

[0092] Figure 7 It is a comparison diagram of different confidence levels and Wasserstein sphere radius in an embodiment of the present invention;

[0093] Figure 8 It is a schematic diagram of the structure of the device in the embodiment of the present invention;

[0094] Fig. 9 Schematic diagram of the structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0095] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0096] like Figure 1 As shown in the figure, a method for day-ahead peak load regulation based on distributed robustness includes the following steps:

[0097] Taking the minimum peak-to-valley difference of the residual load of the receiving grid as the objective function, a photovoltaic-hybrid pumped storage day-ahead peak-shaving dispatching model is constructed;

[0098] Establish hybrid pumped storage constraints that take into account complex hydraulic coupling and operating conditions; linearize the objective function and constraints;

[0099] Based on the day-ahead peak-shaving dispatching model, an opportunity constraint model is established for the photovoltaic output. The distributed robust model based on Wasserstein distance is introduced to transform it into a distributed robust opportunity constraint model, and the distributed robust opportunity constraint model is linearized based on the conditional value at risk approximation.

[0100] The linearized day-ahead peak-shaving scheduling model is solved to obtain the day-ahead peak-shaving scheduling method.

[0101] Taking minimizing the peak-to-valley difference of the residual load of the receiving grid as the objective function and the hybrid pumped storage constraint considering complex hydraulic coupling as the constraint condition, a photovoltaic-hybrid pumped storage day-ahead peak-shaving dispatching model based on distributed blue-rod opportunity constraints is established. Finally, the model is solved to obtain the optimal dispatching result. The optimized dispatching result is shown in Figure 2 As shown, it can be seen that the scheme in this embodiment can fully consider the uncertainty of photovoltaic output, take into account the peak shaving and valley filling effect of the system and photovoltaic consumption, and can formulate a more effective and complete power generation plan compared with the deterministic model.

[0102] In this embodiment, the objective function includes:

[0103] Hybrid pumped storage power stations have better peak-shaving effects and can reduce the rate of abandoned electricity from renewable energy sources. Compared with traditional cascade hydropower stations, they have a new valley-filling function, which increases the load valley value, and can store excess photovoltaic power when the photovoltaic output is high to prevent the phenomenon of abandoned photovoltaic power. Compared with pumped storage power stations, they have natural runoff water, large reservoir water storage capacity, and large power generation flow, which has a better peak-shaving effect.

[0104] In order to reflect the role of hybrid pumped storage in absorbing photovoltaic power and shaving peaks and filling valleys, the minimum peak-to-valley difference of the residual load of the receiving grid is selected as the objective function:

[0105] ;

[0106] ;

[0107] In the formula, The peak-to-valley difference of the residual load of the receiving power grid; is the number of time periods in a scheduling cycle; and They are The original load and residual load of the receiving power grid during the period; for Output of photovoltaic power station during the time period; The number of hybrid pumped storage power stations participating in the dispatch; is the number of hybrid pumped-storage power stations with pumped-storage units; Hybrid pumped storage power station The total number of conventional hydropower units in; Hybrid pumped storage power station Total number of medium pumped storage units; for Time period power station Conventional hydropower units The power generation capacity; for Time period power station Medium Pumped Storage Unit The power generation capacity; for Time period power station Medium Pumped Storage Unit of pumping power.

[0108] The constraints of hybrid pumped storage considering complex hydraulic coupling include:

[0109] The hybrid pumped storage power station is composed of conventional hydropower units and pumped storage units. The discharge flow of the upstream power station will affect the water volume of the downstream reservoir, thereby affecting the pumping flow of the pumped storage unit. In order to ensure the feasibility of the power generation plan obtained by optimizing the dispatch, the water balance constraint of the hybrid pumped storage takes into account the cascade hydraulic coupling upstream and downstream of the reservoir, including the cascade hydropower generation flow, the pumped storage unit generation flow and the pumping flow;

[0110] ;

[0111] ;

[0112] In the formula, For power station exist The reservoir capacity at the end of the time period; The step length of the scheduling calculation period is 1h; For power station exist Inbound flow during the time period; For power station exist The discharge flow in the time period is composed of the power generation flow of conventional hydropower units and the power generation flow and pumping flow of pumped storage units; is the water flow lag between hybrid pumped storage power stations, which is taken as 1h; For power station Conventional hydropower units exist The power generation flow during the period; For power station Medium Pumped Storage Unit exist The power generation flow during the period; For power station Medium Pumped Storage Unit exist Pumping flow rate during the period;

[0113] The complex operating state constraints of the hybrid pumped storage power station are considered, including:

[0114] In order to ensure the operation efficiency of the hybrid pumped storage power station, all pumped storage units cannot generate electricity and pump water at the same time, and conventional hydropower units and pumped storage units cannot generate electricity and pump water at the same time;

[0115] ;

[0116] ;

[0117] In the formula, and Respectively represent power stations Medium Pumped Storage Unit exist Whether the time period is in the power generation and pumping state, if so, it is 1, otherwise it is 0; Indicates power station Conventional hydropower units exist Whether the time period is in the power-on state, if it is in the power-on state, it is 1, otherwise it is 0.

[0118] The constraints of the relationship between the power generation of conventional hydropower units in hybrid pumped storage and the head and discharge flow are linearized, including:

[0119] In a hybrid pumped storage power station, different water heads determine the upper limit of the output of conventional hydropower units, and different downstream flows determine the output of conventional hydropower units. In order to ensure the accuracy of the power generation of the proposed conventional hydropower units, the functional relationship constraints between the power generation of conventional hydropower units and the water head and downstream flow are constructed.

[0120] Power generation constraints of conventional hydropower units:

[0121] ;

[0122] In the formula, It is the functional relationship between the power generation of conventional hydropower units and the water head and downstream flow rate; For power station exist The water head of the period;

[0123] Linearization of the functional relationship between power generation, water head and downstream flow rate:

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] ;

[0129] ;

[0130] ;

[0131] In the formula, For power station No. Water head segmentation points; is the total number of sections of water head; For power station exist Is the time period in A 0-1 variable at each water head segmentation point, if it is, it is 1, otherwise it is 0; For power station exist The water head of the period; For power station In the The specific functional relationship curve between the discharge flow and the power generation at each water head segment point; For power station exist Is the time period in A 0-1 variable for each downstream flow segmentation point, if it is, it is 1, otherwise it is 0; is the total number of sections for the downstream flow; Indicates The first curve of the relationship between the discharge flow and the power generation function segmentation points; It is an efficient piecewise linearization method.

[0132] A distributed robust opportunity constraint model based on Wasserstein distance is established for photovoltaic output, including:

[0133] Convert the photovoltaic output in the objective function into a chance constraint model:

[0134] ;

[0135] ;

[0136] In the formula, is the confidence level of the PV output opportunity constraint;

[0137] Establishing photovoltaic output fuzzy set based on Wasserstein distance:

[0138] ;

[0139] In the formula, is the photovoltaic output fuzzy set based on Wasserstein distance; For the support set The set of all probability distributions in , the support set is taken as ; is the empirical distribution of PV output samples; is any probability distribution of the fuzzy set of photovoltaic output; is the Wasserstein distance between the empirical distribution and any probability distribution of the fuzzy set; is the radius of the Wasserstein sphere;

[0140] Constructing a photovoltaic output distribution robust opportunity constraint model based on Wasserstein distance:

[0141] ;

[0142] ;

[0143] In the formula, A random variable representing the photovoltaic output.

[0144] The distributed robust chance constraint model of photovoltaic output based on Wasserstein distance is linearized, including:

[0145] The distributed robust opportunity constraint model of photovoltaic output is transformed into a conditional risk value expression:

[0146] ;

[0147] ;

[0148] In the formula, is a random variable; Indicates that in a random distribution expectations;

[0149] Linearize the conditional risk value expression:

[0150] ;

[0151] ;

[0152] in, and is a random variable; , , , , and is the dual variable generated in the linearization process; Represents the infinite norm.

[0153] Specifically, the photovoltaic-hybrid pumped storage day-ahead peak-shaving scheduling model based on distributed blue-rod opportunity constraints is solved to obtain a photovoltaic-hybrid pumped storage day-ahead peak-shaving scheduling method, including:

[0154] Yalmip is used to call the Cplex solver to solve the photovoltaic-hybrid pumped storage peak-shaving scheduling model based on distributed blue-rod opportunity constraints, and the conventional hydropower output and pumped storage output of each level of power stations in the hybrid pumped storage power station are obtained, as well as the peak-shaving results under different confidence levels and Wasserstein sphere radii are compared.

[0155] In order to demonstrate the effectiveness of the photovoltaic-hybrid pumped storage day-ahead peak-shaving scheduling method based on distributed blue-rod opportunity constraints provided by the present invention, it is described below in combination with specific application scenarios.

[0156] The embodiment of the present invention includes a photovoltaic power station and a three-stage hybrid pumped-storage power station. The pumped-storage unit is added to the first-stage hybrid pumped-storage power station. A certain photovoltaic output data is used as the mean, and the Monte Carlo method is used to generate 1000 sets of photovoltaic output data as a sample set.

[0157] load:

[0158] [0.5462;0.5210;0.5000;0.4916;0.4874;0.4916;0.5011;0.5502;0.6345;0.8298;0.9160;0.9277;0.7983;0.7101;0.7139;0.7631;0.8627;0.9247;0.9506;0.9454;0.9370;0.8992;0.8270;0.6908]*1600;

[0159] Photovoltaic output average:

[0160] [0;0;0;0;0;0;0.1288;0.3800;0.6013;0.7559;0.8466;0.8655;0.8317;0.7329;0.6662;0.4929;0.2605;0.0601;0.0201;0;0;0;0;0]*100;

[0161] Firstly, the objective function of the photovoltaic-hybrid pumped storage day-ahead peak-shaving dispatch based on the distributed rod opportunity constraint is established as follows:

[0162] ;

[0163] ;

[0164] in, The peak-to-valley difference of the residual load of the receiving power grid; is the number of time periods in a scheduling cycle; and They are The original load and residual load of the receiving power grid during the period; for Output of photovoltaic power station during the time period; The number of hybrid pumped storage power stations participating in the dispatch; is the number of hybrid pumped-storage power stations with pumped-storage units; Hybrid pumped storage power station The total number of conventional hydropower units in; Hybrid pumped storage power station Total number of medium pumped storage units; for Time period power station Conventional hydropower units The power generation capacity; for Time period power station Medium Pumped Storage Unit The power generation capacity; for Time period power station Medium Pumped Storage Unit of pumping power.

[0165] Then, the hybrid pumped storage constraints considering complex hydraulic coupling are established:

[0166] 1) Water balance constraints of each power station reservoir:

[0167] ;

[0168] ;

[0169] in, For power station exist The reservoir capacity at the end of the time period; The step length of the scheduling calculation period is 1h; For power station exist Inbound flow during the time period; For power station exist The discharge flow in the time period is composed of the power generation flow of conventional hydropower units and the power generation flow and pumping flow of pumped storage units; is the water flow lag between hybrid pumped storage power stations, which is taken as 1h; For power station Conventional hydropower units exist The power generation flow during the period; For power station Medium Pumped Storage Unit exist The power generation flow during the period; For power station Medium Pumped Storage Unit exist Pumping flow rate during the period;

[0170] 2) Reservoir capacity constraints:

[0171] ;

[0172] , ;

[0173] in, For power station The maximum adjustable storage capacity; and Power Station The actual storage capacity at the beginning and end of the dispatch period; and Power Station Target control storage capacity at the beginning and end of the dispatch period;

[0174] Conventional hydropower parameters are shown in Table 1:

[0175] Table 1 Conventional hydropower parameters

[0176]

[0177] Pumped storage parameters are shown in Table 2:

[0178] Table 2 Pumped storage parameters

[0179]

[0180] 3) Power generation flow constraints of conventional hydropower units:

[0181] ;

[0182] in, and Power Station Conventional hydropower units The upper and lower limits of power generation flow;

[0183] 4) Constraints on power generation and pumping flow of pumped storage units:

[0184] ;

[0185] ;

[0186] in, and Power Station Medium Pumped Storage Unit The upper and lower limits of power generation flow; and Power Station Medium Pumped Storage Unit The upper and lower limits of pumping flow;

[0187] 5) Constraints on the relationship between water level and reservoir capacity in front of the dam:

[0188] ;

[0189] in, For power station exist The water level at the end of the period; For power station The functional relationship between the water level in front of the dam and the reservoir capacity;

[0190] 6) Water level constraint:

[0191] ;

[0192] in, and Power Station The upper and lower limits of water level;

[0193] 7) Power constraints of conventional hydropower units:

[0194] ;

[0195] ;

[0196] in, It is the functional relationship between the power generation of conventional hydropower units and the water head and downstream flow rate; For power station exist The water head of the period; For power station Conventional hydropower units exist The operating status of the time period, if it is in power generation state, it is 1, otherwise it is 0; and Power Station Conventional hydropower units The upper and lower limits of power generation;

[0197] 8) Constraints on power generation of pumped storage units:

[0198] ;

[0199] ;

[0200] in, For power station Medium Pumped Storage Unit Power generation efficiency; For power station Medium Pumped Storage Unit exist The operating status of the time period, if it is in power generation state, it is 1, otherwise it is 0; and Power Station Medium Pumped Storage Unit The upper and lower limits of power generation;

[0201] 9) Pumping power constraints of pumped storage units:

[0202] ;

[0203] ;

[0204] in, For power station Medium Pumped Storage Unit Pumping efficiency; For power station Medium Pumped Storage Unit exist The operation status of the time period, if it is pumping state, it is 1, otherwise it is 0; and Power Station Medium Pumped Storage Unit The upper and lower limits of pumping power;

[0205] 10) Unit operating status constraints:

[0206] ;

[0207] ;

[0208] ;

[0209] ;

[0210] in, and Respectively represent power stations Conventional hydropower units exist Whether the power-on and power-off operations are performed during the time period. If so, the value is 1, otherwise, the value is 0;

[0211] 11) Minimum start and stop time constraints:

[0212] ;

[0213] ;

[0214] in, and For power station Conventional hydropower units Minimum start and stop time.

[0215] Then the objective function and constraints are linearized including:

[0216] 1) Linearization of objective function:

[0217] ;

[0218] ;

[0219] ;

[0220] ;

[0221] in, and is the maximum and minimum value of the residual load of the receiving power grid; It is the residual load after deducting the output of the hybrid pumped storage power station from the load of the receiving grid;

[0222] 2) Linearization of the relationship between water level and reservoir capacity before the dam:

[0223] ;

[0224] ;

[0225] ;

[0226] in, and is the correlation coefficient between the water level in front of the dam and the reservoir capacity;

[0227] 3) Linearization of the functional relationship between power generation, water head and downstream flow rate:

[0228] ;

[0229] ;

[0230] ;

[0231] ;

[0232] ;

[0233] ;

[0234] ;

[0235] In the formula, For power station No. Water head segmentation points; is the total number of sections of water head; For power station exist Is the time period in A 0-1 variable at each water head segmentation point, if it is, it is 1, otherwise it is 0; For power station exist The water head of the period; For power station In the The specific functional relationship curve between the discharge flow and the power generation at each water head segment point; For power station exist Is the time period in A 0-1 variable for each downstream flow segmentation point, if it is, it is 1, otherwise it is 0; is the total number of sections for the downstream flow; Indicates The first curve of the relationship between the discharge flow and the power generation function segmentation points; It is an efficient piecewise linearization method.

[0236] Then, a distributed robust opportunity constraint model based on Wasserstein distance is established for photovoltaic output, including:

[0237] 1) Convert the photovoltaic output in the objective function into an opportunity constraint model:

[0238] ;

[0239] ;

[0240] in, is the confidence level of the PV output opportunity constraint;

[0241] 2) Establishing the photovoltaic output fuzzy set based on Wasserstein distance:

[0242] ;

[0243] in, is the photovoltaic output fuzzy set based on Wasserstein distance; For the support set The set of all probability distributions in , the support set is taken as ; is the empirical distribution of PV output samples; is any probability distribution of the fuzzy set of photovoltaic output; is the Wasserstein distance between the empirical distribution and any probability distribution of the fuzzy set; is the radius of the Wasserstein sphere;

[0244] 3) Constructing a photovoltaic output distribution and robust opportunity constraint model based on Wasserstein distance:

[0245] ;

[0246] ;

[0247] in, A random variable representing the photovoltaic output.

[0248] Then the distributed robust opportunity constraint model of photovoltaic output based on Wasserstein distance is linearized, including:

[0249] 1) Convert the distributed robust opportunity constraint model of PV output into a conditional risk value expression:

[0250] ;

[0251] ;

[0252] in, is a random variable; Indicates that in a random distribution The expectations below.

[0253] 2) Linearize the conditional risk value expression:

[0254] ;

[0255] ;

[0256] in, and is a random variable; , , , , and is the dual variable generated in the linearization process; Represents the infinite norm.

[0257] Finally, Yalmip is used to call the Cplex solver to solve the photovoltaic-hybrid pumped storage peak-shaving scheduling model based on distributed blue-rod opportunity constraints, and the conventional hydropower output and pumped storage output of each level of power stations in the hybrid pumped storage power station are obtained, as well as the peak-shaving results under different confidence levels and Wasserstein sphere radii are compared.

[0258] The calculation results in this embodiment are as follows Figures 3 to 7 As shown, Figure 3 This is the total output result diagram of conventional hydropower units in No. 1 hybrid pumped storage power station. Figure 4 This is the total output result diagram of conventional hydropower units in No. 2 hybrid pumped storage power station. Figure 5 This is the total output result diagram of conventional hydropower units in No. 3 hybrid pumped storage power station. Figure 6 This is the total output result diagram of the pumped storage unit. Figure 8This is a comparison chart of different confidence levels and Wasserstein sphere radii. It can be seen that the scheme in this embodiment can fully consider the uncertainty of photovoltaic output, taking into account the peak shaving and valley filling effect of the system and photovoltaic absorption. Compared with the deterministic model, it can formulate a more effective and complete power generation plan.

[0259] like Figure 8 As shown, this embodiment also includes a day-ahead peak-shaving scheduling device based on distributed robustness, using the above method, the device includes:

[0260] A modeling unit is used to construct a photovoltaic-hybrid pumped storage day-ahead peak regulation dispatching model with the minimum peak-to-valley difference of the residual load of the receiving power grid as the objective function;

[0261] Linearization unit, used to establish hybrid pumped storage constraints including consideration of complex hydraulic coupling and operating conditions; linearize the objective function and constraints;

[0262] The distributed blue-rod unit is used to establish an opportunity constraint model for the photovoltaic output based on the day-ahead peak-shaving scheduling model, introduce the distributed blue-rod model based on the Wasserstein distance to transform it into a distributed blue-rod opportunity constraint model, and linearize the distributed blue-rod opportunity constraint model based on the conditional value at risk approximation;

[0263] The solving unit is used to solve the linearized day-ahead peak-shaving scheduling model to obtain the day-ahead peak-shaving scheduling method.

[0264] See also Fig. 9 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0265] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0266] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0267] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0268] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0269] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0270] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0271] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0272] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0273] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0274] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for day-ahead peak load regulation based on distributed robustness, characterized in that: The steps include: Taking the minimum peak-to-valley difference of the residual load of the receiving grid as the objective function, a photovoltaic-hybrid pumped storage day-ahead peak-shaving dispatching model is constructed; Establishing hybrid pumped storage constraints that take into account complex hydraulic coupling and operating conditions; linearizing the objective function and constraints; Based on the day-ahead peak-shaving scheduling model, an opportunity constraint model is established for the photovoltaic output, a distributed robust model based on Wasserstein distance is introduced to transform it into a distributed robust opportunity constraint model, and the distributed robust opportunity constraint model is linearized based on conditional value at risk approximation; The linearized day-ahead peak-shaving scheduling model is solved to obtain a day-ahead peak-shaving scheduling method.

2. The method for day-ahead peak load regulation based on distributed robustness according to claim 1, characterized in that: The objective function includes: ； ； In the formula, The peak-to-valley difference of the residual load of the receiving power grid; is the number of time periods in a scheduling cycle; and They are The original load and residual load of the receiving end power grid during the period; for Output of photovoltaic power station during the time period; The number of hybrid pumped storage power stations participating in the dispatch; is the number of hybrid pumped-storage power stations with pumped-storage units; Hybrid pumped storage power station The total number of conventional hydropower units in; Hybrid pumped storage power station Total number of medium pumped storage units; for Time period power station Conventional hydropower units The power generation capacity; for Time period power station Medium Pumped Storage Unit The power generation capacity; for Time period power station Medium Pumped Storage Unit of pumping power.

3. The method for day-ahead peak load regulation based on distributed robustness according to claim 2 is characterized in that: The objective function is linearized, including: ； ； ； ； In the formula, and is the maximum and minimum value of the residual load of the receiving power grid; It is the remaining load after deducting the output of the hybrid pumped-storage power station from the receiving-end power grid load.

4. The method for day-ahead peak load regulation based on distributed robustness according to claim 1, characterized in that: The establishment includes hybrid pumped storage constraints that take into account complex hydraulic coupling and operating conditions, including: Water balance constraints of each power station reservoir: ； ； In the formula, For power station exist The reservoir capacity at the end of the time period; The step length of the scheduling calculation period is 1h; For power station exist Inbound flow during the time period; For power station exist The discharge flow in the time period is composed of the power generation flow of conventional hydropower units and the power generation flow and pumping flow of pumped storage units; is the water flow lag between hybrid pumped storage power stations, which is taken as 1h; For power station Conventional hydropower units exist The power generation flow during the period; For power station Medium Pumped Storage Unit exist The power generation flow during the period; For power station Medium Pumped Storage Unit exist Pumping flow rate during the period; Constraints on the relationship between water level and reservoir capacity before the dam: ； in, For power station exist Water level at the end of the period; For power station The functional relationship between the water level in front of the dam and the reservoir capacity; Power constraints of conventional hydropower units: ； ； in, It is the functional relationship between the power generation of conventional hydropower units and the water head and downstream flow rate; For power station exist The water head of the period; For power station Conventional hydropower units exist The operating status of the time period, if it is in power generation state, it is 1, otherwise it is 0; and Power Station Conventional hydropower units The upper and lower limits of power generation; Unit operating status constraints: ； ； ； ； In the formula, and Respectively represent power stations Conventional hydropower units exist Whether the power-on and power-off operations are performed during the time period. If so, the value is 1; otherwise, the value is 0.

5. The method for day-ahead peak load regulation based on distributed robustness according to claim 4 is characterized in that: The constraint conditions are linearized, including: Linearization of the relationship between the water level in front of the dam and the reservoir capacity function: ； ； ； in, and is the correlation coefficient between the water level in front of the dam and the reservoir capacity; Linearization of the functional relationship between power generation, water head and downstream flow rate: ； ； ； ； ； ； ； In the formula, For power station No. Water head segmentation points; is the total number of sections of water head; For power station exist Is the time period in A 0-1 variable at each water head segmentation point, if it is, it is 1, otherwise it is 0; For power station exist The water head of the period; For power station In the The specific functional relationship curve between the discharge flow and the power generation at each water head segment point; For power station exist Is the time period in A 0-1 variable for each downstream flow segmentation point, if it is, it is 1, otherwise it is 0; is the total number of sections for the downstream flow; Indicates The first curve of the relationship between the discharge flow and the power generation function segmentation points; is a piecewise linearization function.

6. The method for day-ahead peak load regulation based on distributed robustness according to claim 1, characterized in that: The step of establishing an opportunity constraint model for the photovoltaic output and introducing a distributed robust model based on Wasserstein distance to transform it into a distributed robust opportunity constraint model includes the following steps: The photovoltaic output in the objective function is transformed into an opportunity constraint model: ； ； in, is the confidence level of the PV output opportunity constraint; Establishing photovoltaic output fuzzy set based on Wasserstein distance: ； In the formula, is the photovoltaic output fuzzy set based on Wasserstein distance; For the support set The set of all probability distributions in , the support set is taken as ; is the empirical distribution of PV output samples; is any probability distribution of the photovoltaic output fuzzy set; is the Wasserstein distance between the empirical distribution and any probability distribution of the fuzzy set; is the radius of the Wasserstein sphere; Constructing a photovoltaic output distribution robust opportunity constraint model based on Wasserstein distance: ； ； in, A random variable representing the photovoltaic output.

7. The method for day-ahead peak load regulation based on distributed robustness according to claim 6, characterized in that: The method of linearizing the distributed robust opportunity constraint model based on conditional value at risk approximation includes: The distributed robust opportunity constraint model of photovoltaic output is transformed into a conditional risk value expression: ； ； In the formula, is a random variable; Indicates that in a random distribution expectations; Linearize the conditional risk value expression: ； ； In the formula, and is a random variable; , , , , and is the dual variable generated in the linearization process; Represents the infinite norm.

8. The method for day-ahead peak load regulation based on distributed robustness according to claim 1, characterized in that: Solving the linearized day-ahead peak-shaving scheduling model includes: The day-ahead peak-shaving scheduling model is solved by calling the Cplex solver using Yalmip, and the conventional hydropower output and pumped storage output of each level of power stations in the hybrid pumped-storage power station are obtained, as well as the peak-shaving results under different confidence levels and Wasserstein sphere radii are compared.

9. A day-ahead peak-shaving dispatching device based on distributed blue bars, characterized in that: Using the method according to any one of claims 1 to 8, the device comprises: A modeling unit is used to construct a photovoltaic-hybrid pumped storage day-ahead peak regulation dispatching model with the minimum peak-to-valley difference of the residual load of the receiving power grid as the objective function; A linearization unit is used to establish hybrid pumped storage constraint conditions including consideration of complex hydraulic coupling and operating conditions; linearize the objective function and constraint conditions; A distributed blue-robust unit is used to establish an opportunity constraint model for the photovoltaic output based on the day-ahead peak-shaving scheduling model, introduce a distributed blue-robust model based on Wasserstein distance to convert it into a distributed blue-robust opportunity constraint model, and linearize the distributed blue-robust opportunity constraint model based on conditional value at risk approximation; A solving unit is used to solve the linearized day-ahead peak-shaving scheduling model to obtain a day-ahead peak-shaving scheduling method.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

11. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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