Electric power and electric quantity balance margin optimization method and maintenance plan making method and system

Abstract

The invention belongs to the technical field of electric power system optimization, and relates to an electric power and electric quantity balance margin optimization method and a corresponding maintenance plan making method and system.The method comprises the steps that 1, system basic technical data are obtained from an electric power system planning department, 2, market clearing is conducted by considering decision-making behaviors of a power generation company, and a maintenance plan is made. Market clearing results of different power generators are obtained; according to the maintenance plan making method, a power grid company arranges a maintenance plan based on a market clearing result, a start-stop variable, an output variable and a maintenance variable are taken as decision variables, and the minimum system comprehensive cost is taken as an optimization target; according to the method, the quotation behavior of each power generation provider is considered during market clearing, the operation cost is pre-estimated, and then the unit starting mode and the operation cost are considered during maintenance arrangement, so that the unit maintenance plan is reasonably formulated based on the market clearing result. According to the invention, market behaviors of market members can be effectively reflected, and a maintenance plan is made on the premise of ensuring normal clearing of the market.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization, and more specifically, relates to a method for optimizing power and electricity balance margin and a corresponding maintenance plan formulation method and system. Background Art

[0002] At present, the maintenance plan of the power system is mostly aimed at minimizing the comprehensive cost, and the unit maintenance is arranged on the basis of meeting the system's power generation capacity. As the proportion of renewable energy gradually increases, the balance of power and electricity presents a probabilistic characteristic. Under the given maintenance, it may lead to a large amount of wind, solar and water abandonment or the inability to find a feasible unit operation mode. In the power market environment, the introduction of power market operators is responsible for the system's power and electricity balance. It conducts transaction clearing and settlement based on the quotations of buyers and sellers through the trading platform, and announces the transaction results. However, it is worth noting that the impact of unit start-up and shutdown constraints and standby constraints is not considered when the market is cleared, but in the actual market clearing, it has to face the impact of unit start-up and shutdown and standby constraints.

[0003] Power generators decide their own power generation behavior with the goal of maximizing their own profits. Considering the mutual competition between different power generators, the market clearing results may be different from the operating results under the dispatch arrangement in terms of economy and reliability. At this time, if the power grid company wants to optimize the system operation mode, it cannot achieve it through the traditional way of delegating dispatch instructions. It needs to optimize the system operation mode by influencing the market clearing results.

[0004] Properly considering the impact of the power market decision-making equilibrium on the system operation mode and formulating maintenance plans that take the power market into consideration is of great significance for improving the reliability of power system operation. Traditional maintenance plans aim to minimize maintenance costs. After considering the impact of the power market, attention should also be paid to the system operation costs under the maintenance plan to avoid increasing the total system operation costs due to the maintenance plan. On this basis, some solutions propose that the maintenance plan can consider the impact of system electricity prices, but the impact of the decision-making behavior of market players is not recognized. Therefore, while formulating the maintenance plan, the economic dispatch of the power system is carried out, and the system simulation operation mode under the maintenance plan is limited to be more economical than the market clearing result, so as to improve the economy of the system.

[0005] Some existing research jointly optimizes maintenance plans and medium- and long-term operation scheduling. Grid companies can optimize the clearing results to a certain extent through maintenance plans, but there is still a lack of effective solutions on how to formulate maintenance plans in the power market environment. Summary of the invention

[0006] In order to solve the deficiencies in the prior art, the present invention provides a method for optimizing the electric power balance margin and a corresponding maintenance plan formulation method, which fully considers the impact of the decision-making behavior of the power generator on the system operation, and thus carries out a method for formulating a system maintenance plan to solve the above technical problems.

[0007] The present invention adopts the following technical solution. A first aspect of the present invention provides a method for optimizing a power balance margin, comprising the following steps:

[0008] A power producer decision-making bidding model is constructed for a single power producer, and a market clearing model is constructed for the power trading market;

[0009] The constructed power generator decision bidding model and the market clearing model are integrated and transformed into the decision bidding model MPEC problem of the power generator when the market is cleared, and the market clearing model is transformed into the constraint conditions of the decision bidding model MPEC problem of the power generator;

[0010] The MPEC problem of each power producer is solved in turn. When a power producer makes a decision, it is assumed that the behavior of other power producers is known. The diagonalization algorithm is used to solve the game equilibrium point. The bidding and output of each power producer are used as the final bidding strategy and market clearing result.

[0011] Preferably, the construction of a power producer decision bidding model for a single power producer includes:

[0012] For a single power producer, a strategic bidding is conducted with the goal of maximizing its own profit and an objective function is established. For the bids and quantities reported by the power generating units, constraints are established so that the bids and quantities reported by any power generating unit cannot exceed the upper and lower limits.

[0013] Preferably, the construction of a market clearing model for the power trading market includes:

[0014] The market clearing model takes the maximum social welfare as the optimization goal, which is expressed as the minimum revenue of the power generator's bid. Through the market clearing model, the marginal price of each node in the system is calculated, which is then used for the power generator's bidding decision.

[0015] The constraints of the market clearing model include: system safety and stability constraints, unit power constraints and unit start and stop constraints.

[0016] Preferably, the system safety and stability constraints include: satisfying the node power balance, and using the value of its dual variable as the marginal price of the node; limiting the fluctuation of renewable energy power generation within a set range near the predicted output; satisfying the system backup, and the forward and reverse power flows of the system do not exceed the limit; and the system voltage angle does not exceed the limit.

[0017] Preferably, the unit start and stop constraints include: limiting the unit start and stop time, limiting the relationship between the unit start and stop and maintenance variables, and limiting the unit start and stop variables to 0-1 variables.

[0018] Preferably, the integration of the constructed power producer decision bidding model and the market clearing model into the MPEC problem of the power producer decision bidding model when the market is cleared includes:

[0019] Relax the 0-1 binary variable in the market clearing model into a continuous variable in the interval [0,1], including:

[0020] Set the start and stop status u of unit i i,t Relaxation is a continuous variable in the interval [0,1];

[0021] Constructing the dual problem of market clearing models;

[0022] Based on the original problem and its dual problem of the market clearing model, the dual gap problem is constructed;

[0023] The difference between the original market clearing problem and the dual problem is introduced as a penalty term into the power company pricing decision problem.

[0024] Preferably, the MPEC problem of each power generator is solved in turn. When the power generator makes a decision, it is considered that the behaviors of other power generators are known. The diagonalization algorithm is used to solve the game equilibrium point, which includes:

[0025] When all power generators behave the same twice before and after, the equilibrium result is considered to be achieved;

[0026] If the convergence criterion is not met, the bidding information of the generators is updated and the MPEC problems of all generators are solved again until convergence. At this time, the bidding and output of each generator are the final bidding strategy and market clearing result.

[0027] A second aspect of the present invention provides a method for formulating a power system maintenance plan, comprising the following steps:

[0028] Obtaining a clearing result, including the bidding and output of each power generator, wherein the clearing result is obtained by the power and electricity balance margin optimization method;

[0029] Construct a maintenance plan model, including: using start-stop variables, output variables and maintenance variables as decision variables, minimizing system comprehensive cost as optimization goal, and setting constraints;

[0030] Solve the constructed maintenance scheduling model to obtain the start-stop variables, output variables and maintenance variables.

[0031] Preferably, the constraints include: unit maintenance constraints, system power flow constraints and economic constraints;

[0032] The cost of the simulated operation mode under the maintenance plan does not exceed the unit operation cost of the market clearing result.

[0033] A third aspect of the present invention provides a power and quantity balance margin optimization system, which runs the power and quantity balance margin optimization method, including:

[0034] The generator decision bidding configuration module is used to build a generator decision bidding model for a single generator;

[0035] Market clearing configuration module, used to build a market clearing model for the power trading market;

[0036] A game module, used for integrating the constructed decision-making bidding model of power producers and the market clearing model into the decision-making bidding model MPEC problem of power producers when the market is cleared, and the market clearing model is converted into the constraint conditions of the decision-making bidding model MPEC problem of the power producers;

[0037] The solver is used to solve the MPEC problem of each generator in turn, and take the bidding and output of each generator as the final bidding strategy and market clearing result.

[0038] A fourth aspect of the present invention provides a power system maintenance plan formulation system, comprising:

[0039] A data interface for obtaining the clearing results from the power and electricity balance margin optimization system, including the bidding and output of each power generator;

[0040] Maintenance plan generation module, users build maintenance plan model, including: starting and stopping variables, output variables and maintenance variables as decision variables, minimizing system comprehensive cost as optimization goal, and setting constraints;

[0041] The solver is used to solve the maintenance planning model, obtain and output the start and stop variables, output variables and maintenance variables.

[0042] Compared with the prior art, the beneficial effects of the present invention include at least: the method of the present invention can fully consider the strategic behavior of power generators, reasonably formulate maintenance plans, and reduce system risks. Compared with the maintenance plan formulation method under the traditional planning and dispatching mode, the present invention is based on the power market clearing rules, comprehensively considers the bidding behavior of power generators participating in the power market, and implements the formulation of maintenance plans considering the power market from the perspective of power grid companies, which is more in line with the current background of power market reform; therefore, the present invention can effectively reflect the market behavior of market members and formulate maintenance plans under the premise of ensuring normal market clearing. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of a method for optimizing electric power balance margin and a corresponding maintenance plan formulation method of the present invention;

[0044] Figure 2This is a calculation example diagram of the maintenance plan and dispatch results without considering the power market of the present invention;

[0045] Figure 3 It is a calculation example diagram of the maintenance plan and market clearing result considering the strategic behavior of the power market of the present invention;

[0046] Figure 4 It is a comparison diagram of the margin optimization results of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The described embodiments are only embodiments of a part of the present invention, not all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0048] refer to Figures 1 to 4 The present invention provides a method for optimizing the balance margin of electric power and a corresponding maintenance plan formulation method. The method of the present invention is used to fully consider the decision-making behavior of power producers in a market environment, perform market clearing, obtain market clearing results of different power producers, and then formulate a system maintenance plan.

[0049] Specifically, if Figure 1 As shown, embodiment 1 of the present invention provides a method for optimizing a power balance margin, comprising the following steps:

[0050] Step 1: Obtain basic technical data of the system from the power system planning department; the basic technical data of the system includes: technical parameters of various types of power sources in the power system, existing transmission grid and network parameters, load demand and forecast data of new energy power generation;

[0051] Step 2: Construct a generator decision-making bidding model for a single generator and a market clearing model for the power trading market.

[0052] Preferably but not limiting, the step 2 specifically comprises the following steps:

[0053] Step 2.1: Construct a power producer decision bidding model including objective function and constraints.

[0054] Among them, for a single power generator, a strategic bidding is carried out with the goal of maximizing its own profit, and an objective function is established, which is expressed as the following formula (1); for the bids and quantities of the power generation units, constraint conditions are established, and the bids and quantities of any power generation unit cannot exceed the upper and lower limits, which are expressed as the following formulas (2) and (3):

[0055]

[0056] Where:

[0057] f gk represents the interests of generator k;

[0058] t, T represents the index and total number of time;

[0059] i represents the unit index;

[0060] Ω k / Θ n They represent the set of units owned by power generator k and the set of units at node n respectively;

[0061] represents the node marginal price of node n where unit i is located at time t;

[0062] M gi represents the marginal operating cost of unit i;

[0063] P gi,t Indicates the actual output of the unit;

[0064] Represents the actual output of new energy unit j;

[0065] They represent the fluctuation amount and upper and lower limits of the new energy respectively;

[0066] b represents the index of the quotation segment;

[0067] It represents the quotation of unit i in the bth segment and its upper and lower limits;

[0068] Indicates the b-th section report quantity of unit i and its upper and lower limits.

[0069] Step 2.2: Construct a market clearing model including the objective function and constraints.

[0070] The market clearing model takes the maximum social welfare as the optimization goal and the minimum power generator's bid revenue as the goal. Through the market clearing model, the marginal price of each node in the system is calculated, which is then used for power generators to make bidding decisions. Specifically, the objective function of the market clearing model is expressed as follows:

[0071]

[0072] Where:

[0073] f represents the power generator's quoted revenue;

[0074] i represents the unit index;

[0075] t, T represents the index and total number of time;

[0076] bThe index of the price quotation segment;

[0077] w i,b represents the bth segment quotation of unit i;

[0078] P gi,t,b Unit i is scalared in segment b at time t.

[0079] The constraints of the market clearing model include: system safety and stability constraints, unit power constraints and unit start and stop constraints.

[0080] The system safety and stability constraints are expressed as the following formulas (5)-(10). Formula (5) limits the node power balance, and the value of its dual variable is used as the marginal price of the node; Formula (6) limits the fluctuation of renewable energy power generation within a certain range near the predicted output; Formula (7) limits the system reserve, and Formula (8) limits the line transmission capacity, limiting the forward and reverse power flows of the system to not exceed the limit; Formulas (9) and (10) limit the system voltage angle, and node 1 is selected as the voltage angle reference node:

[0081]

[0082] δ1=0;ξ1 (10)

[0083] Where:

[0084] The dual variable corresponding to the constraint condition is after “;”;

[0085] Ω k / Θ n They represent the set of units owned by power generator k and the set of units at node n respectively;

[0086] P gi,t represents the actual output of unit i, Respectively represent the upper and lower limits of the output of unit i;

[0087] represents the actual output of new energy unit j, They represent the fluctuation amount and upper and lower limits of the new energy respectively;

[0088] represents the load at node n;

[0089] Γ n Represents the set of nodes connected to node n;

[0090] B nm represents the admittance of the line between nodes n and m;

[0091] δ n,trepresents the angle of the voltage at node n, δ m,t represents the angle of the voltage at node m;

[0092] λ n,t represents the node marginal electricity price;

[0093] They represent the dual variables of the new energy fluctuation constraint respectively;

[0094] u i,t Indicates the start and stop status of unit i, 0 means shutdown, 1 means startup;

[0095] β represents the reserve coefficient; P d,t Indicates system load; η t dual variable representing the alternate constraint;

[0096] represents the transmission capacity limit of line nm; They represent the dual variables of line power flow constraints respectively;

[0097] They represent the dual variables of node voltage constraints respectively;

[0098] δ1 represents the angle of the voltage at node 1; ξ1 represents the dual variable of the node 1 voltage constraint.

[0099] The unit power constraint is expressed by the following formulas (11)-(14); Formula (11) indicates that the output of each unit cannot exceed its upper and lower limits; Formula (12) limits the unit ramping, and Formulas (13) and (14) limit the unit output and its segmented bid quantity, which is used to characterize the uncertainty of renewable energy power generation:

[0100]

[0101] Where:

[0102] They represent the scalar and its upper limit of unit i in the bth segment at time t respectively;

[0103] They represent the dual variables of the unit segment bid power constraints respectively;

[0104] They represent the dual variables of the unit output constraints respectively;

[0105] R i Indicates the maximum climbing capability of unit i; They respectively represent the dual variables of the unit climbing constraints.

[0106] The unit start and stop constraints are expressed as the following formulas (15)-(18), where formulas (15) and (16) limit the unit start and stop time, formula (17) limits the relationship between the unit start and stop and maintenance variables, and formula (18) limits the unit start and stop variables to 0-1 variables:

[0107]

[0108]

[0109] u i,t ∈{0,1} (18)

[0110] Where:

[0111] u i,n The intermediate variable representing the minimum start-stop constraint of the unit; t, T represent the index and total number of time respectively; T i,on / T i,off They represent the minimum start-up time and minimum shutdown time of unit i respectively; They represent the dual variables of the minimum start-stop constraints of the units respectively;

[0112] Indicates the maintenance status of unit i at time t, 0 means the unit is under maintenance, otherwise 1; τ i,t Dual variable representing the unit state constraint.

[0113] Step 3: The power producer decision bidding model and the market clearing model constructed in step 2 are integrated and converted into the power producer decision bidding model MPEC (Mathematical Programming With Equilibrium Constraint) problem when the market is cleared, that is, the power producer decision bidding model and the market clearing model are converted into a single-layer model, and the market clearing model is converted into the constraints of the power producer decision bidding model MPEC problem.

[0114] Preferably but not limiting, step 3 comprises:

[0115] Step 3.1: Relax the 0-1 binary variable in the market clearing model into a continuous variable in the interval [0,1], including: first, the start and stop state u of unit i i,t Relax to a continuous variable in the interval [0,1], that is, replace formula (18) with the following formula (19):

[0116]

[0117] Where:

[0118] They represent the dual variables of the unit start and stop state constraints respectively.

[0119] It is worth noting that the market clearing problem is transformed from a mixed integer linear programming problem to a continuous linear programming problem, and then its dual problem can be solved. As one of the outstanding substantive features of the present invention, the influence of the unit start-stop constraint and the standby constraint is considered in the market clearing, and the relaxation of the unit start-stop constraint and the corresponding processing are transformed into a model, so that the decision of the power generator can be realized under the market clearing mechanism considering the start-stop of the unit.

[0120] Step 3.2: Construct the dual problem of the market clearing model, including: Express the original market clearing problem as the objective function and its constraints in matrix form, expressed as the following formula (20), where the variables Since the original problem is a mixed integer programming problem, the dual problem cannot be solved. Formula (19) is used to transform it into a continuous convex optimization problem, and then its dual problem can be solved, which is expressed as the following formula (21):

[0121]

[0122] Where:

[0123] x represents the decision variables of the original market clearing problem, including: the unit segment standard quantity P gi,t,b , unit start and stop status u gi,t , node voltage angle δ n,t ;

[0124] c represents the coefficient vector in the objective function; c T represents the transpose of the coefficient vector; A represents the coefficient matrix of the inequality constraint; b represents the constant vector of the inequality constraint; α represents the dual variable of the inequality constraint; B represents the coefficient matrix of the equality constraint; d represents the constant vector of the equality constraint; β represents the dual variable of the equality constraint.

[0125] Step 3.3: Based on the original problem of the market clearing model and its dual problem, a dual gap problem is constructed, which is expressed as the following formula (22). The difference between the original problem of market clearing and the dual problem is introduced as a penalty term into the power generation price decision problem, that is, the decision bidding model MPEC problem of the power generation company when the market is cleared is obtained:

[0126]

[0127] Where:

[0128] x represents the decision variables of the original market clearing problem, including: the unit segment standard quantity P gi,t,b , unit start and stop status u gi,t , node voltage angle δ n,t;

[0129] c represents the coefficient vector in the objective function; c T represents the transpose of the coefficient vector; A represents the coefficient matrix of the inequality constraint; b represents the constant vector of the inequality constraint; α represents the dual variable of the inequality constraint; B represents the coefficient matrix of the equality constraint; d represents the constant vector of the equality constraint; β represents the dual variable of the equality constraint

[0130] It can be understood that the dual gap problem minimizes the dual gap of the original market clearing problem and its dual problem, and the constraints contain the constraints of both the original market clearing problem and its dual problem, and the constraints enforce u i,t ∈{0,1}, keep the variable value consistent with the physical meaning and ensure that the start and stop status of the unit is a 0-1 binary variable.

[0131] It is worth noting that the power company decision bidding model and the market clearing model are converted into a single-layer model. Since the decision variables of the market clearing model include the unit start and stop variables (0-1 variables), the lower model becomes a non-convex problem. Therefore, the lower market clearing model cannot be directly converted into the constraint conditions of the middle-level power company decision equilibrium model through the KKT condition. Therefore, as one of the outstanding substantive features of the present invention, the present invention relaxes the 0-1 binary variables in the market clearing model into continuous variables in the interval [0,1], constructs the dual problem of the market clearing model, and introduces the difference between the original market clearing problem and the dual problem as a penalty term into the power company price decision problem, thereby converting the middle and lower double-layer problem into a single-layer problem, that is, the decision bidding model MPEC of the power company when the market is cleared, which is used to solve the decision variables.

[0132] Step 4: Solve the MPEC problem of each power producer in turn. When the power producer makes a decision, it is assumed that the behavior of other power producers is known. The diagonalization algorithm is used to solve the game equilibrium point. When the behavior of all power producers is the same twice before and after, the equilibrium result is determined to be achieved. If the convergence criterion is not met, the bid information of the power producer is updated, and the MPEC problem of all power producers is solved again until convergence. At this time, the bidding and output of each power producer are the final bidding strategy and market clearing result. Among them, the same behavior of all power producers twice before and after includes: the bid is the same or the error is less than the allowable range.

[0133] It is worth noting that one of the outstanding substantive features of the present invention is that a method for solving the game equilibrium point of different power generators participating in the power market is provided in the power balance margin optimization method, which helps power generators solve their own optimal bidding strategies based on existing information. The equilibrium solution is performed for the decision-making behaviors of all market players, and the decision-making that takes into account the influence of other power generators can be realized under the market clearing mechanism that takes into account the start and stop of the unit.

[0134] like Figure 1 As shown, embodiment 2 of the present invention provides a method for formulating a maintenance plan for a power system, preferably but not restrictively, based on the power balance margin optimization method described in embodiment 1, comprising the following steps:

[0135] Step 1: Obtain the clearing results, including the bids and outputs of each power generator.

[0136] It is worth noting that one of the outstanding substantive features of the present invention is that, in the method for formulating a maintenance plan for a power system, a maintenance plan that takes the power market into consideration is executed. The market clearing result obtained by the power balance margin optimization method described in Example 1 of the present invention includes the bidding behavior of each power generator, which is based on the equilibrium result obtained by each power generator pursuing its own maximum profit. The power grid company optimizes the unit maintenance arrangement based on the power market clearing result, and then fully considers the influence of the strategic behavior of the power generator when formulating the maintenance plan, reasonably formulates the maintenance plan, and reduces the system risk.

[0137] Step 2, construct a maintenance plan model, including: using start-stop variables, output variables and maintenance variables as decision variables, minimizing the system comprehensive cost as the optimization goal, and setting constraints.

[0138] Preferably but not limiting, step 2 specifically comprises:

[0139] Step 2.1: Taking the start-stop variable, output variable and maintenance variable as decision variables and minimizing the system comprehensive cost as the optimization goal, construct the objective function, which is expressed as the following formula (23):

[0140]

[0141] Where:

[0142] f represents the total system cost; a i / b i / c i represents the quadratic coefficient, the primary coefficient and the constant coefficient of the operating cost of unit i; Indicates the maintenance status of unit i at time t, 0 means the unit is undergoing maintenance, otherwise it is 1.

[0143] Step 2.2: Construct constraints, including unit maintenance constraints, system power flow constraints, and economic constraints, expressed as the following formulas (24)-(29):

[0144]

[0145] Where:

[0146] T i G Indicates the unit maintenance time; Indicates the market clearing result.

[0147] In the formula (28), the cost of the simulated operation mode under the maintenance plan cannot exceed the unit operation cost of the market clearing result.

[0148] Step 3, solve the maintenance scheduling model constructed in step 2 to obtain the start-stop variables, output variables and maintenance variables.

[0149] Embodiment 3 of the present invention provides a power and quantity balance margin optimization system, which runs a power and quantity balance margin optimization method according to embodiment 1, including:

[0150] The generator decision bidding configuration module is used to build a generator decision bidding model for a single generator;

[0151] Market clearing configuration module, used to build a market clearing model for the power trading market;

[0152] A game module, used for integrating the constructed decision-making bidding model of power producers and the market clearing model into the decision-making bidding model MPEC problem of power producers when the market is cleared, and the market clearing model is converted into the constraint conditions of the decision-making bidding model MPEC problem of the power producers;

[0153] The solver is used to solve the MPEC problem of each generator in turn, and take the bidding and output of each generator as the final bidding strategy and market clearing result.

[0154] Embodiment 4 of the present invention provides a system for formulating a power system maintenance plan, comprising:

[0155] A data interface, used to obtain the clearing results from the power balance margin optimization system described in Example 3, including the bidding and output of each power generator;

[0156] Maintenance plan generation module, users build maintenance plan model, including: starting and stopping variables, output variables and maintenance variables as decision variables, minimizing system comprehensive cost as optimization goal, and setting constraints;

[0157] The solver is used to solve the maintenance planning model, obtain and output the start and stop variables, output variables and maintenance variables.

[0158] Embodiment 5 of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is loaded into the processor, a method for optimizing a power balance margin according to embodiment 1 is implemented.

[0159] Embodiment 6 of the present invention 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 computer program is loaded into the processor, a method for formulating a power system maintenance plan according to Embodiment 2 is implemented.

[0160] Embodiment 7 of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for optimizing the power balance margin according to embodiment 1 is implemented.

[0161] Embodiment 8 of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for formulating a power system maintenance plan according to Embodiment 2 is implemented.

[0162] In order to verify the effectiveness of the method proposed in this invention, an IEEE 30-node power system is selected for calculation and analysis. The calculation example takes 30 days as the operation cycle. The system generator information is shown in Table 1. The system installed capacity is 3950MW, of which 1000MW is new energy installed capacity, and the maximum load is 2489MW.

[0163]

[0164] Design comparison example:

[0165] Example 1: Without considering the strategic behavior of market players in the power market, the power grid company directly formulates the maintenance plan, that is, only executes the maintenance plan in the process flow chart;

[0166] Example 2: Considering the strategic behavior of market players in the electricity market, the power grid company formulates a maintenance plan based on the market clearing results that consider the strategic behavior of generators, that is, executes all the steps in the flowchart.

[0167] Example 3: Comparative analysis of the margins of Example 1 and Example 2.

[0168] The specific example results are as follows Figure 2 and Figure 3As shown, black indicates maintenance, gray indicates operation, and light gray indicates shutdown. It can be seen that when the power market strategy behavior is not considered, the two maintenance units are scheduled for maintenance in different time periods, which can avoid the problem of insufficient capacity caused by maintenance. Under this maintenance plan, the simulated dispatching scheme carried out by the power grid company has a longer startup time for the unit with low marginal cost (T1) and a shorter startup time for the unit with high marginal cost (T2) compared with the market clearing result, because under the dispatching mode, the power grid company arranges the unit output and formulates the maintenance plan with the goal of minimizing the system operation cost. Secondly, the market clearing result determines the power generation of each unit based on the bidding strategy of each power generator. The number of units started in the same period is large, and the resulting unit cost is large.

[0169] Figure 3 The maintenance plan and market clearing results considering the equilibrium of power market decision making are shown in Figure 2. Figure 2 It can be found that when considering the equilibrium of power market decision-making, the operation mode of the system is directly related to the strategic bidding of power generators, and the diverse bidding behaviors make the operation mode of the units more complicated. The maintenance plan of the two maintenance units has also changed from being at the end of the operation cycle to being dispersed at the beginning and end of the operation cycle, which can avoid capacity shortages caused by maintenance to a greater extent.

[0170] Figure 4 The figure is a comparison chart of the market clearing result margin under the two maintenance plans of Example 1 and Example 2. When the strategic behavior of the generator is not considered, the power grid company formulates the maintenance plan with the goal of minimizing the total cost. Under this maintenance plan, the generator may adjust the bidding behavior, making the margin of the market clearing result not meet the requirements. However, when the maintenance plan is made by considering the strategic behavior of the generator, the margin of the market clearing result can meet the requirements.

[0171] The present invention constructs a three-layer model for system maintenance plan formulation based on the decision-making behavior of different power generators in the market environment. By considering the quotation behavior of each power generator when the market is cleared, the operation mode of the non-maintenance unit and the system operation cost are estimated, and then the unit startup mode and operation cost of the market clearing result are considered when arranging maintenance, so as to reasonably formulate the unit maintenance plan based on the market clearing result, so as to fully consider the impact of the strategic behavior of the power generator on the system operation. The present invention can effectively reflect the market behavior of market members and formulate maintenance plans under the premise of ensuring normal market clearing.

[0172] In summary, the method for formulating maintenance plans for a high proportion of new energy power systems that considers the power market proposed in the present invention can fully consider the strategic behavior of power generators, reasonably formulate maintenance plans, and reduce system risks. Compared with the maintenance plan formulation method under the traditional planning and scheduling mode, the present invention is based on the power market clearing rules, comprehensively considers the bidding behavior of power generators participating in the power market, and realizes the formulation of maintenance plans that consider the power market from the perspective of power grid companies, which is more in line with the current background of power market reforms. The present invention can effectively reflect the market behavior of market members and formulate maintenance plans under the premise of ensuring normal market clearing. Therefore, the present invention is very suitable for pilot provinces in the power market to formulate system maintenance plans after starting the trial operation of the power market.

[0173] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing power balance margin, characterized in that: The following steps are involved: A power producer decision-making bidding model is constructed for a single power producer, and a market clearing model is constructed for the power trading market; The constructed power generator decision bidding model and the market clearing model are integrated and transformed into the decision bidding model MPEC problem of the power generator when the market is cleared, and the market clearing model is transformed into the constraint conditions of the decision bidding model MPEC problem of the power generator; The MPEC problem of each power producer is solved in turn. When a power producer makes a decision, it is assumed that the behavior of other power producers is known. The diagonalization algorithm is used to solve the game equilibrium point. The bidding and output of each power producer are used as the final bidding strategy and market clearing result.

2. The method for optimizing the power balance margin according to claim 1, characterized in that: The power generator decision bidding model constructed for a single power generator includes: For a single power producer, a strategic bidding is conducted with the goal of maximizing its own profit and an objective function is established. For the bids and quantities reported by the power generating units, constraints are established so that the bids and quantities reported by any power generating unit cannot exceed the upper and lower limits.

3. A method for optimizing power balance margin according to claim 1 or 2, characterized in that: The market clearing model constructed for the power trading market includes: The market clearing model takes the maximum social welfare as the optimization goal and the minimum revenue of the power generator's bid as the representation. Through the market clearing model, the marginal price of each node in the system is calculated, which is then used for the power generator's bidding decision. The constraints of the market clearing model include: system safety and stability constraints, unit power constraints and unit start and stop constraints.

4. The method for optimizing the power balance margin according to claim 3, characterized in that: The system safety and stability constraints include: satisfying the node power balance, and using the value of its dual variable as the marginal price of the node; limiting the fluctuation of renewable energy power generation within a set range near the predicted output; satisfying the system reserve, and the system's forward and reverse power flows do not exceed the limit; and the system voltage angle does not exceed the limit.

5. The method for optimizing the power balance margin according to claim 3, characterized in that: The unit start and stop constraints include: limiting the unit start and stop time, limiting the relationship between the unit start and stop and maintenance variables, and limiting the unit start and stop variables to 0-1 variables.

6. A method for optimizing electric power balance margin according to claim 1 or 5, characterized in that: The MPEC problem of integrating the constructed power producer decision bidding model and the market clearing model into the power producer decision bidding model when the market is cleared includes: Relax the 0-1 binary variable in the market clearing model into a continuous variable in the interval [0,1], including: Set the start and stop status u of unit i i,t Relaxation is a continuous variable in the interval [0,1]; Constructing the dual problem of market clearing models; Based on the original problem and its dual problem of the market clearing model, the dual gap problem is constructed; The difference between the original market clearing problem and the dual problem is introduced as a penalty term into the power company pricing decision problem.

7. The method for optimizing the power balance margin according to claim 6, characterized in that: The MPEC problem of each power producer is solved in turn. When a power producer makes a decision, it is considered that the behavior of other power producers is known. The diagonalization algorithm is used to solve the game equilibrium point, including: When all power generators behave the same twice before and after, the equilibrium result is considered to be achieved; If the convergence criterion is not met, the bidding information of the generators is updated and the MPEC problems of all generators are solved again until convergence. At this time, the bidding and output of each generator are the final bidding strategy and market clearing result.

8. A method for formulating a power system maintenance plan, characterized in that: The following steps are involved: Obtaining a clearing result, including the bidding and output of each power generator, wherein the clearing result is obtained by the power balance margin optimization method according to any one of claims 1 to 7; Construct a maintenance plan model, including: using start-stop variables, output variables and maintenance variables as decision variables, minimizing system comprehensive cost as optimization goal, and setting constraints; Solve the constructed maintenance scheduling model to obtain the start-stop variables, output variables and maintenance variables.

9. A method for formulating a power system maintenance plan according to claim 8, characterized in that: Constraints include: unit maintenance constraints, system power flow constraints and economic constraints; The cost of the simulated operation mode under the maintenance plan does not exceed the unit operation cost of the market clearing result.

10. A power balance margin optimization system, running a power balance margin optimization method according to any one of claims 1 to 7, characterized in that: include: The generator decision bidding configuration module is used to build a generator decision bidding model for a single generator; Market clearing configuration module, used to build a market clearing model for the power trading market; A game module, used for integrating the constructed decision-making bidding model of power producers and the market clearing model into the decision-making bidding model MPEC problem of power producers when the market is cleared, and the market clearing model is converted into the constraint conditions of the decision-making bidding model MPEC problem of the power producers; The solver is used to solve the MPEC problem of each generator in turn, and take the bidding and output of each generator as the final bidding strategy and market clearing result.

11. A power system maintenance plan formulation system, characterized in that: include: A data interface for obtaining a clearing result from the power and electricity balance margin optimization system as claimed in claim 10, including the bidding and output of each power producer; Maintenance plan generation module, users build maintenance plan model, including: starting and stopping variables, output variables and maintenance variables as decision variables, minimizing system comprehensive cost as optimization goal, and setting constraints; The solver is used to solve the maintenance planning model, obtain and output the start and stop variables, output variables and maintenance variables.

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