PSA-based day-ahead optimal scheduling method for cascade water-wind-light complementary power generation system

Abstract

A day-ahead optimization scheduling method for a cascade water-wind-light complementary power generation system based on PSA relates to the technical field of multi-energy complementary power generation, and comprises the following steps: firstly, establishing a target function taking source load deviation minimization as a target function, then utilizing a wind power photovoltaic prediction output data set, and performing prediction by using a Monte Carlo generation scene + K-means method to obtain corresponding wind power and photovoltaic output scenes; and according to the constraint condition of each power station, constructing a wind-light-water combined power generation system model. And the optimal solution of the wind-light-water combined system is solved by combining the obtained hydropower output plan scheme and the wind-light prediction curve and utilizing a PSA algorithm. According to the method, the influence of wind power photovoltaic output randomness on the wind-light-water system is considered, the day-ahead output plan of the wind-light-water complementary power generation system is formulated, and it is ensured that the system can operate stably.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-energy complementary power generation, and in particular to a day-ahead optimization scheduling technology for a cascaded hydro-wind-solar complementary power generation system based on a PID-based search optimization algorithm (PSA). Background Art

[0002] As the clean energy with the highest penetration rate at present, wind power and photovoltaic power have the advantages of environmental protection and renewable energy, but their randomness, intermittency and volatility have serious impacts on the power grid, which will bring huge challenges to the safe operation of the power system and the grid dispatching. In order to achieve a clean grid, wind and photovoltaic power stations need to take measures to smooth out fluctuations and reduce the impact on the grid before entering the grid. In this context, cascade hydropower stations have become a key solution for multi-energy complementary dispatching on the power supply side due to their strong regulation capabilities. They effectively smooth out the volatility of wind and solar output, improve the absorption efficiency of the power grid, and are expected to be an effective way to absorb a large amount of new energy in the long run. Therefore, in the current context of promoting the development of clean energy around the world, it is particularly important to accurately analyze the operating characteristics of the water-wind-solar complementary power generation system. This integrated energy system provides strong support for the stable operation and sustainable development of the power system.

[0003] At present, the research on multi-energy complementary scheduling is mainly focused on the multi-energy complementary scheduling of water, wind and solar power in a single hydropower station, while there is little research on the multi-energy complementary scheduling of cascade hydropower stations in a river basin with large-scale wind and solar power grid connection, which does not meet the actual needs of my country to actively promote the construction of large-scale water, wind and solar complementary bases. Compared with a single hydropower station, cascade hydropower stations can obtain better power generation benefits and have the characteristics of flexibility and stability. Using cascade hydropower to make up for the intermittent nature of wind and solar power generation can effectively smooth the output fluctuation of wind and solar power stations. Therefore, a multi-energy complementary power generation system is constructed, but the complex coupling relationship between different energy sources needs to be considered. At the same time, the inherent uncertainty and randomness of renewable energy also increase the difficulty of finding the best solution. This coupling and randomness make the feasible solution space of the multi-energy complementary scheduling problem extremely complex, so a more in-depth and comprehensive model and solution method are needed to solve it. Traditional optimization scheduling methods include dynamic programming step-by-step approximation algorithm, simulated annealing particle swarm algorithm, step-by-step optimization algorithm, etc., which have the disadvantages of low efficiency, slow optimization speed and difficulty in guaranteeing convergence to the global optimal solution. The present invention adopts a novel meta-heuristic algorithm PSA, which has faster convergence speed and higher accuracy. Summary of the invention

[0004] The present invention provides a day-ahead optimization scheduling method for a cascaded hydro-wind-solar complementary power generation system based on PSA, aiming to solve the problems of low efficiency, slow optimization speed and difficulty in converging to the best optimization result in a multi-energy complementary power generation system model;

[0005] The present invention is a day-ahead optimization scheduling method for a cascaded hydro-wind-solar complementary power generation system based on PSA, and the steps are as follows: Step 1: Use Monte Carlo scenario generation method + K-means to predict wind power output under four typical day conditions Photovoltaic output forecast and load forecast power in: Where: represents the predicted wind power output at time t; It represents the predicted photovoltaic output at time t; Indicates the maximum wind power output during period t; represents the maximum photovoltaic output during period t; w represents wind power; s represents photovoltaic power; Step 2: Establish the constraints of the cascade hydro-wind-solar complementary system, which are transmission channel constraints and cascade hydropower constraints. The constraints are: 1) Transmission channel constraints Where: represents the output of the i-th hydropower station; represents the predicted output of wind power of group m; represents the predicted output of the sth group of photovoltaic power; P d represents the channel capacity of the dth transmission section; I represents the number of hydropower stations; M represents wind turbines; N represents the number of photovoltaic units; 2) Constraints of Cascade Hydropower Output Constraint Where: represents the lower limit of the output of the i-th hydropower station at time t; represents the upper limit of the output of the i-th hydropower station at time t; represents the output of the i-th hydropower station at time t; A represents the output coefficient of the cascade hydropower station; Q i,t represents the power generation flow of the i-th hydropower station in the tth period; H i,t represents the water head height of the i-th hydropower station in the tth period; h represents the hydropower station; Load power balance constraints Water balance constraints for cascade hydropower V i,t+1 =V i,t +[I i,t -(Q i,t +S i,t )]·Δt(Formula 5) Where: V i,t+1 represents the storage capacity of the i-th hydropower station at time t+1, V i,t represents the storage capacity of the i-th hydropower station in period t; I i,t represents the inflow of the i-th hydropower station in period t; Δt is the length of the calculation period; S i,t represents the abandoned water flow of the i-th hydropower station in period t; Hydropower storage capacity, water level, and flow constraints Where: represents the lower limit of the storage capacity of the i-th hydropower station at time t, represents the upper limit of the storage capacity of the i-th hydropower station at time t; represents the lower limit of the water level of the i-th hydropower station at time t, represents the upper limit of the water level of the i-th hydropower station at time t; Y i,t represents the water level of the i-th hydropower station at time t; represents the lower limit of the power generation flow of the i-th hydropower station in period t, represents the upper limit of the power generation flow of the i-th hydropower station in period t; represents the initial storage capacity of the hydropower station, Indicates the final storage capacity of the hydropower station; Step 3: Combine the predicted wind power output, predicted photovoltaic output, predicted load power and constraints described in Steps 1 and 2 to establish a day-ahead optimal dispatching model for the cascaded hydro-wind-solar complementary system with the goal of minimizing the source-load deviation; The objective function is as follows: Where: F represents the source charge deviation value; represents the load forecast power at time t; represents the predicted wind power output at time t; It represents the predicted photovoltaic output at time t; represents the hydropower output of the i-th hydropower station at time t; T represents the dispatching period; represents the predicted wind power output of the mth wind turbine group at time t; It represents the predicted photovoltaic output of the nth photovoltaic unit at time t; Step 4: Optimize the day-ahead optimal dispatch model of the cascade hydro-wind-solar hybrid system; Set the population size to B, the number of iterations to A, and the scheduling period to C, the current number of iterations to a; take the water level as the decision variable, generate a set of random data representing the C-hour water level of the cascade hydropower station, record in Represents the water level of the first-level hydropower station, represents the water level of the secondary hydroelectric power station, Represent the water level of the tertiary hydropower station; determine whether x satisfies the constraint conditions of step 2; if not, continue to generate the water level that meets the conditions, if so, form a set of water levels x that meet the conditions; finally, form an initial water level population, denoted as X=(X 1 X 2 X 3 ), where Utilize the obtained X and the relationship between the water level and the power generation flow Q i,t relation and Calculate the output of the hydropower station, and then substitute the wind power prediction output photovoltaic power prediction output and the load value into formula seven to obtain the source-load deviation value, denoted as Find the minimum value F among the B source-load deviation values b1 and the corresponding water level x; judge the current iteration number a, if a = 1, then set the current source-load deviation value F b1 as the target value F b ′1, set the water level x as x′, set the error e as x′ - x, if a > 1, then compare the current source-load deviation value with the previous target value, and set the smaller one as the latest target value F b ′1′, set the water level as x″, and the error e as x″ - x; Dynamically adjust the error signal e through PID to minimize the system deviation and optimize the control process, update the water level population X using formula eight, and judge whether the updated water level x satisfies the constraint conditions of step 2. If not, continue to find the optimal water level. If so, output the obtained water level x. The update strategy is: x(t + 1) = x(t) + η·Δu(t) + (1 - η)·o(t) (formula eight) Judge whether the current iteration number a is greater than or equal to A. If a < A, continue to optimize. If a > A, terminate the optimization and output the final calculated results of the source-load deviation F b ′1′ and the water level x to better track the load curve and achieve the purpose of suppressing the fluctuations of wind and light;

[0006] The beneficial effects of the present invention are as follows: The present invention adopts a new meta-heuristic algorithm, the PSA algorithm, to make up for the deficiencies of traditional algorithms such as poor convergence and slow convergence speed; the PSA algorithm has a faster convergence speed and higher accuracy, enabling the water-wind-light complementary system to obtain a more optimized scheduling method, giving full play to the complementary and seasonal characteristics of water, wind, and light energy, so as to ensure the safe, stable, and economic operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 This is the structure diagram of water-wind-solar complementary power generation. Figure 2 is the model solution flow chart, Figure 3 a is the photovoltaic output prediction curve, Figure 3 b is the wind power output prediction curve, Figure 3 c is the load output prediction curve, Figure 4 This is a bar chart of typical daily optimization results. Figure 5 This is the comparison result of the convergence curves of PSO and PSA. DETAILED DESCRIPTION

[0008] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The embodiment of the present invention provides a day-ahead optimization scheduling method for a cascaded water-wind-solar complementary power generation system based on PSA. The water-wind-solar complementary operation mode refers to the joint operation of three clean energy sources: hydropower, wind power and photovoltaics. The wind and solar output is the main power supply guarantee, and the output fluctuations of wind and solar are adjusted by cascade hydropower to achieve a stable supply of energy. The water-wind-solar complementary optimization model under this mode takes into account the constraints under various factors, and provides an important reference for the sustainable development and safe operation of the power system. The specific steps of the invention are: Step 1: Use Monte Carlo scenario generation method + K-means to predict wind power output under four typical day conditions Photovoltaic output forecast and load forecast power in: Where: represents the predicted wind power output at time t; It represents the predicted photovoltaic output at time t; Indicates the maximum wind power output during period t; represents the maximum photovoltaic output during period t; w represents wind power; s represents photovoltaic power; The wind and solar power station model formula is: Wind power output is mainly affected by wind speed, and its expression is: Where: P N Indicates the rated power of wind power; v in Indicates the cut-in wind speed; v out Indicates the cut-out wind speed; v t represents the wind speed in the tth period; Photovoltaic output forecast Photovoltaic output is mainly affected by solar irradiance, which is related to the position of the sun, the geographical location of the photovoltaic power generation equipment and weather conditions. Its expression is: Where: f(x t ,a,b) represents Beta distribution; B(a,b) represents Beta function; a and b represent shape parameters; P c Indicates the photovoltaic rated power; B g Indicates the light intensity reference value; G e Indicates rated light intensity; x i represents the light intensity at time period t; Step 2: Establish the constraints of the cascade hydro-wind-solar complementary system, which are transmission channel constraints and cascade hydropower constraints. The constraints are: 1) Transmission channel constraints Where: represents the output of the i-th hydropower station; represents the predicted output of wind power of group m; represents the predicted output of the sth group of photovoltaic power; P d represents the channel capacity of the dth transmission section; I represents the number of hydropower stations; M represents wind turbines; N represents the number of photovoltaic units; 2) Constraints of Cascade Hydropower Output Constraint Where: represents the lower limit of the output of the i-th hydropower station at time t; represents the upper limit of the output of the i-th hydropower station at time t; represents the output of the i-th hydropower station at time t; A represents the output coefficient of the cascade hydropower station; Q i,t represents the power generation flow of the i-th hydropower station in the tth period; H i,t represents the water head height of the i-th hydropower station in the tth period; h represents the hydropower station; Load power balance constraints Water balance constraints for cascade hydropower V i,t+1 =V i,t +[I i,t -(Q i,t +S i,t )]·Δt(Formula 7) Where: V i,t+1 represents the storage capacity of the i-th hydropower station at time t+1, V i,t represents the storage capacity of the i-th hydropower station in period t; I i,t represents the inflow of the i-th hydropower station in period t; Δt is the length of the calculation period; S i,t represents the abandoned water flow of the i-th hydropower station in period t; Hydropower storage capacity, water level, and flow constraints Where: represents the lower limit of the storage capacity of the i-th hydropower station at time t, represents the upper limit of the storage capacity of the i-th hydropower station at time t; represents the lower limit of the water level of the i-th hydropower station at time t, represents the upper limit of the water level of the i-th hydropower station at time t; Y i,t represents the water level of the i-th hydropower station at time t; represents the lower limit of the power generation flow of the i-th hydropower station in period t, represents the upper limit of the power generation flow of the i-th hydropower station in period t; represents the initial storage capacity of the hydropower station, Indicates the final storage capacity of the hydropower station; Step 3: Combine the predicted wind power output, predicted photovoltaic output, predicted load power and constraints described in Step 1 and Step 2 to establish a day-ahead optimal scheduling model for the cascaded hydro-wind-solar complementary system with the goal of minimizing source-load deviation. The objective function is as follows: Where: F represents the source charge deviation value; represents the load forecast power at time t; represents the predicted wind power output at time t; It represents the predicted photovoltaic output at time t; represents the hydropower output of the i-th hydropower station at time t; T represents the dispatching period; represents the predicted wind power output of the mth wind turbine group at time t; It represents the predicted photovoltaic output of the nth photovoltaic unit at time t; Step 4: Optimize the day-ahead optimal dispatch model of the cascade hydro-wind-solar hybrid system Set the population size to B, the number of iterations to A, and the scheduling period to C, with the current number of iterations to a. Take the water level as the decision variable and generate a set of random data representing the C-hour water level of the cascade hydropower station. in Represents the water level of the first-level hydropower station, represents the water level of the secondary hydroelectric power station, represents the water level of the third-level hydropower station. Determine whether x satisfies the constraint conditions of step 2. If not, continue to generate water levels that meet the conditions. If yes, form a set of water levels x that meet the conditions. Finally, form an initial water level population, record X = (X 1 X 2 X 3 ),in The above obtained X is used to calculate the water level and power generation flow Q i,t Relational and Calculate the output of the hydropower station, and then add the wind power forecast output obtained in step 1 Photovoltaic output forecast and load value Substitute into formula 7 to find the source charge deviation value, record Find the minimum value F among B source-charge deviation values b1 The corresponding water level x; determine the current iteration number a, if a = 1, then the current source load deviation value F b1 Set to target value F b ′1, the water level x is set to x′, and the error e is set to x′-x. If a>1, the current source load deviation value is compared with the previous target value, and the smaller one is set as the latest target value F b '1', the water level is set to x", and the error e is x"-x; The output of PID regulation is calculated using the target value error of the complementary power generation system. The expression is: Δu(t)=K p ·r2·[e k (t)-e k-1 (t)]+K i ·r3·e k (t)+K d ·r4·[e k (t)-2e k-1 (t)+e k-2 (t)](Formula 10) Where: r2, r3 and r4 represent vectors of random numbers from 0 to 1 in n rows and 1 column; K p , K i and K d Represent the adjustment coefficients of proportion, integration and differentiation respectively. Adding a condition factor can make the water-wind-solar complementary system obtain a better scheduling plan, and the expression is: o(t)=(cos(1-t / T)+λr5·L)·e k (t)(Formula 11) Where: r5 represents a vector of random numbers from 0 to 1 in n rows and d columns; where λ calculates the adjustment coefficient: λ=[ln(T-t+2) / ln(T)] 2 (Formula 12) λ decreases as t increases, which is conducive to the PSA algorithm to fully search for the optimal scheduling solution. Where L is a Levy flight function, defined as Where: u and v respectively represent n×d random number matrices that follow the standard normal distribution; β represents a factor of 1.5. The update of all water levels is related to Δu(t) and o(t). Use Equation VIII to update the water level population X, and determine whether the updated water level x satisfies the constraints in Step 2. If not, continue to find the optimal water level. If satisfied, output the obtained water level x. The update strategy is as follows: x(t + 1) = x(t) + η·Δu(t) + (1 - η)·o(t) (Equation XIV) Determine whether the current iteration number a is greater than or equal to A. If a < A, continue the optimization. If a > A, terminate the optimization and output the final calculated source-load deviation F b ′1′ and the water level x to better track the load curve and achieve the purpose of suppressing the fluctuations of wind and light. The source-load deviations and fluctuations of the water-wind-light complementary system on different typical days are shown in Table 1. Table 1 Comparison of solution indexes on different typical days Step 5: Optimize and compare the water-wind-light complementary system using the PSA in the present invention and the traditional PSO algorithm respectively. The comparison data of the optimization results are shown in Table 2. Table 2 Comparison of optimization results between PSO and PSA In summary, the present invention adopts a new meta-heuristic algorithm, the PSA algorithm, which overcomes the deficiencies of poor convergence and slow convergence speed of traditional algorithms. By comparing the optimization results of the PSA algorithm and the PSO algorithm, it can be seen that the convergence effect is more accurate, obtaining a solution with good representativeness, giving full play to the complementary and seasonal characteristics of water, wind, and light energy, and ensuring the safe, stable, and economic operation of the power system.

Claims

1. A day-ahead optimization dispatching method for a cascade hydro-wind-solar hybrid power generation system based on PSA, characterized in that: The steps are: Step 1: Monte Carlo scenario generation method + K-means are used to predict the wind power output P under four typical day conditions. t w , Photovoltaic predicted output P t s and load forecast power P t D ,in: Where: P t w represents the predicted wind power output at time t; P t s It represents the predicted photovoltaic output at time t; Indicates the maximum wind power output during period t; represents the maximum photovoltaic output during period t; w represents wind power; s represents photovoltaic power; Step 2: Establish the constraints of the cascade hydro-wind-solar complementary system, which are transmission channel constraints and cascade hydropower constraints. The constraints are: 1) Transmission channel constraints Where: P i h represents the output of the i-th hydropower station; represents the predicted output of wind power of group m; represents the predicted output of the sth group of photovoltaic power; P d represents the channel capacity of the dth transmission section; I represents the number of hydropower stations; M represents wind turbines; N represents the number of photovoltaic units; 2) Constraints of Cascade Hydropower Output Constraint Where: represents the lower limit of the output of the i-th hydropower station at time t; represents the upper limit of the output of the i-th hydropower station at time t; represents the output of the i-th hydropower station at time t; A represents the output coefficient of the cascade hydropower station; Q i,t represents the power generation flow of the i-th hydropower station in the tth period; H i,t represents the water head height of the i-th hydropower station in the tth period; h represents the hydropower station; Load power balance constraints Water balance constraints for cascade hydropower V i,t+1 =V i,t +[I i,t -(Q i,t +S i,t )]·Δt (Formula 5) Where: V i,t+1 represents the storage capacity of the i-th hydropower station at time t+1, V i,t represents the storage capacity of the i-th hydropower station in period t; I i,t represents the inflow of the i-th hydropower station in period t; Δt is the length of the calculation period; S i,t represents the abandoned water flow of the i-th hydropower station in period t; Hydropower storage capacity, water level, and flow constraints Where: represents the lower limit of the storage capacity of the i-th hydropower station at time t, represents the upper limit of the storage capacity of the i-th hydropower station at time t; represents the lower limit of the water level of the i-th hydropower station at time t, represents the upper limit of the water level of the i-th hydropower station at time t; Y i,t represents the water level of the i-th hydropower station at time t; represents the lower limit of the power generation flow of the i-th hydropower station in period t, represents the upper limit of the power generation flow of the i-th hydropower station in period t; represents the initial storage capacity of the hydropower station, Indicates the final storage capacity of the hydropower station; Step 3: Combine the predicted wind power output, predicted photovoltaic output, predicted load power and constraints described in Steps 1 and 2 to establish a day-ahead optimal dispatching model for the cascaded hydro-wind-solar complementary system with the goal of minimizing the source-load deviation; The objective function is as follows: Where: F represents the source charge deviation value; represents the load forecast power at time t; P t w represents the predicted wind power output at time t; It represents the predicted photovoltaic output at time t; represents the hydropower output of the i-th hydropower station at time t; T represents the dispatching period; represents the predicted wind power output of the mth wind turbine group at time t; It represents the predicted photovoltaic output of the nth photovoltaic unit at time t; Step 4: Optimize the day-ahead optimal dispatch model of the cascade hydro-wind-solar hybrid system Set the population size to B, the number of iterations to A, and the scheduling period to C, the current number of iterations to a; take the water level as the decision variable, generate a set of random data representing the C-hour water level of the cascade hydropower station, record x = {x 1 ,x 2 ,x 3 },in Represents the water level of the first-level hydropower station, represents the water level of the secondary hydroelectric power station, represents the water level of the third-level hydropower station; determine whether x satisfies the constraint conditions of step 2; if not, continue to generate water levels that meet the conditions; if yes, form a group of water levels x that meet the conditions; finally form an initial water level population, record X = (X 1 X 2 X 3 ),in The above obtained X is used to calculate the water level and power generation flow Q i,t Relational and Calculate the output of the hydropower station, and then use the wind power forecast output P obtained in step 1 t w , Photovoltaic predicted output P t s and load value P t D Substitute into formula 7 to find the source charge deviation value, record Find the minimum value F among B source-charge deviation values b1 The corresponding water level x; determine the current iteration number a, if a = 1, then the current source load deviation value F b1 Set to target value F b ′1, the water level x is set to x′, and the error e is set to x′-x. If a>1, the current source load deviation value is compared with the previous target value, and the smaller one is set as the latest target value F b '1', the water level is set to x", and the error e is x"-x; The error signal e is dynamically adjusted by PID to minimize the system deviation and optimize the control process. The water level population X is updated using formula 8, and it is determined whether the updated water level x meets the constraint conditions of step 2. If not, continue to search for the optimal water level. If it does, output the obtained water level x. The update strategy is: x(t + 1) = x(t) + η·Δu(t) + (1 - η)·o(t) (Equation 8) Determine whether the current iteration number a is greater than or equal to A. If a < A, continue the optimization; if a > A, terminate the optimization and output the final calculated result, the source-load deviation F b '1' and the water level x to better track the load curve and achieve the purpose of suppressing the fluctuations of wind and light.

Images