A method, device, electronic device and storage medium for solving convex hull of unit combination

Abstract

The present invention provides a method, device, electronic device and storage medium for solving the convex hull of a unit combination. The method comprises: constructing a convex hull model of a single thermal power unit and a convex hull model of a single pumped storage unit respectively; constructing a convex hull model of a multi-unit combination based on the convex hull model of the single thermal power unit and the convex hull model of the single pumped storage unit; solving the convex hull model of the multi-unit combination to obtain a unit startup state solution; and determining a set of feasible solutions for the unit startup state solution through a feasible solution recovery algorithm. The scheme converts a mixed integer programming model into a linear programming model by constructing a convex hull model of a multi-unit combination, which can greatly improve the model solving efficiency while ensuring the calculation accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power systems, and in particular relates to a method, device, electronic equipment and storage medium for solving the convex hull of a unit combination. Background Art

[0002] The unit commitment problem aims to provide start-stop and output plans for the units participating in the power system dispatch, so as to ensure the safety and economy of the power system operation. A lot of research has been done on the modeling and solution algorithms of the unit commitment problem. The unit commitment modeling needs to consider system factors such as power balance, backup demand, and line transmission capacity limitations, as well as unit factors such as coal consumption characteristics, ramping capability, output limitations, and start-stop costs. Due to the existence of 0-1 variables such as the start-stop status of the unit, its mathematical model is expressed as a mixed integer programming (Mixed Integer Programming, MIP), which is a high-dimensional discrete non-convex optimization model. The algorithms for solving the unit commitment problem are mainly divided into two categories: deterministic optimal algorithms and relaxed approximation algorithms.

[0003] The core idea of ​​the deterministic optimal algorithm is enumeration, which is essentially an NP-hard problem. Although it can guarantee the global optimal solution, it has a large amount of calculation and needs to balance the quality of the optimal solution with the calculation time. The relaxation approximation algorithm relaxes the original problem and obtains a new problem that is relatively easy to solve, but it cannot obtain the optimal solution of the original problem. The Lagrangian relaxation algorithm is the most widely used approximate algorithm. This algorithm dually decomposes the multi-unit combination problem into a single-unit problem, and sets the dual gap as the convergence criterion, and then constructs a feasible solution based on the converged solution. However, the convergence of the Lagrangian relaxation algorithm is difficult to guarantee, and the convergence is sensitive to the set parameters.

[0004] At present, there is still no algorithm that takes into account both the quality of the optimal solution and the computational efficiency for the unit commitment problem. With the economic development, the electricity demand continues to increase, and the number of dispatchable units has also increased dramatically. Modern power systems have increasingly higher requirements for the real-time acquisition of unit dispatch plans. Therefore, how to quickly and accurately solve large-scale unit commitment is an urgent problem to be solved. Summary of the invention

[0005] The purpose of the embodiments of this specification is to provide a method, device, electronic device and storage medium for solving the convex hull of a unit combination, which can greatly improve the efficiency of model solving while ensuring the calculation accuracy.

[0006] To solve the above technical problems, the embodiments of the present application are implemented in the following ways:

[0007] In a first aspect, the present application provides a method for solving the convex hull of a unit combination, which is applied to a water-fire combined unit combination including a pumped storage unit, and the method comprises:

[0008] Convex hull models of single thermal power unit and single pumped storage unit are constructed respectively;

[0009] Based on the convex hull model of a single thermal power unit and the convex hull model of a single pumped storage unit, a combined convex hull model of multiple units is constructed.

[0010] Solve the convex hull model of multi-machine unit combination to obtain the unit startup state solution;

[0011] The feasible solution recovery algorithm is used to determine a set of feasible solutions for the unit startup state solution.

[0012] In one embodiment, the pumped storage unit includes an upstream reservoir, a downstream reservoir, a pump and a generator set; the generator set and the pump are modeled as a thermal power unit with positive power and a thermal power unit with negative power, respectively;

[0013] Construct a convex hull model of a single pumped storage unit, including:

[0014] Determine the first feasible region of the positive power thermal power unit and the second feasible region of the negative power thermal power unit respectively;

[0015] According to the first feasible region, determining a first convex hull model of the positive power thermal power unit;

[0016] According to the second feasible region, a second convex hull model of the negative power thermal power unit is determined;

[0017] The convex hull model of the single pumped storage unit is determined according to the first convex hull model and the second convex hull model.

[0018] In one of the embodiments, determining the second feasible region of the negative power thermal power unit includes:

[0019] determining a first constraint condition, the first constraint condition comprising a limitation on a pumping capacity of the pumped storage unit;

[0020] Determine a second constraint condition, the second constraint condition including a restriction on a conversion relationship between water consumption and power generation under a constant water head condition;

[0021] determining a third constraint condition, wherein the third constraint condition includes a restriction on an operating state of the water pump;

[0022] determining a fourth constraint condition, wherein the fourth constraint condition includes a restriction that any state of the water pump should last for at least one scheduling period;

[0023] A fifth constraint is determined, the fifth constraint comprising a limit on the rate at which the pump can be raised or lowered.

[0024] In one embodiment, a feasible solution recovery algorithm is used to determine a set of feasible solutions for the startup state solution of the unit, including:

[0025] Release the unit startup status Divided into 3 sets, namely:

[0026]

[0027]

[0028]

[0029] Where t is the time series index, g is the unit index; ∨ represents logical OR;

[0030] Keep set Ω 0,1 Unchanged, adjust the set Ω 0,ε and Ω ε,1 The value of:

[0031]

[0032]

[0033] Determine the feasibility of the above solution, and if feasible, determine a set of feasible solutions;

[0034] If this is not feasible, then the set Ω 0,ε and Ω ε,1 The output of the corresponding unit is set to a preset value, and the optimal sequence method is used to dispatch the set Ω 0,ε and Ω ε,1 Corresponding to the unit, a set of feasible solutions is determined.

[0035] In a second aspect, the present application provides a unit combination convex hull solving device, which is applied to a water-fire combined unit combination including a pumped storage unit, and the device comprises:

[0036] The first construction module is used to construct a convex hull model of a single thermal power unit and a convex hull model of a single pumped storage unit respectively;

[0037] The second construction module is used to construct a combined convex hull model of multiple units based on the convex hull model of a single thermal power unit and the convex hull model of a single pumped storage unit;

[0038] A calculation module is used to solve the convex hull model of the multi-machine unit combination and obtain the unit startup state solution;

[0039] The determination module is used to determine a set of feasible solutions for the unit startup state solution through a feasible solution recovery algorithm.

[0040] In one embodiment, the pumped storage unit includes an upstream reservoir, a downstream reservoir, a pump and a generator set; the generator set and the pump are modeled as a thermal power unit with positive power and a thermal power unit with negative power, respectively;

[0041] The first construction module is also used to:

[0042] Determine the first feasible region of the positive power thermal power unit and the second feasible region of the negative power thermal power unit respectively;

[0043] According to the first feasible region, determining a first convex hull model of the positive power thermal power unit;

[0044] According to the second feasible region, a second convex hull model of the negative power thermal power unit is determined;

[0045] The convex hull model of the single pumped storage unit is determined according to the first convex hull model and the second convex hull model.

[0046] In one embodiment, the first construction module is further used for:

[0047] determining a first constraint condition, the first constraint condition comprising a limitation on a pumping capacity of the pumped storage unit;

[0048] Determine a second constraint condition, the second constraint condition including a restriction on a conversion relationship between water consumption and power generation under a constant water head condition;

[0049] determining a third constraint condition, wherein the third constraint condition includes a restriction on an operating state of the water pump;

[0050] determining a fourth constraint condition, wherein the fourth constraint condition includes a restriction that any state of the water pump should last for at least one scheduling period;

[0051] A fifth constraint is determined, the fifth constraint comprising a limit on the rate at which the pump can be raised or lowered.

[0052] In one embodiment, the determining module is further configured to:

[0053] Release the unit startup status Divided into 3 sets, namely:

[0054]

[0055]

[0056]

[0057] Where t is the time series index, g is the unit index; ∨ represents logical OR;

[0058] Keep set Ω 0,1 Unchanged, adjust the set Ω 0,ε and Ω ε,1 The value of:

[0059]

[0060]

[0061] Determine the feasibility of the above solution, and if feasible, determine a set of feasible solutions;

[0062] If this is not feasible, then the set Ω 0,ε and Ω ε,1 The output of the corresponding unit is set to a preset value, and the optimal sequence method is used to dispatch the set Ω 0,ε and Ω ε,1 Corresponding to the unit, a set of feasible solutions is determined.

[0063] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for solving the convex hull of a unit combination as in the first aspect is implemented.

[0064] In a fourth aspect, the present application provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for solving the convex hull of a unit combination as in the first aspect.

[0065] It can be seen from the technical solution provided in the above embodiments of this specification that the solution constructs a convex hull model of a multi-machine unit combination by separately constructing a convex hull model of a single thermal power unit and a convex hull model of a single pumped-storage unit, and converts the mixed integer programming model into a linear programming model, which can greatly improve the model solving efficiency while ensuring the calculation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0067] Figure 1 A schematic diagram of the flow chart of the method for solving the convex hull of the unit combination provided in this application;

[0068] Figure 2 Schematic diagram of the pumped storage unit provided for this application;

[0069] Figure 3 A schematic diagram of the structure of the unit combination convex hull solving device provided in this application;

[0070] Figure 4 A schematic diagram of the structure of the electronic device provided in this application. DETAILED DESCRIPTION

[0071] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0072] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0073] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present application description without departing from the scope or spirit of the present application. Other embodiments derived from the present application description will be apparent to those skilled in the art. The present application description and examples are exemplary only.

[0074] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0075] Unless otherwise specified, "parts" in this application are all calculated by mass.

[0076] In the prior art, the solution method for unit combination cannot effectively balance the calculation accuracy and calculation efficiency.

[0077] Based on the above defects, this application proposes a method for solving the convex hull of a unit combination, which can improve the efficiency of model solving while ensuring the calculation accuracy.

[0078] The present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0079] Reference Figure 1 , which shows a flow chart of a method for solving the convex hull of a unit combination provided in an embodiment of the present application. The method is applied to a water-fire combined unit combination including a pumped storage unit.

[0080] like Figure 1 As shown, the method for solving the convex hull of the unit combination may include:

[0081] S110, constructing a convex hull model of a single thermal power unit and a convex hull model of a single pumped storage unit respectively;

[0082] S120, constructing a combined convex hull model of multiple units based on the convex hull model of a single thermal power unit and the convex hull model of a single pumped storage unit;

[0083] S130, solving the convex hull model of the multi-machine unit combination to obtain the unit startup state solution;

[0084] S140. Determine a set of feasible solutions for the unit startup state solution through a feasible solution recovery algorithm.

[0085] Specifically, the convex hull model of a single thermal power unit can be constructed by the following steps:

[0086] (1) MIP model of single thermal power unit combination

[0087] The unit commitment problem of the power system aims to solve the optimal operating state of the unit that meets the safety constraints and physical constraints, and provide a unit dispatch plan. The problem involves both continuous decision variables such as unit output and standby, as well as discrete decision variables such as unit start and stop status and switch commands, and is usually expressed as a MIP problem. The unit commitment model of a single unit considering hot start and ramp constraints can be expressed as:

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094] x0=0,u0=0,x t ,u t ∈{0,1},C start ≥0,C shut ≥0 (7)

[0095] Among them, the 0-1 variable x t is the start and stop status of the unit at time t, starting x t =1, shutdown x t =0; 0-1 variable u t is the start-up command of the unit. When the unit changes from shutdown to startup during period t, u t =1, otherwise 0. Parameter P min ,P max Respectively represent the minimum and maximum output of the unit; P t Indicates the actual output of the unit in period t; parameter R0 ,R 1 Respectively represent the maximum value of the unit power climbing up / down at the beginning and end period and the middle period of the unit operation; parameter T on ,T off Respectively represent the minimum start / stop time of the unit; parameter C start ,C shut They respectively represent the starting and stopping costs of the unit.

[0096] Formula (1) indicates that the model takes the minimum operating cost as the goal. Formula (2) defines the relationship between the start-stop state and the start-up command; Formula (3) limits the output range of the unit; Formulas (4)-(5) limit the frequent start-stop of the unit, so that the unit must last for a certain period of time in the start-stop state; Formula (6) limits the maximum value of the change in the unit's output in adjacent time periods; Formula (7) gives the range of the decision variable and defines the initial state of the unit as the shutdown state.

[0097] (2) Convex hull model of a single thermal power unit

[0098] Based on the above single unit combination model, two convex hull models are established: -convex hull model and ε-convex hull model based on optimal state space.

[0099] -The DP model corresponding to the convex hull focuses on the continuous power-on state, and takes the possible continuous power-on period interval as the state space. Let the state space be Then we have:

[0100]

[0101] -The convex hull corresponds to the equivalent LP model expressed as:

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] α, β, γ, χ ≥ 0 (20)

[0114] Among them, the continuous variable α t , β t,k , γ t,k , χ t When the problem is solved optimally, the value is 0 or 1. t Solution Indicates that the unit is started for the first time at time t; variable β t,k Solution Indicates that the unit is turned on at time (t, k) and turned off at time k+1; variable γ t,k Solution Indicates that the unit is shut down during the period (t+1, k-1) and is turned on at time k; variable χ t Solution Indicates that the unit is shut down at time t+1 and remains shut down. Continuous variable It represents the output value at time τ if the unit is started in the period (t, k). The feasible domain of the single unit combination is expressed as -convex hull

[0115]

[0116] The DP model corresponding to the ε-convex hull takes the possible optimal state of the unit (including the optimal start-stop state and output state) as the state space. Assume that the optimal output state space is The optimal state space is ε. For any state e in the state space ε i (initial state e0), definition For state e i The set of possible next states, For state e i The previous state set of . Then the ε-convex hull corresponding equivalent LP model can be expressed as:

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] Among them, the continuous variable λ tij When the problem is solved to obtain the optimal solution, the value is 0 or 1. If the optimal dispatching scheme for the single unit combination is that the unit is in the state e at time t-1 i , the state at time t is e j ,but otherwise, The feasible domain of the unit commitment of a single unit is expressed as the ε-convex hull

[0126]

[0127] Specifically, the convex hull model of a single pumped storage unit can be constructed by the following steps:

[0128] (1) Mixed integer linear programming model for single pumped storage unit combination

[0129] like Figure 2 As shown, a pumped storage unit can absorb or generate electricity, including an upstream reservoir (referred to as the upper reservoir in the figure), a downstream reservoir (referred to as the lower reservoir in the figure), a generator set (such as a turbine generator, referred to as a turbine in the figure) and a pumping pump. The pumped storage unit can undertake the peak load regulation task of the power system. During peak load, the water in the upstream reservoir is released to the downstream reservoir, and the turbine generator provides electricity, and the water is pumped from the downstream reservoir to the upstream reservoir by the pumping pump to consume electricity during low load. The pumped storage unit has three states: pumping, generating electricity or shutting down, and can only operate in one of these states at any time.

[0130] For pumped storage units, both unit constraints and reservoir constraints should be considered. Unit constraints include:

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141] Among them, z t is the start-up instruction of the water pump, W 1min ,W 1max ,W 2min ,W 2max are the minimum water flow of the upper reservoir, the maximum water flow of the upper reservoir, the minimum water flow of the lower reservoir, and the maximum water flow of the lower reservoir respectively; η 1 ,η 2 are the water-to-electricity efficiency conversion coefficients of the turbine and the water pump respectively; P 1 ,P 2 The electric power of the turbine and the water pump respectively; 0-1 variable x t is the start and stop status of the hydropower unit at time t, starting x t =1, shutdown x t =0; 0-1 variable u t is the start-up command of the hydropower unit. When the unit changes from shutdown to startup during period t, u t =1, otherwise 0.

[0142] Constraints (31)-(32) restrict the operating state of the pumped storage unit. Constraints (33)-(34) restrict the minimum on / off time. Constraints (35)-(36) represent the restrictions on water consumption and pumping capacity. Constraints (37)-(38), (40) describe the restrictions on the conversion relationship between water consumption and power generation under constant head conditions. Constraint (39) represents the restrictions on the ramp-up / down rate of the turbine generator.

[0143] Reservoir constraints include:

[0144] V 1min ≤V t 1 ≤V 1max (41)

[0145] V 2min ≤V t 2 ≤V 2max (42)

[0146]

[0147]

[0148] Among them, V1min ,V 1max ,V 2min ,V 2max They are the minimum storage capacity of the upper reservoir, the maximum storage capacity of the upper reservoir, the minimum storage capacity of the lower reservoir, and the maximum storage capacity of the lower reservoir; They represent the real-time storage capacity of the upper reservoir and the lower reservoir at time t respectively; denote the natural water inflows to the upper and lower reservoirs at time t, respectively. Constraints (41)-(42) provide the upper and lower limits of the reservoir volumes. Constraints (43)-(44) represent the reservoir volume balance considering the natural inflows.

[0149] Since the hydroelectric generator unit works without fuel consumption, only the startup / shutdown cost and maintenance cost need to be considered in the objective function. Therefore, the mixed integer linear programming model of a single pumped storage unit can be expressed as:

[0150]

[0151] Ψ MIP ={(P 1 ,P 2 ,x,z,u,v)|(31)-(44)} (46)

[0152] (2) Convex hull model of a single pumped storage unit

[0153] It is very difficult to directly build a convex hull model for a pumped storage unit like a thermal power unit. The reasons are as follows:

[0154] First, the ε convex hull is generated by constructing all possible optimal outputs and optimal on / off state sets. For a thermal power unit that can be regarded as a closed system, its optimal state space is a finite set. In contrast, the optimal output and optimal on / off state set of a pumped storage unit are affected by the external continuous natural water flow, which is a continuous arbitrary value, resulting in an infinite set of optimal output and optimal on / off state sets. Therefore, the ε convex hull cannot be directly applied to pumped storage units.

[0155] Secondly, The convex hull is generated by constructing a set of all possible continuous start intervals, which requires solving the operating cost of each start interval in advance. For thermal power units, the operating cost of any given time interval (t, k) is independent of the on / off state before time t. In contrast, for pumped storage units, the reservoir volume at different times is coupled with water consumption (power generation). Different states before time t will result in different initial reservoir capacity states and operating costs within the time interval (t, k). Therefore, The convex hull cannot be directly applied to pumped storage units either.

[0156] To solve this problem, the turbine generator and the water pump can be modeled as a positive power thermal power unit and a negative power thermal power unit, respectively. Therefore, the pumped storage unit can be equivalent to the upper and lower reservoirs and two coupled thermal units, and its positive power thermal power unit output And negative power thermal power unit output - like Figure 2 shown.

[0157] In one embodiment, constructing a convex hull model of a single pumped storage unit may include:

[0158] Determine the first feasible region of the positive power thermal power unit and the second feasible region of the negative power thermal power unit respectively;

[0159] According to the first feasible region, determining a first convex hull model of the positive power thermal power unit;

[0160] According to the second feasible region, a second convex hull model of the negative power thermal power unit is determined;

[0161] The convex hull model of the single pumped storage unit is determined according to the first convex hull model and the second convex hull model.

[0162] Optionally, determining the second feasible region of the negative power thermal power unit may include:

[0163] determining a first constraint condition, the first constraint condition comprising a limitation on a pumping capacity of the pumped storage unit;

[0164] Determine a second constraint condition, the second constraint condition including a restriction on a conversion relationship between water consumption and power generation under a constant water head condition;

[0165] determining a third constraint condition, wherein the third constraint condition includes a restriction on an operating state of the water pump;

[0166] determining a fourth constraint condition, wherein the fourth constraint condition includes a restriction that any state of the water pump should last for at least one scheduling period;

[0167] A fifth constraint is determined, the fifth constraint comprising a limit on the rate at which the pump can be raised or lowered.

[0168] The feasible region (i.e., the first feasible region) with a positive power thermal power unit (i.e., a hydro-generator) is expressed as:

[0169]

[0170] It can be seen that the above formula has the same form as the mixed integer linear programming model of thermal power units.

[0171] Therefore, the convex hull model (i.e., the first convex hull model) of the positive power thermal power unit (i.e., the hydro-generator) is:

[0172]

[0173] In addition, the feasible domain of the thermal power unit with negative power (i.e., water pump) is expressed as:

[0174]

[0175] This is different from the mixed integer linear programming model with thermal power units. In order to utilize the convex hull model of a single unit thermal power plant, a redundant variable pump startup instruction integer variable v is introduced. t and four redundant constraints as follows:

[0176]

[0177]

[0178]

[0179]

[0180] Among them, constraint (50) limits the operating state of the pump, constraints (51)-(52) indicate that any state of the pump should last for at least one scheduling period, and constraint (53) describes the limit on the rise / fall rate.

[0181] Obviously, redundant constraints will not change the feasible area of ​​the water pump. The feasible domain of the water pump (i.e., the second feasible domain) can be re-expressed as:

[0182]

[0183] Although the above model is more complex than the original model, its form is consistent with the mixed integer linear programming model of thermal power units and can be transformed with the help of the convex hull model of thermal power units.

[0184] It can be understood that formula (36) is the first constraint condition, formula (38) is the second constraint condition, formula (50) is the third constraint condition, formulas (51) and (52) are the fourth constraint condition, and formula (53) is the fifth constraint condition.

[0185] Therefore, the convex hull model (i.e., the second convex hull model) of the negative power unit (i.e., the water pump) is:

[0186]

[0187] Finally, according to (48) and (55), a single UC with a pumped storage unit can be solved by the LP model, and its formula can be expressed as:

[0188]

[0189] Based on the convex hull model of a single thermal power unit and the convex hull model of a single pumped storage unit, a convex hull model of a multi-unit combination is constructed. Specifically:

[0190] (1) Multi-unit combination MIP model

[0191] The MIP model of multi-unit combination can be expressed as

[0192]

[0193]

[0194]

[0195]

[0196]

[0197]

[0198] Among them, G and H are the thermal power unit set and the hydropower unit set respectively, g, b, l are the unit index, bus node index and line index respectively, D b,t is the load of node b at time t.

[0199] The objective function (57) is to minimize the total operating cost, constraints (58)-(59) are the physical constraints of the unit, constraint (60) is the power balance constraint, constraint (61) is the reserve constraint, and constraint (62) is the line transmission capacity constraint.

[0200] (2) Approximate convex hull model of multi-machine combination

[0201] Note that integer variables only exist in the unit physical constraints (57)-(58). Representing each unit (thermal power unit, pumped storage unit) with a convex hull, the approximate convex hull model of the multi-unit combination can be expressed as

[0202]

[0203]

[0204]

[0205]

[0206]

[0207]

[0208] The above formula converts the original MIP model into an approximate convex hull model with a relaxed feasible domain, which is mathematically expressed as a linear programming model. Therefore, the "NP-hard" problem of unit combination is converted into a polynomial time complexity problem through the multi-machine unit combination approximate convex hull model proposed in this application, achieving a major breakthrough in computational efficiency.

[0209] In one embodiment, the unit startup state solution is determined through a feasible solution recovery algorithm to determine a set of feasible solutions, which may include:

[0210] Release the unit startup status According to its value, it is divided into 3 sets, namely:

[0211]

[0212]

[0213]

[0214] Where t is the time series index, g is the unit index; ∧ represents logical AND;

[0215] Keep set Ω 0,1 Unchanged, adjust the set Ω 0,ε and Ω ε,1 The value of:

[0216]

[0217]

[0218] Determine the feasibility of the above solution, and if feasible, determine a set of feasible solutions;

[0219] If this is not feasible, then the set Ω 0,ε and Ω ε,1 The output of the corresponding unit is set to a preset value (for example, 0), and the optimal order method is used to dispatch the set Ω 0,ε and Ω ε,1 Corresponding to the unit, a set of feasible solutions is determined.

[0220] Specifically, based on the optimal solution in the relaxed feasible domain given by formulas (65)-(68), the feasible solution recovery algorithm finds a feasible integer solution near the optimal extreme point. In real power systems, only a few dimensions are fractional values. It is only necessary to adjust the values ​​of these non-integer solutions and determine whether the modified solutions are feasible.

[0221] Understandable, known There are three possible values, namely Based on the value, Divided into three sets:

[0222]

[0223] Where t is the time series index, g is the unit index; ∨ represents logical OR;

[0224] Keep set Ω 0,1 The state of the middle unit remains unchanged, and the set Ω is adjusted 0,ε With Ω ε,1 The unit status is as follows:

[0225]

[0226]

[0227] In this way, a set of unit combination solutions is obtained, and the following necessary and sufficient conditions are used to test whether the solution is a feasible solution

[0228]

[0229] Among them, E 1t is the set of units that are turned on and whose output increases from 0 to the maximum output (without climbing constraints) in a single dispatch period, E 2t is the set of units that are turned on and time t is the first and last start-up period, E 3t is the set of units that are turned on and whose output cannot climb from 0 to the maximum output in a single dispatch period. If the solution is feasible, the economic dispatch problem is solved under the current on / off state of the unit. Then, a set of feasible solutions for the multi-unit combination model can be obtained and the algorithm is stopped.

[0230] If the solution is not feasible, adjust the set Ω 0,ε With Ω ε,1 If the unit is in shutdown state, the power generation of the generator set is less than the load demand, and the part less than the load demand is defined as the unscheduled power (that is, the power balance constraint is not met at this time, resulting in a power shortage). The optimal order method is used to dispatch the set Ω 0,ε With Ω ε,1 The corresponding units can satisfy the unscheduled power (i.e. satisfy the corresponding power shortage) and obtain a set of feasible solutions for the multi-unit combination model and stop the algorithm. Among them, the optimal order method is to sort all units from small to large according to the marginal production cost, and select the first n units that can meet the power.

[0231] The embodiment of the present application constructs a convex hull model of a multi-machine unit combination by respectively constructing a convex hull model of a single thermal power unit and a convex hull model of a single pumped-storage unit, and converts a mixed integer programming model into a linear programming model. This can greatly improve the model solving efficiency while ensuring the calculation accuracy, achieve an effective balance between calculation accuracy and calculation efficiency, and provide a new algorithm for the problem of combining water and fire combined units with pumped storage.

[0232] Actual case studies have shown that the computational efficiency of the convex hull solution method for the unit combination proposed in the embodiment of the present application is more than twice that of the Lagrangian relaxation model and 10-100 times faster than the MIP model. The present invention achieves a major breakthrough in the time complexity of the water-fire combined unit combination containing pumped storage units from NP-difficult to polynomial time, and has important application and practical value.

[0233] Reference Figure 3 , which shows a schematic structural diagram of a unit combination convex hull solving device described according to an embodiment of the present application.

[0234] like Figure 3 As shown, the unit combination convex hull solving device may include:

[0235] The first construction module 310 is used to construct a convex hull model of a single thermal power unit and a convex hull model of a single pumped storage unit respectively;

[0236] The second construction module 320 is used to construct a combined convex hull model of multiple units based on the convex hull model of a single thermal power unit and the convex hull model of a single pumped storage unit;

[0237] The calculation module 330 is used to solve the convex hull model of the multi-machine unit combination to obtain the unit startup state solution;

[0238] The determination module 340 is used to determine a set of feasible solutions for the unit startup state solution through a feasible solution recovery algorithm.

[0239] Optionally, the pumped storage unit includes an upstream reservoir, a downstream reservoir, a pump and a generator set; the generator set and the pump are modeled as a thermal power unit with positive power and a thermal power unit with negative power, respectively;

[0240] The first construction module 310 is also used for:

[0241] Determine the first feasible region of the positive power thermal power unit and the second feasible region of the negative power thermal power unit respectively;

[0242] According to the first feasible region, determining a first convex hull model of the positive power thermal power unit;

[0243] According to the second feasible region, a second convex hull model of the negative power thermal power unit is determined;

[0244] The convex hull model of the single pumped storage unit is determined according to the first convex hull model and the second convex hull model.

[0245] Optionally, the first construction module 310 is further used for:

[0246] determining a first constraint condition, the first constraint condition comprising a limitation on a pumping capacity of the pumped storage unit;

[0247] Determine a second constraint condition, the second constraint condition including a restriction on a conversion relationship between water consumption and power generation under a constant water head condition;

[0248] determining a third constraint condition, wherein the third constraint condition includes a restriction on an operating state of the water pump;

[0249] determining a fourth constraint condition, wherein the fourth constraint condition includes a restriction that any state of the water pump should last for at least one scheduling period;

[0250] A fifth constraint is determined, the fifth constraint comprising a limit on the rate at which the pump can be raised or lowered.

[0251] Optionally, the determination module 340 is further configured to:

[0252] Release the unit startup status Divided into 3 sets, namely:

[0253]

[0254]

[0255]

[0256] Where t is the time series index, g is the unit index; ∨ represents logical OR;

[0257] Keep set Ω 0,1 Unchanged, adjust the set Ω 0,ε and Ω ε,1 The value of:

[0258]

[0259]

[0260] Determine the feasibility of the above solution, and if feasible, determine a set of feasible solutions;

[0261] If this is not feasible, then the set Ω 0,ε and Ω ε,1 The output of the corresponding unit is set to a preset value, and the optimal sequence method is used to dispatch the set Ω 0,ε and Ω ε,1 Corresponding to the unit, a set of feasible solutions is determined.

[0262] The present embodiment provides a unit combination convex hull solving device, which can execute the embodiment of the above method. Its implementation principle and technical effect are similar and will not be repeated here.

[0263] Figure 4 FIG. 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, a structural schematic diagram of an electronic device 400 suitable for implementing an embodiment of the present application is shown.

[0264] like Figure 4 As shown, the electronic device 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage part 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 are also stored. The CPU 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0265] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 406 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.

[0266] In particular, according to the embodiments of the present disclosure, the above reference Figure 1 The described process can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program tangibly contained on a machine-readable medium, and the computer program includes program code for executing the above-mentioned unit combination convex hull solution method. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411.

[0267] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of the code, and the aforementioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0268] The units or modules involved in the embodiments described in the present application may be implemented by software or hardware. The units or modules described may also be arranged in a processor. The names of these units or modules do not constitute limitations on the units or modules themselves in certain circumstances.

[0269] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop, a mobile phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0270] As another aspect, the present application further provides a storage medium, which may be the storage medium included in the aforementioned device in the above embodiment; or it may be a storage medium that exists independently and is not assembled into the device. The storage medium stores one or more programs, and the aforementioned programs are used by one or more processors to execute the unit combination convex hull solution method described in the present application.

[0271] Storage media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0272] It should be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0273] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

Claims

1. A method for solving the convex hull of a unit combination, applied to a water-fire combined unit combination including a pumped storage unit, characterized in that: The method comprises: Convex hull models of single thermal power unit and single pumped storage unit are constructed respectively; Based on the convex hull model of the single thermal power unit and the convex hull model of the single pumped storage unit, a combined convex hull model of multiple units is constructed; Solving the convex hull model of the multi-machine unit combination to obtain a unit startup state solution; Determine a set of feasible solutions for the startup state solution of the unit by using a feasible solution recovery algorithm; The pumped storage unit includes an upstream reservoir, a downstream reservoir, a pump and a generator set; the generator set and the pump are modeled as a thermal power unit with positive power and a thermal power unit with negative power respectively; The method of constructing a convex hull model of a single pumped storage unit comprises: respectively determining a first feasible region of the positive power thermal power unit and a second feasible region of the negative power thermal power unit; Determining a first convex hull model of the positive power thermal power unit according to the first feasible domain; Determining a second convex hull model of the negative power thermal power unit according to the second feasible domain; Determining the convex hull model of the single pumped-storage unit according to the first convex hull model and the second convex hull model; Known unit startup status solution There are three possible values, namely The step of determining a set of feasible solutions for the startup state solution of the unit by using a feasible solution recovery algorithm includes: The unit startup state is unlocked Divided into 3 sets, namely: Where t is the time series index, g is the unit index; ∨ represents logical OR; Keep the set Ω 0,1 Unchanged, adjust the set Ω 0,ε and Ω ε,1 The value of: Determine the feasibility of the above solution, and if feasible, determine the set of feasible solutions; If this is not feasible, then the set Ω 0,ε and Ω ε,1 The output of the corresponding unit is set to a preset value, and the optimal sequence method is used to dispatch the set Ω 0,ε and Ω ε,1 Corresponding to the unit, the set of feasible solutions is determined.

2. The method according to claim 1, characterized in that The second feasible region of determining the negative power thermal power unit includes: determining a first constraint condition, wherein the first constraint condition comprises a limitation on a pumping capacity of the pumped-storage unit; determining a second constraint condition, wherein the second constraint condition includes a restriction on a conversion relationship between water consumption and power generation under a constant water head condition; Determining a third constraint condition, wherein the third constraint condition includes a restriction on an operating state of the water pump; Determining a fourth constraint condition, wherein the fourth constraint condition includes a restriction that any state of the water pump should last for at least one scheduling period; A fifth constraint condition is determined, wherein the fifth constraint condition includes limiting a rate at which the water pump can be raised or lowered.

3. A convex hull solving device for a unit combination, applied to a water-fire combined unit combination including a pumped storage unit, characterized in that: The device comprises: The first construction module is used to construct a convex hull model of a single thermal power unit and a convex hull model of a single pumped storage unit respectively; A second construction module is used to construct a multi-machine unit combined convex hull model based on the convex hull model of the single thermal power unit and the convex hull model of the single pumped storage unit; A calculation module, used for solving the convex hull model of the multi-machine unit combination to obtain a unit startup state solution; A determination module, used for determining a set of feasible solutions for the startup state solution of the unit through a feasible solution recovery algorithm; The pumped storage unit includes an upstream reservoir, a downstream reservoir, a pump and a generator set; the generator set and the pump are modeled as a thermal power unit with positive power and a thermal power unit with negative power respectively; The first construction module is also used to: respectively determining a first feasible region of the positive power thermal power unit and a second feasible region of the negative power thermal power unit; Determining a first convex hull model of the positive power thermal power unit according to the first feasible domain; Determining a second convex hull model of the negative power thermal power unit according to the second feasible domain; Determining the convex hull model of the single pumped-storage unit according to the first convex hull model and the second convex hull model; Known unit startup status solution There are three possible values, namely The determining module is also used for: The unit startup state is unlocked Divided into 3 sets, namely: Where t is the time series index, g is the unit index; ∨ represents logical OR; Keep the set Ω 0,1 Unchanged, adjust the set Ω 0,ε and Ω ε,1 The value of: Determine the feasibility of the above solution, and if feasible, determine the set of feasible solutions; If this is not feasible, then the set Ω 0,ε and Ω ε,1 The output of the corresponding unit is set to a preset value, and the optimal sequence method is used to dispatch the set Ω 0,ε and Ω ε,1 Corresponding to the unit, the set of feasible solutions is determined.

4. The device according to claim 3, characterized in that The first construction module is also used for: determining a first constraint condition, wherein the first constraint condition comprises a limitation on a pumping capacity of the pumped-storage unit; Determining a second constraint condition, wherein the second constraint condition includes a restriction on a conversion relationship between water consumption and power generation under a constant water head condition; Determining a third constraint condition, wherein the third constraint condition includes a restriction on an operating state of the water pump; Determining a fourth constraint condition, wherein the fourth constraint condition includes a restriction that any state of the water pump should last for at least one scheduling period; A fifth constraint condition is determined, wherein the fifth constraint condition includes limiting a rate at which the water pump can be raised or lowered.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for solving the convex hull of the unit combination as described in any one of claims 1-2 is implemented.

6. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for solving the convex hull of a unit combination as described in any one of claims 1-2 is implemented.

Images