A method for constructing an efficient generator unit maintenance model based on unit aggregation and linear relaxation

Abstract

The present application belongs to the field of generator unit maintenance plan arrangement, and relates to a kind of construction method of high-efficiency generator unit maintenance model based on unit aggregation and linear relaxation, based on unit combination model, establishes the basic UC maintenance model, and models the generator unit maintenance problem as a mixed integer programming problem;Further, for the problem that the production and operation constraints of thermal power generating units in the medium and long term maintenance problem of generator unit are complex and difficult to solve, the RCUC model is embedded into the maintenance model by introducing unit aggregation and linear relaxation technology, and the RCUC maintenance model is established;For the maintenance constraints of unit cluster, an improved RCUC maintenance model is proposed, which uses integer variables instead of 0-1 variables for modeling, reduces the size of integer variables and constraints, and improves the solving efficiency of the generator unit maintenance model.

Description

Technical Field

[0001] This invention belongs to the field of generator set maintenance planning, and specifically relates to a method for constructing an efficient generator set maintenance model based on generator set aggregation and linear relaxation. Background Technology

[0002] The efficient and reliable operation of power systems is crucial to meeting the ever-increasing demand for electricity. As the backbone of the power system, generator units are highly susceptible to damage; unexpected failures can impact generation efficiency and reliability, and may even lead to systemic outages or paralysis. Therefore, it is essential to develop regular maintenance plans for generator units in advance to ensure their normal operation and minimize downtime caused by unexpected failures. The generator unit maintenance plans required for long-term operation of power systems involve long time spans, large problem scales, and high solution complexity. Therefore, designing an efficient generator unit maintenance model is a significant challenge for the long-term reliable operation of power systems.

[0003] Generator maintenance issues require consideration of constraints such as the number of maintenance visits, duration, continuity, and minimum time intervals to optimize maintenance resource utilization while minimizing the increase in operating costs caused by preventative maintenance. In the past, maintenance plans were typically developed using simple rule-based methods, scheduling maintenance according to fixed time intervals or predetermined operating times. This approach often fails to consider the dynamic operating characteristics of the power grid, leading to suboptimal maintenance plans and reduced system reliability.

[0004] Currently, scholars both domestically and internationally have conducted extensive research on the problem of generator set maintenance scheduling, with heuristic and mathematical optimization methods being the main approaches. Heuristic methods primarily include the equal reserve (rate) method and the equal risk method. These methods focus on system reliability objectives such as equal reserve or equal risk, using heuristic algorithms to sort the maintenance sequence of generator sets to minimize or equalize the reserve rate or risk throughout the entire system's production and operation cycle. While these methods are computationally fast, they cannot simulate the actual production and operation process of a power system, making it difficult to guarantee the operability and economic efficiency of the results. Mathematical optimization methods are mainly based on mixed integer programming (MIP) to model the production, operation, and maintenance process of generator sets. They can simulate the actual production and operation process, and their objective function considers not only reliability but also the economic efficiency of system operation, better meeting the needs of actual maintenance planning. However, since the time span of generator set maintenance models is usually monthly or annual, using time-series load curves in this case will result in an excessively large scale of the production and operation problem within the model, making it impossible to obtain the optimal solution. Summary of the Invention

[0005] The purpose of this invention is to provide a method for constructing an efficient generator set maintenance model based on unit aggregation and linear relaxation, which solves the problem that existing modeling methods cannot obtain high-quality solutions in a reasonable time.

[0006] This invention is achieved through the following technical solution:

[0007] This invention discloses a method for constructing a high-efficiency generator set maintenance model based on unit aggregation and linear relaxation, comprising the following steps:

[0008] 1) Based on the production and operation characteristics of thermal power units, hydropower units, energy storage devices, pumped storage units, wind power and photovoltaic power, establish a generator unit maintenance model based on the unit combination model;

[0009] 2) Based on the relaxed unit cluster combination method, the unit combination model is linearly relaxed and embedded into the generator set maintenance model to establish an RCUC maintenance model;

[0010] 3) Based on unit aggregation technology, the maintenance variables of the unit cluster in the RCUC maintenance model are modeled using integer variables. With the goal of minimizing the total system cost, an improved RCUC maintenance model is established, thus obtaining the high-efficiency generator unit maintenance model.

[0011] Furthermore, step 1) specifically involves:

[0012] First, establish the production and operation characteristics of thermal power units, hydropower units, energy storage devices, pumped storage units, wind power and photovoltaic power, clarify the constraints that they must meet during production and operation, and establish production and operation constraints based on the unit combination model.

[0013] Establish maintenance constraints for thermal power units and hydropower units;

[0014] Production operation constraints and maintenance constraints constitute the generator set maintenance model.

[0015] Furthermore, the constraints that thermal power units must meet in production and operation include upper and lower limits on output, limits on ramp-up capability, limits on changes in start-up and shutdown status, and limits on minimum continuous start-up / shutdown time. The specific constraints of the mathematical model are as follows:

[0016]

[0017]

[0018]

[0019] u g,t =u g,t-1 +su g,t -sd g,t ,t∈[2,T]Z (4)

[0020]

[0021]

[0022] u g,t ,su g,t ,sd g,t ∈{0,1} (7)

[0023] su g,t +sd g,t ≤1 (8)

[0024] Among them, the 0-1 variable u g,t For the start-up and shutdown state of unit g at time t, the start-up state is u. g,t =1, power off u g,t =0; Cap is the minimum technical output ratio of unit g; g The rated capacity of unit g; These are the start-up ramp rate and the shutdown ramp rate of unit g, respectively. These are the uphill and downhill ramp rates for unit g, respectively. These represent the minimum start-up time and minimum shutdown time for unit g, respectively; P g,t The actual output of thermal power unit g during time period t; su g,t sd represents the starting action variable of thermal power unit g during time period t, where 1 indicates starting and 0 indicates not starting; g,t For the shutdown action of thermal power unit g during time period t, 1 indicates shutdown and 0 indicates no shutdown;

[0025] The operation of energy storage devices needs to meet the constraints of upper and lower limits of charging and discharging power, energy balance equations for adjacent time periods, upper and lower limits of load state, and the constraint that the stored capacity is equal in the initial and final states. The specific mathematical model constraints of the production and operation characteristics of energy storage devices are as follows:

[0026]

[0027]

[0028]

[0029]

[0030]

[0031] in, These represent the charging / discharging power of energy storage device n at time t; This represents the installed capacity of energy storage device n; This represents the amount of energy stored in energy storage device n at time t; These represent the charge / discharge efficiency of energy storage device n; and the State of Charge (SOC). n,t Indicates the load status of the energy storage device; This indicates the maximum load state of the energy storage device. This indicates the minimum load state of the energy storage device. This indicates the maximum energy storage capacity of the energy storage device; This indicates the amount of energy stored in the energy storage device at its initial state. The amount of electricity stored in the energy storage device at the end of its lifecycle;

[0032] Pumped-storage units must meet constraints on pumping / generation power, energy balance, and upper and lower limits on storage capacity. The mathematical model constraints for the production and operation characteristics of pumped-storage units are as follows:

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039] Among them, 0-1 variables These represent the pumping / generating states of pumped-storage unit n at time t, and the pumping state. Power generation status These are the minimum and maximum pumping power of the pumped storage unit, respectively. These are the minimum and maximum generating capacities of the pumped-storage unit, respectively. These refer to the pumping / generating capacity of the pumped storage unit; This represents the amount of electricity stored in pumped-storage unit n at time t; These are the charging / discharging efficiencies of pumped-storage unit n, respectively. This indicates the amount of electricity stored in the pumped-storage unit at its initial state. This indicates the amount of electricity stored in the pumped-storage unit at the end of its lifespan. This indicates the minimum storage capacity of the pumped storage unit; This indicates the maximum storage capacity of the pumped storage unit;

[0040] Hydropower units need to meet minimum technical output and installed capacity constraints, as well as water allocation constraints and non-negative limits on water discharge. The mathematical model constraints for the production and operation characteristics of hydropower units are as follows:

[0041]

[0042]

[0043]

[0044]

[0045] in, The minimum technical output ratio of hydropower unit n; 0-1 variable. Indicates the start-up and shutdown status of the hydroelectric generator unit; T k Let K be the set of time periods within week k; K is the set of weeks within a year. and These represent the power generation and curtailment of hydropower unit n during time period t; variables Let n be the amount of water allocated to hydropower unit n in week k, corresponding to the given power generation. Let n be the total annual electricity generated by hydropower unit n; Let n be the amount of water wasted by hydropower unit n at time t;

[0046] For wind and solar power installations, when their predicted output is too high, the load in the power system is insufficient to absorb all of their predicted output, necessitating wind and solar curtailment. The amount of wind and solar curtailment must be less than their predicted output. The specific constraints of the mathematical model are as follows:

[0047]

[0048]

[0049] in, Let n be the predicted power output of wind turbine n during time period t. Let n be the predicted output value of photovoltaic unit n in time period t, which are all constants; Let n be the amount of wind power curtailed by wind turbine n during time period t; Let n be the amount of solar power curtailed by photovoltaic unit n during time period t;

[0050] Maintenance of thermal power units and hydropower units must meet constraints in terms of frequency, duration, continuity, and time intervals. Specific maintenance constraints for thermal power units and hydropower units are as follows:

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] u g,t +x g,t' ≤1 (32)

[0058]

[0059] The maintenance period index is represented by the subscript t', which distinguishes it from the unit operating period index t; z g,t′ , These are indicators representing the start of maintenance for thermal and hydropower units, respectively, and are 0-1 variables. When maintenance begins in time period t', this 0-1 variable is set to 1; otherwise, it is set to 0. MN g , These represent the number of maintenance operations required for thermal power units and hydropower units, respectively; x g,t′ , MT represents 0-1 variables indicating whether thermal and hydropower units are under maintenance. When a unit is under maintenance during time period t', the variable is 1; otherwise, it is 0. g , These represent the time required for a single overhaul of a thermal power unit and a hydropower unit, respectively; MG g This refers to the minimum interval between the start times of two maintenance operations for a thermal power unit. This refers to the minimum interval between the start times of two maintenance operations for a single hydropower unit.

[0060] Furthermore, the specific process of step 2) is as follows:

[0061] Because the generator maintenance model uses a large number of 0-1 variables, it provides a detailed model of the production and operation processes of thermal power units, hydropower units, and pumped-storage units, but this also increases the difficulty and time required to solve the maintenance schedule. Therefore, a relaxed unit cluster combination method is adopted to achieve complete linearization of the model except for maintenance constraints. The specific constraints are as follows:

[0062] Thermal power unit cluster constraints:

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] Among them, O c,t SU represents the online capacity of unit cluster c at time t; c,t SD c,t These represent the startup capacity and shutdown capacity of unit cluster c at time t, respectively. These are the ramp-up rate and ramp-down rate for unit cluster c, respectively. These are the shortest startup time and shortest shutdown time for unit cluster c, respectively. The actual output of thermal power unit cluster c during time period t;

[0070] In addition, there are connection constraints between the output of thermal power unit clusters and individual units:

[0071]

[0072] 0≤P g,t ≤Cap g (41)

[0073]

[0074] The constraints of energy storage devices are the same as those in formulas (9)-(13);

[0075] After linear relaxation, the upper and lower limits of the output of the pumped storage unit are as shown in formulas (43)-(44). The energy balance constraint and the upper and lower limits of the storage capacity remain unchanged. The constraints of its production and operation are as shown in formulas (17)-(19) and (43)-(44):

[0076]

[0077]

[0078] The output constraints of the hydropower units should be relaxed accordingly:

[0079]

[0080] in, This refers to the online capacity variable of the introduced hydropower units;

[0081] The constraints on wind and solar power curtailment are given in formulas (24)-(25);

[0082] The constraints on the number of maintenance operations, duration, continuity, and time interval are given in formulas (26)-(31), and the constraints on the unit's maintenance status and operating status are given in formulas (45)-(46). Therefore, the maintenance constraints of the RCUC maintenance model are as shown in formulas (26)-(31) and (45)-(46):

[0083]

[0084]

[0085] Furthermore, in step 3), integer variables are used to model the maintenance variables of the unit cluster in the RCUC maintenance model, specifically:

[0086] Assuming that some key characteristic parameters of the units within the cluster are the same, such as the unit capacity, minimum technical output ratio, and ramp rate of a single unit, when modeling the unit maintenance constraints and maintenance variables, it is only necessary to consider the number of units under maintenance within the entire unit cluster. The units within the cluster are not distinguished in detail, and only the maintenance constraints of thermal power units are remodeled using integer variables.

[0087] The maintenance constraints for thermal power units modeled with integer variables are described as follows:

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] The maintenance period index is represented by the subscript t', which distinguishes it from the unit operating period index t; z c,t′ MN represents the number of generating units within the thermal power unit cluster that have begun maintenance; it is an integer variable. c x represents the total number of maintenance visits required for all units within the thermal power unit cluster. c,t′ MT represents the number of units in the thermal power unit cluster that are under maintenance; it is an integer variable. c The total time required for all units in a thermal power unit cluster to complete one maintenance cycle sequentially. This represents the number of generating units contained in thermal power unit cluster c.

[0094] Furthermore, in step 3), with the goal of minimizing the total system cost, it is necessary to construct an objective function that minimizes the system operating cost, specifically:

[0095] minF Z =F G+F P +F ES +F PEN

[0096]

[0097] Among them, F Z F represents the total operating cost of the system. G For thermal power plant operating costs; F P Operating costs of pumped storage units; F ES For the operating cost of energy storage devices; F PEN The system operation penalty cost; t is the time period index number; T is the total number of time periods; g is the thermal power unit index number; N g N represents the total number of thermal power units; c represents the cluster index number of the thermal power units; N c The total number of thermal power unit clusters; P represents the variable cost coefficient for thermal power unit cluster c; C c,t The actual output of thermal power unit cluster c during time period t; These represent the start-up and shutdown costs of a thermal power unit cluster, respectively; SU c,t SD represents the startup capacity of thermal power unit cluster c during time period t. c,t The shutdown capacity of thermal power unit cluster c during time period t; SU c,t and SD c,t All are continuous variables; N p Number of pumped-storage units; CPS n This is the operating cost coefficient for pumped-storage unit n; N represents the pumping power of pumped storage unit n during time period t; es Total number of energy storage devices; CES n Let n be the operating cost coefficient for energy storage device n; Let N be the charging power and discharging power of energy storage device n during time period t, respectively. L The total number of load nodes; CCL is the load shedding penalty coefficient; N represents the load shedding amount of node n during time period t; w The total number of wind turbine units; CSW is the wind curtailment penalty coefficient; N represents the curtailment power of wind turbine n during time period t; pv The total number of photovoltaic installations; CSS is the curtailment penalty coefficient; N represents the curtailment power of photovoltaic device n during time period t; h The total number of adjustable hydropower units; CSH is the water wastage penalty coefficient; Let n be the water discharge power of the adjustable hydropower unit n during time period t.

[0098] Furthermore, the constraints of the improved RCUC maintenance model are specifically configured as follows:

[0099] Thermal power unit cluster constraints:

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] 0≤P gt ≤Cap g

[0108]

[0109] Among them, O c,t SU represents the online capacity of unit cluster c at time t; c,t SD c,t These represent the start-up and shutdown capacities of unit cluster c at time t, respectively; the remaining parameters and variables are similar to those of thermal power units, with only the subscript type differing.

[0110] Constraints of energy storage devices:

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] in, These represent the charging / discharging power of energy storage device n at time t; This represents the installed capacity of energy storage device n; This represents the amount of energy stored in energy storage device n at time t; These represent the charge / discharge efficiency of energy storage device n; and the State of Charge (SOC). n,t Indicates the load status of the energy storage device; This indicates the maximum load state of the energy storage device. This indicates the minimum load state of the energy storage device. This indicates the maximum energy storage capacity of the energy storage device; This indicates the amount of energy stored in the energy storage device at its initial state. The amount of electricity stored in the energy storage device at the end of its lifecycle;

[0117] Constraints of pumped storage units:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] in, This is the maximum pumping power of the pumped storage unit; This is the maximum generating capacity of the pumped storage unit; These refer to the pumping / generating capacity of the pumped storage unit; This represents the amount of electricity stored in pumped-storage unit n at time t; These are the charging / discharging efficiencies of pumped-storage unit n, respectively. This indicates the amount of electricity stored in the pumped-storage unit at its initial state. This indicates the amount of electricity stored in the pumped-storage unit at the end of its lifespan. This indicates the minimum storage capacity of the pumped storage unit; This indicates the maximum energy storage capacity of the pumped storage unit.

[0124] Hydropower unit constraints:

[0125]

[0126]

[0127]

[0128]

[0129] in, The minimum technical output ratio of hydropower unit n; 0-1 variable. Indicates the start-up and shutdown status of the hydroelectric generator unit; T k Let K be the set of time periods within week k; K is the set of weeks within a year. and These represent the power generation and curtailment of hydropower unit n during time period t; variables Let n be the amount of water allocated to hydropower unit n in week k, corresponding to the given power generation. Let n be the total annual electricity generated by hydropower unit n; Let n be the amount of water wasted by hydropower unit n at time t;

[0130] Curtailment of wind and solar power:

[0131]

[0132]

[0133] in, Let n be the predicted power output of wind turbine n during time period t. Let n be the predicted output value of photovoltaic unit n in time period t, which are all constants; Let n be the amount of wind power curtailed by wind turbine n during time period t; Let n be the amount of solar power curtailed by photovoltaic unit n during time period t;

[0134] Maintenance constraints:

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] The maintenance period index is represented by the subscript t', which distinguishes it from the unit operating period index t; z g,t′ , These are indicators representing the start of maintenance for thermal and hydropower units, respectively, and are 0-1 variables. When maintenance begins in time period t', this 0-1 variable is set to 1; otherwise, it is set to 0. MN g , These represent the number of maintenance operations required for thermal power units and hydropower units, respectively; x g,t′ , MT represents 0-1 variables indicating whether thermal and hydropower units are under maintenance. When a unit is under maintenance during time period t', the variable is 1; otherwise, it is 0. g , These represent the time required for a single overhaul of a thermal power unit and a hydropower unit, respectively; MG g This refers to the minimum interval between the start times of two maintenance operations for a thermal power unit. This refers to the minimum interval between the start times of two maintenance operations for a single hydropower unit; c,t′ MN represents the number of generating units within the thermal power unit cluster that have begun maintenance; it is an integer variable. c x represents the total number of maintenance visits required for all units within the thermal power unit cluster. c,t′ MT represents the number of units in the thermal power unit cluster that are under maintenance; it is an integer variable. c The total time required for all units in a thermal power unit cluster to complete one maintenance cycle sequentially. This represents the number of generating units contained in thermal power unit cluster c.

[0144] Furthermore, the operation of generator units in the power system must also meet tie-line capacity constraints and power balance constraints. The tie-line capacity constraints are as follows:

[0145]

[0146] The power balance constraints are as follows:

[0147]

[0148]

[0149]

[0150] in, The power transmitted by the connection line l during time period t; This represents the maximum power transmitted by the connection line l during time period t. These refer to the pumping / generating capacity of the pumped storage unit; and These are the power generation capacity and charging capacity of the energy storage device, respectively. L represents the load shedding amount of node n during time period t; in L out These are the sets of lines that transmit power into and out of the system area, respectively; D n,t Let n be the load demand of node n during time period t.

[0151] Compared with the prior art, the present invention has the following beneficial technical effects:

[0152] This invention discloses a method for constructing an efficient generator set maintenance model based on unit aggregation and linear relaxation. Based on the unit combination model, a basic UC maintenance model is established, modeling the generator set maintenance problem as a mixed integer programming problem. Furthermore, addressing the difficulty in solving the long-term maintenance problem of thermal power units due to complex production and operation constraints, unit aggregation and linear relaxation techniques are introduced to embed the RCUC model into the maintenance model, establishing an RCUC maintenance model. For the maintenance constraints of unit clusters, an improved RCUC maintenance model is proposed, using integer variables instead of 0-1 variables for modeling, reducing the scale of integer variables and constraints, and improving the solution efficiency of the generator set maintenance model. Attached Figure Description

[0153] Figure 1 This is a flowchart of the high-efficiency generator set maintenance model based on unit aggregation and linear relaxation proposed in this invention;

[0154] Figure 2 This is a diagram showing the monthly maintenance schedule of the RCUC maintenance model in this invention.

[0155] Figure 3 This is a monthly maintenance schedule diagram of the improved RCUC maintenance model in this invention;

[0156] Figure 4 This is a diagram showing the annual maintenance schedule of the improved RCUC maintenance model in this invention.

[0157] Figure 5 This is a diagram showing the annual water allocation arrangement for the improved RCUC maintenance model in this invention. Detailed Implementation

[0158] To make the objectives, technical solutions, and advantages of the present invention clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the present invention, and not all of them.

[0159] The components described and illustrated in the accompanying drawings and embodiments of this invention can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the invention provided in the following drawings is not intended to limit the scope of the claimed invention, but merely to illustrate one selected embodiment of the invention. All other embodiments obtained by those skilled in the art based on the accompanying drawings and embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0160] It should be noted that the terms “comprising,” “including,” or any other variations are intended to cover non-exclusive inclusion, such that a process, element, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to the process, element, method, article, or apparatus.

[0161] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0162] See Figure 1 This invention establishes a basic maintenance model for generator sets based on the operating characteristics of various types of generator sets. It employs unit aggregation and linear relaxation techniques to reduce the complexity of modeling unit operation and maintenance, proposing an RCUC maintenance model. Then, by utilizing integer variables in the modeling, the complexity of modeling the maintenance constraints of thermal power unit clusters is further reduced. With the goal of minimizing the total system cost, an improved relaxed unit cluster combined maintenance model is established, ultimately achieving efficient solution of the generator set maintenance model and obtaining the generator set maintenance schedule. Specifically, the invention includes the following steps:

[0163] Step 1: Establish a basic maintenance model for the generator set.

[0164] Based on the production and operation characteristics of power generation units of various energy types, a basic maintenance model for generator units is established. The operation characteristics of thermal power units are characterized by constraints, as shown in formulas (1)-(8):

[0165]

[0166]

[0167]

[0168] u g,t =u g,t-1 +su g,t -sd g,t ,t∈[2,T] Z (4)

[0169]

[0170]

[0171] u g,t ,su g,t ,sd g,t ∈{0,1} (7)

[0172] su g,t +sd g,t ≤1 (8)

[0173] Among them, the 0-1 variable u g,t For the start-up and shutdown state of unit g at time t, the start-up state is u. g,t =1, power off u g,t =0; Cap is the minimum technical output ratio of unit g; g The rated capacity of unit g; These are the start-up ramp rate and the shutdown ramp rate of unit g, respectively. These are the uphill and downhill ramp rates for unit g, respectively. These represent the minimum start-up time and minimum shutdown time for unit g, respectively; P g,t The actual output of thermal power unit g during time period t; su g,t sd represents the starting action variable of thermal power unit g during time period t, where 1 indicates starting and 0 indicates not starting; g,t For the shutdown action of thermal power unit g during time period t, 1 indicates shutdown and 0 indicates no shutdown;

[0174] Constraints on energy storage devices include:

[0175]

[0176]

[0177]

[0178]

[0179]

[0180] in, These represent the charging / discharging power of energy storage device n at time t; This represents the installed capacity of energy storage device n; This represents the amount of energy stored in energy storage device n at time t; These represent the charge / discharge efficiency of energy storage device n; and the State of Charge (SOC). n,t Indicates the load status of the energy storage device; This indicates the maximum load state of the energy storage device. This indicates the minimum load state of the energy storage device. This indicates the maximum energy storage capacity of the energy storage device; This indicates the amount of energy stored in the energy storage device at its initial state. This indicates the amount of electricity stored in the energy storage device at the end of its lifecycle.

[0181] Constraints of pumped storage units include:

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188] Among them, 0-1 variables These represent the pumping / generating states of pumped-storage unit n at time t, and the pumping state. Power generation status These are the minimum and maximum pumping power of the pumped storage unit, respectively. These are the minimum and maximum generating capacities of the pumped-storage unit, respectively. These refer to the pumping / generating capacity of the pumped storage unit; This represents the amount of electricity stored in pumped-storage unit n at time t; These are the charging / discharging efficiencies of pumped-storage unit n, respectively. This indicates the amount of electricity stored in the pumped-storage unit at its initial state. This indicates the amount of electricity stored in the pumped-storage unit at the end of its lifespan. This indicates the minimum storage capacity of the pumped storage unit; This indicates the maximum energy storage capacity of the pumped storage unit.

[0189] The constraints of hydropower units include:

[0190]

[0191]

[0192]

[0193]

[0194] in, The minimum technical output ratio of hydropower unit n; 0-1 variable. Indicates the start-up and shutdown status of the hydroelectric generator unit; T k Let K be the set of time periods within week k; K is the set of weeks within a year. and These represent the power generation and curtailment of hydropower unit n during time period t; variables Let n be the amount of water allocated to hydropower unit n in week k, corresponding to the given power generation. Let n be the total annual electricity generated by hydropower unit n; Let be the amount of water wasted by hydropower unit n at time t.

[0195] The operating constraints for wind power and solar power are:

[0196]

[0197]

[0198] in, Let n be the predicted power output of wind turbine n during time period t. Let n be the predicted output value of photovoltaic unit n in time period t, which are all constants; Let n be the amount of wind power curtailed by wind turbine n during time period t; Let be the amount of solar power wasted by photovoltaic unit n during time period t.

[0199] Unit maintenance needs to meet constraints in terms of frequency, duration, continuity, and time intervals. This model only considers the maintenance of thermal and hydropower units.

[0200]

[0201]

[0202]

[0203]

[0204]

[0205]

[0206] u g,t +x g,t' ≤1 (32)

[0207]

[0208] The maintenance period index is represented by the subscript t', which distinguishes it from the unit operating period index t; z g,t′ , These are indicators representing the start of maintenance for thermal and hydropower units, respectively, and are 0-1 variables. When maintenance begins in time period t', this 0-1 variable is set to 1; otherwise, it is set to 0. MN g , These represent the number of maintenance operations required for thermal power units and hydropower units, respectively; x g,t′ , MT represents 0-1 variables indicating whether thermal and hydropower units are under maintenance. When a unit is under maintenance during time period t', the variable is 1; otherwise, it is 0. g , These represent the time required for a single overhaul of a thermal power unit and a hydropower unit, respectively; MG gThis refers to the minimum interval between the start times of two maintenance operations for a thermal power unit. This refers to the minimum interval between the start times of two maintenance operations for a single hydropower unit.

[0209] Step 2: Establish an RCUC maintenance model based on the relaxed unit cluster combination model.

[0210] By employing a relaxed unit cluster combination method, the model achieves complete linearization except for maintenance constraints. The specific constraints are as follows:

[0211] Thermal power unit cluster constraints:

[0212]

[0213]

[0214]

[0215]

[0216]

[0217]

[0218] Among them, O c,t SU represents the online capacity of unit cluster c at time t; c,t SD c,t These represent the startup capacity and shutdown capacity of unit cluster c at time t, respectively. The minimum technical output ratio for unit cluster c; Cap c This refers to the rated capacity of unit cluster c; These are the startup ramp-up rate and shutdown ramp-up rate for unit cluster C, respectively. These are the ramp-up rate and ramp-down rate for unit cluster c, respectively. These are the shortest startup time and shortest shutdown time for unit cluster c, respectively. The actual output of thermal power unit cluster c during time period t;

[0219] In addition, there are connection constraints between the output of thermal power unit clusters and individual units:

[0220]

[0221] 0≤P g,t ≤Cap g (41)

[0222]

[0223] The constraints of energy storage devices are the same as those in formulas (9)-(13);

[0224] The upper and lower limits of the output of the pumped storage unit are given by linear relaxation as shown in formulas (43)-(44), and the energy balance constraints and the upper and lower limits of the storage capacity are given by formulas (17)-(19) in step 1):

[0225]

[0226]

[0227] Similar to the relaxation method for thermal power unit clusters, the output constraints of hydropower units are relaxed accordingly:

[0228]

[0229] in, This refers to the online capacity variable of the introduced hydropower unit.

[0230] The constraints on wind and solar power curtailment are explained in step 1), and in formulas (24)-(25).

[0231] The constraints on the number of maintenance operations, duration, continuity, and time interval are given in formulas (26)-(31) of step 1). The constraints on the unit's maintenance status and operating status are as follows:

[0232]

[0233]

[0234] Step 3: Model the maintenance variables of the unit cluster using integer variables, and establish an improved RCUC maintenance model with the minimum total system operating cost.

[0235] Assuming that all unit characteristic parameters within the cluster are the same, such as the unit capacity, minimum technical output ratio, and ramp rate of a single unit, when modeling unit maintenance constraints and maintenance variables, it is only necessary to consider the number of units under maintenance within the entire unit cluster, without distinguishing between units within the cluster. That is, integer variables are used to model the maintenance constraints of thermal power units.

[0236] Maintenance constraints for thermal power units modeled with integer variables can be described as follows:

[0237]

[0238]

[0239]

[0240]

[0241]

[0242] The maintenance period index is represented by the subscript t', which distinguishes it from the unit operating period index t; z c,t′ MN represents the number of generating units within the thermal power unit cluster that have begun maintenance; it is an integer variable. c x represents the total number of maintenance visits required for all units within the thermal power unit cluster. c,t′ MT represents the number of units in the thermal power unit cluster that are under maintenance; it is an integer variable. c The total time required for all units in a thermal power unit cluster to complete one maintenance cycle sequentially. This represents the number of generating units contained in thermal power unit cluster c.

[0243] The objective function for minimizing the total operating cost of the system is as follows:

[0244]

[0245] Among them, F Z F represents the total operating cost of the system. G For thermal power plant operating costs; F P Operating costs of pumped storage units; F ES For the operating cost of energy storage devices; F PEN The system operation penalty cost; t is the time period index number; T is the total number of time periods; g is the thermal power unit index number; N g N represents the total number of thermal power units; c represents the cluster index number of the thermal power units; N c The total number of thermal power unit clusters; P represents the variable cost coefficient for thermal power unit cluster c; C c,t The actual output of thermal power unit cluster c during time period t; These represent the start-up and shutdown costs of a thermal power unit cluster, respectively; SU c,t SD represents the startup capacity of thermal power unit cluster c during time period t. c,t The shutdown capacity of thermal power unit cluster c during time period t; SU c,t and SD c,t All are continuous variables; N p Number of pumped-storage units; CPS n This is the operating cost coefficient for pumped-storage unit n; N represents the pumping power of pumped storage unit n during time period t; es Total number of energy storage devices; CES n Let n be the operating cost coefficient for energy storage device n; Let N be the charging power and discharging power of energy storage device n during time period t, respectively. L The total number of load nodes; CCL is the load shedding penalty coefficient; N represents the load shedding amount of node n during time period t;w The total number of wind turbine units; CSW is the wind curtailment penalty coefficient; N represents the curtailment power of wind turbine n during time period t; pv The total number of photovoltaic installations; CSS is the curtailment penalty coefficient; N represents the curtailment power of photovoltaic device n during time period t; h The total number of adjustable hydropower units; CSH is the water wastage penalty coefficient; Let n be the water discharge power of the adjustable hydropower unit n during time period t.

[0246] The operation of generator units in a power system also needs to meet tie-line capacity constraints and power balance constraints. The tie-line capacity constraints are as follows:

[0247]

[0248] The power balance constraints are as follows:

[0249]

[0250] in, The power transmitted by the connection line l during time period t; This represents the maximum power transmitted by the connection line l during time period t. These refer to the pumping / generating capacity of the pumped storage unit; and These are the power generation capacity and charging capacity of the energy storage device, respectively. L represents the load shedding amount of node n during time period t; in L out These are the sets of lines that transmit power into and out of the system area, respectively; D n,t Let n be the load demand of node n during time period t.

[0251] The following are practical examples used to test and verify the effectiveness of the proposed model:

[0252] The system has a maximum load of 27241MW and an annual total electricity generation of 146.67TW·h. It comprises 74 thermal power units with a total installed capacity of 28280MW, distributed across three regions. Based on unit parameters and regional similarities, these units can be categorized into 18 thermal power unit clusters. The system also includes 8 hydropower units with a total installed capacity of 3924MW; 3 energy storage units with a total capacity of 1550MW, achieving both charging and discharging efficiencies of 0.95; and 3 pumped storage units with a total capacity of 1500MW. Wind and solar power output curves are derived from historical statistics of the province, with annual available electricity of 2.03TW·h and 1.73TW·h, respectively. Penalties for wind, solar, and hydropower curtailment are set at 700 yuan / (MW·h), and load shedding penalties are set at 50000 yuan / (MW·h). Solve the generator unit maintenance model.

[0253] Monthly maintenance schedule for the RCUC maintenance model: See Figure 2 The monthly maintenance plan derived from the RCUC maintenance model schedules a larger portion of maintenance capacity at the end of the month, when the system load pressure is low. Around the fourth day before the system load peak, maintenance capacity is almost non-existent or minimal, which helps alleviate the pressure on the system's supply.

[0254] Improved monthly maintenance schedule for the RCUC maintenance model: See [link / reference] Figure 3 The improved RCUC maintenance model results in a monthly maintenance plan that schedules more maintenance capacity at the end of the month, when the system load pressure is low. Around the fourth day before the system load peak, maintenance capacity is almost not scheduled or is very small, which helps to reduce the pressure on the system to ensure supply.

[0255] Improved annual maintenance plan for the RCUC maintenance model: See Figure 4 Peak values ​​for total unit maintenance capacity typically occur during periods of lower weekly peak load, such as weeks 22, 23, 24, and 38, 39, 40. Week 23 has the highest total maintenance capacity at 7750MW, while the corresponding peak load is only 18246MW. Conversely, when the weekly peak load is higher, meaning generators require more output, the annual maintenance plan allocates less capacity. For example, in week 27, the peak load is approximately 26234MW, requiring more generators to contribute output to meet load balancing and reserve requirements; therefore, the total maintenance capacity for that week is only 360MW. Similarly, the peak load occurs in week 51 at 27471MW, with a total maintenance capacity of only 1230MW. Only weeks 1, 5, 33, and 47 have no scheduled unit maintenance. Distributing maintenance capacity across weeks helps reduce system load balancing and reserve pressure.

[0256] Improved annual water allocation scheme for the RCUC maintenance model: See Figure 5 Before solving the problem using the improved RCUC maintenance model, the weekly average water volume before optimization was mainly related to the season and the corresponding inflow. If power generation was carried out based on the inflow without adjustment, it might lead to an increase in the peak-valley difference of the system load. After optimizing the water volume allocation using the improved RCUC maintenance model, the weekly allocation value of hydropower volume is basically positively correlated with the weekly load power value. That is, the higher the weekly load power, the more water is allocated in that week, and vice versa. In other words, by optimizing the water volume allocation of adjustable hydropower, the improved RCUC maintenance model makes the net load of the system tend to be stable, which is conducive to the efficient and reliable operation of the system.

[0257] The computational efficiency of the improved RCUC maintenance model is shown in Tables 1 and 2. Table 1 presents the solution results of the modified IEEE-RTS79 example system, and Table 2 presents the solution results of an actual example from a provincial power system. As can be seen from Table 1, the RCUC maintenance model and the improved RCUC maintenance model significantly improve the solution speed in the monthly maintenance schedule example by employing unit aggregation and linear relaxation techniques for thermal power units. Compared to the basic UC maintenance model, the RCUC model's solution time is reduced by 86.45%. The improved RCUC model further enhances the solution speed by using integer variable modeling for thermal power unit maintenance constraints, reducing the solution time by 95.78% compared to the basic UC maintenance model. Regarding the error in the optimization results, the improved RCUC maintenance model has a gap value of 0.09% when it finally stops calculating, compared to a gap value of 0.19% for the RCUC maintenance model. This means that a more accurate feasible solution was obtained with less solution time.

[0258] As shown in Table 2, when the system scale increases, the basic generator set maintenance model can no longer obtain the optimization result within a limited time. However, both the RCUC maintenance model and the improved RCUC maintenance model can obtain the optimization result satisfying Gap ≤ 0.5%. The improved RCUC maintenance model reduces the solution time by 18.56% compared to the original RCUC maintenance model. Through the above comparison, the efficient generator set maintenance model based on unit aggregation and linear relaxation proposed in this invention can solve the generator set maintenance schedule faster while ensuring a certain level of solution accuracy, thus verifying the effectiveness of the model.

[0259] Table 1. Solution results of the modified IEEE-RTS79 example system.

[0260]

[0261]

[0262] Table 2 shows the solution results of the actual example system.

[0263]

[0264] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions 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 constructing a high-efficiency generator set maintenance model based on unit aggregation and linear relaxation, characterized in that, Includes the following steps: 1) Based on the production and operation characteristics of thermal power units, hydropower units, energy storage devices, pumped storage units, wind power and photovoltaic power, establish a generator unit maintenance model based on the unit combination model; 2) Based on the relaxed unit cluster combination method, the unit combination model is linearly relaxed and embedded into the generator set maintenance model to establish an RCUC maintenance model; 3) Based on unit aggregation technology, the maintenance variables of the unit cluster in the RCUC maintenance model are modeled using integer variables. With the goal of minimizing the total system cost, an improved RCUC maintenance model is established, thus obtaining the high-efficiency generator unit maintenance model. The specific process of step 2) is as follows: Because the generator maintenance model uses a large number of 0-1 variables, it provides a detailed model of the production and operation processes of thermal power units, hydropower units, and pumped-storage units, but this also increases the difficulty and time required to solve the maintenance schedule. Therefore, a relaxed unit cluster combination method is adopted to achieve complete linearization of the model except for maintenance constraints. The specific constraints are as follows: Thermal power unit cluster constraints: （34） （35） （36） （37） （38） （39） in, For unit cluster c exist t Online capacity at any given moment; , They are respectively unit clusters c exist t Startup and shutdown capacity at any given time; 、 They are respectively unit clusters c The uphill and downhill rates; , They are respectively unit clusters c The shortest boot time and the shortest shutdown time; For thermal power unit clusters c exist t Actual output during the time period; In addition, there are connection constraints between the output of thermal power unit clusters and individual units: （40） （41） （42） Constraints on constructing energy storage devices; The upper and lower limits of the output of the pumped storage unit, after linear relaxation, are shown in formulas (43)-(44): （43） （44） The output constraints of the hydropower units should be relaxed accordingly: in, Oh n,t This refers to the online capacity variable of the introduced hydropower units; For hydroelectric generator units n The minimum technical output ratio; For hydroelectric generator units n exist t Power generation during a given time period; Establish constraints on wind and solar power curtailment; The maintenance constraints of the RCUC maintenance model include maintenance frequency, duration, continuity and time interval constraints, and unit maintenance and operation status constraints; the unit maintenance and operation status constraints are shown in formulas (45)-(46): （45） （46）； In step 3), integer variables are used to model the maintenance variables of the unit cluster in the RCUC maintenance model, specifically: Assuming that some key characteristic parameters of the units within the cluster are the same, such as the unit capacity, minimum technical output ratio, and ramp rate of a single unit, when modeling the unit maintenance constraints and maintenance variables, it is only necessary to consider the number of units under maintenance within the entire unit cluster. The units within the cluster are not distinguished in detail, and only the maintenance constraints of thermal power units are remodeled using integer variables. The maintenance constraints for thermal power units modeled with integer variables are described as follows: （47） （48） （49） （50） （51） The maintenance period index uses subscripts. t’ This indicates the index of the unit's operating period. t Distinguish between phases; This represents the number of units within the thermal power unit cluster that have begun maintenance; it is an integer variable. This represents the total number of maintenance operations required for all units within the thermal power unit cluster. This represents the number of units in the thermal power unit cluster that are under maintenance; it is an integer variable. The total time required for all units in a thermal power unit cluster to complete one maintenance cycle sequentially. This represents the number of generating units contained in thermal power unit cluster c.

2. The method for constructing a high-efficiency generator set maintenance model based on unit aggregation and linear relaxation as described in claim 1, characterized in that, Step 1) Specifically: First, establish the production and operation characteristics of thermal power units, hydropower units, energy storage devices, pumped storage units, wind power and photovoltaic power, clarify the constraints that they must meet during production and operation, and establish production and operation constraints based on the unit combination model. Establish maintenance constraints for thermal power units and hydropower units; Production operation constraints and maintenance constraints constitute the generator set maintenance model.

3. The method for constructing a high-efficiency generator set maintenance model based on unit aggregation and linear relaxation according to claim 2, characterized in that, The constraints that thermal power units must meet in production and operation include upper and lower limits on output, limits on ramp-up capability, limits on changes in start-up and shutdown status, and limits on minimum continuous start-up / shutdown time. The specific constraints of the mathematical model are as follows: （1） （2） （3） （4） （5） （6） （7） （8） Among them, 0-1 variables For the unit g exist t Start-up and stop status at all times, power on =1, power off =0; For the unit g The minimum technical output ratio; For the unit g Rated capacity; , The units g Power-on ramp-up rate and power-off ramp-up rate; 、 The units g The uphill and downhill rates; , The units g The shortest boot time and the shortest shutdown time; For thermal power units g exist t Actual output during the time period; For thermal power units g exist t The start action variable for the time period, where 1 indicates start and 0 indicates not start; For thermal power units g exist t The shutdown action variable for a given time period, where 1 indicates shutdown and 0 indicates no shutdown; The operation of energy storage devices needs to meet the constraints of upper and lower limits of charging and discharging power, energy balance equations for adjacent time periods, upper and lower limits of load state, and the constraint that the stored capacity is equal in the initial and final states. The specific mathematical model constraints of the production and operation characteristics of energy storage devices are as follows: （9） （10） （11） （12） （13） in, , Energy storage devices n exist t The charging / discharging power at any given moment; Indicates energy storage device n The installed capacity; Indicates energy storage device n exist t The amount of electricity stored at any given time; , Energy storage devices n The charge / discharge efficiency; Indicates the load status of the energy storage device; This indicates the maximum load state of the energy storage device. This indicates the minimum load state of the energy storage device. This indicates the maximum energy storage capacity of the energy storage device; This indicates the amount of energy stored in the energy storage device at its initial state. The amount of electricity stored in the energy storage device at the end of its lifecycle; Pumped-storage units must meet constraints on pumping / generation power, energy balance, and upper and lower limits on storage capacity. The mathematical model constraints for the production and operation characteristics of pumped-storage units are as follows: （14） （15） （16） （17） （18） （19） Among them, 0-1 variables , pumped storage units n exist t The pumping / power generation status at any given time, pumping status =1, power generation status =1; , These are the minimum and maximum pumping power of the pumped storage unit, respectively. , These are the minimum and maximum generating capacities of the pumped-storage unit, respectively. , These refer to the pumping / generating capacity of the pumped storage unit; Indicates pumped storage unit n exist t The amount of electricity stored at any given time; , pumped storage units n The charge / discharge efficiency; This indicates the amount of electricity stored in the pumped-storage unit at its initial state. This indicates the amount of electricity stored in the pumped-storage unit at the end of its lifespan. This indicates the minimum storage capacity of the pumped storage unit; This indicates the maximum storage capacity of the pumped storage unit; Hydropower units need to meet minimum technical output and installed capacity constraints, as well as water allocation constraints and non-negative limits on water discharge. The mathematical model constraints for the production and operation characteristics of hydropower units are as follows: （20） （21） （22） （23） in, For hydroelectric generator units n Minimum technical output ratio; 0-1 variable Indicates the start-up and shutdown status of the hydroelectric generator unit; For the first k Weekday time slots; A set of weeks within a year; and Hydropower units n exist t Power generation and power curtailment during a given period; variables For hydroelectric generator units n In the k The weekly water allocation corresponds to the given power generation. For hydroelectric generator units n Total annual electricity consumption; For hydroelectric generator units n In the t The amount of water wasted at any given moment; For wind and solar power installations, when their predicted output is too high, the load in the power system is insufficient to absorb all of their predicted output, necessitating wind and solar curtailment. The amount of wind and solar curtailment must be less than their predicted output. The specific constraints of the mathematical model are as follows: （24） （25） in, For wind turbines n exist t The predicted power output for the time period, For photovoltaic units n exist t The predicted output values ​​for each time period are all constants; For wind turbines n exist t The amount of wind power curtailed during a given period; For photovoltaic units n exist t The amount of electricity wasted during the period; Maintenance of thermal power units and hydropower units must meet constraints in terms of frequency, duration, continuity, and time intervals. Specific maintenance constraints for thermal power units and hydropower units are as follows: （26） （27） （28） （29） （30） （31） （32） （33） The maintenance period index uses subscripts. t’ This indicates the index of the unit's operating period. t Distinguish between phases; , These are indicators that signify the start of maintenance for thermal and hydropower units, respectively, and are 0-1 variables. t’ When the maintenance period begins, this 0-1 variable takes the value 1; otherwise, it takes the value 0. , These are the number of maintenance operations required for thermal power units and hydropower units, respectively. , These are 0-1 variables representing whether thermal power and hydropower units are under maintenance. t’ When the time period is under maintenance, this 0-1 variable takes the value 1; otherwise, it takes the value 0. , These are the times required for a single maintenance of thermal power units and hydropower units, respectively. This refers to the minimum interval between the start times of two maintenance operations for a thermal power unit. This refers to the minimum interval between the start times of two maintenance operations for a single hydropower unit.

4. The method for constructing a high-efficiency generator set maintenance model based on unit aggregation and linear relaxation according to claim 1, characterized in that, In step 3), with the goal of minimizing the total system cost, it is necessary to construct an objective function that minimizes the system operating cost, specifically: in, Total system operating cost; For thermal power plant operating costs; For the operating costs of pumped storage units; For the operating cost of energy storage devices; Penalty costs for system operation; t For time period index number; T Total number of time periods; g Index number for thermal power units; This represents the total number of thermal power units. c For thermal power unit cluster index number; The total number of thermal power unit clusters; For thermal power unit clusters c Variable cost coefficient; PC c,t For thermal power unit clusters c exist t Actual output during the time period; , These are the operating capacity cost and shutdown capacity cost of a thermal power unit cluster, respectively. For thermal power unit clusters c exist t Startup capacity for a given time period; For thermal power unit clusters c exist t Shutdown capacity during specific time periods; and All are continuous variables; This refers to the number of pumped storage units; For pumped storage units n Operating cost coefficient; For pumped storage units n exist t Pumping power during a given time period; This represents the total number of energy storage devices; For energy storage devices n Operating cost coefficient; , Energy storage devices n exist t Charging and discharging power during the same period; N L This represents the total number of load nodes. This is the load shedding penalty factor; For nodes n exist t Load shedding amount during the time period; This represents the total number of wind turbine units. This is the wind curtailment penalty coefficient; For wind turbines n exist t Wind curtailment power during the period; This represents the total number of photovoltaic installations. This is the penalty coefficient for discarded light; For photovoltaic devices n exist t The amount of light discarded during a given period; This represents the total number of adjustable hydroelectric generator units; This is the penalty coefficient for water abandonment; Adjustable hydroelectric generator set n exist t Water discharge capacity during a given time period.

5. The method for constructing a high-efficiency generator set maintenance model based on unit aggregation and linear relaxation according to claim 1, characterized in that, The constraints of the improved RCUC maintenance model are specifically as follows: Thermal power unit cluster constraints: in, For unit cluster c exist t Online capacity at any given moment; , They are respectively unit clusters c exist t The start-up and shutdown capacities at any given time; the remaining parameters and variables are similar to those of thermal power units, with only the subscript type differing. Constraints of energy storage devices: in, , Energy storage devices n exist t The charging / discharging power at any given moment; Indicates energy storage device n The installed capacity; Indicates energy storage device n exist t The amount of electricity stored at any given time; , Energy storage devices n The charge / discharge efficiency; Indicates the load status of the energy storage device; This indicates the maximum load state of the energy storage device. This indicates the minimum load state of the energy storage device. This indicates the maximum energy storage capacity of the energy storage device; This indicates the amount of energy stored in the energy storage device at its initial state. The amount of electricity stored in the energy storage device at the end of its lifecycle; Constraints of pumped storage units: in, This is the maximum pumping power of the pumped storage unit; This is the maximum generating capacity of the pumped storage unit; , These refer to the pumping / generating capacity of the pumped storage unit; Indicates pumped storage unit n exist t The amount of electricity stored at any given time; , pumped storage units n The charge / discharge efficiency; This indicates the amount of electricity stored in the pumped-storage unit at its initial state. This indicates the amount of electricity stored in the pumped-storage unit at the end of its lifespan. This indicates the minimum storage capacity of the pumped storage unit; This indicates the maximum storage capacity of the pumped storage unit; Hydropower unit constraints: in, For hydroelectric generator units n Minimum technical output ratio; 0-1 variable Indicates the start-up and shutdown status of the hydroelectric generator unit; For the first k Weekday time slots; A set of weeks within a year; and Hydropower units n exist t Power generation and power curtailment during a given period; variables For hydroelectric generator units n In the k The weekly water allocation corresponds to the given power generation. For hydroelectric generator units n Total annual electricity consumption; For hydroelectric generator units n In the t The amount of water wasted at any given moment; Curtailment of wind and solar power: in, For wind turbines n exist t The predicted power output for the time period, Ppv n,t For photovoltaic units n exist t The predicted output values ​​for each time period are all constants; For wind turbines n exist t The amount of wind power curtailed during a given period; For photovoltaic units n exist t The amount of electricity wasted during the period; Maintenance constraints: The maintenance period index uses subscripts. t’ This indicates the index of the unit's operating period. t Distinguish between phases; , These are indicators that signify the start of maintenance for thermal and hydropower units, respectively, and are 0-1 variables. t’ When the maintenance period begins, this 0-1 variable takes the value 1; otherwise, it takes the value 0. , These are the number of maintenance operations required for thermal power units and hydropower units, respectively. , These are 0-1 variables representing whether thermal power and hydropower units are under maintenance. t’ When the time period is under maintenance, this 0-1 variable takes the value 1; otherwise, it takes the value 0. , These are the times required for a single maintenance of thermal power units and hydropower units, respectively. This refers to the minimum interval between the start times of two maintenance operations for a thermal power unit. This refers to the minimum interval between the start times of two maintenance operations for a single hydropower unit. This represents the number of units within the thermal power unit cluster that have begun maintenance; it is an integer variable. This represents the total number of maintenance operations required for all units within the thermal power unit cluster. This represents the number of units in the thermal power unit cluster that are under maintenance; it is an integer variable. The total time required for all units in a thermal power unit cluster to complete one maintenance cycle sequentially. This represents the number of generating units contained in thermal power unit cluster c.

6. The method for constructing a high-efficiency generator set maintenance model based on unit aggregation and linear relaxation according to claim 5, characterized in that, The operation of generator units in a power system also needs to meet tie-line capacity constraints and power balance constraints. The tie-line capacity constraints are as follows: The power balance constraints are as follows: in, For connecting lines l exist t Power value transmitted during a specific time period; For connecting lines l exist t Maximum power value transmitted during a time period; , These refer to the pumping / generating capacity of the pumped storage unit; and These are the power generation capacity and charging capacity of the energy storage device, respectively. For nodes n exist t Load shedding amount during the time period; , These are the sets of lines that transmit power into and out of the system area, respectively. For nodes n exist t Load demand during a given time period.

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