A method and system for constructing a flexible water-power interval considering new energy uncertainty

Abstract

The present application belongs to the technical field of power system balance analysis and intelligent dispatching, and relates to a method and system for constructing a hydroelectric flexible interval considering new energy uncertainty, the method comprising: 1. obtaining basic technical data of a power system containing new energy; 2. constructing a day-ahead power balance analysis model and establishing a system hydroelectric flexible interval value function; 3. constructing a two-stage robust optimization model based on decision-dependent uncertainty set; dividing the problem into a main problem and a sub-problem for iterative solution; and 4. multi-objective automatic optimization based on mixed integer linear programming. The present application improves the response capability of the system to uncertain factors under the premise of ensuring that the time and space boundaries of hydroelectric resources are not exceeded, effectively solves the problem of imbalance between real-time clearing results and medium and long-term decomposition strategies, further reduces the complexity of the real-time clearing model, and improves the overall clearing efficiency.

Description

Technical Field

[0001] This invention belongs to the field of power system balance analysis and intelligent dispatching technology, specifically relating to a method and system for constructing flexible hydropower intervals that take into account the uncertainty of new energy sources. Background Technology

[0002] In power system operation, day-ahead power balance analysis is crucial for ensuring reliable power supply and economical system operation. Especially with the integration of a high proportion of renewable energy, fully leveraging the flexibility of hydropower to optimize day-ahead power balance and achieve real-time clearing and settlement has become an increasingly popular research topic.

[0003] Regarding the value of flexibility, some scholars have constructed a framework for evaluating hydropower flexibility, qualitatively analyzing the flexible adjustment capabilities of different types of hydropower in providing ancillary services such as rapid start-up and shutdown, ramp-up ability, and reserve capacity, using a "good, medium, poor" grading system. Some studies have also established flexibility demand indicators and added flexibility supply-demand matching constraints to scheduling models, thereby quantifying the flexible adjustment capabilities provided by hydropower and improving hydropower flexibility through rational scheduling and operation methods. However, the flexible adjustment capacity of hydropower determined by this method is calculated based on the physical characteristics of the generating units, resulting in a relatively fixed adjustable space for the units throughout the entire time period. This makes it difficult to consider the impact of medium- and long-term power boundaries on the short-term adjustable flexibility space of hydropower. Furthermore, studies focus primarily on the impact of hydropower flexibility on supply-demand balance, with less attention paid to the matching relationship of upstream and downstream cascade flow, which can easily lead to flow mismatch problems.

[0004] As a prerequisite for day-ahead power market clearing, day-ahead power balancing provides a reasonable generator start-up and shutdown plan and output range for the clearing market. However, in real-time market clearing, the clearing model, in pursuit of minimizing short-term system costs, exhibits significant short-sightedness, easily leading to a mismatch between day-ahead power plans and actual generation results. To address this, some scholars have adopted a dual-settlement mechanism, making minor adjustments to real-time hydropower output based on the day-ahead clearing results. This has partially resolved the connection between day-ahead power boundaries and short-term generation plans, but it overlooks the short-term adjustment potential of hydropower, meaning that hydropower can be finely adjusted near the day-ahead plan. Therefore, how to decouple day-ahead power boundaries from short-term generation plans and provide a flexible hydropower adjustment range to fully leverage the flexible adjustment potential of hydropower resources has become an issue that cannot be ignored. Meanwhile, existing clearing rules have some potential problems when dealing with cascade hydropower and hydropower with multiple ownership entities. For example, this clearing method cannot guarantee matching of generation flow between upstream and downstream power stations in a cascade, potentially leading to structural water wastage or power shortages in the cascade.

[0005] Therefore, the potential for flexibility in existing hydropower resources needs to be further explored. The limitations on power generation and flow coupling caused by the inherent characteristics of hydropower greatly affect the adjustable flexibility of hydropower. How to decouple these limitations of hydropower and characterize the adjustable flexibility of hydropower has become a difficult problem to be solved. Summary of the Invention

[0006] This invention provides the following technical solution: a method for constructing flexible hydropower ranges that takes into account the uncertainties of new energy sources, comprising the following steps: Step 1: Obtain basic technical data for power systems containing new energy sources.

[0007] Step 2: Construct a day-ahead power balance analysis model based on basic technical data, and establish a flexible interval value function for hydropower based on electricity price signals.

[0008] Step 3: Based on the value function of the flexible range of hydropower, construct a two-stage robust optimization model based on the decision-dependent uncertainty set; divide the problem into a main problem and sub-problems for iterative solution. The main problem is to maximize the value of the flexible range of hydropower, and the sub-problems are to perform spatiotemporal coupling feasibility verification and system index feasibility verification of cascade hydropower in the feasible range of hydropower solved by the main problem, and determine whether the current hydropower operation range is feasible.

[0009] Step 4: Automatic multi-objective optimization based on mixed-integer linear programming.

[0010] Preferably, the basic technical data in step 1 includes: operating parameters of various generating units, and predicted values ​​of new energy sources and load.

[0011] Preferably, the objective function of the day-ahead power balance analysis model in step 2 is:

[0012] in, For the binary variables in the model, For continuous variables in the model, The start-up and shutdown costs for thermal and hydropower plants, For the operating costs of thermal power units, , , These are respectively water curtailment penalties, renewable energy curtailment penalties, and load shedding penalties.

[0013] More preferably, the hydropower flexible interval value function in step 2 is: a hydropower interval construction form that considers the value differences at different times; when the electricity price signal is high, i.e., the load demand is large, the model tends to solve for the hydropower operation interval with a higher upper limit of output, thereby increasing the hydropower output during that period and improving the system's economy; when the electricity price signal is low, i.e., the power is relatively redundant, the model tends to solve for the hydropower operation interval with a lower lower limit of output, thereby reducing the hydropower output during that period and improving the system's absorption level.

[0014] Preferably, in step 3, the hydropower flexible interval value function is:

[0015] in, As a dual multiplier, , This indicates the upper and lower limits of the output range of a single hydropower unit. , The upper and lower limits of the flexible range for hydropower in the model decision-making process. For the collection of hydroelectric generating units, This represents the number of time periods throughout the entire cycle.

[0016] More preferably, the constraints of the hydropower flexible interval value function include: Hydropower flexible range upper and lower limit constraints:

[0017] in, Indicates the operating status of the hydroelectric power unit; Hydropower output upper and lower limit constraints:

[0018] in, This refers to the operating power of the thermal power unit. Constraints for the feasibility verification of spatiotemporal coupling of cascade hydropower:

[0019] In the formula, For the set of constraints for normal operation The remaining constraints after removing the upper and lower limits of hydropower generation and the hydropower coupling constraints throughout the entire time period are: dual multipliers The corresponding system output plan, , These represent the positive and negative slack variables of hydropower generation over the entire time period. , These are the positive and negative relaxation variables of the hydroelectric coupling constraint, respectively. In order to be in Based on this, a relaxed feasible region is constructed by relaxing the upper and lower limit constraints of hydropower and the coupling constraints of cascade hydropower throughout the entire time period. These are power generation efficiency and turbine efficiency, respectively. These represent the density of water and the acceleration due to gravity, respectively. Indicates net head of electricity generation. This indicates the total power generation flow of the hydropower station. Let V be the uncertain set of hydropower output, and let V be the uncertain set of renewable energy output. , The lower and upper limits of the available power supply for unit i on day d; System operation indicator feasibility verification constraints:

[0020] in, This refers to the threshold coefficients for system load shedding rate and power curtailment rate. These are the matrix coefficients corresponding to the model variables in the objective function. Let i be the load demand of node i at time t. Provide energy output for node i at time t.

[0021] More preferably, the main problem is:

[0022] The objective function of the main problem is the hydropower flexible interval value function. These are the upper and lower limits for the hydropower range; These are the remaining continuous variables within the model. This is a set for spatiotemporal feasibility verification of cascade hydropower projects. This is a set of feasibility verification metrics for the system. To find the extreme hydropower operating power within a given hydropower range, , As a dual multiplier, , For the set of poles, Let the scenery be a random variable. Let A be an uncertain set, and let A, J, K, M, N, O, E, F, and G be the matrix coefficients corresponding to the model variables in the constraints.

[0023] More preferably, the sub-problems include: Feasibility verification model for spatiotemporal coupling of cascade hydropower:

[0024] System indicator feasibility verification model:

[0025]

[0026] in, The feasible domain for system output. This is a relaxation variable added to the constraints on the upper and lower limits of hydropower generation throughout the entire time period and the spatiotemporal coupling constraints of cascade hydropower.

[0027] More preferably, in the subproblem, if There is a solution and If the current hydropower section is deemed to have converged through the feasibility verification model of the spatiotemporal coupling of cascade hydropower and the feasibility verification model of system indicators, then it will be considered converged; otherwise, it will continue to be... , The dual multipliers obtained from the solution are fed back to the main problem, and iterative solutions are continued.

[0028] This invention also discloses a hydropower flexible range construction system that takes into account the uncertainty of new energy sources. This system is used to implement the above-described hydropower flexible range construction method. The hydropower flexible range construction system includes: The data module acquires basic technical data for power systems that include new energy sources.

[0029] The construction module, based on the basic technical data of the power system including new energy sources obtained from the data module, constructs a day-ahead power balance calculation and a two-stage robust optimization model based on the decision-dependent uncertainty set.

[0030] The solution module, based on the two types of models obtained from the construction module, performs day-ahead power balance calculation and solves the flexible operation range of cascade hydropower, respectively, to obtain the day-ahead electricity price signal and construct the value function of the flexible operation range of hydropower. Then, through the collaborative solution of the main and sub-problems in the flexible operation range model of cascade hydropower, the optimal flexible operation range of hydropower is obtained.

[0031] The output module, based on the flexible operating range of hydropower obtained from the solution module, performs subsequent clearing calculations.

[0032] The beneficial effects of this invention are: 1. This invention acquires power system data, constructs a day-ahead power balance analysis model to solve for day-ahead balance, provides electricity price signals for each time period, and then constructs a value function for the flexible hydropower interval based on the electricity price signals. Through feasibility verification of the spatiotemporal coupling of cascade hydropower and feasibility verification of system indicators, the optimal flexible hydropower operating interval is finally solved. This method, through a feasible interval construction strategy for cascade hydropower, decomposes the spatiotemporal coupling constraints of cascade hydropower into time-by-time output operating intervals. While ensuring that the spatiotemporal boundaries of hydropower resources are not exceeded, it improves the system's responsiveness to uncertainties, effectively solves the problem of imbalance between real-time clearing results and medium-to-long-term decomposition strategies, and further reduces the complexity of the real-time clearing model, thus improving overall clearing efficiency.

[0033] 2. This invention constructs a hydropower flexible interval value function, which uses electricity price signals to characterize the balance risk of each time period, and accurately estimates the value weight of hydropower flexible intervals in different time periods.

[0034] 3. This invention constructs a two-stage robust optimization model based on decision-dependent uncertainty sets, dividing the interval solution problem into a main problem and a sub-problem for iterative solution. The main problem includes two unique types of feasibility verification constraints for the spatiotemporal coupling of cascade hydropower and feasibility verification constraints for system indicators. Through these verification constraints, the flexible operating domain of hydropower with the highest interval value is efficiently generated.

[0035] 4. This invention verifies the model by constructing sub-problems, quickly locates extreme scenarios under the current hydropower flexible range, and feeds back to the main problem in the form of dual multipliers, narrowing the feasible region of the main problem's hydropower flexible range and assisting in the rapid solution of the main problem. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating a method and system for constructing flexible hydropower ranges that takes into account the uncertainties of new energy sources, according to the present invention. Figure 2 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 3 This is a chip block diagram according to an embodiment of the present invention; Figure 4 This is a diagram showing the flexible operation range of the entire hydropower system of this invention. Detailed Implementation

[0037] The relevant technologies of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0038] like Figures 1-4 As shown, this embodiment of a method for constructing a flexible hydropower range that takes into account the uncertainty of new energy sources includes the following steps: S1. Obtain basic technical data for power systems containing new energy sources; Basic technical data for power systems that include new energy sources include: operating parameters of various generating units, and predicted values ​​of new energy sources and load.

[0039] S2. Based on the basic technical data of the power system containing new energy obtained in step S1, construct the day-ahead power balance analysis model and reveal the extreme risks in the system balance. Based on the electricity price signal, establish the system hydropower flexible interval value function to measure the importance of the hydropower flexible adjustment capability in different time periods. The current power balance analysis model includes: unit state logic constraints, ramping constraints, output range constraints, and shortest start-up and shutdown time constraints for thermal power units; output range constraints, hydropower coupling constraints, flow range constraints, reservoir capacity range constraints, water level range constraints, water balance constraints, new energy unit output constraints, load shedding constraints, and system power balance constraints. The hydropower flexible interval value function refers to the construction form of hydropower intervals that considers the value differences at different times. That is, when the electricity price signal is high and the load demand is high, the model tends to solve for hydropower operation intervals with a higher upper limit of output, thereby increasing hydropower output during that period and improving the system's economy. When the electricity price signal is low and the power is relatively redundant, the model tends to solve for hydropower intervals with a lower lower limit of output, thereby reducing hydropower output during that period and improving the system's absorption capacity.

[0040] Specifically, a day-ahead power balance analysis model is constructed, which consists of two parts: an objective function and constraints. The objective function is as follows:

[0041] in, These are binary variables in the model, including variables such as unit start-up and shutdown, and unit operating status; These are continuous variables in the model, including relevant variables such as unit operating power, cascade reservoir flow, water level, reservoir capacity, and load shedding power; The start-up and shutdown costs for thermal and hydropower plants, For the operating costs of thermal power units, , , These are penalties for water curtailment, penalties for renewable energy curtailment, and load shedding, and the specific forms of each cost are as follows:

[0042]

[0043]

[0044]

[0045]

[0046]

[0047] in , For the start-up and shutdown costs of thermal power plants, The start-up cost of hydropower Let the operating cost function of thermal power units be _____. This refers to the operating power of thermal power units. , , These are the water curtailment penalty coefficient, the renewable energy curtailment penalty coefficient, and the load shedding penalty coefficient, respectively. , , These are respectively the power of abandoned water, the power of abandoned renewable energy, and the power of load shedding.

[0048] The constraints include operational constraints for thermal power units, operational constraints for cascade hydropower in river basins, operational constraints for new energy sources, load shedding constraints, and power balance constraints. The electricity price signal is based on the dual multiplier of the power balance constraint. Given a set of conditions, we can reveal the equilibrium risk of the system at different time periods and then construct a flexible interval value function for hydropower. The constraints are as follows: Operating constraints of thermal power units:

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] In the formula: This indicates the operating status of the thermal power unit. If the thermal power unit is in operation, its value is 1. , Indicates the start-up and shutdown status of the thermal power unit; These represent the minimum start-up and shutdown times for thermal power units, respectively. These represent the maximum and minimum technical output of the thermal power unit, respectively. These represent the uphill and downhill rates of the thermal power unit, respectively.

[0055] Constraints on the operation of cascade hydropower in the basin:

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] The operational constraints of cascade hydropower mainly include constraints on the start-up and shutdown of hydropower units, power generation flow constraints, water discharge constraints, power generation head, tailrace water level constraints, water level and reservoir capacity constraints, reservoir water balance constraints, reservoir capacity constraints, and minimum outflow constraints. Among these, the constraints on the start-up and shutdown of hydropower units include… This indicates the operating status of the hydropower unit. If the hydropower unit is in operation, its value is 1. , This indicates the start-up and shutdown status of the hydropower unit. (Power generation flow constraint) This indicates the minimum and maximum power generation flow rates of the hydropower station. This represents the total power generation flow of the hydropower station. In the constraint of water discharge, This indicates the maximum flood discharge (water abandonment) of the hydropower station. (During the power generation head constraint...) These represent the net head for power generation, reservoir water level, tailrace water level, and head loss height, respectively. This constraint describes the relationship between the net head for power generation and the reservoir water level, tailrace water level, and head loss height. The water level-storage capacity constraint describes the amount of water stored in the reservoir. Reservoir height The relationship between these factors is often not a linear function, so directly incorporating them into the system's mathematical model will make the optimization problem difficult to solve, requiring some linearization. The reservoir water balance constraint describes the flow balance relationship of the spatiotemporal coupling of cascade hydropower. The reservoir capacity constraint restricts the boundary of the reservoir water level and provides an initial reservoir water level. The minimum outflow constraint restricts the sum of the minimum power generation flow and the minimum water discharge flow of the reservoir. This is the minimum ecological flow required downstream, and it is usually given in advance.

[0069] Meanwhile, hydropower units also include some hydropower output coupling constraints, namely:

[0070]

[0071]

[0072] In the formula, , This indicates the upper and lower limits of the output range of a single hydropower unit, which are determined based on the unit's installed capacity and minimum technical output. These represent the maximum and minimum daily power generation of the hydropower station, respectively. These represent the density of water and the acceleration due to gravity, respectively. These are the power generation efficiency and the turbine efficiency, respectively, which are approximated as constant values ​​in this invention, generally ranging from 0.85 to 0.95.

[0073] New energy operation constraints:

[0074] The operational constraints of new energy sources mainly include curtailment power constraints and actual output constraints.

[0075] Unload constraint:

[0076] Power balance constraints:

[0077] S3. Based on the hydropower flexible interval value function obtained in step S2, construct a two-stage robust optimization model based on the decision-dependent uncertainty set. Divide the problem into a main problem and a sub-problem for iterative solution. The main problem is to maximize the value of the hydropower flexible interval, while the sub-problem is to perform spatiotemporal coupling feasibility verification and system index feasibility verification on the hydropower feasible interval solved by the main problem, and determine whether the current hydropower operation interval is feasible.

[0078] For a two-stage robust optimization model based on decision-dependent uncertainty sets, the principal and sub-problems specifically include: For the main problem, based on the hydropower flexible interval value function constructed in step S2, the hydropower flexible interval that maximizes it is solved and passed to the sub-problem for system feasibility verification; For the sub-problem, based on the optimal hydropower flexibility range obtained from solving the main problem, it is necessary to conduct feasibility checks on the spatiotemporal coupling of cascade hydropower and on the feasibility of system indicators. The spatiotemporal coupling feasibility check requires that, within the current flexible hydropower range, any hydropower output can satisfy the upper and lower limits of hydropower output and the upstream and downstream water balance constraints of the cascade hydropower projects throughout the entire time period. The system indicator feasibility check requires that, within the current flexible hydropower range, the optimized system indicator results are not lower than a given threshold. When both types of feasibility checks are satisfied, the current flexible hydropower range is considered feasible.

[0079] In the main problem, the objective function is to maximize the value function of the hydropower flexible interval, that is:

[0080] , To determine the upper and lower limits of the hydropower flexible range for model decision-making, and based on different electricity price levels at different times, the upper limit of the hydropower range is raised during periods of power shortage to increase the system's output, while the lower limit of the hydropower range is lowered during periods of difficulty in absorbing renewable energy to make room for renewable energy absorption. By constructing the value cost of the hydropower range in a differentiated manner, the optimal hydropower flexible range for the overall economic benefits of the system is obtained.

[0081] The constraints include upper and lower limits of the flexible range of hydropower, upper and lower limits of hydropower output, constraints of uncertain sets of new energy sources, constraints for the feasibility verification of spatiotemporal coupling of cascade hydropower, and constraints for the feasibility verification of system indicators.

[0082] Hydropower flexible range upper and lower limit constraints:

[0083] Hydropower output upper and lower limit constraints:

[0084] Constraints for the feasibility verification of spatiotemporal coupling of cascade hydropower:

[0085] In the formula, For the set of constraints for normal operation The remaining constraints after removing the upper and lower limits of hydropower generation and hydropower coupling constraints throughout the entire time period are as follows: This includes the operating constraints of thermal power units, the operating constraints of cascade hydropower in the basin, the hydropower coupling constraints, the operating constraints of new energy sources, the load shedding constraints, and the power balance constraints mentioned above. dual multipliers The corresponding system output plan, , These represent the positive and negative slack variables of hydropower generation over the entire time period. , These are the positive and negative relaxation variables of the hydropower coupling constraint, respectively. In this verification constraint, the hydropower power is a random variable. The aim is to ensure that, within the given flexible range of hydropower, the extreme operating power sets fed back in the sub-problem can satisfy the spatiotemporal coupling constraint of the cascade hydropower, and that there will be no situation where the power exceeds the limit or the upstream and downstream flow cannot be matched.

[0086] System operation indicator feasibility verification constraints:

[0087] In the formula, These are pre-defined threshold coefficients for system load shedding rate and power curtailment rate. This verification model aims to locate the most extreme wind and solar power output scenario in the current uncertain set, requiring that the corresponding system load shedding rate and power curtailment rate in this scenario do not exceed the given thresholds.

[0088] When solving the main problem, considering its essence as a large-scale linear optimization problem, feasible cuts can be fed back through enumeration. At the same time, the decision variables of the spatiotemporal coupling verification constraint of the cascade hydropower project can be used. Since the hydropower operation range is determined by the main problem, this verification model is a robust optimization problem based on the uncertainty set dependent on the decision. The uncertainty set is variable, and this can be addressed by enumeration. The method of enumeration leads to an infinite number of possibilities, making it difficult to solve. Therefore, strong duality theory can be used to transform the problem into a dual problem by finding the set of extreme points of the dual problem. This involves enumerating and filtering extreme cases, then feeding back the feasible cuts of these extreme cases to the main problem at the higher level. That is:

[0089] Obtain the corresponding set of dual multipliers Then, for each dual multiplier The extreme scenarios corresponding to the dual multipliers can be characterized by KKT conditions. Thus, the dual-layer optimization objective in the feasibility verification constraint of cascade hydropower is transformed into constraint conditions and merged into the main problem constraint.

[0090]

[0091]

[0092] in This is the set of constraints after KKT transformation. , To correspond to the dual multipliers of the original problem constraints, the KKT internal structure, from top to bottom, consists of the original problem constraints, complementary relaxation constraints, stability constraints, and dual feasible constraints.

[0093] Regarding the feasibility verification constraints for system indicators, considering that their uncertain set is given in advance, the corresponding set of dual multipliers or extreme scenario set and corresponding dual multipliers can be directly solved, and then fed back to the upper-level main problem. The transformation form here is almost identical to the feasibility verification constraint of spatiotemporal coupling of cascade hydropower; its transformation process is referenced here and will not be described in detail.

[0094] In the subproblem, the two types of feasibility checks in the main problem are solved. Considering that both types of feasibility check models need to find the corresponding extreme boundary cases under different scenarios, and then obtain their corresponding dual multiplication subsets, they need to be transformed in a certain form.

[0095] For the feasibility verification model of spatiotemporal coupling of cascade hydropower, sub-problems are defined. ,Right now:

[0096] The maximum value of this problem is denoted as... The most severe water-wind-solar power coupling scenario and its dual multiplier. Then, return the feedback to the main question.

[0097] For the system indicator feasibility verification model, define the sub-problems. AND problem Sub-problems This is used to locate the set of most severe wind and light scenes within the current flexible range of hydropower. With dual multipliers subproblems That is in Feasibility verification is performed on the obtained set of extreme scenarios.

[0098]

[0099]

[0100] like There is a solution and If the current hydropower section passes the feasibility verification of spatiotemporal coupling of cascade hydropower and the feasibility verification of system indicators, the model is considered to have converged; otherwise, the model will continue to be used. , The dual multipliers obtained from the solution are fed back to the main problem, and iterative solutions are continued.

[0101] S4. Based on the original unit combination model sub-problem obtained in step S3, solve the model to obtain the optimal flexible operation range of hydropower.

[0102] Example This embodiment selects JPES-ROTS for case analysis and, based on this, expands the installed capacity of hydropower, wind power, and solar power in the system to construct a typical hydropower-wind-solar system example. The improved virtual system includes 54 units with a total installed capacity of 43,980 MW, including 32 thermal power units with an installed capacity of 14,400 MW and 16 hydropower units with an installed capacity of 10,080 MW. The system has 4 hydropower plants, each with 4 hydropower units, and 9 wind and 9 solar power units, with a corresponding installed capacity of 19,500 MW. The maximum load in the system is 19,353.29 MW. In this system, the installed capacity of renewable energy is 44.34%. The model sets that the available hydropower volume throughout the day fluctuates within 10% of the planned hydropower volume for the day-ahead period, with a load shedding rate not exceeding 0.01% and a curtailment rate not exceeding 20%.

[0103] All calculations were performed in Matlab software using the Gurobi 10.0.2 solver. The computer operating system was Windows 11, the CPU was an Intel i5-12500 with a clock speed of 3.00 GHz, and the memory was 32GB.

[0104] To verify the effectiveness of the proposed algorithm, this invention constructs four comparative examples to compare with the proposed algorithm. Through real-time clearing calculations throughout the entire period, the ability of various algorithms to cope with random fluctuations in new energy sources is compared. At the same time, the cumulative electricity consumption results throughout the entire period are statistically analyzed to determine whether the electricity consumption constraints and the upstream and downstream flow matching constraints of the cascade are valid. The corresponding examples are set as follows: Case 1: The proposed method limits the output of hydropower units based on the aforementioned flexible operating range for hydropower during real-time clearing. It also fully considers the impact of wind and solar forecasting errors. Before real-time clearing, N random wind and solar output curves are generated based on the day-ahead forecast power. Finally, the results of N sets of real-time clearing are statistically analyzed, and the changes in the system's average curtailment rate and load shedding rate are compared. Both wind and solar outputs use a normal distribution model; here, N is set to 10, the wind power forecasting error is taken as 10% of the predicted output, and the solar power forecasting error is taken as 5% of the predicted output. Case 2: In real-time clearing, the output of hydropower units is limited based on the minimum and maximum technical output of hydropower, while the other conditions remain the same as in Case 1. Case 3: Limit the upper and lower limits of hydropower output based on the upper and lower limits of available electricity throughout the day. Decompose the upper and lower limits of hydropower output based on load trends and construct the upper and lower limits of hydropower output in intervals. At the same time, check the upstream and downstream flow of the cascade basin for the output boundary of hydropower units at each time interval to ensure that the upstream and downstream flow always match the hydropower output in this interval. The other conditions are consistent with Case 1. Case 4: In real-time clearing, the output of hydropower units is limited based on the minimum and maximum technical output of hydropower. At the same time, fine variable head modeling of cascade hydropower is introduced to consider the upstream and downstream flow balance relationship of the cascade basin. The other conditions are consistent with Case 1. Case 5: In real-time clearing, the output of hydropower units is limited based on the minimum and maximum technical output of hydropower. At the same time, fine variable head modeling of cascade hydropower is introduced to consider the upstream and downstream flow balance relationship of the cascade basin. In order to avoid a large deviation between the real-time clearing power results and the available power in the day, a certain adjustment range is set based on the planned water level of the cascade given by the day-ahead balance. It is required that the water level change shall not exceed the given range. The other conditions are consistent with Case 1. Figure 4 The optimal hydropower output range for the entire system is obtained after calculation. When the hydropower units are freely optimized within the current flexible range, the power output constraints and the balance constraints of the upstream and downstream reservoirs in the cascade basin can be satisfied throughout the entire time period, and there will be no situation where the power output and flow exceed the limits.

[0105]

[0106] Table 1 shows the real-time clearing results for each case. It can be seen that Case 2 has the best economic benefits because it does not impose full-time power limits or upstream and downstream matching limits on hydropower output. Hydropower output can be freely optimized between minimum and maximum output, which has the best flexibility. Therefore, the overall curtailment rate and load shedding rate are the lowest. However, long-term use of this method for clearing will lead to a serious imbalance between short-term clearing results and medium- and long-term allocation plans. It has strong short-sightedness and is not conducive to the balance of the system throughout the entire cycle. Cases 4 and 5, building upon Case 2, further consider reservoir modeling for cascade hydropower and the matching constraints of upstream and downstream flow rates. This somewhat limits the flexibility of hydropower units, thus increasing costs and model complexity while reducing computational efficiency. Case 5, in addition to considering the limitations of medium- and long-term allocated power generation on real-time clearing, is slightly more expensive than Case 4. Case 3 fully considers the limitations of all-time hydropower generation and the matching constraints of upstream and downstream flow rates. It constructs the output range of hydropower units based on load guidance, which can effectively connect with medium- and long-term allocated hydropower plans. However, this range construction only considers load response requirements and pays less attention to the uncertainty of new energy sources. Therefore, its response to new energy fluctuations is weak, which limits the flexible adjustment capability of hydropower and results in a significant degree of power curtailment and load shedding. Case 1 combines the advantages of other examples, with a cost deviation of 1.29% from Case 5 and a clearing efficiency improvement of 36.54%. This method fully considers the medium- and long-term power allocation plan and cascade flow matching constraints, and maximizes the construction of the flexible operation range of hydropower. While ensuring the optimal economic benefits of the system, it reduces the complexity of the model and improves the computational efficiency.

[0107]

[0108] Table 2 shows the exceedance of spatiotemporal constraints for each example of cascade hydropower. Table 2 presents the exceedance of hydropower and flow limits in the real-time clearing results for different examples. It can be seen that the hydropower units in Case 2 experienced varying degrees of exceedance of power and flow limits throughout the entire time period because the output range of the hydropower units was not greatly restricted. Cases 4 and 5 considered the spatial coupling relationship of cascade hydropower, so the flow of upstream and downstream power plants was matched, but the power consumption still exceeded the upper limit of the given power plan. Cases 1 and 3 fully considered the limitations of spatiotemporal constraints on the regulation capacity of hydropower units and did not have exceedance situations, thus verifying the effectiveness of the proposed method.

[0109] In summary, this invention constructs feasible intervals for cascade hydropower by using day-ahead balance risk to characterize the value of flexible operation intervals for hydropower. Furthermore, based on two models—spatiotemporal coupling feasibility verification and system index feasibility verification—the spatiotemporal coupling constraints of cascade hydropower are decomposed into time-by-time output operation intervals. This improves the system's responsiveness to uncertainties while ensuring that the spatiotemporal boundaries of hydropower resources are not exceeded. It effectively solves the problem of imbalance between real-time clearing results and medium-to-long-term decomposition strategies, further reducing the complexity of the real-time clearing model and improving overall clearing efficiency.

[0110] It should be emphasized that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for constructing flexible hydropower ranges considering the uncertainties of new energy sources, characterized in that, Includes the following steps: Step 1: Obtain basic technical data for power systems containing new energy sources; Step 2: Construct a day-ahead power balance analysis model based on the aforementioned basic technical data, and establish a flexible interval value function for hydropower based on electricity price signals; Step 3: Based on the value function of the flexible hydropower interval, construct a two-stage robust optimization model based on the decision-dependent uncertainty set; divide the problem into a main problem and sub-problems for iterative solution. The main problem is to maximize the value of the flexible hydropower interval, and the sub-problems are to perform spatiotemporal coupling feasibility verification and system index feasibility verification on the feasible hydropower interval solved by the main problem to determine whether the current hydropower operation interval is feasible. Step 4: Automatic multi-objective optimization based on mixed-integer linear programming; The basic technical data in step 1 includes: operating parameters of various generating units, and predicted values ​​of new energy sources and load; The objective function of the daytime power balance analysis model in step 2 is: in, For the binary variables in the model, For continuous variables in the model, The start-up and shutdown costs for thermal and hydropower plants, For the operating costs of thermal power units, , , These are respectively: water curtailment penalty, renewable energy curtailment penalty, and load shedding penalty; The hydropower flexible interval value function in step 2 is as follows: the hydropower interval construction form considers the value difference at different times; when the electricity price signal is high, i.e., the load demand is large, the model tends to solve for the hydropower operation interval with a higher upper limit of output, thereby increasing the hydropower output during that period and improving the system's economy; when the electricity price signal is low, i.e., the power is relatively redundant, the model tends to solve for the hydropower operation interval with a lower lower limit of output, thereby reducing the hydropower output during that period and improving the system's absorption level. In step 3, the flexible interval value function for hydropower is: in, As dual multipliers, , This indicates the upper and lower limits of the output range of a single hydropower unit. , The upper and lower limits of the flexible range for hydropower in the model decision-making process. For the collection of hydroelectric generating units, This refers to the number of time periods throughout the entire cycle; The two-stage robust optimization model in step 2 has a main problem and sub-problems, specifically: For the main problem, based on the hydropower flexible interval value function constructed in step S2, the hydropower flexible interval that maximizes it is solved and passed to the sub-problems for system feasibility verification; For the sub-problems, based on the optimal hydropower flexible interval obtained from the solution of the main problem, the spatiotemporal coupling feasibility verification of cascade hydropower and the system index feasibility verification are performed accordingly. The spatiotemporal coupling feasibility verification is that within the current hydropower flexible interval, any hydropower power can make the upper and lower limits of hydropower power and the upstream and downstream water balance constraints of cascade hydropower hold. The system index feasibility verification includes that within the current hydropower flexible interval, the optimized system index result is not lower than a given threshold; When both types of feasibility verifications are satisfied, the current hydropower flexible interval is deemed feasible. Step S4 specifically includes solving the model based on the original unit combination model sub-problem obtained in step S3 to obtain the optimal flexible operation range of hydropower.

2. The method for constructing a flexible hydropower range considering the uncertainty of new energy sources according to claim 1, characterized in that, The constraints of the hydropower flexible interval value function include: Hydropower flexible range upper and lower limit constraints: in, Indicates the operating status of the hydroelectric power unit; Hydropower output upper and lower limit constraints: in, This refers to the operating power of thermal power units; Constraints for the feasibility verification of spatiotemporal coupling of cascade hydropower: In the formula, For the set of constraints for normal operation The remaining constraints after removing the upper and lower limits of hydropower generation and the hydropower coupling constraints throughout the entire time period are: dual multipliers The corresponding system output plan, , These represent the positive and negative slack variables of hydropower generation over the entire time period. , These are the positive and negative relaxation variables of the hydroelectric coupling constraint, respectively. In order to be in Based on this, a relaxed feasible region is constructed by relaxing the upper and lower limit constraints of hydropower and the coupling constraints of cascade hydropower. These are power generation efficiency and turbine efficiency, respectively. These represent the density of water and the acceleration due to gravity, respectively. Indicates net head of electricity generation. This indicates the total power generation flow of the hydropower station. Let V be the uncertain set of hydropower output, and let V be the uncertain set of renewable energy output. , The lower and upper limits of the available power of unit i on day d. To find the extreme hydropower operating power within a given hydropower range, For scenery, random variables; System operation indicator feasibility verification constraints: in, This refers to the threshold coefficients for system load shedding rate and power curtailment rate. The coefficients of the matrix corresponding to the model variables in the objective function. Let be the load demand of node i at time t. It provides energy output to node i at time t.

3. The method for constructing a flexible hydropower range considering the uncertainty of new energy sources according to claim 2, characterized in that, The main problem is: The objective function of the main problem is the hydropower flexible interval value function. These are the upper and lower limits for the hydropower range; These are the remaining continuous variables within the model. This is a set for spatiotemporal feasibility verification of cascade hydropower projects. This is a set of feasibility verification metrics for the system. , As dual multipliers, , For the set of poles, Let A, J, K, M, N, O, E, F, and G be the matrix coefficients corresponding to the model variables in the constraints. This is a relaxation variable added to the constraints on the upper and lower limits of hydropower generation throughout the entire time period and the spatiotemporal coupling constraints of cascade hydropower.

4. The method for constructing a flexible hydropower range considering the uncertainty of new energy sources according to claim 3, characterized in that, The sub-problems include: Feasibility verification model for spatiotemporal coupling of cascade hydropower: System indicator feasibility verification model: in, The feasible domain for contributing to the system.

5. The method for constructing a flexible hydropower range considering the uncertainty of new energy sources according to claim 4, characterized in that, In the sub-problem, if There is a solution and If the current hydropower section is deemed to have converged through the feasibility verification model of the spatiotemporal coupling of cascade hydropower and the feasibility verification model of system indicators, then it will be considered converged; otherwise, it will continue to be... , The dual multipliers obtained from the solution are fed back to the main problem, and iterative solutions are continued.

6. A flexible hydropower range construction system that takes into account the uncertainty of new energy sources, characterized in that, The hydropower flexible interval construction system is used to implement the hydropower flexible interval construction method according to any one of claims 1 to 5, and the hydropower flexible interval construction system includes: The data module acquires basic technical data for power systems including new energy sources; The construction module is based on the basic technical data of the power system including new energy obtained from the data module to construct the day-ahead power balance calculation and a two-stage robust optimization model based on the decision-dependent uncertainty set; The solution module, based on the two types of models obtained from the construction module, performs day-ahead power balance calculation and solves the flexible operation range of cascade hydropower, respectively, to obtain the day-ahead electricity price signal and construct the value function of the flexible operation range of hydropower. Then, through the joint solution of the main and sub-problems in the flexible operation range model of cascade hydropower, the optimal flexible operation range of hydropower is obtained. The output module, based on the flexible operating range of hydropower obtained from the solution module, performs subsequent clearing calculations.

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