A method and system for evaluating operation benefits and risks of a water, wind and light system with multiple functions

Abstract

The application discloses a water-wind-solar multi-energy complementary system operation benefit and risk assessment method and system, comprising the following steps: constructing a water-wind-solar multi-energy complementary risk benefit evaluation index system; based on the evaluation index system, taking the maximum total power generation benefit of the water-wind-solar complementary system as an objective function, combining the reservoir water level, power generation reference flow, discharge flow, water balance, daily water level amplitude and transmission channel capacity constraint conditions, constructing a water-wind-solar multi-energy complementary short-term optimization scheduling model; adopting a moth flame optimization algorithm to solve the short-term optimization scheduling model to obtain an optimized scheduling process; inputting wind-solar predicted output data and runoff data into an electricity-fixed water model to generate a day-ahead generation plan, and correcting the optimized scheduling process by using measured data in actual operation to obtain a corrected scheduling process; and evaluating the corrected scheduling process according to the constructed risk benefit evaluation index system.

Description

Technical Field

[0001] This invention relates to a method and system for assessing the operational benefits and risks of a multi-energy complementary system of water, wind and solar, belonging to the field of optimized scheduling technology for multi-energy complementary systems of water, wind and solar. Background Technology

[0002] Short-term optimal scheduling model and solution for multi-energy complementary hydropower, wind power and solar power: With the goal of maximizing power generation, a short-term optimal scheduling model for multi-energy complementary power generation is constructed with constraints such as reservoir water level, power generation flow, outflow, and water balance. The day-ahead power generation plan is obtained by solving the model through dynamic programming (DP) or particle swarm optimization (PSO) algorithm. Commonly used evaluation indicators for multi-energy complementary systems include total power generation, output fluctuation and load shedding probability, but these have the characteristics of isolated perspective and one-sided function.

[0003] While existing short-term optimization scheduling models support the operation planning of hydro-wind-solar hybrid systems to some extent, commonly used dynamic programming and particle swarm optimization algorithms often face bottlenecks such as slow convergence speed or getting trapped in local optima when solving complex problems with high dimensions and multiple constraints. This limits the optimization effect and real-time performance of the scheduling scheme. Furthermore, the constraints set by the models are often incomplete, failing to fully consider key limitations such as the capacity of the transmission channels. At the same time, existing technologies focus more on the implementation of functions and have not established a quantitative comprehensive evaluation index system. This makes it impossible to scientifically evaluate the synergistic performance of the hybrid system in multiple dimensions such as power supply reliability, output stability, reservoir operation safety, and comprehensive economy. As a result, potential risks in system operation are difficult to identify, and there is a large deviation between planned and actual output. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for assessing the operational benefits and risks of a hydro-wind-solar multi-energy complementary system. It addresses the problem that existing technologies cannot scientifically identify operational risks due to their single evaluation dimension, by considering four aspects: the reliability of power supply, the stability of system output, the safety of reservoir operation, and the economic efficiency of the comprehensive power generation benefits of the complementary system.

[0005] To achieve the above objectives / to solve the above technical problems, the present invention is implemented using the following technical solution:

[0006] First aspect: A method for assessing the operational benefits and risks of a multi-energy complementary system of water, wind, and solar power, the method comprising:

[0007] A risk and benefit evaluation index system for multi-energy complementarity of water, wind and solar is constructed. The index system includes power supply reliability evaluation, energy system stability evaluation, reservoir operation safety evaluation, and complementary system economic evaluation.

[0008] Based on the evaluation index system, with the objective function of maximizing the total power generation benefit of the hydro-wind-solar complementary system, and combined with wind and solar predicted output data, runoff data, initial and final water levels, power generation flow, downstream flow, water balance, daily water level fluctuation and transmission channel capacity constraints, a short-term optimal scheduling model for hydro-wind-solar multi-energy complementary system is constructed.

[0009] The moth-flame optimization algorithm is used to solve the short-term optimization scheduling model to obtain the optimized scheduling process;

[0010] The measured power output data of wind and solar power and runoff data are input into the electricity-determined water model to update the scheduling process;

[0011] The updated scheduling process is evaluated based on the established risk-benefit evaluation index system.

[0012] Optionally, the power supply reliability evaluation includes:

[0013] Maximum missing load L max The maximum difference between system output and load demand when the output of the complementary system cannot meet the load requirements;

[0014] Probability of load loss L p The proportion of the number of time periods in which the actual output of the complementary system did not match the load demand to the total number of time periods;

[0015] Maximum duration L of single continuous loss of load last The length of time during which the output of the complementary system fails to meet the load plan;

[0016] Cumulative missing power J sum : The total difference between the actual power generation and the planned power generation during the period.

[0017] Optionally, the energy system stability evaluation uses the output difference coefficient (CV). P The calculation formula for this evaluation indicator is as follows:

[0018] ;

[0019] ;

[0020] In the formula, n is the number of time periods. The average actual output of the hydro-wind-solar multi-energy complementary system during the specified time period. CV represents the actual output of the complementary system in the i-th time period. P The smaller the value, the more stable the system output.

[0021] Optionally, the reservoir operation safety assessment includes:

[0022] Reservoir water level stability is measured by the maximum absolute value of the difference between the reservoir water levels before and after a certain period. The smaller the difference, the safer the reservoir operation.

[0023] Discharge flow stability: Measured by the discharge flow difference coefficient, which is calculated as the ratio of the standard deviation of discharge flow to the average discharge flow.

[0024] Optionally, the economic evaluation of the complementary system uses the total power generation benefit of the complementary system as an indicator, and the calculation formula is as follows:

[0025] ;

[0026] Where E represents the total power generation benefit of the complementary system. , and The electricity prices are for wind power, solar power, and hydropower, respectively. , , Δt represents the output of wind power, solar power, and hydropower during time period i, respectively, and Δt is the length of time period i.

[0027] The formula for calculating hydropower generation is as follows:

[0028] ;

[0029] In the formula, Let i be the power generation flow rate during the i-th time period. Let be the head of the hydropower station in the i-th time period, and k be the comprehensive output coefficient.

[0030] Optionally, the constraints specifically include:

[0031] Reservoir water level constraints: In the formula, This is the minimum allowable water level for the reservoir, designated as the dead water level. Z(i) represents the maximum allowable water level of the reservoir in the i-th time period, which is set as the normal storage level; Z(i) represents the water level in the i-th time period.

[0032] Power generation flow constraints: In the formula, Let be the power generation flow rate in the i-th time period. This represents the maximum flow rate used by the unit.

[0033] Downflow constraint: In the formula, The total discharge flow in the i-th time period consists of the power generation flow and the water discharge. This represents the minimum ecological water demand downstream. The maximum allowable discharge flow rate;

[0034] Water balance constraints: In the formula, , These represent the reservoir capacity at the end of time intervals i+1 and i, respectively. Let i be the inflow rate into the reservoir during the i-th time period;

[0035] Daily water level fluctuation constraints: In the formula, For daily water level fluctuations, This represents the maximum daily water level fluctuation.

[0036] Conveyor channel capacity constraints: In the formula, , , These represent the electricity prices for wind power, solar power, and hydropower during time period i, respectively. This refers to the capacity of the transport channel.

[0037] Optionally, the solution process of the moth-flame optimization algorithm includes:

[0038] Initialize the moth population vector set and fitness matrix, and generate the flame matrix and flame fitness matrix according to the fitness values. The flame represents the optimal solution in the current iteration search.

[0039] Position Update: Individual moths move in a logarithmic spiral curve around their corresponding flames. The update formula is: ;in, moth Around the first A flame The new position after the logarithmic spiral motion; For the first The flame and the first The Euclidean distance between individual moths; To adjust the constant parameters of the logarithmic spiral curve; A random number, rounded to four decimal places, within the range of [-1, 1], representing the proximity of the next position of the current moth individual to the flame;

[0040] Moth flame abandonment mechanism: As the number of iterations increases, the number of flames is adaptively reduced. The formula for calculating the number of flames is: ;

[0041] In the formula: The current number of flames; To round up; This represents the current iteration number; This represents the maximum number of iterations specified.

[0042] Optionally, the step of inputting measured wind and solar power output data and runoff data into the electricity-determined water model to update the scheduling process includes:

[0043] (1) Divide the scheduling period into k time periods and initialize the starting time period i=1;

[0044] (2) Input the predicted wind and solar power output data and inflow runoff data for the i-th to (i+k−1) time periods into the short-term optimization scheduling model, and set the water levels at the beginning and end of the time periods;

[0045] (3) The MFO optimization algorithm is used to solve the model and formulate the day-ahead power generation plan;

[0046] (4) Obtain the measured wind and solar power output data for the i-th to (i+k−1) time period, input them and the inflow runoff data into the conventional scheduling model, and update the hydropower station scheduling process hourly according to the principle of "determining water supply based on electricity";

[0047] (5) Take the updated water level at the end of the (i+k−1)th time period as the initial water level for the next round of power generation plan, and let i=i+k;

[0048] (6) Repeat steps (2) to (5) until i>T, where T represents the total number of time periods;

[0049] (7) Output the final scheduling result.

[0050] Optionally, the short-term optimal scheduling model is established with the objective of maximizing the total power generation benefit of the hydro-wind-solar hybrid system:

[0051] ;

[0052] in, , and The electricity prices are for wind power, solar power, and hydropower, respectively. , , Δt represents the electricity price for wind power, solar power, and hydropower during time period i, respectively, and Δt is the duration of time period i.

[0053] The second aspect: a system for assessing the operational benefits and risks of a multi-energy complementary system of water, wind, and solar power, the system comprising:

[0054] The evaluation index module is configured to construct a risk and benefit evaluation index system for multi-energy complementarity of water, wind and solar power. The index system includes power supply reliability evaluation, energy system stability evaluation, reservoir operation safety evaluation, and complementary system economic evaluation.

[0055] The module is configured to build a short-term optimal scheduling model for multi-energy complementarity of water, wind and solar power based on the evaluation index system, with the objective function of maximizing the total power generation benefit of the water-wind-solar complementary system. It combines wind and solar power predicted output data, runoff data and initial and final water levels, power generation flow, downstream flow, water balance, daily water level fluctuation and transmission channel capacity constraints to build a short-term optimal scheduling model for multi-energy complementarity of water, wind and solar power.

[0056] The calculation module is configured to solve the short-term optimization scheduling model using the moth flame optimization algorithm to obtain the optimized scheduling process;

[0057] The scheduling and adjustment module is configured to input measured wind and solar power output data and runoff data into the power-to-water model to update the scheduling process.

[0058] The evaluation module is configured to evaluate the updated scheduling process based on a constructed risk-benefit evaluation index system.

[0059] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0060] The beneficial effects of this invention stem from its unique core technical features, achieving a leap from single-objective evaluation to systematic collaborative management and control. Specifically, by constructing a multi-dimensional risk-benefit evaluation index system covering power supply reliability, output stability, reservoir safety, and economy, this invention achieves for the first time a comprehensive quantitative assessment of the operational performance of a hydro-wind-solar multi-energy complementary system, initially solving the problem that existing technologies cannot scientifically identify operational risks due to their single evaluation dimensions.

[0061] At the level of optimization scheduling, the complex constraint short-term optimization scheduling model is solved by the moth flame optimization algorithm. It has excellent global optimization ability and convergence efficiency, and can quickly generate the optimal power generation plan under the actual engineering constraints such as transmission channel capacity and daily water level fluctuation. Attached Figure Description

[0062] Figure 1 The diagram shown is a schematic of the solution process of the MFO algorithm of this invention;

[0063] Figure 2 The diagram shown is a simulation evaluation flowchart of the daily power generation plan preparation and actual dispatch process according to the present invention.

[0064] Figure 3 The figure shows the cumulative missing load duration in 2021 according to the present invention;

[0065] Figure 4 The figure shows the cumulative power loss in 2021 according to this invention;

[0066] Figure 5 The figure shows the power output deficit of the complementary system in 2021 according to the present invention;

[0067] Figure 6 The figure shows the power generation flow of the multi-energy complementary system of the present invention in 2020 and 2021;

[0068] Figure 7 The figure shown is a typical daily load tracking chart for the four seasons of 2020 according to the present invention;

[0069] Figure 8 The figure shown is a typical daily load tracking chart for the four seasons of 2021 according to the present invention;

[0070] Figure 9 The diagram shown is a schematic diagram of the output difference coefficient before and after the complementarity of the present invention.

[0071] Figure 10 The diagram shown is a comparison of reservoir water level fluctuations before and after system complementation in 2020 according to the present invention.

[0072] Figure 11 The diagram shown illustrates the difference coefficient of downstream flow rate before and after system complementation in 2020 according to this invention.

[0073] Figure 12 The diagram shown illustrates the discharge, power generation, and wastewater discharge of the complementary system in 2020 according to this invention.

[0074] Figure 13 The diagram shown illustrates the typical daily power output of this invention in 2020.

[0075] Figure 14 The diagram shown illustrates the power generation before and after the complementarity of the present invention.

[0076] Figure 15 The diagram shown illustrates the power generation benefits before and after the complementarity of this invention. Detailed Implementation

[0077] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0078] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are used only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0079] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0080] Example 1: A method for assessing the operational benefits and risks of a hydro-wind-solar multi-energy complementary system is disclosed. The method includes:

[0081] Step 1: Construct a risk and benefit evaluation index system for multi-energy complementarity of water, wind and solar. The index system includes power supply reliability evaluation, energy system stability evaluation, reservoir operation safety evaluation, and complementary system economic evaluation.

[0082] Step 2: Based on the evaluation index system, with the objective function of maximizing the total power generation benefit of the hydro-wind-solar complementary system, and combining the predicted output data of wind and solar power, runoff data, initial and final water levels, power generation flow, downstream flow, water balance, daily water level fluctuation and transmission channel capacity constraints, a short-term optimization scheduling model for hydro-wind-solar multi-energy complementarity is constructed.

[0083] Step 3: The moth-flame optimization algorithm is used to solve the short-term optimization scheduling model to obtain the optimized scheduling process;

[0084] Step 4: Input the measured power output data of wind and solar power and the runoff data into the power-to-water model to update the scheduling process;

[0085] Step 5: Evaluate the updated scheduling process based on the constructed risk-benefit evaluation index system.

[0086] In this embodiment, the "electricity-based water supply" model refers to, given the initial water level of the reservoir and the predicted inflow sequence, using a high-efficiency iterative algorithm to back-calculate the required power generation flow for each time period, with the power generation plan as the objective, and simultaneously calculating water level changes and water wastage. The specific formula is as follows:

[0087] ;

[0088] In the formula, Let i be the power generation flow rate during the i-th time period. Let be the head of the hydropower station in the i-th time period, and k be the comprehensive output coefficient.

[0089] In this embodiment, step 1, the power supply reliability evaluation includes:

[0090] (1) Maximum load missing amount L maxThe maximum difference between system output and load demand when the output of the complementary system cannot meet the load requirements;

[0091] (2) Probability of load failure L p The proportion of the number of time periods in which the actual output of the complementary system did not match the load demand to the total number of time periods;

[0092] (3) Maximum duration of single continuous load loss L last The length of time during which the output of the complementary system fails to meet the load plan;

[0093] (4) Accumulated missing power J sum : The total difference between the actual power generation and the planned power generation during the period.

[0094] In this embodiment, during step 1, the energy system stability evaluation uses the output difference coefficient (CV). P The calculation formula for this evaluation indicator is as follows:

[0095] ;

[0096] ;

[0097] In the formula, n is the number of time periods. The average actual output of the hydro-wind-solar multi-energy complementary system during the specified time period. CV represents the actual output of the complementary system in the i-th time period. P The smaller the value, the more stable the system output.

[0098] In the specific implementation process of this embodiment, step 1, the reservoir operation safety evaluation includes:

[0099] (1) Reservoir water level stability: The maximum value of the absolute value of the difference between the reservoir water levels before and after the time period is used as the measure. The smaller the difference, the safer the reservoir operation.

[0100] (2) Stability of discharge flow: The discharge flow difference coefficient is used to measure it. The calculation formula is the ratio of the standard deviation of discharge flow to the average discharge flow.

[0101] In this embodiment, during the specific implementation process, in step 1, the economic evaluation of the complementary system uses the total power generation benefit of the complementary system as the indicator, and the calculation formula is as follows:

[0102] ;

[0103] Where E represents the total power generation benefit of the complementary system. , and The electricity prices are for wind power, solar power, and hydropower, respectively. , , Δt represents the output of wind power, solar power, and hydropower during time period i, respectively, and Δt is the length of time period i.

[0104] The formula for calculating hydropower generation is as follows:

[0105] ;

[0106] In the formula, Let i be the power generation flow rate during the i-th time period. Let be the head of the hydropower station in the i-th time period, and k be the comprehensive output coefficient.

[0107] In the specific implementation of this embodiment, step 2 includes the following constraints:

[0108] (1) Reservoir water level constraints: In the formula, This is the minimum allowable water level for the reservoir, designated as the dead water level. Z(i) represents the maximum allowable water level of the reservoir in the i-th time period, which is set as the normal storage level; Z(i) represents the water level in the i-th time period.

[0109] (2) Power generation flow constraints: In the formula, Let be the power generation flow rate in the i-th time period. This represents the maximum flow rate used by the unit.

[0110] (3) Downflow constraint: In the formula, The total discharge flow in the i-th time period consists of the power generation flow and the water discharge. This represents the minimum ecological water demand downstream. The maximum allowable discharge flow rate;

[0111] (4) Water balance constraints: In the formula, , These represent the reservoir capacity at the end of time intervals i+1 and i, respectively. Let i be the inflow rate into the reservoir during the i-th time period;

[0112] (5) Daily water level fluctuation constraints: In the formula, For daily water level fluctuations, This represents the maximum daily water level fluctuation.

[0113] (6) Conveying channel capacity constraints: In the formula, , , These represent the electricity prices for wind power, solar power, and hydropower during time period i, respectively. This refers to the capacity of the transport channel.

[0114] In this embodiment, step 3, the solution process of the moth flame optimization algorithm, includes:

[0115] (1) Initialize the moth population vector set and fitness matrix, and generate the flame matrix and flame fitness matrix according to the fitness size. The flame represents the optimal solution in the current iteration search.

[0116] (2) Position Update: Individual moths move in a logarithmic spiral curve around the corresponding flame. The update formula is: ;in, moth Around the first A flame The new position after the logarithmic spiral motion; For the first The flame and the first The Euclidean distance between individual moths; To adjust the constant parameters of the logarithmic spiral curve; A random number, rounded to four decimal places, within the range of [-1, 1], representing the proximity of the next position of the current moth individual to the flame;

[0117] (3) Moth flame abandonment mechanism: As the number of iterations increases, the number of flames is adaptively reduced. The formula for calculating the number of flames is: ;

[0118] In the formula: The current number of flames; To round up; This represents the current iteration number; This represents the maximum number of iterations specified.

[0119] In this embodiment, step 4, which involves inputting measured wind and solar power output data and runoff data into the power-to-water model to update the scheduling process, specifically includes the following steps:

[0120] (1) Divide the scheduling period into k time periods and initialize the starting time period i=1;

[0121] (2) Input the predicted wind and solar power output data and inflow runoff data for the i-th to (i+k−1) time periods into the short-term optimization scheduling model, and set the water levels at the beginning and end of the time periods;

[0122] (3) The MFO optimization algorithm is used to solve the model and formulate the day-ahead power generation plan;

[0123] (4) Obtain the measured wind and solar power output data for the i-th to (i+k−1) time period, input them and the inflow runoff data into the conventional scheduling model, and update the hydropower station scheduling process hourly according to the principle of "determining water supply based on electricity";

[0124] (5) Take the updated water level at the end of the (i+k−1)th time period as the initial water level for the next round of power generation plan, and let i=i+k;

[0125] (6) Repeat steps (2) to (5) until i>T, where T represents the total number of time periods;

[0126] (7) Output the final scheduling result.

[0127] In this embodiment, during the specific implementation process, in step 5, the short-term optimization scheduling model is established with the objective of maximizing the total power generation benefit of the hydro-wind-solar hybrid system.

[0128] ;

[0129] in, , and The electricity prices are for wind power, solar power, and hydropower, respectively. , , Δt represents the electricity price for wind power, solar power, and hydropower during time period i, respectively, and Δt is the duration of time period i.

[0130] The invention is further elaborated upon in conjunction with the implementation method 1: For example, the predicted wind and solar power output data, runoff data, and initial and final water levels of a power station in 2020 and 2021 are input into a short-term optimization scheduling model to formulate a day-ahead power generation plan. Then, combined with measured wind and solar power output data and runoff data, the annual reservoir operation process is calculated through a rolling update using a "power-determined water" approach. The differences between the day-ahead power generation plan and the actual power generation process of the hydro-wind-solar system are compared. Based on the constructed evaluation index system for the hydro-wind-solar multi-energy complementary system, the risks and benefits of wind and solar energy integration and wind and solar prediction errors on the power generation system are evaluated from four aspects: reliability, stability, safety, and economy. This experiment integrates all wind and solar power output, meaning no wind or solar curtailment is allowed, with water curtailment only permitted during the flood season.

[0131] (1) Power supply reliability evaluation

[0132] Table 1 shows the reliability evaluation results for the entire year of 2021:

[0133] Table 1 Reliability Indicators in 2021

[0134]

[0135] According to Table 1, Figure 3 and Figure 4 Statistical analysis shows that the hydro-wind-solar multi-energy complementary system experienced 32 load loss events in 2021, mainly distributed from the end of May to the end of June. The system's annual load loss probability L... pThe percentage was 5.76%, with the maximum missing workload L. max The total capacity reached 81.84 MW. In terms of cumulative power loss, June 14th saw the most severe loss, reaching 285,200 kWh; June 6th saw the lowest loss, at 29,500 kWh. In terms of duration, June 7th and June 13th had the longest single load shedding periods, both reaching 20 hours; June 26th had the shortest, at 6 hours. It is worth noting that the system did not experience any load shedding in 2020.

[0136] The output deficit of the multi-energy complementary system was statistically analyzed, and the water level situation throughout 2021 was examined. Figure 5 As shown, the water level gradually receded from January to June and gradually rose from July to December throughout the year. In June of that year, the water level dropped to near the dead storage level, and a concentrated period of power outages occurred, with an average power deficit of 13.34 MW on June 15th. The lowest water level in 2020 was 701 m.

[0137] according to Figure 6 The statistical results of power generation flow in the two experimental years show that the power generation flow in 2020, with abundant rainfall, was significantly greater than that in 2021, with less rainfall. In January 2021, affected by abnormal water inflow and high water levels, the power generation flow peaked at 520 m³ / s, with relatively mild fluctuations throughout the year, only showing significant output in January, May, and July. In contrast, in 2020, rainfall was mainly concentrated in July and September, resulting in significantly increased output during these periods, with a maximum power generation flow of 716 m³ / s. The annual output fluctuations were also more pronounced. Both experimental years exhibited a common characteristic of relatively low output at the end of the year.

[0138] according to Figure 7 and Figure 8 The analysis results show that wind power output exhibited a seasonal pattern of higher output in spring and summer and lower output in autumn and winter over the two years. In 2020, abundant rainfall resulted in no load shortages throughout the year, demonstrating good adjustment capabilities to wind and solar power forecast errors. In the autumn, the system even operated at full capacity continuously, transmitting 1040 MW of power with 100% channel utilization. In contrast, 2021 saw lower rainfall and load shortages in the summer, with daytime power generation plans not being fully met, especially during days when wind and solar power output fell short of expectations, leading to more significant load deficits.

[0139] Comprehensive analysis shows that during July to April of the following year, the complementary system exhibits good regulation performance due to higher reservoir water levels. When actual wind and solar power output falls below predicted values, the system can increase power output and generation by increasing the hydropower station's flow rate, thereby meeting the day-ahead power generation plan and ensuring system reliability. However, during May and June, when reservoir water levels are low, the head effect decreases, and the inflow is relatively limited, making it difficult to compensate for wind and solar forecast errors by lowering the water level to increase the flow rate. This leads to a deviation between the system's actual output and the day-ahead plan. Therefore, the reliability of this multi-energy complementary system significantly decreases when operating at low reservoir water levels during the dry season. Overall, dry years are more prone to load shedding, while wet years offer stronger load assurance capabilities.

[0140] (2) Energy system stability evaluation

[0141] According to the data in Table 2, after system complementarity in 2020 and 2021, the output difference coefficients for wind power and solar power were 0.73 and 1.39, respectively, indicating that the output fluctuation of wind power was significantly smaller than that of solar power. Combined with... Figure 9 Analysis shows that, from a diurnal perspective, wind power output is mainly affected by wind energy, and its intraday changes are usually relatively stable; while photovoltaic output is significantly affected by the alternation of day and night and weather factors, resulting in a larger coefficient of variation. However, during April, July, and August, due to the drastic fluctuations in intraday wind speed, the coefficient of variation for wind power output was even higher than that for photovoltaic.

[0142] From an annual cycle perspective, wind power exhibits distinct seasonal characteristics, with high output and small fluctuations in spring and summer, and low output but increased fluctuations in autumn and winter; photovoltaic power, on the other hand, shows relative stability on an annual scale with smaller seasonal differences.

[0143] The overall output difference coefficient of the multi-energy complementary system was significantly lower than that of wind and solar power operating alone, at 0.16 in 2020 and 0.23 in 2021. In the wet year of 2020, abundant water flow resulted in strong hydropower regulation capabilities and more stable system output. In the dry year of 2021, the regulation capabilities decreased due to lower water flow and low water levels. Notably, the system output difference coefficient dropped to 0 in mid-to-late September 2020. At this time, abundant water flow and high water levels allowed hydropower units to operate at full capacity, and the power transmission channel utilization rate reached 100%, fully demonstrating the effective role of hydropower in mitigating wind and solar power fluctuations within the multi-energy complementary system.

[0144] Table 2. Statistics on differences in output coefficients

[0145]

[0146] (3) Reservoir operation safety assessment

[0147] according to Figure 10A comparative analysis of reservoir water level fluctuations before and after system complementation in 2020 can be summarized into the following three typical scenarios: ① Increased water level fluctuations after complementation: This mainly occurred in January and October-December. During this period, the reservoir operated at a high water level, close to the normal storage level. Before complementation, the water level fluctuations were relatively small. After the system participated in complementation, in order to smooth out fluctuations in wind and solar power output, the reservoir adjusted the power generation flow, resulting in significantly increased intraday water level fluctuations. ② Fluctuations before and after complementation were basically consistent: From February to August, the reservoir water level experienced a process of decline and rise, with relatively large fluctuations. However, the complementation operation did not have a significant impact on the original water level change trend. ③ Fluctuations tended to be gentler after complementation: In mid-to-late September, due to the actual wind and solar power output being higher than the predicted value, hydropower actively reduced its output to balance the error, causing the water level to rise to the normal storage level and remain stable. During this period, water level fluctuations approached zero.

[0148] Comprehensive analysis shows that the maximum difference in reservoir water level fluctuation before and after system complementarity did not exceed 0.02 m / h, indicating that the wind and solar power integration has a small overall impact on the reservoir water level, and the water level regulation is within a controllable range.

[0149] according to Figure 11 The comparison of outflow before and after system complementation can be summarized into two typical patterns: ① Increased fluctuations in outflow after complementation: During January and October to December, the difference coefficient of outflow after system complementation was significantly greater than before complementation. During this period, the reservoir operated at a higher water level, and to effectively mitigate fluctuations in wind and solar power output, it was necessary to adjust the power generation flow to achieve power balance, resulting in increased intraday fluctuations in outflow. ② Flow fluctuations before and after complementation were basically consistent: During February to September, the reservoir experienced a typical drawdown and storage process, with the water level first decreasing and then rising, eventually returning to near the normal storage level. During this stage, through the coordinated adjustment of outflow and water level, the system could mitigate fluctuations in wind and solar power output without significantly changing the flow fluctuation characteristics; therefore, the difference coefficient of outflow before and after complementation did not change significantly.

[0150] A typical 48-hour period in mid-to-late September 2020 was selected to analyze the relationship between the discharge flow, power generation flow, and water wastage flow of the complementary system. Figure 12 During this period, the hydropower-wind-solar multi-energy complementary system operated at full capacity, and the power transmission channels were running at full load. The total output of hydropower, wind power, and photovoltaic power reached 1040 MW. Figure 13 .

[0151] Analysis shows that the actual wind and solar power output during this period was higher than predicted. If hydropower generation continues as planned, it will trigger competition for transmission channels. To mitigate wind and solar power fluctuations and meet the capacity limitations of transmission channels, hydropower output needs to be reduced. However, at this time, the reservoir water level is already at the normal storage level, and it is impossible to reduce the outflow by further raising the water level, resulting in a situation where hydropower is not operating at full capacity but water is being wasted, with a maximum wasted flow rate of 388 m³ / s.

[0152] Therefore, under the condition of high water level operation during the flood season, the prediction deviation of wind and solar power output may exacerbate the water wastage situation and affect the economic efficiency and regulation flexibility of the system operation.

[0153] (4) Economic evaluation of complementary systems

[0154] According to Table 3 and Table 4, Figure 14 and Figure 15 Comparative analysis shows that after introducing the multi-energy complementary operation mode of hydropower, wind power and solar power, the overall power generation capacity and economic benefits of the system have been significantly improved.

[0155] From the perspective of power generation, the actual total power generation of the complementary system reached 3.5723 billion kWh in 2020, an increase of 338.1 million kWh compared to 3.2342 billion kWh when hydropower was operating alone before the complementary system was established, representing a growth rate of 10.45%. Although hydropower generation declined slightly, the overall power output structure of the system was optimized by introducing wind and solar power. In 2021, the actual total power generation of the system was 2.8073 billion kWh, an increase of 412.4 million kWh compared to 2.3949 billion kWh before the complementary system was established, representing a growth rate of 17.22%.

[0156] In terms of economic benefits, the actual power generation benefit of the complementary system was RMB 1.1056 billion in 2020, an increase of RMB 118.8 million, or 12.04%, compared with before the complementary system. In 2021, the actual benefit of the system reached RMB 872.1 million, an increase of RMB 141.1 million, or 19.30%, compared with before the complementary system. The increase in economic benefits was higher than the increase in power generation, mainly due to the higher grid-connected electricity price of wind and solar power.

[0157] The planned power output of wind and solar power in 2021 was 383.6 million kWh, while the actual output was 338.4 million kWh.

[0158] There was a power output shortfall of 0.452 billion kWh. Through the coordinated optimization of the hydro-wind-solar hybrid system, this shortfall was effectively mitigated, and the actual total power generation of the system still maintained a high degree of completion compared with the planned value, demonstrating the key role of hydropower regulation in ensuring the stable operation of the system.

[0159] Table 3 Comparison of annual power generation before and after complementarity

[0160]

[0161] Table 4 Comparison of annual power generation benefits before and after complementarity

[0162]

[0163] In summary, the hydro-wind-solar multi-energy complementary system has a good adjustment effect on the error between the predicted and actual output of wind and solar power, and can make up for the power shortage to a certain extent. Since the price of wind and solar power is higher than that of hydropower, the complementary operation of multiple energy sources can not only increase the system's power generation, but also significantly improve the power generation efficiency.

[0164] By constructing a risk-benefit evaluation system for a multi-energy complementary system, calculations and analyses were conducted on specific cases, verifying that the complementary system has high power supply reliability, improves the output stability of the energy system with minimal impact on the safe operation of the reservoir, and simultaneously increases power generation efficiency, providing a model basis and feasibility analysis for subsequent capacity allocation.

[0165] Example 2: A system for assessing the operational benefits and risks of a hydro-wind-solar multi-energy complementary system is disclosed. The system includes:

[0166] The evaluation index module is configured to construct a risk and benefit evaluation index system for multi-energy complementarity of water, wind and solar power. The index system includes power supply reliability evaluation, energy system stability evaluation, reservoir operation safety evaluation, and complementary system economic evaluation.

[0167] The module is configured to build a short-term optimal scheduling model for multi-energy complementarity of water, wind and solar power based on the evaluation index system, with the objective function of maximizing the total power generation benefit of the water-wind-solar complementary system. It combines wind and solar power predicted output data, runoff data and initial and final water levels, power generation flow, downstream flow, water balance, daily water level fluctuation and transmission channel capacity constraints to build a short-term optimal scheduling model for multi-energy complementarity of water, wind and solar power.

[0168] The calculation module is configured to solve the short-term optimization scheduling model using the moth flame optimization algorithm to obtain the optimized scheduling process;

[0169] The scheduling and adjustment module is configured to input measured wind and solar power output data and runoff data into the power-to-water model to update the scheduling process.

[0170] The evaluation module is configured to evaluate the updated scheduling process based on a constructed risk-benefit evaluation index system.

[0171] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for assessing the operational benefits and risks of a hydro-wind-solar multi-energy complementary system, characterized in that, The method includes: A risk and benefit evaluation index system for multi-energy complementarity of water, wind and solar is constructed. The index system includes power supply reliability evaluation, energy system stability evaluation, reservoir operation safety evaluation, and complementary system economic evaluation. Based on the evaluation index system, with the objective function of maximizing the total power generation benefit of the hydro-wind-solar complementary system, and combined with wind and solar predicted output data, runoff data, initial and final water levels, power generation flow, downstream flow, water balance, daily water level fluctuation and transmission channel capacity constraints, a short-term optimal scheduling model for hydro-wind-solar multi-energy complementary system is constructed. The moth-flame optimization algorithm is used to solve the short-term optimization scheduling model to obtain the optimized scheduling process; The measured power output data of wind and solar power and runoff data are input into the electricity-determined water model to update the scheduling process; The updated scheduling process is evaluated based on the established risk-benefit evaluation index system.

2. The method for assessing the operational benefits and risks of a multi-energy complementary system based on water, wind, and solar power as described in claim 1, is characterized in that... The power supply reliability evaluation includes: Maximum missing load L max The maximum difference between system output and load demand when the output of the complementary system cannot meet the load requirements; Probability of load loss L p The proportion of the number of time periods in which the actual output of the complementary system did not match the load demand to the total number of time periods; Maximum duration L of single continuous loss of load last The length of time during which the output of the complementary system fails to meet the load plan; Cumulative missing power J sum : The total difference between the actual power generation and the planned power generation during the period.

3. The method for assessing the operational benefits and risks of a multi-energy complementary system based on water, wind, and solar power as described in claim 1, is characterized in that... The energy system stability assessment uses the output difference coefficient (CV). P The calculation formula for this evaluation indicator is as follows: ； ； In the formula, n is the number of time periods. The average actual output of the hydro-wind-solar multi-energy complementary system during the specified time period. CV represents the actual output of the complementary system in the i-th time period. P The smaller the value, the more stable the system output.

4. The method for assessing the operational benefits and risks of a multi-energy complementary system based on water, wind, and solar power as described in claim 1, is characterized in that... The safety assessment of the reservoir operation includes: Reservoir water level stability is measured by the maximum absolute value of the difference between the reservoir water levels before and after a certain period. The smaller the difference, the safer the reservoir operation. Discharge flow stability: Measured by the discharge flow difference coefficient, which is calculated as the ratio of the standard deviation of discharge flow to the average discharge flow.

5. The method for assessing the operational benefits and risks of a multi-energy complementary system based on water, wind, and solar power according to claim 1, characterized in that, The economic evaluation of the complementary system uses the total power generation benefit of the complementary system as the indicator, and the calculation formula is as follows: ； Where E represents the total power generation benefit of the complementary system. , and The electricity prices are for wind power, solar power, and hydropower, respectively. , , Δt represents the output of wind power, solar power, and hydropower during time period i, respectively, and Δt is the length of time period i. The formula for calculating hydropower generation is as follows: ； In the formula, Let i be the power generation flow rate during the i-th time period. Let be the head of the hydropower station in the i-th time period, and k be the comprehensive output coefficient.

6. The method for assessing the operational benefits and risks of a multi-energy complementary system based on water, wind, and solar power according to claim 1, is characterized in that... The constraints specifically include: Reservoir water level constraints: In the formula, This is the minimum allowable water level for the reservoir, designated as the dead water level. Z(i) represents the maximum allowable water level of the reservoir in the i-th time period, which is set as the normal storage level; Z(i) represents the water level in the i-th time period. Power generation flow constraints: In the formula, Let be the power generation flow rate in the i-th time period. This represents the maximum flow rate used by the unit. Downflow constraint: In the formula, The total discharge flow in the i-th time period consists of the power generation flow and the water discharge. This represents the minimum ecological water demand downstream. The maximum allowable discharge flow rate; Water balance constraints: In the formula, , These represent the reservoir capacity at the end of time intervals i+1 and i, respectively. Let i be the inflow rate into the reservoir during the i-th time period; Daily water level fluctuation constraints: In the formula, For daily water level fluctuations, This represents the maximum daily water level fluctuation. Conveyor channel capacity constraints: In the formula, , , These represent the electricity prices for wind power, solar power, and hydropower during time period i, respectively. This refers to the capacity of the transport channel.

7. The method for assessing the operational benefits and risks of a multi-energy complementary system based on water, wind, and solar power according to claim 1, characterized in that, The solution process of the moth-flame optimization algorithm includes: Initialize the moth population vector set and fitness matrix, and generate the flame matrix and flame fitness matrix according to the fitness values. The flame represents the optimal solution in the current iteration search. Position Update: Individual moths move in a logarithmic spiral curve around their corresponding flames. The update formula is: ;in, moth Around the first A flame The new position after the logarithmic spiral motion; For the first The flame and the first The Euclidean distance between individual moths; To adjust the constant parameters of the logarithmic spiral curve; A random number, rounded to four decimal places, within the range of [-1, 1], representing the proximity of the next position of the current moth individual to the flame; Moth flame abandonment mechanism: As the number of iterations increases, the number of flames is adaptively reduced. The formula for calculating the number of flames is: ; In the formula: The current number of flames; To round up; This represents the current iteration number; This represents the maximum number of iterations specified.

8. The method for assessing the operational benefits and risks of a multi-energy complementary system based on water, wind, and solar power according to claim 1, characterized in that, The process of inputting measured wind and solar power output data and runoff data into the power-to-water model to update the scheduling includes: (1) Divide the scheduling period into k time periods and initialize the starting time period i=1; (2) Input the predicted wind and solar power output data and inflow runoff data for the i-th to (i+k−1) time periods into the short-term optimization scheduling model, and set the water levels at the beginning and end of the time periods; (3) The MFO optimization algorithm is used to solve the model and formulate the day-ahead power generation plan; (4) Obtain the measured wind and solar power output data for the i-th to (i+k−1) time period, input them and the inflow runoff data into the conventional scheduling model, and update the hydropower station scheduling process hourly according to the principle of "determining water supply based on electricity"; (5) Take the updated water level at the end of the (i+k−1)th time period as the initial water level for the next round of power generation plan, and let i=i+k; (6) Repeat steps (2) to (5) until i>T, where T represents the total number of time periods; (7) Output the final scheduling result.

9. The method for assessing the operational benefits and risks of a multi-energy complementary system based on water, wind, and solar power according to claim 1, characterized in that, The short-term optimal scheduling model is established with the objective of maximizing the total power generation benefit of the hydro-wind-solar hybrid system. ； in, , and The electricity prices are for wind power, solar power, and hydropower, respectively. , , Δt represents the electricity price for wind power, solar power, and hydropower during time period i, respectively, and Δt is the duration of time period i.

10. A system for assessing the operational benefits and risks of a multi-energy complementary system based on water, wind, and solar power, characterized in that: The system includes: The evaluation index module is configured to construct a risk and benefit evaluation index system for multi-energy complementarity of water, wind and solar power. The index system includes power supply reliability evaluation, energy system stability evaluation, reservoir operation safety evaluation, and complementary system economic evaluation. The module is configured to build a short-term optimization scheduling model for multi-energy complementarity of water, wind and solar power based on the evaluation index system, with the objective function of maximizing the total power generation benefit of the water-wind-solar complementary system, and combining wind and solar predicted output data, runoff data and initial and final water levels, power generation flow, downstream flow, water balance, daily water level fluctuation and transmission channel capacity constraints. The calculation module is configured to solve the short-term optimization scheduling model using the moth flame optimization algorithm to obtain the optimized scheduling process; The scheduling and adjustment module is configured to input measured wind and solar power output data and runoff data into the power-to-water model to update the scheduling process. The evaluation module is configured to evaluate the updated scheduling process based on a constructed risk-benefit evaluation index system.

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