Cascade hydropower decoupling optimal complementary scheduling method under multi-source uncertain demand

Abstract

The cascaded hydropower decoupling optimization complementary scheduling method under multi-source uncertainty demand belongs to the field of hydropower scheduling. In view of the multi-source uncertainty demand such as new energy output fluctuation and power spot market clearing power fluctuation faced by the power system, the method proposes a new decoupling scheduling paradigm of "interval commitment-autonomous response" to solve the problems of response lag, low decision efficiency and limited adjustment capacity existing in the traditional "plan reporting-negotiation adjustment" scheduling mode. First, the decoupling scheduling framework of cascaded hydropower is established, so that each power station can independently calculate and report the dynamic adjustable power interval based on its own complex hydraulic constraints. On this basis, the cascaded hydropower decoupling optimization complementary scheduling model under multi-source uncertainty demand is constructed to accurately quantify the adjustment capacity boundary of hydropower in the multi-source uncertainty environment. Finally, through the two-stage solving algorithm combining linearization reconstruction and strong duality theory, the original problem is transformed into a mixed integer linear programming model to realize efficient solution.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of water power dispatching, and particularly relates to a method for optimal dispatching of a cascade hydropower system, and more particularly to a method for decoupled optimal complementary dispatching of a cascade hydropower system under multi-source uncertainty demand. BACKGROUND

[0002] With the sharp increase in the scale of wind and light renewable energy grid connection, the volatility and intermittency of the output of the renewable energy bring huge flexibility gaps to the power system. Hydropower, which relies on huge reservoirs, joint dispatching of reservoirs and high response efficiency, is a natural flexible supply resource. Therefore, implementing hydropower joint other energy to form a multi-energy complementary system and collaborative operation with the receiving end power grid will be an important way to support the centralized consumption of new energy in the present and a long period of time in the future. The current typical collaborative operation mode of hydropower and the receiving end power grid usually follows the following process:

[0003] (1) Day-ahead plan submission: the cascade hydropower formulates a power generation plan based on its own operation constraints and submits it to the receiving end power grid. At the same time, the receiving end power grid configures system-wide spinning reserve capacity for the cascade hydropower.

[0004] (2) Plan negotiation adjustment: the receiving end power grid adjusts the submitted plan by considering system power and energy balance factors, and issues the modified power generation plan to the cascade hydropower.

[0005] (3) Intraday real-time adjustment: during the operation day, the hydropower continuously adjusts its planned output according to the actual output of new energy and prediction deviation, power spot market clearing power fluctuation and other uncertain factors, in order to maintain system power balance or pursue economic benefits.

[0006] (4) Adjustment plan reporting: the hydropower reports the final power generation plan after adjustment to the receiving end power grid.

[0007] Although this mode achieves new energy consumption to some extent, its inherent coordinated dispatching mechanism has significant drawbacks: (1) The multiple rounds of plan submission, negotiation, issuance, adjustment and reporting take a long time, which is difficult to adapt to the rapid changes of new energy output deviation, power spot market clearing power fluctuation and other uncertain demand sources, resulting in that the dispatching decision lags behind the actual operation state. (2) Frequent information exchange and centralized decision-making between the power grid dispatching center and the cascade hydropower increase the communication cost and computational burden. (3) Since the upstream and downstream power stations of the cascade hydropower are relatively independent operation decision units, and even some power stations belong to different interest subjects, the responsibility ambiguity leads to repeated negotiation on the proportion of spinning reserve capacity borne by each power station, and each power station often adopts the most conservative strategy to respond in order to ensure its own operation safety or economic benefits, so that the overall adjustment capacity of the cascade hydropower is difficult to be fully utilized, and its autonomous decision-making space for responding to new energy fluctuations and adapting to market changes is compressed.

[0008] To address the above challenges, the present application proposes a cascade hydropower decoupling optimization complementary scheduling method under multi-source uncertainty demand, aiming to realize the paradigm shift of scheduling mode. The main idea is as follows:

[0009] (1) Determine the decoupling scheduling process of cascade hydropower.

[0010] (2) Determine the maximum adjustable power range of cascade hydropower, so that the range can cover the change range of multi-source uncertainty demand to the greatest extent.

[0011] The adjustable power range of hydropower proposed in the present application is different from the traditional sense of hydropower spinning reserve capacity. Its essence is the paradigm shift from "top-down instructive capacity allocation" to "bottom-up autonomous adjustment capability reporting". Existing researches focus on the spinning reserve capacity optimization allocation model dominated by the receiving end power grid. When determining the maximum reserve capacity of cascade hydropower, it often relies on conservative empirical trial and error, deviating from the physical limit. When decomposing it to each power station, it often uses static allocation proportion, which fails to reflect the inherent spatio-temporal coupling characteristics of cascade upstream and downstream. Although existing researches have provided a unified framework for the quantification of power system flexibility and established a theoretical basis for flexibility evaluation, the construction of flexible power source adopts a relatively general form without considering hydropower, an important flexible power source. The maximum adjustable power range of hydropower proposed in the present application is a dynamic and time-varying power adjustment range actively reported by each power station of cascade upstream and downstream based on their own complex hydraulic constraints optimization. This range is not a passive and fixed allocation share, but an active quantification of hydropower adjustment capability, and its boundary is directly determined by the operation decision of hydropower itself, thereby ensuring the accuracy of adjustment capability evaluation and the feasibility of scheduling implementation at the root.

[0012] In addition, in terms of uncertainty modeling, traditional methods usually regard uncertainty as an exogenous random variable. However, in actual power system operation, the boundary of uncertainty parameter is often affected by the system's previous decisions, which is called decision-dependent uncertainty. Decision-dependent uncertainty has been preliminarily studied in the fields of power system scheduling, reliability evaluation, demand side response, etc. However, existing researches on decision-dependent uncertainty in the field of power system have not considered hydropower, an important flexible power source. As an important supply source of power system flexibility, the maximum adjustable power range of hydropower directly affects the maximum change range of uncertainty demand such as new energy output and spot market clearing power fluctuation. Therefore, for the unique non-convex, nonlinear, high-dimensional complex constraint system of hydropower, it is a technical problem to be solved to develop a model and method that can effectively embed such constraints and reliably solve the maximum adjustable power range of hydropower to meet the multi-source uncertainty demand.

[0013] To this end, the application proposes a decoupling scheduling framework for cascade hydropower, and on this basis, a cascade hydropower decoupling optimization complementary scheduling model under multi-source uncertainty demand is constructed to show the influence of decision on the boundary of uncertain parameters. And for the complex space-time hydraulic coupling relationship of cascade upstream and downstream under multi-source uncertainty demand, a bias accumulation mechanism is constructed to represent the cumulative effect of reservoir capacity bias in time sequence and the transmission characteristics in space caused by the response of water and electricity to multi-source uncertainty demand. And by developing a two-stage solving algorithm combining linear reconstruction and strong duality theory, the original model is converted into a mixed integer linear programming model to realize efficient solving. SUMMARY

[0014] The technical problem to be solved by the application is to provide a cascade hydropower decoupling optimization complementary scheduling method under multi-source uncertainty demand. The decoupling scheduling framework replaces the traditional scheduling mode of "plan reporting-coordination adjustment" to simplify the scheduling process and realize the paradigm shift of the scheduling mode.

[0015] The technical scheme adopted by the application is as follows:

[0016] The application proposes a cascade hydropower decoupling optimization complementary scheduling method under multi-source uncertainty demand, mainly including: constructing a cascade hydropower decoupling scheduling framework, establishing a cascade hydropower decoupling optimization complementary scheduling model under multi-source uncertainty demand, and transforming and reconstructing the scheduling model. Specifically as follows:

[0017] Step one, construction of cascade hydropower decoupling scheduling framework:

[0018] In the coordinated scheduling mode of traditional multi-energy complementary system and receiving end power grid, cascade hydropower reports generation plan to receiving end power grid in day-ahead, and the power grid adjusts the generation plan according to uncertain factors such as new energy prediction bias in real time and issues it to each department in day-ahead operation stage. The water power adjusts its own generation plan after receiving the issued plan and reports it to the receiving end power grid again. This plan submission mode has a complicated process and takes a long time. The application proposes a decoupling scheduling framework for cascade hydropower, which follows the following steps:

[0019] Step 1, each hydropower station of cascade upstream and downstream reports a dynamic maximum adjustable power generation interval range to the receiving end power grid, which is closely related to its own decision.

[0020] Step 2, after receiving the maximum adjustable power generation interval range reported by each hydropower station, the receiving end power grid determines whether it can cover the fluctuation range of the uncertainty factors. If it can, the hydropower station autonomously and flexibly adjusts its output within the committed interval to respond to the rapid changes of the multi-source uncertainty demand in real time without frequent reporting of adjustment plans or waiting for grid dispatching instructions. If it cannot, the grid considers calling other flexible power sources. The uncertainty factors include new energy prediction deviation, power spot market clearing power fluctuation, and basin inflow uncertainty.

[0021] In this decoupled scheduling mode of "interval commitment-autonomous response", each power station of different interest subjects in the cascade upstream and downstream can autonomously respond within the committed interval, thereby realizing decoupled scheduling operation between power stations of different interest subjects in the cascade upstream and downstream. After receiving the adjustable interval of the hydropower station, the receiving end power grid can consider it as a kind of flexible resource constraint and consider it in the scheduling model, thereby optimizing the arrangement of other units or calling other flexible resources, so as to realize decoupled scheduling operation between the grid and the cascade hydropower station. Compared with the traditional "plan reporting-negotiation adjustment" coordinated scheduling mode, this decoupled scheduling mode of "interval commitment-autonomous response" greatly simplifies the scheduling process and realizes the paradigm shift of the scheduling mode.

[0022] Step 2, establish a cascade hydropower decoupled optimization complementary scheduling model under multi-source uncertainty demand:

[0023] To maximize the ability of the hydropower station to respond to multi-source uncertainty demand in decoupled scheduling, the following objective function is constructed with the maximum adjustable power generation interval of the cascade hydropower station as the target:

[0024] (1)

[0025] In the formula: respectively represent the upper and lower limits of the adjustable power interval of the hydropower station in the time period; is the total number of time periods in the scheduling period; is the total number of cascade hydropower stations. The cascade hydropower operation constraints in the planning stage are as follows:

[0026]

[0027] 1) water balance constraint:

[0028] (2)

[0029] In the formula: represents the reservoir capacity of the hydropower station in the time period; ​​​​​​Interval flow, power generation flow, abandoned water flow of the period; The period step length is calculated for scheduling; to ensure the maximum utilization of water energy resources, the abandoned water is limited to .

[0030] 2) Water power operation boundary constraint:

[0031] (3)

[0032] (4)

[0033] (5)

[0034] In the formula: respectively represent the upper and lower limits of the output of the hydropower station In the period; represent the output of the hydropower station in period; respectively represent the upper and lower limits of the power generation flow of the hydropower station in period; respectively represent the upper and lower limits of the reservoir capacity of the hydropower station in period; formula (3) to formula (5) respectively represent the output, power generation flow and reservoir capacity boundary constraints of the hydropower station in the planning stage.

[0035] 3) Water power initial and final reservoir capacity control constraint:

[0036] (6)

[0037] (7)

[0038] In the formula: respectively represent the reservoir capacity of the hydropower station at the beginning of the scheduling period and at the end of the scheduling period; respectively represent the reservoir capacity control values of the hydropower station at the beginning of the scheduling period and at the end of the scheduling period.

[0039] 4) Water power generation function:

[0040] In short-term scheduling, the daily water level fluctuation of the hydropower station has little effect on the water consumption rate. According to the actual operation of the research object, the fixed water consumption rate is used to calculate the output. The water power generation function is represented as follows:

[0041] (8)

[0042] In the formula: represent the output of the hydropower station in water level depletion rate of the period.

[0043] 5) water level-storage capacity relationship function:

[0044] (9)

[0045] wherein: denotes the water level of the hydropower station at the dam in the period; denotes the water level of the hydropower station .

[0046] 6) Hydropower cross-section constraint:

[0047] (10)

[0048] wherein: denotes the upper and lower limits of the cross-section constraint of the hydropower station in the period, respectively; denotes the set of hydropower stations related to the cross-section constraint. The purpose of introducing this constraint is to prevent potential infeasibility related to transmission constraints.

[0049] The constraints of cascade hydropower operation under multi-source uncertain demand are as follows:

[0050] Since hydropower is affected by multi-source uncertain demand in actual operation, in response to multi-source uncertain demand, the actual decision of hydropower may deviate randomly from the planned value, resulting in that the output, power generation flow and water level of cascade hydropower are random variables, and further resulting in that the decoupling result of hydropower generation is a random variable. For any , its value should be within the upper and lower limits of the hydropower adjustable power interval, i.e.:

[0051] (11)

[0052] For convenience of representation, a random variable is introduced to represent the deviation of the decoupling result of actual hydropower generation from the planned value:

[0053] (12)

[0054] 1) Hydropower output and power generation flow constraint under multi-source uncertain demand:

[0055] The output and power generation flow under multi-source uncertain demand are obtained by adding the deviation term caused by uncertainty to the respective planned value, i.e.:

[0056] (13)

[0057] (14)

[0058] wherein: denote the reservoir storage of hydropower stations at time period and the power generation flow, respectively.

[0059] Equations (13)-(14) are constraints containing random variables, which need to be satisfied for all random variables . The same applies to Equations (15)-(20), which will not be elaborated hereinafter.

[0060] 2) Water balance equation under multi-source uncertain demand:

[0061] Substituting Equations (13)-(14) into Equation (2), the water balance equation under multi-source uncertain demand is obtained as shown in Equation (15):

[0062] (15)

[0063] wherein: denote the reservoir storage of hydropower stations at time period and the power generation flow, respectively.

[0064] 3) Operation boundary constraint of hydropower under multi-source uncertain demand:

[0065] The same as the operation boundary constraint in the planning stage of cascade hydropower, the power generation flow, the output and the reservoir storage of cascade hydropower under multi-source uncertain demand need to satisfy Equations (16)-(18), respectively:

[0066] (16)

[0067] (17)

[0068] (18)

[0069] 4) Cross-section constraint of hydropower under multi-source uncertain demand:

[0070] In response to multi-source uncertain demand, the cascade hydropower also needs to satisfy the cross-section constraint to prevent potential infeasibility related to power transmission constraints:

[0071] (19)

[0072] 5) Guarantee reservoir storage constraint of hydropower under multi-source uncertain demand:

[0073] The water level at the end of the scheduling period will be affected by the multi-source uncertainty factors in each period, so it is difficult to strictly control according to the planned water level. The cascade hydropower operation undertakes the energy storage guarantee task, responds to multi-source uncertainty factors, and should avoid affecting the medium and long-term plan and guarantee requirements. Therefore, it is necessary to limit the disturbance range of reservoir capacity at the end of the scheduling period. Therefore, the guarantee reservoir capacity constraint at the end of the scheduling period of cascade hydropower under the consideration of multi-source uncertainty demand is introduced:

[0074] (20)

[0075] In the formula: respectively represent the upper and lower limits of the guarantee reservoir capacity constraint of the hydropower station under the multi-source uncertainty demand.

[0076] 6) Hydropower operation stability constraint under multi-source uncertainty demand:

[0077] In order to ensure that the cascade hydropower can adjust the generation capacity interval as smoothly as possible between periods when responding to multi-source uncertainty factors, the cascade hydropower operation stability constraint formula (21) is introduced:

[0078] (21)

[0079] In the formula: represents the upper limit of the stability operation constraint of the hydropower station under the multi-source uncertainty demand.

[0080] 7) Uncertainty demand tracking constraint:

[0081] In order to ensure that the cascade hydropower can respond to the tracking of uncertainty demand to different degrees, the cascade hydropower uncertainty demand tracking constraint formula (22) is introduced, which represents that the cascade hydropower can respond to the fluctuation of uncertainty interval to different degrees when

[0082] (22)

[0083] In the formula, represents the fluctuation range of uncertainty demand; represents the fluctuation trend of the uncertainty demand in the period.

[0084] Step three, transformation and reconstruction of the scheduling model:

[0085] ​​​​The cascade hydropower decoupling optimal complementary scheduling model under multi-source uncertain demand is a high-dimensional spatio-temporal deep coupling nonlinear stochastic optimization model, whose solution is restricted by a large number of nonlinear constraints and a large number of randomness constraints. Therefore, the nonlinear constraints are linearized, and the constraint terms containing random variables are reconstructed to realize efficient solution of the model.

[0086] Reconstruction of output, power flow and cross-section constraints under multi-source uncertain demand:

[0087] For any In the interval , Let a new random variable be expressed as:

[0088] (23)

[0089] Then is transformed into:

[0090] (24)

[0091] First, the output boundary constraint formula (16) under multi-source uncertain demand is transformed, and formula (13), formula (24) are substituted into formula (16):

[0092] (25)

[0093] Formula (25) is equivalent to for any Under the condition, the maximum value of the output is less than the upper limit of the output, and the minimum value of the output is greater than the lower limit of the output:

[0094] (26)

[0095] (27)

[0096] Therefore, the output boundary constraint reconstruction under multi-source uncertain demand is obtained as:

[0097] (28)

[0098] Similarly, the power flow boundary and cross-section constraint reconstruction under multi-source uncertain demand is obtained as:

[0099] (29)

[0100] (30)

[0101] Transformation of reservoir capacity boundary and guaranteed reservoir capacity constraints under multi-source uncertain demand:

[0102] 1) Cumulative deviation water balance equation:

[0103] The cascade hydropower responds to multi-source uncertainty demand, from the beginning of the scheduling period to any time, the inflow and power generation flow of the hydropower station deviate from the planned value, causing the storage capacity deviation, the storage capacity of any period is affected by the storage capacity of the previous period and will continue to affect the subsequent period. Therefore, the cumulative deviation water balance equation of the cascade hydropower under multi-source uncertainty demand is established by substituting equation (14) and equation (24) into equation (15) and iterating multiple times as shown in equation (31):

[0104] (31)

[0105] In the formula, represents the scheduling period number between the initial period of the scheduling period and period; represents the interval flow of the hydropower station in the period; represents the power generation flow of the hydropower station in the period; represents the water level water consumption rate of the hydropower station in the period.

[0106] 2) Construction of reservoir capacity extreme value model under multi-source uncertainty demand:

[0107] Introducing variables , respectively represent the maximum value and the minimum value of the reservoir capacity of the hydropower station in the period due to the response to multi-source uncertainty demand, respectively represented as equation (32)-equation (33). The interval formed by the maximum value and the minimum value of any time is the change range of the reservoir capacity under multi-source uncertainty demand:

[0108] (32)

[0109] (33)

[0110] 3) Reconstruction of reservoir capacity upper bound constraint under multi-source uncertainty demand:

[0111] The operation boundary constraint equation (18) and the guarantee reservoir capacity constraint equation (20) under multi-source uncertainty demand need to be satisfied respectively. In order to unify the expression, the constraint equation (18) and equation (20) are combined, and let represent the reservoir capacity upper bound constraint under multi-source uncertainty demand, then satisfies , represented as:

[0112] (34)

[0113] Therefore, the upper bound constraint on storage capacity under multi-source uncertain demand is expressed as:

[0114] (35)

[0115] Equations (32) and (33) of the storage capacity extreme value model under multi-source uncertainty demand are linear optimization problems with random variables as decision variables. Duality theory is introduced to solve them. First, model (32) is solved. In model (32) The feasible region is transformed into constraint form:

[0116] (36)

[0117] In the formula: This indicates that the corresponding random variable The dual variable of the feasible region. For ease of expression, let... Then we obtain the Lagrange function (37) of equation (32):

[0118] (37)

[0119] In the formula, They represent hydroelectric power stations. exist The upper and lower limits of the adjustable power range for different time periods; Indicates hydroelectric power station exist Efforts during specific time periods.

[0120] Based on the Lagrange function (37), construct the dual problem equation (38) of the original problem. Since model equation (32) is a linear convex optimization model, the original problem and the dual problem have strong duality, that is, the optimal solution of the dual problem is consistent with that of the original problem. Therefore, the optimal solution of the dual problem equation (38) is: .

[0121] (38)

[0122] The primal problem is solved by solving the dual problem, which transforms the minimization problem into an existence problem. The result of the reconstruction in response to the upper bound constraint of the storage capacity is as follows:

[0123] (39)

[0124] 4) Reconstruction of lower bound constraints for storage capacity under multi-source uncertain demand:

[0125] The reconstruction method of the lower bound constraint of the reservoir capacity under the multi-source uncertain demand is the same as the reconstruction method of the upper bound constraint, and will not be described again. The reconstruction result is shown in formula (40):

[0126] (40).

[0127] The beneficial effects of the present application are: the present application proposes a new paradigm of decoupled scheduling between cascade hydropower and receiving end power grid to solve the problems of response lag, low decision efficiency and limited adjustment capacity existing in the traditional scheduling mode of "plan reporting-coordination adjustment". On this basis, a cascade hydropower decoupled optimization complementary scheduling model under multi-source uncertain demand is established to show the influence of hydropower decision on the boundary of multi-source uncertain parameters. And the deviation accumulation mechanism is introduced to accurately represent the accumulation effect of reservoir capacity deviation in time sequence and the transmission characteristics in space caused by the response of hydropower to multi-source uncertain demand, so as to accurately quantify the maximum adjustable power generation capacity interval of hydropower under multi-source uncertain environment. Finally, by developing a two-stage solving algorithm combining linear reconstruction and strong duality theory, the complex high-dimensional time-space deep coupling nonlinear random optimization original model is converted into a mixed integer linear programming model, and the efficient solution of the model is realized. BRIEF DESCRIPTION OF DRAWINGS

[0128] Figure 1 is a new energy output prediction value and its fluctuation interval diagram;

[0129] Figure 2 is a hydropower adjustable power generation capacity interval diagram under different degrees of tracking new energy output trend, wherein (a) is complete tracking, and (b) is 43.2% incomplete tracking;

[0130] Figure 3 is a hydropower plan output diagram under different degrees of tracking new energy output trend, wherein (a) is complete tracking, and (b) is 43.2% incomplete tracking;

[0131] Figure 4 is a flexibility allocation proportion diagram under different degrees of tracking new energy output trend, wherein (a) is complete tracking, and (b) is 43.2% incomplete tracking;

[0132] Figure 5 is a hydropower adjustable power generation capacity interval and plan output diagram under different degrees of tracking new energy output trend, wherein (a) is a flood season hydropower adjustable power generation capacity interval, (b) is a flood season hydropower output, (c) is a dry season hydropower adjustable power generation capacity interval, and (d) is a dry season hydropower output. DETAILED DESCRIPTION

[0133] The present application will be further described below in combination with the drawings and implementation cases.

[0134] A cascade hydropower group consisting of four hydropower stations in LCJ basin of a province in southwest China was taken as a research example to verify the proposed method. The total installed capacity of the cascade hydropower is 13070MW, which is an important peak and frequency regulating power source of China Southern Power Grid and a power source point of West-to-East Power Transmission. Table 1 shows the basic information of each power station in the upstream and downstream order of the cascade. The simulation calculation was carried out on the typical days of flood and dry seasons in 2024, and the initial and final reservoir storage, interval runoff, upper and lower limits of output in the model input are the actual parameters of the power station. The invention is verified by taking the uncertainty of new energy output as an example, but the proposed method is also applicable to the fluctuation of power clearing electricity in the power spot market and other multi-source uncertainty demands, and has universality. The program is realized by Python3.8, and GUROBI10.0.1 is called for solving. The calculation environment is Intel(R) Core(TM) i7-12700H CPU@2.30 GHz, 16GB RAM, Win 11 operating system.

[0135] Table 1 Basic data information of hydropower stations

[0136]

[0137] Figure 1 The output trend of new energy is shown when the new energy output is the predicted value and when the output fluctuation range is 43.2% above and below the predicted value. Figure 2 (a) in (a) represents the adjustable power range of the hydropower when the new energy output is according to the predicted value, compared with Figure 1 , it can be seen that the hydropower can completely track the trend of new energy output in this scenario. The new energy output is larger at the time period 12:00-15:00, at which time the hydropower has a larger adjustable power range and sufficient flexible adjustment capacity. The new energy output is smaller at the time period 6:00-8:00 and 18:00-20:00, and the adjustable power range of the hydropower is correspondingly reduced. Figure 2 (b) in (b) when the new energy output fluctuates 43.2% up and down, the adjustable power range of the hydropower at each time period increases significantly, compared with Figure 2 (a) The overall adjustable power range of the cascade increases by 58018MWh. It can be seen that under the proposed method, the hydropower can respond to the uncertainty of new energy output to different degrees.

[0138] Figure 3 The generation plan of the hydropower when tracking the fluctuation of new energy output to different degrees is shown. The overall planned output of the cascade is 166753.4971MW in both cases, but the planned output of each power station at each time period changes, indicating that when the hydropower tracks the trend of new energy output to different degrees, it does not affect the overall planned power generation of the cascade, but the generation plan of each power station at each time period will be adjusted to adapt to the fluctuation of new energy to different degrees. Figure 4 The distribution proportion of flexibility of each power station at each time period under the two conditions is shown fromFigure 4 (a) and Figure 4 As can be seen from (b) above, hydropower stations with strong regulation capabilities, such as XW and NZD, have a higher proportion of flexibility, while hydropower stations with weak regulation capabilities, such as MW and DCS, have a lower proportion of flexibility. (Comparison) Figure 4 (a) and Figure 4 (b) in the figure illustrates that when hydropower tracks the output trend of new energy sources to different degrees, the flexibility of each power station at different times will be redistributed to ensure that the overall adjustable power range of the cascade is maximized and to adapt to different fluctuations of new energy sources.

[0139] Figure 5 It demonstrates the adjustable power range and power generation plan for hydropower under different water inflow conditions. Figure 5 (a) and Figure 5 (b) in the figure represents the adjustable hydropower range and the power generation plan during the flood season, respectively. Figure 5 (c) and Figure 5 In the figure, (d) represents the adjustable hydropower capacity range during the dry season and the planned power generation. The adjustable hydropower capacity ranges obtained during the flood season and the dry season are 17258.6959 MWh and 34517.3917 MWh, respectively. Figure 1 This shows that hydropower can track the changes in new energy output trends to a certain extent during both the flood and dry seasons. Under different inflow conditions, the adjustable power range and output plan of each cascade power station at different times vary significantly. This is because the overall inflow situation of the basin is different, and the inflow of different power stations is also different, which directly affects the arrangement of power generation plans of each power station at different times, and thus affects the flexibility and flexibility allocation method.

Claims

1. A cascade hydropower decoupling optimal complementary scheduling method under multi-source uncertain demand, characterized in that: The construction of the cascade hydropower decoupling scheduling framework, the establishment of the cascade hydropower decoupling optimization complementary scheduling model under the multi-source uncertainty demand, the transformation and reconstruction of the scheduling model; as follows: Step one, the construction of the cascade hydropower decoupling scheduling framework: Step 1, each hydropower station in the cascade upstream and downstream reports a dynamic maximum adjustable power generation interval range closely related to its own decision to the receiving end power grid; Step 2, after receiving the maximum adjustable power generation interval range reported by each hydropower station, the receiving end power grid judges whether it can cover the fluctuation range of the uncertainty factors, if it can, the hydropower station autonomously and flexibly adjusts its output within its commitment interval to respond to the rapid changes of multi-source uncertainty demand in real time without frequent reporting of adjustment plans or waiting for power grid scheduling instructions, if it cannot, the power grid considers calling other flexible power sources; the uncertainty factors include new energy prediction deviation, power spot market clearing power fluctuation and uncertainty of basin inflow; Step two, establish the cascade hydropower decoupling optimization complementary scheduling model under the multi-source uncertainty demand: In order to maximize the ability of hydropower to respond to multi-source uncertainty demand in decoupling scheduling, the following objective function is constructed with the maximum adjustable power generation interval of cascade hydropower as the target: (1) In the formula: respectively represent Period hydropower station The upper and lower boundaries of the adjustable power interval of the hydropower station; The total number of time periods of the dispatch period; The total number of cascade hydropower stations; The cascade hydropower operation constraints in the planning stage are as follows: 1) Water balance constraint: (2) In the formula: represents the hydropower station In the storage capacity of the time period; respectively represents the hydropower station In the interval flow, power generation flow and abandoned water flow of the time period; is the scheduling calculation time period step; in order to maximize the utilization of water energy resources, the abandoned water is limited to ; 2) Hydropower operation boundary constraint: (3) (4) (5) In the formula: respectively represent the output of the hydropower station In the upper and lower limits of the output of the time period; respectively represent the output of the hydropower station In the output of the time period; respectively represent the output of the hydropower station In the upper and lower limits of the flow of the time period; respectively represent the output of the hydropower station In the upper and lower limits of the storage capacity of the time period; formula (3) - formula (5) respectively represent the output, flow, and storage capacity boundary constraints of the hydropower station in the planning stage; 3) Hydropower initial and final reservoir capacity control constraint: (6) (7) In the formula: respectively represent the water power station the reservoir capacity at the beginning and end of the dispatching period; respectively represent the water power station the reservoir capacity control value at the beginning and end of the dispatching period; 4) Hydropower generation function: In short-term scheduling, the daily water level fluctuation of hydropower station has little effect on water consumption rate, and the output is calculated by using fixed water consumption rate according to the actual operation of the research object; the hydropower generation function is as follows: (8) In the formula: represents a hydropower station At water level consumption rate of the period; 5) Water level-storage capacity relationship function: (9) wherein: represents a hydropower station At the dam water level at the time period; represents a hydropower station the water level-storage capacity relationship curve function of the hydropower station 6) Hydropower section constraint: (10) where: respectively represent the set of hydropower plants the upper and lower bounds of the periodical section constraints; represents the set of hydropower plants related to the section constraints; the purpose of the introduction of this constraint is to prevent potential infeasibility related to the transmission constraints; The cascade hydropower operation constraints under multi-source uncertainty demand are as follows: Because of the influence of multi-source uncertainty demand in actual operation, the actual decision of hydropower in response to multi-source uncertainty demand may deviate from the planned value, resulting in that the output, power generation flow and water level of cascade hydropower are random variables, which further leads to the decoupling result of hydropower generation capacity is a random variable; for any The value should be within the upper and lower bounds of the hydropower adjustable power interval, that is: (11) For convenience of representation, introduce random variable represents the deviation of the actual generation of hydropower from the decoupling result and the planned value: (12) 1) Hydropower output and generation flow constraint under multi-source uncertainty demand: The output and generation flow under multi-source uncertainty demand are obtained by adding the deviation term caused by uncertainty to the respective planned value, that is: (13) (14) In the formula: respectively represent the output and power generation flow of the hydropower station under multi-source uncertain demand In the time period Equations (13) - (14) are constraints involving random variables that must hold for all random variables satisfying Equations (15) - (20) are also constraints involving random variables that must hold for all random variables satisfying​ 2) Water balance equation under multi-source uncertainty demand: Substitute formula (13) to formula (14) into formula (2) to obtain the water balance equation under multi-source uncertainty demand as shown in formula (15): (15) In the formula: represents the water power station under the multi-source uncertain demand At the storage capacity of the time period; 3) Hydropower operation boundary constraint under multi-source uncertainty demand: The same as the operation boundary constraint in the planning stage of cascade hydropower, the generation flow, output and reservoir capacity of cascade hydropower under multi-source uncertainty demand need to meet the operation boundary constraints formula (16) to formula (18): (16) (17) (18) 4) Hydropower section constraint under multi-source uncertainty demand: While responding to multi-source uncertainty demand, cascade hydropower also needs to meet the section constraint to prevent potential infeasibility related to transmission constraints: (19) 5) Hydropower guarantee capacity constraint under multi-source uncertainty demand: The water level at the end of the dispatching period will be affected by multi-source uncertainty factors in each period, so it is difficult to strictly control the water level at the end of the plan. The cascade hydropower operation bears the energy storage guarantee task, and should avoid affecting the long-term plan and guarantee requirements while responding to multi-source uncertainty factors. Therefore, the guarantee reservoir capacity constraint at the end of the dispatching period under multi-source uncertainty demand is introduced: (20) In the formula: respectively represent the multi-source uncertain demand of hydropower stations Upper and lower limits of the safeguard reservoir capacity constraint at the end of the dispatch period 6) Hydropower operation stability constraint under multi-source uncertainty demand: To ensure that the adjustable generation capacity interval of each hydropower station in each period is as stable as possible when responding to multi-source uncertainty factors, the hydropower operation stability constraint (21) is introduced: (21) In the formula: represents the upper limit of the station's stationary operation constraint under multi-source uncertain demand; represents the upper limit of the station's stationary operation constraint under multi-source uncertain demand; 7) Uncertainty demand tracking constraint: To ensure that the cascade hydropower energy responds to the uncertain demand with different degrees, the uncertain demand tracking constraint (22) of cascade hydropower is introduced, when represents the fluctuation of the uncertain interval of the cascade hydropower energy with different degrees of response as the demand changes. (22) wherein represents the fluctuation range of uncertainty demand; represents the fluctuation trend of period uncertainty demand; Step three, transformation and reconstruction of the scheduling model: The decoupling optimization complementary scheduling model of cascade hydropower under multi-source uncertainty demand is a high-dimensional space-time deeply coupled nonlinear random optimization model, which is restricted by a large number of nonlinear constraints and a large number of randomness constraints. Therefore, the nonlinear constraints are linearized, and the constraint terms containing random variables are reconstructed to realize efficient solution of the model. Reconstruction of output, power generation flow and cross-section constraints under multi-source uncertainty demand: For any In the interval , By introducing a new random variable is expressed as: (23) then is converted to: (24) First, transform the output boundary constraint (16) under multi-source uncertainty demand, substitute (13) and (24) into (16), and get: (25) Equation (25) is equivalent to for any In the case where the maximum of the output is smaller than the upper limit of the output and the minimum of the output is larger than the lower limit of the output: (26) (27) Thus, the output boundary constraint under multi-source uncertainty demand is reconstructed as: (28) Similarly, the power generation flow boundary and cross-section constraint under multi-source uncertainty demand is reconstructed as: (29) (30) Transformation of reservoir capacity boundary and guarantee reservoir capacity constraint under multi-source uncertainty demand: 1) Cumulative deviation water balance equation: To respond to multi-source uncertainty demand, the inflow and power generation flow of hydropower stations deviate from the planned value from the beginning of the dispatching period to any time, causing reservoir capacity deviation. The reservoir capacity at any period is affected by the reservoir capacity of the previous period and will continue to affect the subsequent period. Therefore, substitute (14) and (24) into (15) and iterate multiple times to establish the cumulative deviation water balance equation of cascade hydropower under multi-source uncertainty demand as shown in equation (31): (31) In the formula, denotes the scheduling period number between the initial period of the scheduling period and the period; denotes the water power station the interval flow of the period; denotes the water power station the power generation flow of the period; denotes the water power station the water level depletion rate of the period; denotes the water power station the water level depletion rate of the period; denotes the water power station 2) Construction of reservoir capacity extreme value model under multi-source uncertainty demand: Introducing variables , respectively, represent the water power station At The maximum and minimum values of the reservoir capacity caused by the multi-source uncertain demand in the time period are represented as equations (32) and (33), respectively; the maximum and minimum values at any time The interval formed by the maximum and minimum values at any time is the change range of the reservoir capacity under multi-source uncertain demand: (32) (33) 3) Reconstruction of reservoir capacity upper bound constraint under multi-source uncertainty demand: The operation boundary constraint (18) and the guarantee reservoir capacity constraint (20) under the multi-source uncertainty demand need to be satisfied respectively. To express uniformly, the constraint (18) and the constraint (20) are integrated, and The reservoir capacity upper limit constraint under the multi-source uncertainty demand is represented by The satisfaction of is represented as: (34) Therefore, the reservoir capacity upper bound constraint under multi-source uncertainty demand is represented as: (35) The reservoir capacity extreme value model formula (32) and formula (33) under multi-source uncertain demand are linear optimization problems with random variables as decision variables, and the dual theory is introduced to solve them; firstly, the model formula (32) is solved; the feasible region of the model formula (32) is transformed into a constraint form: ​ (36) In the formulae: denotes the corresponding random variable the dual variable of the feasible region; for convenience of expression, let then the Lagrangian function of formula (32) is formula (37): (37) In the formula, respectively represent the hydropower stations In the upper and lower boundaries of the adjustable power interval of the time period; respectively represent the hydropower stations In the output of the time period; According to the Lagrange function formula (37), the dual problem formula (38) of the original problem is constructed; since the model formula (32) is a linear convex optimization model, the original problem and the dual problem have strong duality, that is, the optimal solution of the dual problem is consistent with that of the original problem, so the optimal solution of the dual problem formula (38) is ; (38) Solve the dual problem to solve the original problem, and convert the minimization problem into an existence problem. The response reservoir capacity upper bound constraint reconstruction result is: (39) 4) Reconstruction of reservoir capacity lower bound constraint under multi-source uncertainty demand: The reconstruction method of reservoir capacity lower bound constraint under multi-source uncertainty demand is the same as that of upper bound constraint reconstruction, which is not repeated here. The reconstruction result is shown in equation (40): (40)。

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