Unit commitment optimization model construction and production optimization method and system of multi-source system

Abstract

The invention discloses a unit commitment optimization model construction and production optimization method and system of a multi-source system, and relates to the technical field of production resource regulation and control. The operation parameters and the operation constraints of the pumped storage power station are subjected to linearization processing, so that interference of nonlinear integer variables in the pumped storage power station is weakened; meanwhile, the renewable energy sources are modeled through the uncertain set, a large number of scenes do not need to be generated, and the solving efficiency of solving the uncertain unit commitment optimization problem is improved; according to the method, two-stage robust optimization and a rank generation algorithm are used for conversion, so that variables and constraints in the unit commitment optimization problem of the multi-source system meet the strong duality required by solution, the unit commitment optimization problem is conveniently solved, the mapping relation between the variables and the constraints in the unit commitment optimization problem is simplified, and the optimization efficiency of the unit commitment optimization problem is improved. The calculation speed for solving the unit commitment optimization problem of the multi-source system is increased, and an accurate calculation result is obtained.

Description

Technical Field

[0001] The present application relates to the technical field of production resource control, and in particular to the construction of a unit combination optimization model, a production optimization method and a system for a multi-source system. Background Art

[0002] When the flexibility regulation resources of the power system are insufficient, it is easy to cause a large amount of wind and solar power abandonment, which reduces the utilization rate of renewable energy. At the same time, the increase in the penetration rate of renewable energy will reduce the rotating reserve capacity of the power system, further leading to insufficient flexibility regulation resources of the power system. In order to improve the reliability and security of a high proportion of renewable energy in the power system, it is necessary to increase the flexibility resources of the power system to cope with the volatility of renewable energy such as wind power and photovoltaic power generation. Hydropower, as an excellent regulation resource, has temporal and spatial complementarity with wind power and photovoltaic power generation. However, excessive autonomous scheduling of hydropower stations will reduce the flexibility of the power system. Therefore, it is necessary to study the joint optimization operation of multi-source systems including wind power, photovoltaic power, hydropower and pumped storage systems.

[0003] In terms of the short-term operation of multi-source systems, obtaining a unit combination of hydropower units combined with pumped storage functions can ensure the operational safety of the multi-source system. Due to the nonlinear constraints of coupled hydraulic and electric power, the uncertainty of wind and solar power output, and the uncertainty of natural inflow in the multi-source system, the unit combination optimization production of the multi-source system is uncertain. Currently, the public methods for solving the problem of unit combination optimization production of multi-source systems with uncertainty include stochastic optimization; stochastic optimization uses probability density functions or scenario trees to describe the uncertainty of unit combination optimization production problems, thereby solving the uncertainty problem in unit combination optimization production.

[0004] At present, the methods for solving unit combination optimization problems with uncertainty include stochastic optimization. Stochastic optimization uses probability density functions or scenario trees to describe uncertainty, which requires the generation of a large number of scenarios. The generation of a large number of scenarios greatly increases the computational complexity, resulting in low efficiency in the solution process. Summary of the invention

[0005] In order to solve the problem of low efficiency in the solution process of solving the unit combination optimization problem with uncertainty by using random optimization, the present invention proposes a unit combination optimization model construction, production optimization method and system for a multi-source system. In the unit combination optimization model construction method for a multi-source system provided by the present invention, the interference of nonlinear integer variables in the pumped storage power station is weakened by linearizing the operating parameters and operating constraints of the pumped storage power station; at the same time, the renewable energy is modeled by using uncertain sets, and a large number of scenarios do not need to be generated, thereby improving the solution efficiency of solving the uncertain unit combination optimization problem; using two-stage robust optimization and row and column generation algorithm conversion, the variables and constraints in the unit combination optimization problem of the multi-source system meet the strong duality required for solution, which is convenient for solving the unit combination optimization problem, simplifies the mapping relationship between variables and constraints in the unit combination optimization problem, and improves the calculation speed of solving the unit combination optimization problem of the multi-source system.

[0006] On the one hand, the present invention proposes a method for constructing a unit commitment optimization model of a multi-source system, comprising:

[0007] According to the operation parameters and operation constraints of the pumped storage power station in the multi-source system, the pumped storage model is obtained by linearization processing;

[0008] According to the operating parameters and operating constraints of wind turbines, photovoltaic units, and hydropower units with natural inflow in the multi-source system, the uncertainty set is used to model and obtain the uncertainty model of renewable energy;

[0009] Based on the renewable energy uncertainty model and the pumped storage model, the initial unit commitment optimization model is constructed by taking the maximum proportion of renewable energy power generation in the multi-source system and the minimum operating cost of the multi-source system as the optimization objectives;

[0010] The initial unit commitment optimization model is reconstructed using a two-stage robust optimization and row-column generation algorithm to obtain a final unit commitment optimization model.

[0011] Optionally, the initial unit commitment optimization model includes: initial optimization functions and constraints;

[0012] The initial optimization function includes: taking the maximum proportion of renewable energy power generation in the multi-source system and the minimum operating cost of the multi-source system as the optimization target; the maximum proportion of renewable energy power generation in the multi-source system includes: the minimum reduction cost of renewable energy power generation in the multi-source system; the reduction cost of renewable energy power generation includes: the start-stop cost of hydropower units and water pumps in the pumped storage power station in the multi-source system, the penalty cost of abandoned light and abandoned wind, and the penalty cost of abandoned water;

[0013] The constraints of the initial optimization function include: the start-stop cost constraints of the hydropower units and water pumps in the pumped storage power station, the penalty cost constraints for abandoned solar power and abandoned wind power, and the penalty cost constraints for abandoned water power;

[0014] The renewable energy sources in the multi-source system include wind power, light and natural inflow of water.

[0015] Optionally, the initial unit commitment optimization model is reconstructed using a two-stage robust optimization and row-column generation algorithm to obtain a final unit commitment optimization model, including:

[0016] For the initial unit combination optimization model, the initial unit combination optimization model is reconstructed by using a two-stage robust optimization algorithm to obtain a first-stage function and a second-stage function; the first-stage function includes taking the unit combination operation cost of the multi-source system as an optimization target, and the second-stage function includes taking the cost reduction of renewable energy power generation as an optimization target; according to the second-stage function, the constraints of the second-stage function are obtained;

[0017] Using the inner column and constraint generation method, the integer variables in the constraints of the two-stage function are replaced and equivalent, and the equivalent two-stage function and constraints are obtained;

[0018] By replacing the dual variables, the constraints of the equivalent two-stage function are transformed to obtain the final two-stage function and constraints;

[0019] The first-stage function and the final second-stage function and constraints are combined to determine the final unit combination optimization model.

[0020] Optionally, the operating parameters and operating constraints of the pumped storage power station based on the multi-source system are linearized to obtain a pumped storage model, including:

[0021] Based on the operation parameters and operation constraints of the pumped storage power station of the multi-source system, an initial pumped storage model is constructed, wherein the initial pumped storage model includes: a mapping relationship between the active output of the hydropower unit, the reservoir water level and the reservoir water storage capacity;

[0022] Performing linearization processing on nonlinear variables in the active power output of the hydropower unit to obtain a linearized approximate result of the active power output of the hydropower unit;

[0023] Linearizing the nonlinear variables in the mapping relationship between the reservoir water level and the reservoir water storage capacity to obtain a linearized approximate result of the mapping relationship between the reservoir water level and the reservoir water storage capacity;

[0024] The linearized approximation result of the active output of the hydropower unit and the linearized approximation result of the mapping relationship between the reservoir water level and the reservoir water storage capacity are combined to serve as the final pumped storage model.

[0025] Optionally, the linearizing of the nonlinear variables in the active output of the hydropower unit to obtain a linearized approximate result of the active output of the hydropower unit includes:

[0026] Based on the active output of the hydropower unit, the turbine efficiency is replaced with a constant value and simplified to obtain a bilinear function;

[0027] By using the linear envelope method, a convex envelope is constructed at the boundary of the bilinear function to obtain an inequality group for the active output of the hydropower unit, and the inequality group is used as a linearized approximate result of the active output of the hydropower unit.

[0028] Optionally, linearizing the nonlinear variables in the mapping relationship between the reservoir water level and the reservoir water storage capacity to obtain a linearized approximate result of the mapping relationship between the reservoir water level and the reservoir water storage capacity, including:

[0029] Based on the mapping relationship between the reservoir water level and the reservoir water storage capacity, the nonlinear variables of the function are linearly approximated using the SOS-2 constraint to obtain a linear approximation result of the mapping relationship between the reservoir water level and the reservoir water storage capacity.

[0030] Optionally, the renewable energy uncertainty model satisfies the following expression:

[0031]

[0032]

[0033] Where UD is the uncertain set of wind turbines, photovoltaic turbines and hydropower units corresponding to natural inflow in the multi-source system; N Wind is the set of wind turbines in the multi-source system; N PV is the set of photovoltaic units in the multi-source system; N HPP is the set of hydropower units in the multi-source system; T is the time set; is the available output of wind turbine m at time t; is the available output of photovoltaic unit n at time t; r x,t is the natural inflow of hydropower unit x at time t, i.e., the output of hydropower unit x; is the predicted average power output of wind turbine m at time t; is the power output of the wind turbine m at the upper limit of the interval at time t; is the power output of the wind turbine m at the lower limit of the interval at time t; is the predicted average power output of PV unit n at time t; is the power output of the upper limit of the interval of PV unit n at time t; is the power output of the lower limit of the interval of PV unit n at time t; is the predicted average power output of hydropower unit x at time t; is the power output of the hydropower unit x at the upper limit of the interval at time t; is the power output of the hydropower unit x at the lower limit of the interval at time t; is a binary variable for the output of wind turbine m at time t, which is used to indicate that the output of wind turbine m at time t is the upper bound. or lower bound Or the predicted value is a binary variable for the output of photovoltaic unit n at time t, used to indicate that the output of photovoltaic unit n at time t is the upper bound or lower bound Or the predicted value is a binary variable for the natural inflow of hydropower station x at time t, which is used to indicate that the natural inflow of hydropower unit x at time t is the upper bound. or lower bound Or the predicted value Γ TW To adjust the conservativeness of the uncertainty set of wind turbines in the time dimension; Γ TS is the conservative degree of the uncertainty set of photovoltaic units in the time dimension; Γ TR is the conservative degree of the uncertainty set of hydropower units corresponding to the natural inflow in the time dimension; Γ KW To adjust the conservativeness of the uncertainty set of wind turbines in the spatial dimension; Γ KS To adjust the conservativeness of the uncertainty set of the photovoltaic unit in the spatial dimension; Γ KR To adjust the conservativeness of the uncertainty set of hydropower units corresponding to the natural inflow in the spatial dimension.

[0034] On the other hand, the present invention also proposes a unit combination model building system for a multi-source system, comprising:

[0035] A pumped storage model building unit is used to obtain a pumped storage model by linearization processing according to the operating parameters and operating constraints of the pumped storage power station of the multi-source system;

[0036] A renewable energy uncertainty model building unit is used to model the operation parameters and operation constraints of wind turbines, photovoltaic turbines and hydropower units with natural inflow in the multi-source system using uncertainty sets to obtain a renewable energy uncertainty model;

[0037] A unit combination optimization model building unit is used to build an initial unit combination optimization model based on the renewable energy uncertainty model and the pumped storage model, taking the maximum proportion of renewable energy power generation in the multi-source system and the minimum operating cost of the multi-source system as the optimization objectives;

[0038] The unit commitment model conversion unit is used to reconstruct the initial unit commitment optimization model by using a two-stage robust optimization and row and column generation algorithm to obtain a final unit commitment optimization model.

[0039] In another aspect, the present invention further proposes a method for optimizing unit combination production of a multi-source system, comprising:

[0040] According to the current operating parameters of the pumped storage power station, wind turbines, photovoltaic units and hydropower units with natural inflow in the multi-source system, the unit combination optimization model is solved to obtain the unit combination optimization results;

[0041] Determine the production optimization plan of the hydropower units and water pumping stations in the pumped storage power station according to the unit combination optimization result;

[0042] The unit combination optimization model is constructed using the unit combination production optimization model construction method for a multi-source system as described in the above technical solution.

[0043] Optionally, solving the unit commitment optimization model to obtain the unit commitment optimization result includes:

[0044] Solving the unit commitment decision variables of the unit commitment optimization model to obtain a first optimization result;

[0045] According to the first optimization result, respectively updating the upper bound and the lower bound of the unit commitment optimization model to obtain updated upper bound and lower bound;

[0046] Based on the updated upper bound and lower bound, solving the economic dispatch variables of the wind turbine, photovoltaic turbine, and hydropower unit of the unit combination optimization model to obtain a second optimization result;

[0047] updating the upper bound and the lower bound of the unit commitment optimization model according to the second optimization result;

[0048] According to the updated upper and lower bounds, the unit combination decision variables of the unit combination optimization model are iteratively optimized using the clamping criterion, the first optimization result and the second optimization result are updated, and the upper and lower bounds of the unit combination optimization model are updated until the upper and lower bounds of the updated unit combination optimization model meet the iteration termination condition, and the first optimization result and the second optimization result when the upper and lower bounds of the updated unit combination optimization model meet the iteration termination condition are used as the unit combination optimization result.

[0049] In another aspect, the present invention further provides a unit combination production optimization system for a multi-source system, comprising:

[0050] The result solving unit is used to solve the unit combination optimization model according to the current operating parameters of the pumped storage power station, wind turbine, photovoltaic unit and hydropower unit with natural inflow in the current multi-source system to obtain the unit combination optimization result;

[0051] A production optimization unit, used to confirm the production optimization plan of the hydropower units and water pumping stations in the pumped storage power station according to the unit combination optimization result;

[0052] The pre-constructed unit combination model is constructed using the unit combination model construction method for a multi-source system as described in the above technical solution.

[0053] In another aspect, the present invention further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0054] The memory is used to store one or more programs;

[0055] When the one or more programs are executed by the at least one processor, the unit combination optimization model construction method of the multi-source system as described in the above technical solution or the unit combination production optimization method of the multi-source system as described in the above technical solution is implemented.

[0056] On the other hand, the present invention also proposes a readable storage medium having an execution program stored thereon. When the execution program is executed, it implements the unit combination optimization model construction method of the multi-source system as described in the above technical solution or the unit combination production optimization method of the multi-source system as described in the above technical solution.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] In the method for constructing a unit combination optimization model for a multi-source system provided by the present invention, the interference of nonlinear integer variables in the pumped-storage power station is weakened by linearizing the operating parameters and operating constraints of the pumped-storage power station; at the same time, an uncertain set is used to model renewable energy, and there is no need to generate a large number of scenarios, thereby improving the efficiency of solving the uncertain unit combination optimization problem; a two-stage robust optimization and row and column generation algorithm is used for transformation, so that the variables and constraints in the unit combination optimization problem of the multi-source system meet the strong duality required for solving, which facilitates the solution of the constructed unit combination optimization problem, simplifies the mapping relationship between variables and constraints in the unit combination optimization problem, and improves the calculation speed of solving the unit combination optimization problem of the multi-source system compared with the current use of random optimization to solve the unit combination optimization problem with uncertainty.

[0059] In the unit combination optimization method for a multi-source system proposed in the present invention, the unit combination optimization model is solved by utilizing the operating parameters and operating constraints in the current multi-source system to obtain the unit combination optimization results, and the unit combination optimization results are utilized to optimize the production plans of the hydropower units and water pumping stations in the pumped-storage power station in the multi-source system to ensure that the proportion of renewable energy in the multi-source system is maximized and the operating cost of the multi-source system is minimized, thereby improving the calculation speed for solving the unit combination optimization problem of the multi-source system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A flow chart of a method for constructing a unit commitment optimization model for a multi-source system of the present invention;

[0061] Figure 2 A flow chart for obtaining a pumped storage model in a method for constructing a unit combination optimization model for a multi-source system of the present invention;

[0062] Figure 3 A flow chart of obtaining a final unit commitment optimization model in the unit commitment optimization model construction method of a multi-source system of the present invention;

[0063] Figure 4 A structural schematic diagram of a unit commitment optimization model building system for a multi-source system of the present invention;

[0064] Figure 5 A schematic diagram of the structure of the IEEE-24 bus system provided by the present invention;

[0065] Figure 6 It is a structural schematic diagram of a unit combination production optimization system of a multi-source system of the present invention;

[0066] Figure 7 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0067] Embodiment 1:

[0068] Reference Figure 1 The present invention proposes a method for constructing a unit combination optimization model of a multi-source system, comprising:

[0069] S1, according to the operating parameters and operating constraints of the pumped storage power station in the multi-source system, the pumped storage model is obtained by linearization processing;

[0070] S2, based on the operating parameters and operating constraints of wind turbines, photovoltaic units, and hydropower units with natural inflow in the multi-source system, the uncertainty set is used to model and obtain the uncertainty model of renewable energy;

[0071] S3, based on the renewable energy uncertainty model and the pumped storage model, taking the maximum proportion of renewable energy power generation in the multi-source system and the minimum operating cost of the multi-source system as the optimization objectives, and constructing an initial unit commitment optimization model;

[0072] S4, reconstructing the initial unit commitment optimization model by using a two-stage robust optimization and row-column generation algorithm to obtain a final unit commitment optimization model.

[0073] At present, random optimization algorithms or robust optimization algorithms are used to solve the unit combination optimization problem with uncertainty. The random optimization algorithm uses probability density functions or scenario trees to describe uncertainty. The optimization process of the random optimization algorithm requires the generation of a large number of scenarios, but the generation of a large number of scenarios will greatly increase the complexity of the calculation, resulting in low efficiency in solving the unit combination optimization problem with uncertainty.

[0074] Robust optimization algorithms use uncertainty sets or intervals to solve uncertainty problems. Since the results obtained by single-stage robust optimization are too conservative, a two-stage robust model is currently more commonly used. The robust model is used to optimize the unit combination variables in the first stage and optimize the worst-case scheduling results in the second stage. However, there are integer variables in the second stage of the robust model, and the unit combination optimization problem with mixed integer variables does not meet the strong duality required for solution, which makes the second stage optimization problem of the robust model difficult to solve.

[0075] Compared with the random optimization algorithm or robust optimization algorithm currently used to solve the unit combination optimization problem with uncertainty, the present invention reduces the interference of nonlinear integer variables in the pumped-storage power station by linearizing the operating parameters and operating constraints of the pumped-storage power station; at the same time, the uncertain set is used to model renewable energy, without generating a large number of scenarios, thereby improving the efficiency of solving the uncertain unit combination optimization problem; the two-stage robust optimization and row and column generation algorithm conversion are used to make the variables and constraints in the multi-source system unit combination optimization problem meet the strong duality required for solving, which is convenient for solving the constructed unit combination optimization problem, simplifies the mapping relationship between variables and constraints in the unit combination optimization problem, and improves the calculation speed of solving the unit combination optimization problem of the multi-source system.

[0076] The pumped storage power station in the multi-source system in S1 uses a water pump station to lift water resources from a lower position in nature to a higher position, thereby increasing the gravitational potential energy of the water resources. Subsequently, the hydropower units of the pumped storage power station are used to convert the higher water resources into electrical energy, and the generated electrical energy is connected to the power grid to cope with the volatility of wind and photovoltaic power generation in the multi-source system.

[0077] Reference Figure 2 , optional, S1 includes:

[0078] S11, constructing an initial pumped storage model based on the operating parameters and operating constraints of the pumped storage power station of the multi-source system, wherein the initial pumped storage model includes: a mapping relationship between the active output of the hydropower unit, the reservoir water level and the reservoir water storage capacity;

[0079] S12, performing linearization processing on the nonlinear variables in the active output of the hydropower unit to obtain a linearized approximate result of the active output of the hydropower unit;

[0080] S13, linearizing the nonlinear variables in the mapping relationship between the reservoir water level and the reservoir water storage capacity to obtain a linearized approximate result of the mapping relationship between the reservoir water level and the reservoir water storage capacity;

[0081] S14, combining the linearized approximation result of the active power output of the hydropower unit and the linearized approximation result of the mapping relationship between the reservoir water level and the reservoir water storage capacity as the final pumped storage model.

[0082] S11 is specifically implemented as follows:

[0083] The operation constraints of pumped storage power stations in multi-source systems include the switching states and logic constraints of hydropower units and water pumps.

[0084] For example, it is assumed that the pumped storage power station in the multi-source system includes N HPP Hydropower stations and NPS The index set of hydropower stations is N HPP ={1,2,…,N HPP}, the index set of the pump station is N PS ={1,2,…,N PS}. At the same time, each hydropower station contains multiple hydropower units, and each pump station also contains multiple pumps. The set of all hydropower units is N H ={1,2,…,N H}, the set of all pumps is N pu ={1,2,…,N pu}; Set the scheduling time index set to T = {1, 2, .., T}, and the time interval between adjacent scheduling is Δt.

[0085] The switch status and logic constraints of each hydropower unit and each water pump satisfy the following expressions:

[0086]

[0087] In the formula, v H,i,t 、w H,i,t Both represent the switching action of hydropower unit i at time t. When v H,i,t or H,i,t When the value is 1, it means that the hydropower unit i is turned on or off at time t; when v H,i,t or H,i,t When the value is 0, it means that the hydropower unit i has no switching action at time t; the binary variable u H,i,t represents the working state of hydropower unit i at time t. H,i,t =1 indicates that the working state of hydropower unit i at time t is on, u H,i,t =0 means that the working state of hydropower unit i at time t is shutdown; u H,i,t-1 represents the working status of hydropower unit i at time t-1; v Pu,j,t 、w Pu,j,t Both represent the switching action of pump j at time t. Pu,j,t or Pu,j,t When the value is 1, it means that the pump j is turned on or off at time t; when v Pu,j,t or Pu,j,t When the value is 0, it means that the pump j has no switching action at time t; the binary variable u Pu,j,t represents the working state of pump j at time t. Pu,j,t =1 indicates that the working state of pump j is on, u Pu,j,t =0 means the working state of pump j is off; u Pu,j,t-1 Represents the working status of pump j at time t-1.

[0088] The power generated by hydropower unit i is P H,i,t is about the turbine efficiency η tu,i,t (h D,x,t ,q H,i,t ), generator efficiency η gen,i , turbine displacement q H,i,t and water head D,x,t The nonlinear function satisfies the following expression:

[0089]

[0090] Among them, P H,i,t represents the electric energy generated by hydropower unit i at time t; η tu,i,t (h D,x,t ,q H,i,t ) represents the efficiency of the turbine corresponding to the hydropower unit i; η gen,i represents the efficiency of the generator corresponding to the hydropower unit i; ρ represents the density of water; g represents the gravitational acceleration of water; h D,x,t represents the water head of the hydropower station x at time t; q H,i,t represents the discharge of the turbine corresponding to the hydropower unit i at time t; the set Ω x Represents the hydropower unit in hydropower station x.

[0091] Water head D,x,t is the reservoir water level h x,t and tailwater level h T,x,t The difference between them satisfies the following expression:

[0092]

[0093] Among them, h D,x,t represents the water head of the hydropower station x at time t; h x,t represents the reservoir water level corresponding to the hydropower station x; h T,x,t represents the tailwater level of hydropower station x at time t; h loss,x,t represents the head loss of hydropower station x at time t.

[0094] Head loss h loss,x,t It is related to the friction of water in the pressure pipe and is considered as a quadratic function of the turbine flow rate, satisfying the following expression:

[0095]

[0096] In the formula, k loss,x represents the friction coefficient of the pressure pipe in the hydropower station x; q H,i,t represents the displacement of the turbine corresponding to the hydropower unit i at time t, and the set Ω x Represents the hydropower unit in hydropower station x.

[0097] Tail water level hT,x,t Related to the outflow of the reservoir, the expression is as follows:

[0098]

[0099] Among them, the function χ x (·) represents the tailwater level h T,x,t and turbine displacement q H,i,t , overflow flow s x,t The constraints of the power generated by the hydropower station and the water level satisfy the following expression:

[0100]

[0101] Where P H,i,t represents the active output of hydropower unit i at time t; Indicates the minimum output value of hydropower unit i; represents the maximum output value of hydropower unit i; u H,i,t represents the working status of hydropower unit i at time t; q H,i,t represents the discharge of the turbine corresponding to hydropower unit i at time t; represents the minimum discharge of hydropower unit i; represents the maximum discharge of hydropower unit i; λ H,i represents the maximum ramp rate of hydropower unit i; Δt represents the time interval between adjacent scheduling; represents the active output of hydropower unit i; P H,i,t+1 represents the active power output of hydropower unit i at time t+1; v H,i,t 、w H,i,t Both represent the switching action of hydropower unit i at time t; h x,t represents the water level of hydropower station x at time t; represents the minimum water level of hydropower station x; Represents the maximum water level of hydropower station x.

[0102] The power consumption of pump j is P Pu,j,t With pumping flow q Pu,j,t 、Water head h L,y,t and pumping efficiency η Pu,j (h L,y,t ) and satisfy the following expression:

[0103]

[0104] Among them, P Pu,j,t represents the power consumption of pump j at time t; η Pu,j (h L,y,t ) represents the pumping efficiency of water pump j; ρ represents the density of water; g represents the gravitational acceleration of water; q Pu,j,trepresents the pumping flow rate of pump j at time t; h L,y,t represents the water head of pumping station y at time t; the set Ω y represents the pumps in pumping station y.

[0105] In actual engineering practice, water pumps are usually fixed at the optimal working point. Therefore, the above water pump model can be simplified as follows:

[0106]

[0107] Among them, P Pu,j,t represents the power consumption of pump j at time t; represents the power consumption of pump j at the optimal working point; represents the pumping flow rate of pump j at the optimal working point, u Pu,j,t represents the working state of pump j at time t; q Pu,j,t represents the pumping flow of pump j at time t; the set N pu Represents a collection of pumps.

[0108] The spinning reserve capacity of the hydropower unit and water pump is calculated as follows:

[0109]

[0110] in, represents the upward rotation reserve capacity of hydropower unit i at time t; represents the maximum output value of hydropower unit i; P H,i,t represents the active output of hydropower unit i at time t; u H,i,t represents the working status of hydropower unit i at time t; represents the downward rotation reserve capacity of hydropower unit i at time t; represents the upward rotation reserve capacity of pump j at time t; P Pu,j,t represents the power consumption of pump j at time t; u Pu,j,t represents the working status of pump j at time t; represents the maximum power consumption of pump j; Represents the downward rotation reserve capacity of pump j at time t.

[0111] The water balance equation includes the reservoir water storage V x,t , turbine displacement q H,i,t , pumping flow q Pu,j,t , overflow flow s x,t and the natural inflow rate r x,t , and use the adjacent scheduling time interval Δt to perform numerical normalization to satisfy the following expression:

[0112]

[0113] Among them, V x,t V represents the water storage capacity of the reservoir of hydropower station x at time t; x,t-1 represents the reservoir water storage capacity of hydropower station x at time t-1; Δt represents the time interval between adjacent dispatching; Indicates that the corresponding turbine of hydropower unit i is at t-τ x-1 -1 moment of displacement; It means that the hydropower station x-1 is at t-τ x-1 -1 overflow flow; q H,i,t-1 represents the discharge of the turbine corresponding to hydropower unit i at time t-1; s x,t-1 represents the overflow flow of hydropower station x at time t-1; τ x represents the time required for water to flow from hydropower station x to hydropower station x+1; Indicates that pump j is at t-τ x-1 -1 moment of displacement; q Pu,j,t-1 represents the displacement of pump j at time t-1; r x-1,t-1 represents the natural inflow rate of hydropower station x-1 at time t-1, which includes natural inflow factors such as evaporation, leakage, river diversion and confluence; Ω y-1 represents the water pump in the water pump station y-1; Ω y represents the water pump in the water pump station y; Ω x-1 represents the hydropower unit in the hydropower station x-1; Ω x Represents the hydropower unit in hydropower station x.

[0114] The water storage capacity of a hydropower station is also limited by factors such as dead water level capacity, beneficial reservoir capacity, and total reservoir capacity to achieve flood control.

[0115] In addition, the initial value of the water storage capacity of the hydropower station and the final value It is usually fixed within the scheduling period and satisfies the following expression:

[0116]

[0117] In the formula, represents the maximum capacity allowed for hydropower station x; represents the minimum capacity allowed for hydropower station x; V x,t represents the water storage capacity of hydropower station x at time t; V x,1 represents the water storage capacity of hydropower station x at the initial moment; represents the initial value of the water storage capacity of the hydropower station x; V x,T represents the water storage capacity of hydropower station x at the final moment; represents the final value of the reservoir water storage of hydropower station x.

[0118] Overflow flow refers to the water discharged directly from the hydropower station through the flood discharge facilities without passing through the turbines, so it cannot be used for power generation. The overflow flow is limited by the capacity of the flood discharge facilities, and the overflow flow satisfies the following expression:

[0119]

[0120] In the formula, s x,t represents the overflow flow of hydropower station x at time t; Represents the maximum overflow flow of hydropower station x.

[0121] The water level of the reservoir of the hydropower station x,t and reservoir water storage V x,t The functional relationship between them is related to the active power output equation (5) of the hydropower unit and the water balance formula (19). For most hydropower reservoirs, this mapping relationship needs to be determined based on the survey of the reservoir terrain and is usually highly nonlinear.

[0122] For example, the reservoir water level h x,t and reservoir water storage V x,t The mapping relationship between Satisfies the following expression:

[0123]

[0124] In the formula, h x,t represents the reservoir water level of hydropower station x at time t; V x,t represents the reservoir water storage capacity of hydropower station x at time t.

[0125] In engineering practice, pumped storage systems are also required to meet requirements in flood control, agricultural and industrial water supply, shipping, and environmental ecology. x,t and maximum flow rate ramp rate Establish constraints to satisfy the following expressions:

[0126]

[0127] In the formula, q H,i,t represents the displacement of the turbine corresponding to hydropower unit i at time t; s x,t represents the overflow flow of hydropower station x at time t; q Pu,j,t represents the pumping flow rate of pump j at time t; q H,i,t-1 represents the discharge of the turbine corresponding to hydropower unit i at time t-1; s x,t-1 represents the overflow flow of hydropower station x at time t-1; q Pu,j,t-1 represents the pumping flow rate of pump j at time t-1; Q x,t represents the minimum environmental flow of hydropower station x at time t; Represents the maximum flow ramp rate of hydropower station x.

[0128] The above is an initial pumped storage model obtained based on the operating parameters and operating constraints of the pumped storage power station of the multi-source system, which is used to represent the mapping relationship between the active output of the hydropower units of the pumped storage power station of the multi-source system, the reservoir water level and the reservoir water storage capacity.

[0129] The active power output equation (5) of the hydropower unit in the initial pumped storage model is about the turbine efficiency η tu,i,t (h D,k,t ,q H,i,t ), generator efficiency η gen,i , turbine displacement q H,i,t and water head D,j,t In order to improve the efficiency of subsequent unit combination optimization solution, it is necessary to linearize the active output equation of the hydropower unit. In short-term optimization operation, it is usually assumed that the turbine efficiency η tu,i,t (h D,k,t ,q H,i,t ) is a constant value, which is also the initial value in the adjacent dispatching time. Therefore, under the constant turbine efficiency, the active power output equation (5) of the hydropower unit is simplified to a bilinear function.

[0130] S12 can be implemented as:

[0131] Based on the active output of the hydropower unit, the turbine efficiency is replaced with a constant value and simplified to obtain a bilinear function;

[0132] By using the linear envelope method, a convex envelope is constructed at the boundary of the bilinear function to obtain an inequality group for the active output of the hydropower unit, and the inequality group is used as a linearized approximate result of the active output of the hydropower unit.

[0133] For example, since the convex linear envelope method can ensure convexity while maintaining a sufficiently tight boundary, the convex linear envelope method is used to convexly relax the active output equation (5) of the hydropower unit. By constructing a convex envelope around the bilinear function, the original equality constraint is replaced by a set of inequality constraints that satisfy the following expression:

[0134]

[0135] Among them, P H,i,t represents the active power output of hydropower unit i at time t; represents the minimum discharge of hydropower unit i; represents the maximum discharge of hydropower unit i; q H,i,t represents the displacement of the turbine corresponding to hydropower unit i at time t; h D,x,trepresents the water head of hydropower station x at time t; represents the minimum head of hydropower station x; represents the maximum water head of the hydropower station x; a i is the efficiency constant of the turbine-generator corresponding to hydropower unit i.

[0136] The efficiency constant a of the turbine-generator corresponding to the hydropower unit i i Satisfies the following calculation formula:

[0137] a i =η tu,i,1 (h D,x,1 ,q H,i,1 )η gen,i ρg, (32)

[0138] Where η tu,i,1 (h D,x,1 ,q H,i,1 ) represents the efficiency of the turbine corresponding to the hydropower unit i; η gen,i represents the efficiency of the generator corresponding to the hydropower unit i; ρ represents the density of water; g represents the gravitational acceleration of water.

[0139] S13 can be implemented as follows:

[0140] Based on the mapping relationship between the reservoir water level and the reservoir water storage capacity, the nonlinear variables of the function are linearly approximated using the SOS-2 constraint to obtain a linear approximation result of the mapping relationship between the reservoir water level and the reservoir water storage capacity.

[0141] In order to facilitate the subsequent calculation of nonlinear quantities in the pumped storage model, the reservoir water level h x,t and reservoir water storage V x,t The mapping relationship between (24) can be approximated as K by using the SOS-2 constraint h Linear function of the segment.

[0142] For example, assuming that the reservoir water volume range is V x Each segment point of can be expressed as The corresponding reservoir water levels are

[0143] By introducing variables Reservoir water level h x,t and reservoir water storage V x,t Satisfies the following expression:

[0144]

[0145] Where V x,trepresents the water storage capacity of the reservoir of hydropower station x at time t; Indicates the introduction of variables; represents the reservoir water storage corresponding to multiple segments approximated by SOS-2 constraints; h x,t represents the reservoir water level of hydropower station x at time t;

[0146] Reservoir water level h x,t and reservoir water storage V x,t The mapping relationship between them; R represents a constant.

[0147] In order to indicate the reservoir water level h x,t The segment interval is introduced by introducing a binary variable when When x,t falls into the kth segment.

[0148] Therefore, the introduced binary variables satisfy the following expression:

[0149]

[0150]

[0151] In the formula, Indicates the introduced variables; represents the introduced variable corresponding to hydropower station 1; represents the variable introduced corresponding to the hydropower station x+1; represents the introduced binary variable; represents the introduced binary variable corresponding to hydropower station 1; Represents the k-1th data in the binary variable introduced corresponding to the hydropower station x.

[0152] When V x,t Falling and In between, and are greater than 0, and the rest are equal to

[0153] Furthermore, the pumped storage model also includes: the functional relationship between the head loss and the displacement of the turbine corresponding to the hydropower unit, for the head loss h loss,x,t The displacement q of the turbine corresponding to the hydropower unit H,i,t The linearization of the functional relationship between includes the following steps:

[0154] By introducing q H,x,t express Assume q H,x,tThe value range can be divided into K loss part, The corresponding head loss value is

[0155] By introducing variables Head loss h loss,x,t The displacement q of the turbine corresponding to the hydropower unit H,i,t Satisfies the following expression:

[0156]

[0157] In the formula, q H,x,t Indicates the introduced alternative value, used to replace h represents the variable that introduces the correction for the displacement of the corresponding turbine of the hydropower unit; loss,x,t Indicates head loss.

[0158] In order to indicate the displacement q of the turbine corresponding to the hydropower unit H,x,t The segment interval, introducing binary variables

[0159] when When H,x,t falls in the kth segment, otherwise The introduced binary variables should satisfy the following expression:

[0160]

[0161]

[0162] In the formula, represents the introduced variable corresponding to the hydropower station x; represents the introduced variable corresponding to hydropower station 1; represents the variable introduced corresponding to the hydropower station x+1; represents the introduced binary variable corresponding to the hydropower station x; represents the introduced binary variable corresponding to hydropower station 1; Represents the k-1th data in the binary variable corresponding to the hydropower station x introduced.

[0163] When calculating the rotating reserve capacity of the hydropower unit and the water pump according to formulas (15)-(18), there are nonlinear terms formed by the multiplication of integer variables and continuous variables, which can be converted into linear terms according to the actual operating characteristics. H,i,t = 0, the hydropower unit stops operating at time t. According to formula (8), P H,i,t =0. When u H,i,t=1, according to formula (8) Therefore, it can be seen that no matter u H,i,t What value should P take? H,i,t u H,i,t =P H,i,t Always true.

[0164] Similarly, we can also deduce P Pu,i,t u Pu,i,t =P Pu,i,t Formulas (15)-(18) can be modified into the following expressions:

[0165]

[0166] In the formula, represents the upward rotation reserve capacity of hydropower unit i at time t; represents the maximum output value of hydropower unit i; P H,i,t represents the active output of hydropower unit i at time t; u H,i,t represents the working status of hydropower unit i at time t; represents the downward rotation reserve capacity of hydropower unit i at time t; represents the upward rotation reserve capacity of pump j at time t; P Pu,j,t represents the power consumption of pump j at time t; u Pu,j,t represents the working status of pump j at time t; represents the maximum power consumption of pump j; Represents the downward rotation reserve capacity of pump j at time t.

[0167] S14 is used to comprehensively perform linear processing results on the initial pumped storage model to obtain a pumped storage model for constructing a multi-source system unit combination model.

[0168] S14 specifically includes: using the convex linear envelope method to convexly relax the active output equation of the hydropower unit, and approximate the mapping relationship between the reservoir water level and the reservoir water storage capacity as a linear function, and at the same time linearize the functional relationship between the head loss and the displacement of the corresponding turbine of the hydropower unit, and comprehensively combine the convex relaxation results of the active output equation of the hydropower unit, the linear approximation results of the mapping relationship between the reservoir water level and the reservoir water storage capacity, and the linearization results of the functional relationship between the head loss and the displacement of the corresponding turbine of the hydropower unit, and use the comprehensive linearization processing results as the final pumped storage model.

[0169] S2 specifically includes: using the uncertainty set UD to model the operating parameters and operating constraints of wind turbines, photovoltaic turbines, and hydropower units with natural inflow in the multi-source system, and constructing a renewable energy uncertainty model that satisfies the following expression:

[0170]

[0171] Where UD is the uncertain set of wind turbines, photovoltaic turbines and hydropower units corresponding to natural inflow in the multi-source system; N Wind is the set of wind turbines in the multi-source system; N Pv is the set of photovoltaic units in the multi-source system; N HPP is the set of hydropower units in the multi-source system; T is the time set; is the available output of wind turbine m at time t; is the available output of photovoltaic unit n at time t; r x,t is the natural inflow of hydropower unit x at time t, i.e., the output of hydropower unit x; is the predicted average power output of wind turbine m at time t; is the power output of the wind turbine m at the upper limit of the interval at time t; is the power output of the wind turbine m at the lower limit of the interval at time t; is the predicted average power output of PV unit n at time t; is the power output of the upper limit of the interval of PV unit n at time t; is the power output of the lower limit of the interval of PV unit n at time t; is the predicted average power output of hydropower unit x at time t; is the power output of the hydropower unit x at the upper limit of the interval at time t; is the power output of the hydropower unit x at the lower limit of the interval at time t; is a binary variable for the output of wind turbine m at time t, which is used to indicate that the output of wind turbine m at time t is the upper bound. or lower bound Or the predicted value is a binary variable for the output of photovoltaic unit n at time t, used to indicate that the output of photovoltaic unit n at time t is the upper bound or lower bound Or the predicted value is a binary variable for the natural inflow of hydropower station x at time t, which is used to indicate that the natural inflow of hydropower unit x at time t is the upper bound. or lower bound Or the predicted value Γ TW To adjust the conservativeness of the uncertainty set of wind turbines in the time dimension; Γ TS is the conservative degree of the uncertainty set of photovoltaic units in the time dimension; Γ TRis the conservative degree of the uncertainty set of hydropower units corresponding to the natural inflow in the time dimension; Γ KW To adjust the conservativeness of the uncertainty set of wind turbines in the spatial dimension; Γ KS To adjust the conservativeness of the uncertainty set of the photovoltaic unit in the spatial dimension; Γ KR To adjust the conservativeness of the uncertainty set of hydropower units corresponding to the natural inflow in the spatial dimension.

[0172] S3 comprehensive pumped storage model and renewable energy uncertainty model. Since the unit combination optimization problem of the multi-source system is a multi-objective optimization problem, it is necessary to ensure that the proportion of renewable energy power generation in the multi-source system is the largest, while also ensuring that the operating cost of the multi-source system is the smallest. The unit combination of the multi-source system includes wind power generation, photovoltaic power generation, hydropower generation corresponding to natural inflows, and pumped storage power generation. The operating parameters and operating constraints of wind power generation, photovoltaic power generation, hydropower generation corresponding to natural inflows, and pumped storage power generation are integrated to construct the initial unit combination optimization model.

[0173] The initial unit commitment optimization model constructed using S3 includes: initial optimization functions and constraints;

[0174] The initial optimization function includes: taking the maximum proportion of renewable energy power generation in the multi-source system and the minimum operating cost of the multi-source system as the optimization target; the maximum proportion of renewable energy power generation in the multi-source system includes: the minimum reduction cost of renewable energy power generation in the multi-source system; the reduction cost of renewable energy power generation includes: the start-stop cost of hydropower units and water pumps in the pumped storage power station in the multi-source system, the penalty cost of abandoned light and abandoned wind, and the penalty cost of abandoned water;

[0175] The constraints of the initial optimization function include: the start-stop cost constraints of the hydropower units and water pumps in the pumped storage power station, the penalty cost constraints for abandoned solar power and abandoned wind power, and the penalty cost constraints for abandoned water power;

[0176] The renewable energy sources in the multi-source system include wind power, light and natural inflow of water.

[0177] Exemplarily, the multi-source system includes thermal power generation, wind power generation, photovoltaic power generation, hydropower generation corresponding to natural inflow, and pumped storage power generation. In order to use pumped storage power generation to suppress the volatility caused by the connection of wind power generation or photovoltaic power generation in the multi-source system, thermal power generation, wind power generation, photovoltaic power generation, hydropower generation corresponding to natural inflow, and pumped storage power generation are integrated to form an initial combination optimization model.

[0178] The constraints of the initial optimization function include: operating cost constraints of thermal power units, start-up and shutdown cost constraints of thermal power units, start-up and shutdown cost constraints of hydropower units and water pumps in pumped storage power stations, penalty cost constraints for abandoned solar and wind power, and penalty cost constraints for abandoned water.

[0179] Thermal power unit operating cost C Tc Satisfies the following expression:

[0180]

[0181] Start-up and shutdown cost of thermal power units C Ts Satisfies the following expression:

[0182]

[0183] When the proportion of renewable energy generation in a multi-source system is the largest, the power of renewable energy generation connected to the multi-source system is the largest. At this time, the reduction of renewable energy in the multi-source system is the smallest, and the reduction cost of renewable energy is the smallest. The reduction cost of renewable energy generation includes: the start-stop cost of hydropower units and water pumps in the pumped storage power station in the multi-source system, the penalty cost of abandoned light and wind, and the penalty cost of abandoned water, which satisfies the following expression:

[0184] The start-up and shutdown costs of hydropower units and pumps in pumped storage power stations C Hs Satisfies the following expression:

[0185]

[0186] Penalty cost for wind and solar curtailment C RN Satisfies the following expression:

[0187]

[0188] Water abandonment penalty cost C W Satisfies the following expression:

[0189]

[0190] In the formula, C Tc Represents the operating cost of thermal power units; N T Represents the set of thermal power units in a multi-source system; C Tc,k,t represents the operating cost of thermal power unit k at time t; α k , β k and γ k All represent the cost coefficient of thermal power unit k; T,k,t represents the output of thermal power unit k at time t; u T,k,t Represents the binary state of the thermal power unit. T,k,t =1 means the thermal power unit is in operation; C Ts represents the start-up and shutdown cost of thermal power units; v T,k,t and w T,k,t Both represent the operating status of thermal power units. When v T,k,t=1 means that thermal power unit k is started at time t, when w T,k,t =1 means that thermal power unit k is shut down at time t; represents the cost coefficient for starting up thermal power unit k; represents the cost coefficient of shutting down thermal power unit k; is the startup cost coefficient of hydropower unit i; represents the startup cost coefficient of water pump j; ω Wind Represents the penalty coefficient for wind abandonment; ω PV Indicates the penalty coefficient for abandoning light; ω H represents the penalty coefficient for abandoning water; represents the wind abandonment volume of wind turbine m at time t; It indicates the amount of abandoned light of PV unit n at time t.

[0191] Furthermore, y is defined to represent the unit combination decision variable made in the first stage, and x is defined to represent the economic dispatch variable of thermal power, pumped storage power station and renewable energy in the second stage; since wind power, photovoltaic power generation and natural inflow are affected by uncertainties, these uncertainties are represented by the random variable u described by the uncertainty set UD; the constraints Q(y,u) in the second stage include constraints on thermal power units, renewable energy and transmission lines.

[0192] Then the initial optimization function of the initial unit commitment optimization model is:

[0193]

[0194] In the formula, C Ts represents the start-up and shutdown cost of thermal power units; C Tc Represents the operating cost of thermal power units; C Hs represents the start-up and shutdown costs of the hydropower units and water pumps in the pumped storage power station; C RN represents the penalty cost for abandoning wind and solar power; C W represents the penalty cost for abandoning water.

[0195] The constraints of the initial optimization function are:

[0196]

[0197]

[0198] (1)-(4), (6)-(11), (13)-(14), (19)-(23) and (25)-(54), (85)

[0199] In the formula, u T,k,t Represents the binary state of the thermal power unit. T,k,t =1 means the thermal power unit is in operation; u T,k,1Represents the binary state of the thermal power unit in the first time period; N T is the collection of thermal power units in the multi-source system; G k represents the start time of thermal power unit k in the first time period; Indicates the time required for the start-up of thermal unit k; E k Indicates the startup time of thermal power unit k before the first time period; Indicates the shutdown time required for thermal unit k; B k represents the downtime of thermal power unit k in the first time period; v T,k,t and w T,k,t Both represent the operating status of thermal power units. When v T,k,t =1 means that thermal power unit k is started at time t, when w T,k,t =1 means that thermal power unit k is shut down at time t; J k represents the downtime of thermal power unit k before the first time period; represents the minimum output power of thermal power unit k; represents the maximum output power of thermal power unit k; P T,k,t represents the output power of thermal power unit k at time t; P T,k,t+1 represents the output power of thermal power unit k at time t+1; T,k represents the ramp rate of thermal power unit k; Δt represents the time interval between adjacent scheduling; represents the upper spinning reserve capacity of thermal power unit k at time t; represents the lower spinning reserve capacity of thermal power unit k at time t; P Wind,m,t represents the output power of wind turbine m at time t; P PV,n,t represents the output power of photovoltaic generator n at time t; represents the available output power of wind turbine m at time t; represents the wind abandonment amount of wind turbine m at time t; represents the available output power of photovoltaic generator n at time t; represents the amount of abandoned light from photovoltaic generator n at time t; L b,t Represents the load on bus b at time t; Ω H,b represents the set of hydropower units assigned to bus b; Ω Pu,b represents the set of pumps assigned to bus b; Ω T,b represents the set of hydro-thermal power units assigned to bus b; Ω Wind,b represents the set of wind turbines assigned to bus b; Ω PV,b represents the set of PV units assigned to bus b; It represents the set of branches into which the active power flow enters from busbar b; Indicates the set of branches where active power flows out from busbar b; L u % represents the demand factor of upper spinning reserve capacity; L l % represents the demand factor of the spinning reserve capacity; f l,t represents the power flow of branch l at time t; It represents the phase angle of the bus voltage at the head end of branch l at time t; represents the phase angle of the bus voltage at the end of branch l at time t; X l is the reactance of branch l; Indicates the thermal stability limit of branch l; N b Represents the set of busbars b.

[0200] Formula (64) represents the initial optimization function of the initial unit combination optimization model; Formulas (65)-(85) represent the constraints of the initial optimization function; specifically: Formulas (65)-(69) represent the minimum startup and shutdown time constraints of thermal power units; Formulas (70)-(71) represent the startup and shutdown logic constraints of thermal power units; Formulas (72)-(75) represent the operation constraints of thermal power units in multi-source systems; Formulas (76)-(79) represent the operation constraints of renewable energy in multi-source systems; Formulas (80)-(84) represent the operation constraints of transmission lines; Formula (85) includes Formulas (1)-(4), (6)-(11), (13)-(14), (19)-(23) and (25)-(54) which represent the operation constraints of pumped storage power stations in multi-source systems.

[0201] Reference Figure 3 In some embodiments, in order to improve the efficiency of solving the initial optimization model of the multi-source system in the subsequent process, the embodiment of the present invention provides a method for reconstructing the initial unit combination optimization model based on a two-stage robust optimization algorithm (Column and Constraint Generation, C&CG). The S4 provided by the embodiment of the present invention includes:

[0202] S41, for the initial unit combination optimization model, using a two-stage robust optimization algorithm, reconstructing the initial unit combination optimization model to obtain a first-stage function and a second-stage function; the first-stage function includes taking the unit combination operation cost of the multi-source system as an optimization target, and the second-stage function includes taking the cost reduction of renewable energy power generation as an optimization target; according to the second-stage function, obtaining the constraints of the second-stage function;

[0203] S42, using the inner column and constraint generation method, replacing and equating the integer variables in the constraints of the two-stage function to obtain an equivalent two-stage function and constraints;

[0204] S43, transforming the constraints of the equivalent two-stage function by replacing the dual variables to obtain the final two-stage function and constraints;

[0205] S44, comprehensively considering the first-stage function and the final second-stage function and constraints to determine the final unit commitment optimization model.

[0206] Since the unit combination problem corresponding to the initial unit combination optimization model is a multi-objective optimization problem, the initial optimization function in the initial unit combination optimization model includes: maximizing the proportion of renewable energy power generation in the multi-source system and minimizing the operating cost of the multi-source system as the optimization objectives; the maximizing proportion of renewable energy power generation in the multi-source system includes: minimizing the reduction cost of renewable energy power generation in the multi-source system; the reduction cost of renewable energy power generation includes: starting and stopping costs of hydropower units and water pumps in pumped-storage power stations in the multi-source system, penalty costs for abandoning solar power and wind power, and penalty costs for abandoning water.

[0207] The operating cost of the multi-source system corresponds to the operating cost of the multi-source system composed of thermal power, wind power, photovoltaics, and pumped storage systems. Renewable energy power generation accounts for the largest proportion, including the smallest renewable energy reduction cost, and the renewable energy reduction cost includes the start-up and shutdown costs of hydropower units and water pumps, the penalty costs for abandoning light and wind, and the penalty costs for abandoning water. The penalty costs for abandoning water are caused by the overflow flow in the natural inflow.

[0208] Since the fuel cost of a thermal power unit is a quadratic function, it is approximated by P piecewise linear functions to satisfy the following expression:

[0209]

[0210] In the formula, C Tc,k,t represents the operating cost of thermal power unit k at time t; P T,k,t represents the output of thermal power unit k at time t; and are the corrected cost coefficients of thermal power units, calculated according to formula (87); α k , β k and γ k Both represent the cost coefficient of thermal power unit k before approximation; u T,k,t Represents the binary state of the thermal power unit. T,k,t =1 means the thermal power unit is in operation; represents the pth interval within the output power range of thermal power unit k; Represents the p+1th interval within the output power range of thermal power unit k.

[0211] Since the optimization objective function of the initial unit combination optimization model is essentially a two-stage robust optimization model, the initial combination optimization model can be reconstructed into a one-stage function and a two-stage function through S41. The one-stage function corresponds to the function of the unit combination decision variables of the initial unit combination optimization model, and the two-stage function corresponds to the economic dispatch variables of thermal power, pumped storage power stations and renewable energy.

[0212] For example, S41 can be expressed as the following canonical expression:

[0213]

[0214] st Ay≥b, (89)

[0215] Ex+Gz≥f-Ru-Dy, (90)

[0216] Tz ≥ V, (91)

[0217]

[0218]

[0219] Where c, d, g, A, b, E, G, f, R, D, T and V all represent constant matrices; u represents the uncertain variables in the uncertainty set UD; y represents the unit commitment decision variable made in the first stage; x represents the economic dispatch variable of thermal power, pumped storage power station and renewable energy in the second stage; Q(y,u) represents the constraints including hydropower units, renewable energy and transmission lines.

[0220] Formula (89) represents the constraint that only contains the first-stage variable y; Formula (91) represents the constraint that only contains the second-stage variable z; Formula (90) represents the constraint on the remaining variables except the first-stage variable y and the second-stage variable z.

[0221] To this end, the original problems (88)-(91) are transformed into one-stage functions and two-stage functions. The one-stage function pair includes the main problem, which includes equations (95)-(99), and the two-stage function pair includes sub-problems, which include equations (100)-(102).

[0222] The first-stage function satisfies the following expression,

[0223]

[0224] st Ay≥b, (96)

[0225]

[0226] In the formula, u l* ∈UD, η is an auxiliary variable.

[0227] For the problem corresponding to the two-stage function, the finite subset is constructed by solving a separate subproblem that determines the worst case to be added and provides a lower bound on the optimal solution.

[0228] The subproblem corresponding to the second-stage function is a two-level maximum and minimum problem (two-level max-min problem), which includes the subproblem solved in the first stage. The two-stage function satisfies the following expression:

[0229]

[0230] Tz≥V, (102)

[0231] The solution for the two-stage function can be derived with a finite optimal value x * &z * and the corresponding solution for the worst case u * , or find a u * , so that the subproblem does not exist, that is

[0232] When the first-stage solution is given Finally, the worst case can be obtained by using the solution of the two-stage function, which is used as the lower bound and an upper bound is provided. By clamping the upper and lower bounds, an algorithm for obtaining an exact solution can be proposed. Let LB and UB denote the lower and upper bounds respectively, and ε denote the optimality tolerance.

[0233] However, due to the existence of integer variables, the subproblem corresponding to the two-stage function is difficult to solve, which makes the strong duality condition no longer hold, resulting in the existing general two-level problem algorithm being unsuitable for solving this subproblem. Therefore, it is necessary to use the inner layer sequence and constraint generation equations to replace and equate the integer variables of the constraints of the two-stage function based on the countability of the integer variable z.

[0234] Exemplarily, S42 may be implemented as follows:

[0235] By using the inner column and constraint generation method, the integer variable z in the constraint of the two-stage function is replaced and equivalent, and the equivalent two-stage function and constraint are obtained, which satisfies the following expression:

[0236]

[0237] Among them, x r Yes rThe corresponding decision variables. Each constraint problem is a linear programming problem that meets the use requirements of the KKT (Karush-Kuhn-Tucker, a necessary condition for the optimal solution of nonlinear programming) conditions.

[0238] Exemplarily, S43 includes: setting π r Represents the dual variable of the rth constraint in the equivalence problem, transforms the constraints of the equivalent two-stage function, and obtains the final two-stage function and constraints, that is, transforms equations (103)-(105) to satisfy the following expression:

[0239]

[0240] Equations (110)-(111) are dual constraints and can be solved using the Big M method (a method for solving linear programming problems). Therefore, equations (103)-(105) are converted to equations (106)-(111) that can be solved.

[0241] S44 can be implemented as: combining equations (95)-(99) and equations (106)-(111) as the final unit combination optimization model. The final unit combination optimization model is used to obtain the production method of the pumped-storage power station in the multi-selection system according to the power generation data of renewable energy and thermal power units in the multi-source system, and optimize the production status of the hydropower units and water pumps in the pumped-storage power station according to the production method, so as to ensure that the proportion of renewable energy power generation is maximized and the operating cost of the multi-source system is minimized.

[0242] By linearizing the operating parameters and operating constraints of the pumped-storage power station, the interference of nonlinear integer variables in the pumped-storage power station is weakened; at the same time, the uncertain set is used to model renewable energy, without generating a large number of scenarios, thereby improving the efficiency of solving the uncertain unit combination optimization problem; the two-stage robust optimization and row and column generation algorithm are used for transformation, so that the variables and constraints in the unit combination optimization problem of the multi-source system meet the strong duality required for solving, which is convenient for solving the unit combination optimization problem, simplifies the mapping relationship between variables and constraints in the unit combination optimization problem, and improves the calculation speed of solving the unit combination optimization problem of the multi-source system and obtains accurate calculation results.

[0243] Embodiment 2:

[0244] Reference Figure 4 The present invention also proposes a unit combination model building system for a multi-source system, comprising:

[0245] A pumped storage model building unit is used to obtain a pumped storage model by linearization processing according to the operating parameters and operating constraints of the pumped storage power station of the multi-source system;

[0246] A renewable energy uncertainty model building unit is used to model the operation parameters and operation constraints of wind turbines, photovoltaic turbines and hydropower units with natural inflow in the multi-source system using uncertainty sets to obtain a renewable energy uncertainty model;

[0247] A unit combination optimization model building unit is used to build an initial unit combination optimization model based on the renewable energy uncertainty model and the pumped storage model, taking the maximum proportion of renewable energy power generation in the multi-source system and the minimum operating cost of the multi-source system as the optimization objectives;

[0248] The unit commitment model conversion unit is used to reconstruct the initial unit commitment optimization model by using a two-stage robust optimization and row and column generation algorithm to obtain a final unit commitment optimization model.

[0249] Optionally, the initial unit commitment model includes optimization functions and constraints;

[0250] The optimization function includes: taking the maximum proportion of renewable energy power generation in the multi-source system and the minimum operating cost of the multi-source system as the optimization target; the maximum proportion of renewable energy power generation in the multi-source system includes: the minimum reduction cost of renewable energy power generation in the multi-source system; the reduction cost of renewable energy power generation includes: the start-stop cost of hydropower units and water pumps in the pumped storage power station in the multi-source system, the penalty cost of abandoned solar power and abandoned wind power, and the penalty cost of abandoned water power;

[0251] The constraints of the optimization function include: the start-stop cost constraints of the hydropower units and water pumps in the pumped storage power station, the penalty cost constraints for abandoned solar power and abandoned wind power, and the penalty cost constraints for abandoned water power;

[0252] The renewable energy sources in the multi-source system include wind power, light and natural inflow of water.

[0253] Optionally, the unit commitment optimization model conversion unit includes:

[0254] The model decomposition subunit is used to reconstruct the initial unit combination optimization model by using a two-stage robust optimization algorithm to obtain a first-stage function and a second-stage function; the first-stage function includes taking the unit combination operation cost of the multi-source system as an optimization target, and the second-stage function includes taking the cost reduction of renewable energy power generation as an optimization target;

[0255] A constraint acquisition subunit, used for acquiring the constraint of the two-stage function according to the two-stage function;

[0256] A first conversion subunit is used to replace and equate integer variables in the constraints of the two-stage function by using an inner column and constraint generation method to obtain an equivalent two-stage function and constraint;

[0257] The second transformation subunit is used to transform the constraints of the equivalent two-stage function by using dual variable substitution to obtain the final two-stage function and constraints;

[0258] The unit commitment optimization model output subunit is used to integrate the first-stage function and the final second-stage function and constraints to determine the final unit commitment optimization model.

[0259] Optionally, the pumped storage model building unit includes:

[0260] A pumped storage model construction subunit is used to construct an initial pumped storage model based on the operating parameters and operating constraints of the pumped storage power station of the multi-source system, wherein the initial pumped storage model includes: a mapping relationship between the active output of the hydropower unit, the reservoir water level and the reservoir water storage capacity;

[0261] A first linearization subunit is used to perform linearization processing on the nonlinear variables in the active power output of the hydropower unit to obtain a linearized approximate result of the active power output of the hydropower unit;

[0262] The second linearization subunit is used to linearize the nonlinear variables in the mapping relationship between the reservoir water level and the reservoir water storage capacity, so as to obtain a linearized approximate result of the mapping relationship between the reservoir water level and the reservoir water storage capacity;

[0263] The pumped storage model generation subunit is used to synthesize the linearized approximation result of the active power output of the hydropower unit and the linearized approximation result of the mapping relationship between the reservoir water level and the reservoir water storage capacity as the final pumped storage model.

[0264] Optionally, the first linearization subunit is specifically used for:

[0265] Based on the active output of the hydropower unit, the turbine efficiency is replaced with a constant value and simplified to obtain a bilinear function;

[0266] By using the linear envelope method, a convex envelope is constructed at the boundary of the bilinear function to obtain an inequality group for the active output of the hydropower unit, and the inequality group is used as a linearized approximate result of the active output of the hydropower unit.

[0267] Optionally, the second linearization subunit is specifically used for:

[0268] Based on the mapping relationship between the reservoir water level and the reservoir water storage capacity, the nonlinear variables of the function are linearly approximated using the SOS-2 constraint to obtain a linear approximation result of the mapping relationship between the reservoir water level and the reservoir water storage capacity.

[0269] Optionally, the renewable energy uncertainty model satisfies the following expression:

[0270]

[0271]

[0272] Where UD is the uncertain set of wind turbines, photovoltaic turbines and hydropower units corresponding to natural inflow in the multi-source system; N Wind is the set of wind turbines in the multi-source system; N PV is the set of photovoltaic units in the multi-source system; N HPP is the set of hydropower units in the multi-source system; T is the time set; is the available output of wind turbine m at time t; is the available output of photovoltaic unit n at time t; r x,t is the natural inflow of hydropower unit x at time t, i.e., the output of hydropower unit x; is the predicted average power output of wind turbine m at time t; is the power output of the wind turbine m at the upper limit of the interval at time t; is the power output of the wind turbine m at the lower limit of the interval at time t; is the predicted average power output of PV unit n at time t; is the power output of the upper limit of the interval of PV unit n at time t; is the power output of the lower limit of the interval of PV unit n at time t; is the predicted average power output of hydropower unit x at time t; is the power output of the hydropower unit x at the upper limit of the interval at time t; is the power output of the hydropower unit x at the lower limit of the interval at time t; is a binary variable for the output of wind turbine m at time t, which is used to indicate that the output of wind turbine m at time t is the upper bound. or lower bound Or the predicted value is a binary variable for the output of photovoltaic unit n at time t, used to indicate that the output of photovoltaic unit n at time t is the upper bound or lower bound Or the predicted value is a binary variable for the natural inflow of hydropower station x at time t, which is used to indicate that the natural inflow of hydropower unit x at time t is the upper bound. or lower bound Or the predicted value Γ TWTo adjust the conservativeness of the uncertainty set of wind turbines in the time dimension; Γ TS is the conservative degree of the uncertainty set of photovoltaic units in the time dimension; Γ TR is the conservative degree of the uncertainty set of hydropower units corresponding to the natural inflow in the time dimension; Γ KW To adjust the conservativeness of the uncertainty set of wind turbines in the spatial dimension; Γ KS To adjust the conservativeness of the uncertainty set of the photovoltaic unit in the spatial dimension; Γ KR To adjust the conservativeness of the uncertainty set of hydropower units corresponding to the natural inflow in the spatial dimension.

[0273] Embodiment 3:

[0274] The present invention also proposes a unit combination production optimization method for a multi-source system, comprising:

[0275] S51, solving the unit combination optimization model according to the current operating parameters of the pumped storage power station, wind turbines, photovoltaic units and hydropower units with natural inflow in the multi-source system to obtain the unit combination optimization result;

[0276] S52, determining a production optimization plan for the hydropower units and the water pumping station in the pumped-storage power station according to the unit combination optimization result;

[0277] The unit combination optimization model is constructed using the unit combination production optimization model construction method for a multi-source system as described in the above technical solution.

[0278] Optionally, S51 disclosed in the present invention includes:

[0279] S511, solving the unit commitment decision variables of the unit commitment optimization model to obtain a first optimization result;

[0280] S512, updating the upper bound and the lower bound of the unit commitment optimization model respectively according to the first optimization result to obtain updated upper bound and lower bound;

[0281] S513, based on the updated upper bound and lower bound, solving the economic dispatch variables of the wind turbine generator set, the photovoltaic turbine generator set, and the hydropower generator set of the unit combination optimization model to obtain a second optimization result;

[0282] S514, updating the upper bound and the lower bound of the unit commitment optimization model according to the second optimization result;

[0283] S515, according to the updated upper and lower bounds, using the clamping criterion, iteratively optimize the unit combination decision variables of the unit combination optimization model, update the first optimization result and the second optimization result, and update the upper and lower bounds of the unit combination optimization model until the upper and lower bounds of the updated unit combination optimization model meet the iteration termination condition, and use the first optimization result and the second optimization result when the upper and lower bounds of the updated unit combination optimization model meet the iteration termination condition as the unit combination optimization result.

[0284] When solving the unit combination optimization model, the initial value of the upper bound of the unit combination optimization model is first set to infinity, and the initial value of the lower bound of the unit combination optimization model is set to infinitesimal, and the current iteration algebra k is assigned a value of 0. The iteration termination condition is used to determine the unit combination optimization result that satisfies the unit combination optimization model obtained by the clamping criterion. The iteration termination condition may include an allowable difference ε, and the difference between the upper bound and the lower bound is evaluated using the allowable difference ε, so as to determine whether the number of iterative updates meets the termination condition.

[0285] The main problem includes the unit combination decision variables of the unit combination optimization model, which corresponds to the first-stage function of the unit combination optimization model construction method in the same inventive concept; the sub-problem includes the economic dispatch variables of wind turbines, photovoltaic units, and hydropower units in the unit combination optimization model, which corresponds to the second-stage function of the unit combination optimization model construction method in the same inventive concept.

[0286] Exemplarily, solving the unit commitment optimization model to obtain the unit commitment optimization result includes:

[0287] Step 1: Set LB = -∞, UB = +∞, k = 0;

[0288] Step 2: Solve the main problem and obtain (y (k+1)* ,η (k+1)* ,z 1* ,…,z k* ,x 1* ,…,x k* ), update LB=c according to the solution result T y (k+1)* +η (k+1)* ,

[0289] At the same time, the difference between the updated upper bound and the lower bound is calculated, and the difference between the updated upper bound and the lower bound is evaluated based on the allowable difference ε;

[0290] If UB-LB≤ε, return y (k+1)* , and (y (k+1)* ,η (k+1)* ,z 1* ,…,zk* ,x 1* ,…,x k* ) as the unit combination optimization result of the unit combination optimization model, and end the iteration;

[0291] If UB-LB>ε, go ​​to step 3;

[0292] Step 3: Solve the subproblems The optimal solution (z (k+1)* ,x (k+1)* ), and use the optimal solution to continue updating the upper and lower bounds, expressed as follows: Update LB = max{LB,d T x (k+1)* +g T z (k+1)*},renew

[0293] Step 4: Update the main problem with additional constraints, and use the updated main problem to update the upper and lower bounds of step 2. The additional constraints satisfy the following expression:

[0294] η≥d T x k+1 +g T z k+1 ,

[0295] Ex k+1 +Gz k+1 ≥f-Ru (k+1)* -Dy,

[0296] Tz k+1 ≥V,

[0297] Among them, u (k+1)* Solve through step 3 get.

[0298] Step 5: Update k=k+1 and jump to step 2.

[0299] The difference between the upper bound and the lower bound is gradually reduced by using the clamping criterion, so that the optimal values ​​of the first optimization result and the second optimization result are obtained by using the updated upper bound and lower bound, and the first optimization result and the second optimization result are used as the unit combination optimization result, and the unit combination optimization result is used to realize the regulation of the production mode of the pumped storage power station in the current multi-source system.

[0300] Exemplarily, in order to evaluate the effect of the proposed method, an IEEE-24 bus system for verification testing is constructed in the embodiment of the present invention. The unit combination optimization production method of the current multi-source system is modeled in MATLAB (a matrix data calculation software) using YALMIP (an optimization modeling toolbox for simplifying the modeling and solution process of optimization problems), and the test is carried out on a computer configured with an Intel Core 7 CPU (2.90GHz) and 16GB RAM (Random Access Memory), and the mixed integer linear programming problem is solved by GUROBI 9.1.1 (an optimization solver).

[0301] like Figure 5 As shown, in the IEEE-24 bus power system, the power plants located at buses 7 and 16 are replaced by two wind farms, the power plants located at buses 6 and 20 are replaced by two photovoltaic power stations, four hydropower stations are set up on buses 3, 9, 14, and 18, and a water pumping station is set up on bus 3.

[0302] In the initial period of dispatch, the water storage capacity of each hydropower station is 2.08×10 9 m 3 , 6.9×10 8 m 3 , 1.34×10 8 m 3 and 1.72×10 9 m 3 The optimal operating point of the two pumps in the pump station is set at 180MW and the pumping flow rate is 150m 3 / s, and the pump station is located between hydropower station #1 and hydropower station #2. The total operation cycle is set to 24 hours, and the time resolution is 1 hour.

[0303] Reservoir water level h x,t and reservoir water storage V x,t The mapping relationship between (Equation 24) can be approximated as K by using the SOS-2 constraint h = a linear function of 3 segments. For 4 reservoirs, we have

[0304]

[0305] In order to verify the accuracy of the proposed method for optimizing the production of pumped storage power station units in the multi-source system in terms of linearization, the solutions of the pumped storage model before and after linearization are compared, and the hydropower output power P is compared. H,i,t The total operating cost C of the multi-source system consisting of thermal power, wind / solar, and pumped storage systems total The initial scenarios for the optimization production method of pumped storage power station unit portfolio include:

[0306] Scenario 1: The initial reservoir water levels of the four hydropower stations are 97.4m, 94.1m, 61.5m and 95.3m respectively.

[0307] Scenario 2: The initial reservoir water levels of the four hydropower stations are 123.0m, 121.2m, 66.1m and 105.7m respectively.

[0308] Scenario 3: The discharge ranges of the four hydropower stations are [250,650], [200,800], [200,900] and [150,800] m 3 / s.

[0309] Scenario 4: The discharge ranges of the four hydropower stations are [250,700], [200,900], [100,1200] and [100,1100] m 3 / s.

[0310] The calculation results of the pumped storage model before linearization are regarded as the benchmark values, and the comparison values ​​of the pumped storage model before and after linearization are obtained, as shown in the following table:

[0311] Table 1 Comparison of pumped storage model before and after linearization

[0312]

[0313]

[0314] Among them, Δ(P H,i,t ) represents the average absolute error between the approximate value and the accurate value of the hydropower station output power before and after linearization, δ(P H,i,t ) represents the average relative error between the approximate value and the accurate value of the hydropower station output power before and after linearization, δ(C total ) represents the average relative error between the approximate value and the accurate value of the objective function before and after linearization. As can be seen from Table 1, the maximum absolute error of the hydropower station output power is 24.06MW, and the relative error is 0.892%; the maximum relative error of the objective function is 0.05%, both of which are within an acceptable range.

[0315] In summary, the multi-source system unit combination optimization method proposed in the present invention can not only obtain an accurate production optimization method to ensure that the proportion of renewable energy in the multi-source system is maximized and the operating cost of the multi-source system is minimized, but also improve the calculation speed of solving the unit combination optimization problem of the multi-source system and greatly reduce the calculation time.

[0316] Embodiment 4:

[0317] Reference Figure 6Based on the same inventive concept, the present invention also proposes a unit combination production optimization system for a multi-source system, comprising:

[0318] The result solving unit is used to solve the unit combination optimization model according to the current operating parameters of the pumped storage power station, wind turbine, photovoltaic unit and hydropower unit with natural inflow in the current multi-source system to obtain the unit combination optimization result;

[0319] A production optimization unit, used to confirm the production optimization plan of the hydropower units and water pumping stations in the pumped storage power station according to the unit combination optimization result;

[0320] The pre-constructed unit combination model is constructed using the unit combination model construction method for a multi-source system as described in the above technical solution.

[0321] Optionally, the result solving unit includes:

[0322] A first solving subunit is used to solve the unit combination decision variables of the unit optimization combination model to obtain a first optimization result;

[0323] A first boundary updating subunit, configured to update the upper bound and the lower bound of the unit commitment optimization model according to the first optimization result, to obtain updated upper bound and lower bound;

[0324] A second solving subunit is used to solve the economic dispatch variables of the wind turbines, photovoltaic turbines and hydropower units in the unit combination optimization model based on the updated upper bound and lower bound to obtain a second optimization result;

[0325] A second boundary updating subunit, configured to update an upper bound and a lower bound of the unit commitment optimization model according to the second optimization result;

[0326] The result output subunit is used to iteratively optimize the unit combination decision variables of the unit combination optimization model according to the updated upper and lower bounds and using the clamping criterion, update the first optimization result and the second optimization result, and update the upper and lower bounds of the unit combination optimization model until the upper and lower bounds of the updated unit combination optimization model meet the iteration termination condition, and use the first optimization result and the second optimization result when the upper and lower bounds of the updated unit combination optimization model meet the iteration termination condition as the unit combination optimization result.

[0327] Embodiment 5:

[0328] like Figure 7As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0329] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method flows or corresponding functions, so as to implement a unit combination optimization model construction method for a multi-source system or a step of a unit combination production optimization method for a multi-source system in the above-mentioned embodiment.

[0330] Embodiment 6:

[0331] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It is understandable that the storage medium here can include both a built-in storage medium in an electronic device and an extended storage medium supported by the electronic device. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a unit combination optimization model construction method for a multi-source system or a unit combination production optimization method for a multi-source system in the above-mentioned embodiment.

[0332] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0333] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0334] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0335] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0336] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for constructing a unit commitment optimization model for a multi-source system, characterized in that: include: According to the operation parameters and operation constraints of the pumped storage power station in the multi-source system, the pumped storage model is obtained by linearization processing; According to the operating parameters and operating constraints of wind turbines, photovoltaic units, and hydropower units with natural inflow in the multi-source system, the uncertainty set is used to model and obtain the uncertainty model of renewable energy; Based on the renewable energy uncertainty model and the pumped storage model, the initial unit commitment optimization model is constructed by taking the maximum proportion of renewable energy power generation in the multi-source system and the minimum operating cost of the multi-source system as the optimization objectives; The initial unit commitment optimization model is reconstructed using a two-stage robust optimization and row-column generation algorithm to obtain a final unit commitment optimization model.

2. The method according to claim 1, characterized in that The initial unit commitment optimization model includes: initial optimization functions and constraints; The initial optimization function includes: taking the maximum proportion of renewable energy power generation in the multi-source system and the minimum operating cost of the multi-source system as the optimization target; the maximum proportion of renewable energy power generation in the multi-source system includes: the minimum reduction cost of renewable energy power generation in the multi-source system; the reduction cost of renewable energy power generation includes: the start-stop cost of hydropower units and water pumps in the pumped storage power station in the multi-source system, the penalty cost of abandoned light and abandoned wind, and the penalty cost of abandoned water; The constraints of the initial optimization function include: the start-stop cost constraints of the hydropower units and water pumps in the pumped storage power station, the penalty cost constraints for abandoned solar power and abandoned wind power, and the penalty cost constraints for abandoned water power; The renewable energy sources in the multi-source system include wind power, light and natural inflow of water.

3. The method according to claim 2, characterized in that The initial unit commitment optimization model is reconstructed using a two-stage robust optimization and row-column generation algorithm to obtain a final unit commitment optimization model, including: For the initial unit combination optimization model, the initial unit combination optimization model is reconstructed by using a two-stage robust optimization algorithm to obtain a first-stage function and a second-stage function; the first-stage function includes taking the unit combination operation cost of the multi-source system as an optimization target, and the second-stage function includes taking the cost reduction of renewable energy power generation as an optimization target; according to the second-stage function, the constraints of the second-stage function are obtained; Using the inner column and constraint generation method, the integer variables in the constraints of the two-stage function are replaced and equivalent, and the equivalent two-stage function and constraints are obtained; By replacing the dual variables, the constraints of the equivalent two-stage function are transformed to obtain the final two-stage function and constraints; The first-stage function and the final second-stage function and constraints are combined to determine the final unit combination optimization model.

4. The method according to claim 1, characterized in that The operation parameters and operation constraints of the pumped storage power station based on the multi-source system are linearized to obtain a pumped storage model, including: Based on the operation parameters and operation constraints of the pumped storage power station of the multi-source system, an initial pumped storage model is constructed, wherein the initial pumped storage model includes: a mapping relationship between the active output of the hydropower unit, the reservoir water level and the reservoir water storage capacity; Performing linearization processing on nonlinear variables in the active power output of the hydropower unit to obtain a linearized approximate result of the active power output of the hydropower unit; Linearizing the nonlinear variables in the mapping relationship between the reservoir water level and the reservoir water storage capacity to obtain a linearized approximate result of the mapping relationship between the reservoir water level and the reservoir water storage capacity; The linearized approximation result of the active output of the hydropower unit and the linearized approximation result of the mapping relationship between the reservoir water level and the reservoir water storage capacity are combined to serve as the final pumped storage model.

5. The method according to claim 4, characterized in that The linearization of the nonlinear variables in the active output of the hydropower unit to obtain a linearized approximate result of the active output of the hydropower unit includes: Based on the active output of the hydropower unit, the turbine efficiency is replaced with a constant value and simplified to obtain a bilinear function; By using the linear envelope method, a convex envelope is constructed at the boundary of the bilinear function to obtain an inequality group for the active output of the hydropower unit, and the inequality group is used as a linearized approximate result of the active output of the hydropower unit.

6. The method according to claim 4, characterized in that The nonlinear variables in the mapping relationship between the reservoir water level and the reservoir water storage capacity are linearized respectively to obtain the linearized approximate result of the mapping relationship between the reservoir water level and the reservoir water storage capacity, including: Based on the mapping relationship between the reservoir water level and the reservoir water storage capacity, the nonlinear variables of the function are linearly approximated using the SOS-2 constraint to obtain a linear approximation result of the mapping relationship between the reservoir water level and the reservoir water storage capacity.

7. The method according to claim 1, characterized in that The renewable energy uncertainty model satisfies the following expression: Where UD is the uncertain set of wind turbines, photovoltaic turbines and hydropower units corresponding to natural inflow in the multi-source system; N Wind is the set of wind turbines in the multi-source system; N PV is the set of photovoltaic units in the multi-source system; N HPP is the set of hydropower units in the multi-source system; T is the time set; is the available output of wind turbine m at time t; is the available output of photovoltaic unit n at time t; r x,t is the natural inflow of hydropower unit x at time t, i.e., the output of hydropower unit x; is the predicted average power output of wind turbine m at time t; is the power output of the wind turbine m at the upper limit of the interval at time t; is the power output of the wind turbine m at the lower limit of the interval at time t; is the predicted average power output of PV unit n at time t; is the power output of the upper limit of the interval of PV unit n at time t; is the power output of the lower limit of the interval of PV unit n at time t; is the predicted average power output of hydropower unit x at time t; is the power output of the hydropower unit x at the upper limit of the interval at time t; is the power output of the hydropower unit x at the lower limit of the interval at time t; is a binary variable for the output of wind turbine m at time t, which is used to indicate that the output of wind turbine m at time t is the upper bound. or lower bound Or the predicted value is a binary variable for the output of photovoltaic unit n at time t, used to indicate that the output of photovoltaic unit n at time t is the upper bound or lower bound Or the predicted value is a binary variable for the natural inflow of hydropower station x at time t, which is used to indicate that the natural inflow of hydropower unit x at time t is the upper bound. or lower bound Or the predicted value Γ TW To adjust the conservativeness of the uncertainty set of wind turbines in the time dimension; Γ TS is the conservative degree of the uncertainty set of photovoltaic units in the time dimension; Γ TR is the conservative degree of the uncertainty set of hydropower units corresponding to the natural inflow in the time dimension; Γ KW To adjust the conservativeness of the uncertainty set of wind turbines in the spatial dimension; Γ KS To adjust the conservativeness of the uncertainty set of the photovoltaic unit in the spatial dimension; Γ KR To adjust the conservativeness of the uncertainty set of hydropower units corresponding to the natural inflow in the spatial dimension.

8. A unit combination model building system for a multi-source system, characterized in that: include: A pumped storage model building unit is used to obtain a pumped storage model by linearization processing according to the operating parameters and operating constraints of the pumped storage power station of the multi-source system; A renewable energy uncertainty model building unit is used to model the operation parameters and operation constraints of wind turbines, photovoltaic turbines and hydropower units with natural inflow in the multi-source system using uncertainty sets to obtain a renewable energy uncertainty model; A unit combination optimization model building unit is used to build an initial unit combination optimization model based on the renewable energy uncertainty model and the pumped storage model, taking the maximum proportion of renewable energy power generation in the multi-source system and the minimum operating cost of the multi-source system as the optimization objectives; The unit commitment model conversion unit is used to reconstruct the initial unit commitment optimization model by using a two-stage robust optimization and row and column generation algorithm to obtain a final unit commitment optimization model.

9. The system according to claim 8, characterized in that The initial unit commitment model includes optimization functions and constraints; The optimization function includes: taking the maximum proportion of renewable energy power generation in the multi-source system and the minimum operating cost of the multi-source system as the optimization target; the maximum proportion of renewable energy power generation in the multi-source system includes: the minimum reduction cost of renewable energy power generation in the multi-source system; the reduction cost of renewable energy power generation includes: the start-stop cost of hydropower units and water pumps in the pumped storage power station in the multi-source system, the penalty cost of abandoned solar power and abandoned wind power, and the penalty cost of abandoned water power; The constraints of the optimization function include: the start-stop cost constraints of the hydropower units and water pumps in the pumped storage power station, the penalty cost constraints for abandoned solar power and abandoned wind power, and the penalty cost constraints for abandoned water power; The renewable energy sources in the multi-source system include wind power, light and natural inflow of water.

10. The system according to claim 8, characterized in that The unit commitment optimization model conversion unit comprises: The model decomposition subunit is used to reconstruct the initial unit combination optimization model by using a two-stage robust optimization algorithm to obtain a first-stage function and a second-stage function; the first-stage function includes taking the unit combination operation cost of the multi-source system as an optimization target, and the second-stage function includes taking the cost reduction of renewable energy power generation as an optimization target; A constraint acquisition subunit, used for acquiring the constraint of the two-stage function according to the two-stage function; A first conversion subunit is used to replace and equate integer variables in the constraints of the two-stage function by using an inner column and constraint generation method to obtain an equivalent two-stage function and constraint; The second transformation subunit is used to transform the constraints of the equivalent two-stage function by using dual variable substitution to obtain the final two-stage function and constraints; The unit commitment optimization model output subunit is used to integrate the first-stage function and the final second-stage function and constraints to determine the final unit commitment optimization model.

11. The system according to claim 8, characterized in that The pumped storage model building unit comprises: A pumped storage model construction subunit is used to construct an initial pumped storage model based on the operating parameters and operating constraints of the pumped storage power station of the multi-source system, wherein the initial pumped storage model includes: a mapping relationship between the active output of the hydropower unit, the reservoir water level and the reservoir water storage capacity; A first linearization subunit is used to perform linearization processing on the nonlinear variables in the active power output of the hydropower unit to obtain a linearized approximate result of the active power output of the hydropower unit; The second linearization subunit is used to linearize the nonlinear variables in the mapping relationship between the reservoir water level and the reservoir water storage capacity, so as to obtain a linearized approximate result of the mapping relationship between the reservoir water level and the reservoir water storage capacity; The pumped storage model generation subunit is used to synthesize the linearized approximation result of the active power output of the hydropower unit and the linearized approximation result of the mapping relationship between the reservoir water level and the reservoir water storage capacity as the final pumped storage model.

12. The system according to claim 11, characterized in that The first linearization subunit is specifically used for: Based on the active output of the hydropower unit, the turbine efficiency is replaced with a constant value and simplified to obtain a bilinear function; By using the linear envelope method, a convex envelope is constructed at the boundary of the bilinear function to obtain an inequality group for the active output of the hydropower unit, and the inequality group is used as a linearized approximate result of the active output of the hydropower unit.

13. The system according to claim 11, characterized in that The second linearization subunit is specifically used for: Based on the mapping relationship between the reservoir water level and the reservoir water storage capacity, the nonlinear variables of the function are linearly approximated using the SOS-2 constraint to obtain a linear approximation result of the mapping relationship between the reservoir water level and the reservoir water storage capacity.

14. The system of claim 8, wherein: The renewable energy uncertainty model satisfies the following expression: Where UD is the uncertain set of wind turbines, photovoltaic turbines and hydropower units corresponding to natural inflow in the multi-source system; N Wind is the set of wind turbines in the multi-source system; N PV is the set of photovoltaic units in the multi-source system; N HPP is the set of hydropower units in the multi-source system; T is the time set; is the available output of wind turbine m at time t; is the available output of photovoltaic unit n at time t; r x,t is the natural inflow of hydropower unit x at time t, i.e., the output of hydropower unit x; is the predicted average power output of wind turbine m at time t; is the power output of the wind turbine m at the upper limit of the interval at time t; is the power output of the wind turbine m at the lower limit of the interval at time t; is the predicted average power output of PV unit n at time t; is the power output of the upper limit of the interval of PV unit n at time t; is the power output of the lower limit of the interval of PV unit n at time t; is the predicted average power output of hydropower unit x at time t; is the power output of the hydropower unit x at the upper limit of the interval at time t; is the power output of the hydropower unit x at the lower limit of the interval at time t; is a binary variable for the output of wind turbine m at time t, which is used to indicate that the output of wind turbine m at time t is the upper bound. or lower bound Or the predicted value is a binary variable for the output of photovoltaic unit n at time t, used to indicate that the output of photovoltaic unit n at time t is the upper bound or lower bound Or the predicted value is a binary variable for the natural inflow of hydropower station x at time t, which is used to indicate that the natural inflow of hydropower unit x at time t is the upper bound. or lower bound Or the predicted value Γ TW To adjust the conservativeness of the uncertainty set of wind turbines in the time dimension; Γ TS is the conservative degree of the uncertainty set of photovoltaic units in the time dimension; Γ TR is the conservative degree of the uncertainty set of hydropower units corresponding to the natural inflow in the time dimension; Γ KW To adjust the conservativeness of the uncertainty set of wind turbines in the spatial dimension; Γ KS To adjust the conservativeness of the uncertainty set of the photovoltaic unit in the spatial dimension; Γ KR To adjust the conservativeness of the uncertainty set of hydropower units corresponding to the natural inflow in the spatial dimension.

15. A method for optimizing unit combination production of a multi-source system, characterized in that: include: According to the current operating parameters of the pumped storage power station, wind turbines, photovoltaic units and hydropower units with natural inflow in the multi-source system, the unit combination optimization model is solved to obtain the unit combination optimization results; Determine the production optimization plan of the hydropower units and water pumping stations in the pumped storage power station according to the unit combination optimization result; The unit commitment optimization model is constructed using the unit commitment production optimization model construction method for a multi-source system according to any one of claims 1 to 7.

16. The method according to claim 15, characterized in that Solving the unit commitment optimization model to obtain the unit commitment optimization result includes: Solving the unit commitment decision variables of the unit commitment optimization model to obtain a first optimization result; According to the first optimization result, respectively updating the upper bound and the lower bound of the unit commitment optimization model to obtain updated upper bound and lower bound; Based on the updated upper bound and lower bound, solving the economic dispatch variables of the wind turbine, photovoltaic turbine, and hydropower unit of the unit combination optimization model to obtain a second optimization result; updating the upper bound and the lower bound of the unit commitment optimization model according to the second optimization result; According to the updated upper and lower bounds, the unit combination decision variables of the unit combination optimization model are iteratively optimized using the clamping criterion, the first optimization result and the second optimization result are updated, and the upper and lower bounds of the unit combination optimization model are updated until the upper and lower bounds of the updated unit combination optimization model meet the iteration termination condition, and the first optimization result and the second optimization result when the upper and lower bounds of the updated unit combination optimization model meet the iteration termination condition are used as the unit combination optimization result.

17. A unit combination production optimization system for a multi-source system, characterized in that: include: The result solving unit is used to solve the unit combination optimization model according to the current operating parameters of the pumped storage power station, wind turbine, photovoltaic unit and hydropower unit with natural inflow in the current multi-source system to obtain the unit combination optimization result; A production optimization unit, used to confirm the production optimization plan of the hydropower units and water pumping stations in the pumped storage power station according to the unit combination optimization result; The pre-built unit commitment model is constructed using the unit commitment model construction method for a multi-source system according to any one of claims 1 to 7.

18. The system of claim 17, wherein: The result solving unit comprises: A first solving subunit is used to solve the unit combination decision variables of the unit optimization combination model to obtain a first optimization result; A first boundary updating subunit, configured to update the upper bound and the lower bound of the unit commitment optimization model according to the first optimization result, to obtain updated upper bound and lower bound; A second solving subunit is used to solve the economic dispatch variables of the wind turbines, photovoltaic turbines and hydropower units in the unit combination optimization model based on the updated upper bound and lower bound to obtain a second optimization result; A second boundary updating subunit, configured to update an upper bound and a lower bound of the unit commitment optimization model according to the second optimization result; The result output subunit is used to iteratively optimize the unit combination decision variables of the unit combination optimization model according to the updated upper and lower bounds and using the clamping criterion, update the first optimization result and the second optimization result, and update the upper and lower bounds of the unit combination optimization model until the upper and lower bounds of the updated unit combination optimization model meet the iteration termination condition, and use the first optimization result and the second optimization result when the upper and lower bounds of the updated unit combination optimization model meet the iteration termination condition as the unit combination optimization result.

19. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the unit combination optimization model building method for a multi-source system according to any one of claims 1 to 7 or the unit combination production optimization method for a multi-source system according to claim 15 or 16 is implemented.

20. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, the unit combination optimization model construction method of the multi-source system as described in any one of claims 1 to 7 or the unit combination production optimization method of the multi-source system as described in claim 15 or 16 is implemented.

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