Redundant constraint identification and elimination method for large-scale power market clearing simulation

Abstract

The invention discloses a redundancy constraint identification and elimination method for large-scale power market clearing simulation, and the method comprises the steps: building a safety constraint unit combination and safety constraint economic dispatching optimization model in which a tracking unit, a new energy unit and an energy storage power station jointly participate in market clearing; factors such as unit self-constraint, new energy randomness and energy storage charge-discharge balance are comprehensively considered; according to a large number of line power flow constraints involved in the clearing process, the maximum possible transmission power of a power transmission line is calculated, the conversion relation between unit power and load requirements on the line is combined, and redundancy of safety constraints is rapidly recognized and screened through an analysis method, so that efficient elimination is achieved; and solving the node marginal electricity price based on the solved unit combination start-stop scheme result and the declared unit output plan. The method is more suitable for a large-scale market environment, can effectively reduce the consumption of computing resources, improves the real-time performance of market clearing, and optimizes the operation management of a power transmission network.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrical engineering, specifically relates to the technical field of power market optimization calculation and large-scale optimization problem solving, and particularly relates to a method for identifying and eliminating redundant constraints for large-scale power market clearing simulation. Background Art

[0002] With the global energy structure transforming towards low-carbon and clean, the penetration rate of new energy in the power system is continuously increasing. At the same time, the rapid development of energy storage technology has led to the gradual emergence of a large number of independent market players in the system. Due to the volatility and uncertainty of new energy sources such as wind power and photovoltaic power, coupled with the increasingly complex power grid structure, in the case of multiple market players such as unified regulation units, new energy units, and energy storage systems jointly participating in the power market clearing, there are high-dimensional variable spaces and more dynamic constraints in the market bidding process, such as the start-stop constraints, ramp rates of generating units, charge-discharge constraints of energy storage, and security limitations of the transmission network, which greatly increases the computational complexity. Therefore, the solution speed of the large-scale power market day-ahead clearing optimization operation is slow, or even unsolvable.

[0003] Currently, optimization algorithms such as mixed integer linear programming (MILP) are usually used for power market clearing. However, with the expansion of the power grid scale and the increase of the time dimension, the computational complexity increases exponentially. Especially, the number of transmission security constraints is huge, and the constraint scale increases exponentially with the number of lines and time periods, resulting in the solver dealing with a large number of redundant constraints and seriously slowing down the calculation speed. Existing methods such as Lagrangian relaxation and Benders decomposition often require multiple iterations to converge. Each iteration requires recalculating and evaluating a large number of constraints or relying on posteriori checks, and the redundant constraints are not identified sufficiently or the invalid constraints cannot be quickly eliminated. Therefore, for large-scale power market clearing, it has important theoretical and engineering value to study the rapid identification and elimination of redundant constraints that do not affect the market clearing results during the security-constrained unit commitment (SCUC) and security-constrained economic dispatch (SCED) processes. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for identifying and eliminating redundant constraints for large-scale power market clearing simulation in view of the deficiencies of the prior art. The present invention is oriented to large-scale power markets and combines mathematical analysis methods with market clearing models to achieve the purpose of quickly identifying and eliminating redundant constraints.

[0005] The purpose of the present invention is achieved by the following technical solutions: A method for identifying and eliminating redundant constraints for large-scale power market clearing simulation, comprising the following steps:

[0006] (1) Pre - processing: Considering the constraint conditions of various types of units in the power system, with the goal of minimizing the operating cost of the power system, a complete day - ahead clearing simulation model and its corresponding multiple constraint conditions for the jointly participating power grid - regulated units, new - energy units, and energy storage power stations in the power market are constructed according to the power - system topological structure and parameters. Among them, the complete day - ahead clearing simulation model includes a security - constrained unit commitment model and a security - constrained economic dispatch model.

[0007] (2) Redundant constraint determination: Based on the day - ahead clearing simulation model constructed in step (1), by performing power - flow calculations on the transmission network, combining the generating power of units and load demand during the power - market clearing process, considering the contribution coefficients of each unit and the influence coefficients of loads, the maximum possible transmission power of each transmission line is derived and calculated. The maximum possible transmission power of each transmission line is compared with its corresponding line - security - constraint threshold, and then according to the determination theorem of redundant constraints, it is judged whether the line - flow constraint of this transmission line is a redundant constraint.

[0008] (3) Fast identification and elimination of redundant constraints: Based on the constraint relaxation and feasible - solution - space characteristics of the optimization model, the line - flow constraints that do not affect the optimal solution are quickly determined and preliminarily eliminated through a linearization strategy, that is, the line - flow constraints of the transmission lines determined as redundant constraints in step (2) are eliminated, simplifying the calculation scale of the mixed - integer linear - programming problem, and the unit - commitment start - stop plan results are obtained by solving.

[0009] (4) Solving nodal prices: Based on the unit - commitment start - stop plan results obtained in step (3) and the declared unit output plans, economic dispatch optimization is carried out to solve the Lagrange multipliers of the relevant constraint conditions in the security - constrained economic dispatch model. The electricity - energy marginal component and congestion - marginal component of each node are calculated according to the Lagrange multipliers corresponding to the constraints to obtain the marginal price of each node as the finally obtained nodal price.

[0010] Furthermore, the power - generation entities in the power market include power - grid - regulated units, new - energy units, and energy - storage power stations. The power - grid - regulated units include thermal power units and coal - fired power units, and the new - energy units include wind - power units and photovoltaic units.

[0011] Furthermore, the objective function of the security - constrained unit commitment model is:

[0012]

[0013] where, N is the number of power - grid - regulated units in the power system; T is the number of time periods considered in the clearing process; P i t is the output of the i - th power - grid - regulated unit at time t; is the operating - cost function of the i - th power - grid - regulated unit at time t; and They are the start-up and shutdown costs of the i-th regulated unit in period t, respectively.

[0014] Furthermore, the system power balance constraint of the security-constrained unit commitment model is:

[0015]

[0016] Among them, N W , N PV and N S are the numbers of wind turbines, photovoltaic units and energy storage power stations in the power system, respectively; is the declared output of the j-th wind turbine in period t, is the declared output of the k-th photovoltaic unit in period t; and are the discharge power and charge power of the s-th energy storage power station in period t, respectively; NL t is the net load value of the power system in period t;

[0017] The system reserve capacity constraint of the security-constrained unit commitment model is:

[0018]

[0019] Among them, represents the start-stop state of the i-th regulated unit in the power system in period t, is "1" indicating the on state, is "0" indicating the off state; and are the maximum technical output and minimum technical output of the i-th regulated unit in period t, respectively; and are the positive reserve capacity demand and negative reserve capacity demand of the power system in period t, respectively;

[0020] The upper and lower limits of unit output constraint of the security-constrained unit commitment model are:

[0021]

[0022] The unit output ramp rate constraint of the security-constrained unit commitment model is:

[0023]

[0024] Among them, R U,i and R D,i are the maximum up-ramp rate and maximum down-ramp rate of the i-th regulated unit, respectively;

[0025] The unit start-stop time constraint of the security-constrained unit commitment model is:

[0026]

[0027] Among them, T ON,i and T OFF,i are respectively the shortest continuous startup time and the shortest continuous shutdown time of the i-th unified regulation unit; temp is the intermediate variable of the time period;

[0028] The upper and lower limits of the gas volume of the units in the security-constrained unit commitment model are:

[0029]

[0030] Among them, and are respectively the upper limit value and the lower limit value of the gas volume of the gas turbine unit group in the unified regulation units; GS is the gas turbine unit in the unified regulation units; η GE is the gas-electricity conversion coefficient;

[0031] The charging and discharging power constraints of the energy storage in the security-constrained unit commitment model are:

[0032]

[0033] Among them, and respectively represent the charging state and the discharging state of the s-th energy storage power station at time t; and are respectively the maximum charging power and the maximum discharging power of the s-th energy storage power station;

[0034] The energy storage capacity constraints of the security-constrained unit commitment model are:

[0035]

[0036] Among them, is the capacity of the s-th energy storage power station at time t; and are respectively the maximum capacity and the minimum capacity of the s-th energy storage power station; η S,s is the charge-discharge efficiency of the s-th energy storage power station;

[0037] The line power flow constraints of the security-constrained unit commitment model are:

[0038]

[0039] Among them, is the power flow of line l at time t; is the forward transmission power limit of line l.

[0040] Furthermore, the objective function of the security-constrained economic dispatch model is as follows:

[0041]

[0042] where N is the number of unified dispatch units in the power system; T is the number of time periods considered in the clearing process; P i t is the output of the i-th unified dispatch unit at time t; is the operating cost function of the i-th unified dispatch unit at time t.

[0043] Furthermore, the system power balance constraint, system reserve capacity constraint, upper and lower limits of unit output constraint, unit output ramp rate constraint, upper and lower limits of unit gas volume constraint, energy storage charge and discharge power constraint, energy storage capacity constraint, and line power flow constraint of the security-constrained economic dispatch model are the same as the corresponding constraint conditions in the security-constrained unit commitment model; among them, all operating state variables in the security-constrained economic dispatch model are known results obtained from solving the security-constrained unit commitment model.

[0044] Furthermore, the determination theorem of the redundant constraint is specifically as follows:

[0045] By constructing a small-scale mixed integer linear programming problem, calculate the maximum possible transmission power of each transmission line on the premise of meeting the system power balance constraint; if the maximum possible transmission power of a certain transmission line is strictly less than its corresponding line security constraint threshold, then the line power flow constraint of this transmission line is a redundant constraint; where the line security constraint threshold corresponding to the transmission line is the forward transmission power limit of this transmission line.

[0046] Furthermore, the maximum possible transmission power of each transmission line is derived and calculated by using a mathematical analysis method, and its calculation formula is:

[0047]

[0048] where BL max,l is the maximum power flow of line l under the current system load demand and structural parameters, that is, the maximum possible transmission power of line l; Gen is the set of all unified dispatch units in the system; α i,l is the power contribution coefficient of the i-th unified dispatch unit to line l; Bus is the set of all load nodes in the system; β j,l is the power influence coefficient of the j-th load node on line l.

[0049] Furthermore, the elimination rule of the redundant constraint is specifically as follows:

[0050] The redundant constraints are determined by the feasible region defined by the system load demand and transmission security constraints; if a line power flow constraint is identified as inactive by the sufficient condition for parsing, then as long as the power balance constraint of the system is satisfied, it is inactive for all possible unit commitment states; then if the maximum possible transmission power BL of the calculated transmission line max,l is less than the forward transmission power limit of line l then the corresponding line power flow constraint is a redundant constraint and is removed.

[0051] Furthermore, the calculation formula for the marginal price of the node is:

[0052]

[0053] where LMP b,t is the marginal electricity price of node b at time t; λ t is the Lagrange multiplier of the system power balance constraint at time t, which is used to represent the electricity marginal component of the marginal electricity price of the node; is the Lagrange multiplier of the maximum forward power flow constraint of line l at time t; is the Lagrange multiplier of the maximum reverse power flow constraint of line l at time t; N L is the number of transmission lines in the power system; G l-b is the sensitivity factor of node b to line l; represents the network congestion marginal component of the marginal electricity price of the node.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] (1) For the situation where multiple market players such as regulated units, new energy units, and energy storage systems jointly participate in the electricity market clearing, the present invention adopts a redundant constraint elimination technology that combines mathematical analysis methods with small-scale optimization screening. By calculating the maximum possible carrying power of the transmission line and combining the conversion relationship between unit output and load demand, redundant constraints are quickly identified for screening and elimination, thereby significantly reducing the scale of the optimization model and accelerating the market clearing speed.

[0056] (2) The present invention adopts a fast constraint elimination method and combines a mixed integer linear programming (MILP) solver, which can complete the market clearing in a shorter time and support real-time markets and rolling optimization. The shortened calculation time enables the dispatching center to adjust the clearing plan more frequently, improving market flexibility and adapting to complex operating conditions such as sudden changes in new energy output and load fluctuations.

[0057] (3) Based on mathematical analysis methods, the present invention can effectively be compatible with existing market clearing architectures and solvers, such as mainstream optimization tools like CPLEX and GUROBI, and can be seamlessly integrated into existing power trading platforms and dispatching systems. Meanwhile, this method supports parallel computing and distributed solving, which can further improve the computing performance and provide an efficient and stable clearing solution for ultra-large-scale power grids and cross-regional markets. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flowchart of the method for identifying and eliminating redundant constraints for large-scale power market clearing simulation of the present invention;

[0059] Figure 2 is a schematic diagram of the power market clearing simulation process of the present invention;

[0060] Figure 3 is a schematic diagram of the redundant constraints of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0062] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the", and "said" used in the present invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0063] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0064] The present invention will be described in detail below with reference to the drawings. Without conflict, the features in the following embodiments and implementation manners can be combined with each other.

[0065] See Figure 1, the method for identifying and eliminating redundant constraints for large-scale power market clearing simulation of the present invention specifically includes the following steps:

[0066] (1) Pretreatment: Considering the constraint conditions of various types of units in the power system, with the goal of minimizing the operating cost of the power system, a complete day-ahead clearing simulation model in which unified regulation units, new energy units, and energy storage power stations jointly participate in the power market and its corresponding multiple constraint conditions are constructed according to the topological structure and parameters of the power system. Among them, the complete day-ahead clearing simulation model includes a security-constrained unit commitment (SCUC) model and a security-constrained economic dispatch (SCED) model.

[0067] Furthermore, the power generation entities in the power market include unified regulation units, new energy units, and energy storage power stations. The unified regulation units include thermal power units and coal-fired power units, and the new energy units include wind power units and photovoltaic units.

[0068] In this embodiment, the clearing simulation process for specifically calculating the nodal price is as Figure 2 shown. The complete simulation clearing process includes security-constrained unit commitment and security-constrained economic dispatch. Comprehensively considering the scheduling constraints of various types of units in the power system (such as unit start-stop, operation, load matching, etc.) and system demand, specifically, considering characteristics such as unit output constraints, start-stop constraints, and ramp rate constraints, integrating the output randomness of new energy units, and optimizing the wind and light output using prediction data; aiming at the operating characteristics of the energy storage power station, establishing charge-discharge power constraints and energy storage limits, and optimizing the charge-discharge strategy in combination with market price signals. With the goal of minimizing the operating cost of the power system, the actual power system is simplified, and a mathematical model for large-scale clearing of multiple market entities is established to obtain a complete day-ahead clearing simulation model in which unified regulation units, new energy units, and energy storage power stations jointly participate in the power market. Among them, when simplifying the actual power system, only key parameters such as the maximum line capacity, the maximum power generation of units, and the network topology structure are retained, and line losses, section parameters, etc. are ignored to ensure that the calculation results can quantitatively reflect the value of redundant constraint identification and elimination.

[0069] It should be noted that the SCED model is further optimized based on the results of the SCUC model. On the basis of the SCUC model, the objective function of the SCED model changes, and at the same time, some constraint conditions are reduced. Therefore, the objective function and constraint conditions of the SCED model will be described in subsequent steps. The SCUC model and the SCED model are two processes of actual market clearing. The two models are similar but not exactly the same. The SCUC process needs to be carried out first, and then the SCED process can be carried out based on the results of the SCUC. Among them, the SCUC model aims to minimize the unit start-stop cost and operating cost; while the SCED model only aims to minimize the unit operating cost, and the content of the solution focused by the two is different.

[0070] Furthermore, the objective function of the security-constrained unit commitment model is as follows:

[0071]

[0072] where N is the number of regulated units in the power system; T is the number of time periods considered in the clearing process; P i t is the output of the i-th regulated unit at time period t; is the operating cost function of the i-th regulated unit at time period t, which is determined by the regulated unit's day-ahead declaration; and are the start-up and shut-down costs of the i-th regulated unit at time period t, respectively.

[0073] Furthermore, the system power balance constraint of the security-constrained unit commitment model is as follows:

[0074]

[0075] where N W , N PV and N S are the numbers of wind turbines, photovoltaic units, and energy storage power stations in the power system, respectively; is the declared output of the j-th wind turbine at time period t, is the declared output of the k-th photovoltaic unit at time period t; and are the discharge power and charge power of the s-th energy storage power station at time period t, respectively; NL t is the net load value of the power system at time period t.

[0076] Furthermore, the system reserve capacity constraint of the security-constrained unit commitment model is as follows:

[0077]

[0078] where represents the start-stop state of the i-th regulated unit in the power system at time period t, is a variable, is "1" when the unit is in the on state, is "0" when the unit is in the off state; and are the maximum and minimum technical outputs of the i-th regulated unit at time period t, respectively; and are the positive and negative reserve capacity requirements of the power system at time period t, respectively.

[0079] Furthermore, the upper and lower limits of the unit output constraint of the security-constrained unit commitment model are as follows:

[0080]

[0081] Furthermore, the ramp rate constraint of the unit output in the security-constrained unit commitment model is as follows:

[0082]

[0083] where, R U,i and R D,i are respectively the maximum upward ramp rate and the maximum downward ramp rate of the i-th regulated unit.

[0084] Furthermore, the start-up and shutdown time constraint of the unit in the security-constrained unit commitment model is as follows:

[0085]

[0086] where, T ON,i and T OFF,i are respectively the shortest continuous on-time and the shortest continuous off-time of the i-th regulated unit; temp is the intermediate variable of the time period.

[0087] Furthermore, the upper and lower limits constraint of the gas volume of the unit in the security-constrained unit commitment model is as follows:

[0088]

[0089] where, and are respectively the upper limit value and the lower limit value of the gas volume of the gas turbine unit group in the regulated units; GS is the gas turbine unit in the regulated units; η GE is the gas-electricity conversion coefficient.

[0090] Furthermore, the charge and discharge power constraint of the energy storage in the security-constrained unit commitment model is as follows:

[0091]

[0092] where, and respectively represent the charging state and the discharging state of the s-th energy storage power station at time t; and are respectively the maximum charging power and the maximum discharging power of the s-th energy storage power station.

[0093] Furthermore, the energy storage capacity constraint of the security-constrained unit commitment model is as follows:

[0094]

[0095] where, is the capacity of the s-th energy storage power station at time t; and are the maximum capacity and minimum capacity of the s-th energy storage power station respectively; η S,s is the charge-discharge efficiency of the s-th energy storage power station.

[0096] Furthermore, the line power flow constraint of the security-constrained unit commitment model is:

[0097]

[0098] where is the power flow of line l at time period t; is the forward transmission power limit of line l.

[0099] Furthermore, the objective function of the security-constrained economic dispatch (SCED) model is:

[0100]

[0101] where N is the number of centrally dispatched units in the power system; T is the number of time periods considered in the clearing process; P i t is the output of the i-th centrally dispatched unit at time period t; is the operating cost function of the i-th centrally dispatched unit at time period t.

[0102] Furthermore, the constraint conditions of the security-constrained economic dispatch model are basically the same as those of the security-constrained unit commitment model. The system power balance constraint, system reserve capacity constraint, unit output upper and lower limit constraint, unit output ramping constraint, unit gas volume upper and lower limit constraint, energy storage charge and discharge power constraint, energy storage capacity constraint and line power flow constraint of the security-constrained economic dispatch model are the same as the corresponding constraint conditions in the security-constrained unit commitment model, which will not be elaborated here. There is no unit start-stop time constraint in the security-constrained economic dispatch model. Among them, all operating state variables in the security-constrained economic dispatch model are known results obtained from the solution of the security-constrained unit commitment model. At this time, the SCED model no longer contains 0-1 integer variables, and SCED is a linear programming optimization problem.

[0103] (2) Redundant constraint determination: Based on the day-ahead clearing simulation model constructed in step (1), by performing power flow calculations on the transmission network, combining the generator power and load demand during the power market clearing process, considering the contribution coefficients of each unit and the influence coefficients of the loads, the maximum possible transmission power of each transmission line is deduced and calculated; the maximum possible transmission power of each transmission line is compared with its corresponding line security constraint threshold, and then according to the determination theorem of redundant constraints, it is judged whether the line power flow constraint of this transmission line is a redundant constraint.

[0104] It should be understood that after the construction of the current-day clearing simulation model is completed, a preliminary determination of redundant constraints is carried out. The schematic diagram of the definition of redundant constraints is as shown in Figure 3 as shown. According to the determination theorem of redundant constraints, it is judged whether the line power flow constraint of a transmission line is a redundant constraint. If the line power flow constraint of a certain transmission line is determined to be a redundant constraint, it enters the subsequent elimination stage, and these redundant constraints are preliminarily eliminated.

[0105] Furthermore, the definition of redundant constraints is specifically as follows: If the line power flow constraint of a certain transmission line satisfies one of the following conditions, it is called a redundant constraint or an inactive constraint:

[0106] ① The existence of this constraint does not affect the feasible solution set of the security-constrained unit commitment (SCUC) model, that is, the feasible solution set remains unchanged after this constraint is deleted;

[0107] ② This constraint does not affect the optimal solution of the security-constrained unit commitment (SCUC) model, that is, the value of the optimization objective function remains unchanged after this constraint is deleted.

[0108] Furthermore, the determination theorem of redundant constraints is specifically as follows: By constructing a small-scale mixed-integer linear programming (MILP) problem, calculate the maximum possible transmission power of each transmission line on the premise of satisfying the system power balance constraint; if the maximum possible transmission power of a certain transmission line is strictly less than its corresponding line security constraint threshold, the line power flow constraint of this transmission line is a redundant constraint and can be deleted. The line security constraint threshold corresponding to the transmission line is the forward transmission power limit of this transmission line

[0109] Furthermore, the maximum possible transmission power of each transmission line is derived and calculated by using a mathematical analysis method, and its calculation formula is:

[0110]

[0111] where, BL max,l is the maximum power flow of line l under the current system load demand and structural parameters, that is, the maximum possible transmission power of line l; Gen is the set of all centrally dispatched units in the system; α i,l is the power contribution coefficient of the i-th centrally dispatched unit to line l; Bus is the set of all load nodes in the system; β j,l is the power influence coefficient of the j-th load node on line l.

[0112] (3) Redundant constraint rapid identification and elimination: Based on the constraint relaxation property of the optimization model and the characteristics of the feasible solution space, rapidly determine the line power flow constraints that do not affect the optimal solution, and perform preliminary elimination through a linearization strategy, that is, eliminate the line power flow constraints of the transmission lines determined as redundant constraints in step (2). In this way, it can be ensured that all the remaining constraints have a substantial impact on the final optimization result, simplify the calculation scale of the complex mixed-integer linear programming (MILP) problem, reduce the calculation scale, ensure that the eliminated constraints have no substantial impact on the market clearing result, and solve to obtain the unit commitment start-stop plan result.

[0113] It should be understood that by using a mathematical analysis method, the redundancy of the line power flow constraints of the line can be calculated by analyzing the transmission network parameters, unit output, and load distribution. Then, based on the constraint relaxation property of the optimization model and the characteristics of the feasible solution space, rapidly determine the line power flow constraints that do not affect the optimal solution, and perform preliminary elimination through a linearization strategy. Since in an actual power system, many constraints are nonlinear (such as the AC power flow equation); the linearization strategy converts complex nonlinear constraints into linear inequalities through methods such as Taylor expansion and DC power flow approximation, making it easier to handle in the MILP optimization model. In this application, the resistance is ignored and the voltage amplitude is assumed to be constant, converting the nonlinear power equation into a linear constraint.

[0114] It should be noted that the constraint relaxation property means that in the optimization process, if a certain constraint is relaxed (or even removed) and the optimal solution remains unchanged, it indicates that this constraint has a weak binding force on the final solution and may be redundant, that is, on a line with a very high transmission capacity, even if the line power flow constraint is not considered, the unit commitment plan will not exceed the physical limit. The characteristics of the feasible solution space refer to the distribution of all solutions that satisfy the constraints in a multi-dimensional space; some constraints may not change the boundary of the feasible solution space, that is, they do not limit the range of solutions. Geometrically, these constraints may be covered by other tighter constraints and thus do not affect the actual shape of the solution space, as Figure 3 shown. ① Relaxation degree check: Slightly reduce the constraint (for example, reduce the safety margin), and observe whether the objective function value and the optimal solution change. If they do not change, this constraint may be redundant. ② Spatial dominance relationship: Analyze the geometric position of the constraint in the solution space to determine whether it is dominated by other tighter constraints; for example, if multiple line constraints are superimposed in the same direction, the one with the least impact can be eliminated.

[0115] Furthermore, the specific rule for eliminating redundant constraints is as follows: Redundant constraints are determined by the feasible region defined by the system load demand and transmission security constraints. If a line power flow constraint is identified as inactive by the sufficient condition of the analysis, then as long as the power balance constraint of the system is satisfied, it is inactive for all possible unit commitment states; therefore, if the maximum possible transmission power BL of the calculated transmission linemax,l Less than the forward transmission power limit of line l Then the corresponding line flow constraint is a redundant constraint and can be ignored and removed.

[0116] It should be noted that after removing the redundant constraints, solve the MILP problem. In the MILP problem, the objective function is the objective function of the security-constrained unit commitment model, and the constraint conditions are multiple constraint conditions of the security-constrained unit commitment model. That is, under multiple constraint conditions of the security-constrained unit commitment model, solve the objective function of the security-constrained unit commitment model. Finally, the unit commitment start-stop plan result can be solved, that is, the start-stop state of each unit in each clearing period, that is and In the MILP problem, all variables to be solved are unknowns. Combining the known data such as the provided network framework and load, solving the optimization model can obtain the unit commitment start-stop plan, and the start-stop plan is a 0-1 matrix.

[0117] (4) Solve the nodal price: Based on the unit commitment start-stop plan result obtained in step (3) and the declared unit output plan, perform economic dispatch optimization to solve the Lagrange multipliers of the relevant constraint conditions in the security-constrained economic dispatch model; calculate the energy marginal component and congestion marginal component of each node according to the Lagrange multipliers of the corresponding constraints to obtain the marginal price of each node as the finally obtained nodal price.

[0118] Furthermore, the calculation formula for the marginal price of a node is:[[]]

[0119]

[0120] where LMP b,t is the marginal price of node b at time t; λ t is the Lagrange multiplier of the system power balance constraint at time t, which is used to represent the energy marginal component of the marginal price of the node; is the Lagrange multiplier of the maximum forward power flow constraint of line l at time t; is the Lagrange multiplier of the maximum reverse power flow constraint of line l at time t; N L is the number of transmission lines in the power system; G l-b is the sensitivity factor of node b to line l; represents the network congestion marginal component of the marginal price of the node.

[0121] In summary, the present invention can ensure that, on the premise of ensuring system security, it reduces the consumption of computing resources by eliminating redundant constraints, improves the market clearing efficiency, and accurately calculates the nodal electricity price, providing strong technical support for the efficient dispatching and safe operation of large-scale power markets. Compared with traditional optimization methods, the present invention is more suitable for large-scale market environments, can effectively reduce the consumption of computing resources, improve the real-time performance of market clearing, and at the same time optimize the operation management of the transmission network, providing a basis for market operators to identify key transmission lines.

[0122] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0123] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for identifying and eliminating redundant constraints for large-scale power market clearing simulation, characterized in that It includes the following steps: (1) Pretreatment: Considering the constraint conditions of various units in the power system, with the goal of minimizing the operation cost of the power system, a complete day-ahead clearing simulation model in which the unified dispatch units, new energy units, and energy storage power stations jointly participate in the power market and its corresponding multiple constraint conditions are constructed according to the power system topology and parameters; among them, the complete day-ahead clearing simulation model includes a security-constrained unit commitment model and a security-constrained economic dispatch model; (2) Redundant constraint determination: Based on the day-ahead clearing simulation model constructed in step (1), by performing power flow calculations on the transmission network, combining the generator power and load demand in the power market clearing process, considering the contribution coefficients of each unit and the influence coefficients of the load, the maximum possible transmission power of each transmission line is deduced and calculated; compare the maximum possible transmission power of each transmission line with its corresponding line security constraint threshold, and then judge whether the line power flow constraint of this transmission line is a redundant constraint according to the determination theorem of redundant constraints; (3) Fast identification and elimination of redundant constraints: Based on the constraint relaxation and feasible solution space characteristics of the optimization model, quickly determine the line power flow constraints that do not affect the optimal solution, and perform preliminary elimination through a linearization strategy, that is, eliminate the line power flow constraints of the transmission lines determined to be redundant constraints in step (2), simplify the calculation scale of the mixed-integer linear programming problem, and solve to obtain the unit commitment start-stop plan result; (4) Solve the nodal price: Based on the unit commitment start-stop plan result obtained in step (3) and the declared unit output plan, perform economic dispatch optimization, and solve the Lagrange multipliers of the relevant constraint conditions in the security-constrained economic dispatch model; calculate the energy marginal component and congestion marginal component of each node according to the Lagrange multipliers corresponding to the constraints to obtain the marginal price of each node as the finally obtained nodal price.

2. The redundant constraint identification and elimination method for large-scale power market clearing simulation according to claim 1, characterized in that The power generation entities in the power market include unified dispatch units, new energy units, and energy storage power stations. The unified dispatch units include thermal power units and coal-fired power units, and the new energy units include wind power units and photovoltaic units.

3. The method for identifying and eliminating redundant constraints for large-scale power market clearing simulation according to claim 1, characterized in that The objective function of the security-constrained unit commitment model is: Among them, N is the number of unified dispatch units in the power system; T is the number of time periods considered in the clearing process; P i t is the output of the i-th unified dispatch unit in the t-th time period; is the operating cost function of the i-th unified dispatch unit in the t-th time period; and are the start-up and shutdown costs of the i-th unified dispatch unit in the t-th time period, respectively.

4. The redundancy constraint identification and elimination method for large-scale power market clearing simulation according to claim 1, characterized in that The system power balance constraint of the security-constrained unit commitment model is: Among them, N W , N PV and N S are the numbers of wind turbines, photovoltaic units and energy storage power stations in the power system respectively; is the declared output of the j-th wind turbine at time t, is the declared output of the k-th photovoltaic unit at time t; and are the discharge power and charge power of the s-th energy storage power station at time t respectively; NL t is the net load value of the power system at time t; The system reserve capacity constraint of the security-constrained unit commitment model is: Among them, represents the start-stop state of the i-th centrally dispatched unit in the power system at time t, when it is "1", it represents the on state, when it is "0", it represents the off state; and are respectively the maximum technical output and the minimum technical output of the i-th centrally dispatched unit at time t; and are respectively the positive reserve capacity demand and the negative reserve capacity demand of the power system at time t; The upper and lower limits of unit output constraints of the security-constrained unit commitment model are: The unit output ramp rate constraint of the security-constrained unit commitment model is: where R U,i and R D,i are respectively the maximum upward ramp rate and the maximum downward ramp rate of the i-th unit under unified dispatching; The unit start-stop time constraint of the security-constrained unit commitment model is: Among them, T ON,i and T OFF,i are respectively the shortest consecutive running time and the shortest consecutive shutdown time of the i-th unit under unified adjustment; temp is an intermediate variable for the time period; The upper and lower limits of unit gas volume constraints of the security-constrained unit commitment model are: Among them, and are the upper limit value and the lower limit value of the gas volume of the gas turbine unit group in the unified regulation unit respectively; GS is the gas turbine unit in the unified regulation unit; η GE is the gas-electricity conversion coefficient; The energy storage charge and discharge power constraint of the security-constrained unit commitment model is: Among them, and respectively represent the charging state and discharging state of the s-th energy storage power station at time t; and are respectively the maximum charging power and maximum discharging power of the s-th energy storage power station; The energy storage capacity constraint of the security-constrained unit commitment model is: Among them, is the capacity of the s-th energy storage power station at time t; and are the maximum capacity and minimum capacity of the s-th energy storage power station respectively; η S,s is the charge-discharge efficiency of the s-th energy storage power station; The line power flow constraint of the security-constrained unit commitment model is: Among them, is the power flow of line l during time period t; is the forward transmission power limit of line l.

5. The method for identifying and eliminating redundant constraints for large-scale power market clearing simulation according to claim 1, characterized in that The objective function of the security-constrained economic dispatch model is: where, N is the number of unified dispatching units in the power system; T is the number of time periods considered in the clearing process; P i t is the output of the i-th unified dispatching unit at time period t; is the operating cost function of the i-th unified dispatching unit at time period t.

6. The redundant constraint identification and elimination method for large-scale electricity market clearing simulation according to claim 1, wherein The system power balance constraint, system reserve capacity constraint, upper and lower limits of unit output constraint, unit output ramp rate constraint, upper and lower limits of unit gas volume constraint, energy storage charge and discharge power constraint, energy storage capacity constraint, and line power flow constraint of the security-constrained economic dispatch model are the same as the corresponding constraint conditions in the security-constrained unit commitment model; among them, all operating state variables in the security-constrained economic dispatch model are known results obtained from solving the security-constrained unit commitment model.

7. The method for identifying and eliminating redundant constraints for large-scale power market clearing simulation according to claim 1, wherein The determination theorem of the redundant constraint is specifically as follows: By constructing a small-scale mixed integer linear programming problem, calculate the maximum possible transmission power of each transmission line on the premise of meeting the system power balance constraint; If the maximum possible transmission power of a certain transmission line is strictly less than its corresponding line security constraint threshold, then the line power flow constraint of this transmission line is a redundant constraint; where the line security constraint threshold corresponding to the transmission line is the forward transmission power limit of this transmission line.

8. The method for identifying and eliminating redundant constraints for large-scale power market clearing simulation according to claim 1, characterized in that The maximum possible transmission power of each transmission line is derived and calculated by using a mathematical analysis method, and its calculation formula is: Among them, BL max,l is the maximum power flow of line l under the current system load demand and structural parameters, that is, the maximum possible transmission power of line l; Gen is the set of all grid-connected units in the system; α i,l is the power contribution coefficient of the i-th grid-connected unit to line l; Bus is the set of all load nodes in the system; β j,l is the power influence coefficient of the j-th load node on line l.

9. The redundancy constraint identification and elimination method for large-scale power market clearing simulation according to claim 1, characterized in that The elimination rule of the redundant constraint is specifically as follows: The redundant constraint is determined by the feasible region defined by the system load demand and transmission security constraints; if a line power flow constraint is identified as inactive by the sufficient conditions for analysis, then as long as the power balance constraint of the system is satisfied, it is inactive for all possible unit commitment states; then if the maximum possible transmission power BL of the calculated transmission line max,l is less than the forward transmission power limit of line l then the corresponding line power flow constraint is a redundant constraint and is removed.

10. The method for identifying and eliminating redundant constraints for large-scale power market clearing simulation according to claim 1, characterized in that, The calculation formula of the marginal price of the node is: Among them, LMP b,t is the marginal electricity price of node b at time period t; λ t is the Lagrange multiplier of the system power balance constraint at time period t, used to represent the electricity marginal component of the marginal electricity price of the node; is the Lagrange multiplier of the maximum forward power flow constraint of line l at time period t; is the Lagrange multiplier of the maximum reverse power flow constraint of line l at time period t; N L is the number of transmission lines in the power system; G l-b is the sensitivity factor of node b to line l; represents the network congestion marginal component of the marginal electricity price of the node.

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