Multi-source system unit maintenance and unit combination collaborative optimization method considering peak regulation adequacy

Abstract

A multi-source system unit maintenance and unit combination collaborative optimization method considering peak regulation adequacy comprises the following steps: establishing a multi-source system which is composed of a pumped storage power station, a wind power plant, a thermal power plant, a hydropower station and a user load and has energy storage and power generation functions, and calculating the total peak regulation capability of the multi-source system; calculating a peak regulation demand of the multi-source system; deriving peak regulation adequacy, wherein the peak regulation adequacy represents the relationship between the peak regulation capability and the peak regulation demand of the power system; a multi-source system unit maintenance and unit combination collaborative optimization model is constructed, so that the unit maintenance cost, the unit operation cost and the system peak regulation excess or vacancy punishment cost are minimum; and solving to obtain the optimal maintenance and operation state of each unit. The method can provide a basis for a power grid dispatching department to make a generator set maintenance plan, and can improve the economical efficiency and reliability of power system operation.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and dispatching, and in particular to a method for collaborative optimization of unit maintenance and unit combination in a multi-source system taking into account peak load margin. Background Art

[0002] Wind power is gradually being connected to the power grid at a high rate due to its mature technology and large-scale development conditions. Wind power has strong uncertainty, volatility and anti-peak characteristics, which are different from traditional conventional generators. In particular, the peak-shaving problem caused by anti-peaking poses a great challenge to the safe and stable operation of the power system. It is not enough to rely solely on the existing peak-shaving capacity of the system. It is necessary to establish a multi-source complementary coordination mechanism to maximize the development of the flexible peak-shaving capacity of the power system.

[0003] Generator maintenance and unit combination planning are key components of optimizing the power system. The generator maintenance plan will affect the generator combination plan in the operation and dispatch, and thus affect the peak-shaving capacity of the system. Because the peak-shaving capacity of different types of generators varies greatly, if some regions ignore the coordination of the system's peak-shaving capacity when arranging generator maintenance, it will lead to insufficient peak-shaving capacity of power grid units after maintenance and a large amount of wind power abandonment during low load periods.

[0004] Unit maintenance and unit combination are to meet the safety and economic goals of power grid dispatching and operation by optimizing the start and stop plans of units, and the two affect each other. Under the current operation mode, the power grid company first formulates the unit maintenance plan, and then uses the maintenance plan as a constraint condition for unit combination to determine the unit start and stop plan. However, under the current operation mode, the maintenance plan formulated in advance is likely to constrain the optimization space of unit combination.

[0005] When large-scale wind power is integrated into the power grid, the volatility, randomness and anti-peaking characteristics of wind power will affect the safe and stable operation of the power grid, especially the peaking problem is more serious. Therefore, when arranging the unit maintenance and unit combination plan, it is necessary to carefully consider the arrangement of the maintenance and combination plan of the units with strong peaking capacity and weak peaking capacity. In this case, the unit maintenance and unit combination plan must not only ensure that the system meets the stability requirements of each time period, but also ensure that the peaking capacity of the units in each time period matches the peaking demand of the system.

[0006] In summary, the large-scale connection of wind power with anti-peaking characteristics to the grid has brought new problems to the peak-shaving of the power system. The maintenance plan of the generator set will affect the unit combination in the operation and dispatch, and thus affect the peak-shaving capacity of the system. It is urgent to solve how the multi-source system unit maintenance and unit combination collaborative optimization model taking into account the peak-shaving margin can balance the economy and reliability of the power system. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides a method for collaborative optimization of unit maintenance and unit combination of a multi-source system taking into account the peak-shaving margin. The method can provide a basis for the power grid dispatching department to formulate a generator unit maintenance plan, and can improve the economy and reliability of the power system operation.

[0008] The technical solution adopted by the present invention is:

[0009] The method for coordinated optimization of unit maintenance and unit commitment of a multi-source system taking into account the peak load margin comprises the following steps:

[0010] Step 1: Establish a multi-source system consisting of a pumped storage power station, a wind farm, a thermal power plant, a hydropower station and user loads with energy storage and power generation functions, and calculate the total peak-shaving capacity of the multi-source system, which is the sum of the peak-shaving capacity of the thermal power unit, the peak-shaving capacity of the hydropower unit and the peak-shaving capacity of the pumped storage unit;

[0011] Step 2: Calculate the peak load demand of the multi-source system;

[0012] Step 3: Derive the peak-shaving margin, which is an indicator to measure the peak-shaving margin of the power system, and is expressed as the relationship between the peak-shaving capacity and peak-shaving demand of the power system;

[0013] Step 4: Construct a collaborative optimization model of unit maintenance and unit combination in a multi-source system to minimize the unit maintenance cost, unit operation cost, and penalty cost for excess or shortage of system peak load regulation;

[0014] Step 5: Use the CPLEX solver to obtain the optimal maintenance and operating status of each unit.

[0015] In step 1, the operating principle of the multi-source system is as follows: when the load forecast value of the power grid system is less than the sum of the minimum output power of the thermal power plant and the hydropower plant and the predicted wind power, the pumped storage power station will perform pumped storage, and the system will convert and store the excess electric energy; when the load forecast value of the power grid system is greater than the sum of the output power of the thermal power plant and the hydropower plant and the predicted wind power, the pumped storage power station will discharge water for power generation, release the stored electric energy to fill the lack of load power, and ensure that the power grid is in a balanced state.

[0016] In step 1, the total peak-shaving capacity of the multi-source system is the sum of the peak-shaving capacities of different units in normal operation, including the following three parts:

[0017]

[0018] Where: P ability is the total peak-shaving capacity of the system; It is the total peak load regulation capacity of thermal power units in operation; is the total peak load regulation capacity of the hydropower units in operation; It is the total peak-shaving capacity of the pumped-storage unit in operation.

[0019]

[0020]

[0021]

[0022] Where: T is the total duration of the calculation cycle, t is the time period; P g,max is the maximum technical output of thermal power unit g; P g,min is the minimum technical output of thermal power unit g; U g,t is the operating status of thermal power unit g in period t, which is a 0 / 1 variable. If it is running, it is 1, otherwise it is 0; N G N is the number of thermal power units. H is the number of hydropower units; P h,e is the rated power of the hydropower unit h; O h When it is 1, it indicates the dry season; h When it is 0, it indicates the flood season; h,t is the operating status of the hydropower unit h in period t, which is a 0 / 1 variable. If it is running, it is 1, otherwise it is 0. K is the number of pumped storage units; is the maximum power generation level of pumped storage unit k; is the maximum energy storage level of pumped storage unit k; U k,t is the operating status of pumped storage unit k in period t, which is a 0 / 1 variable. If it is operating, it is 1, otherwise it is 0.

[0023] In step 2, the most important factors affecting the peak-shaving demand of the multi-source system include two parts: first, the change in electricity load. The greater the difference between the peak and valley values ​​of the system load, the greater the peak-shaving demand; second, wind power has obvious anti-peaking characteristics, and the peak-shaving demand is expressed as: the daily maximum peak-to-valley difference of the electricity load; the difference between the power generation level of wind power generation at the low load and the power generation level of wind power generation at the peak load.

[0024] In step 2, in the power system, the most important factors affecting the system peak load demand include two parts. The first is the change in power load. The greater the difference between the peak and valley values ​​of the system load, the greater the peak load demand. Secondly, wind power has obvious anti-peak load characteristics.

[0025] P need =D L +D W (6);

[0026] Where: P need is the total peak load demand of the system; D LPeak load demand caused by power load fluctuation; D W is the peak load demand caused by wind power output. Their expressions are as follows:

[0027] D L =M+ε M (7);

[0028] D W =(W F,t1 +ε w,t1 )-(W F,t2 +ε w,t2 )(8);

[0029] Where: M is the predicted value of the maximum peak-to-valley difference of the daily power load; ε M is the prediction error of the maximum peak-to-valley difference of the power load on a daily basis. F,t is the predicted value of wind farm output power in period t; w,t is the prediction error of wind farm output power in period t; t1 is the load valley moment; t2 is the load peak moment.

[0030] In step 3, the peak-shaving margin is an indicator to measure the abundance or insufficiency of the peak-shaving of the power system, which is expressed as the relationship between the peak-shaving capacity and the peak-shaving demand of the power system, that is, the system has sufficient regulation capacity to maintain the stability and reliability of the power grid. The arrangement of the maintenance plan and the combination of units will affect the peak-shaving capacity of the system. Too much peak-shaving capacity will increase the operating cost of the system, and too little peak-shaving capacity will cause wind abandonment and load shedding. Therefore, the penalty cost part of the peak-shaving surplus or shortage is introduced into the objective function to coordinate the balance between the peak-shaving capacity and the peak-shaving demand during the maintenance and combination process.

[0031] The expression of peak load margin is as follows:

[0032]

[0033] Where: β P is the peak load margin of the system; ability With P need They are the peak-shaving capacity and peak-shaving demand of the system respectively.

[0034] In step 4, the objective function of the multi-source system unit maintenance and unit combination collaborative optimization model includes the unit operation cost F run , unit maintenance cost F mc ; Peak load excess or shortage penalty cost F pc ;

[0035] The constraints include: system power balance constraints, unit maintenance status constraints, unit maintenance time constraints, unit maintenance continuity constraints, unit maintenance resource constraints, unit mutually exclusive maintenance constraints, thermal power unit output constraints, thermal power unit climbing constraints, thermal power unit continuous start and stop constraints, hydropower unit output constraints, wind power output constraints, and pumped storage unit output constraints. The goal of the multi-source system unit maintenance and unit combination collaborative optimization model is to minimize the unit maintenance cost and operating cost. In addition, the arrangement of maintenance plans and unit combinations will affect the peak-shaving capacity of the system. Too much peak-shaving capacity will increase the operating cost of the system, and too little peak-shaving capacity will result in wind abandonment and load shedding. Therefore, the peak-shaving excess or shortage penalty cost part is introduced in the objective function to coordinate the balance between peak-shaving capacity and peak-shaving demand during maintenance and combination. The objective function is specifically expressed as follows:

[0036] F E =min(F run +F mc +F pc )(9);

[0037] Where: F E is the total cost of the system; F run F is the unit operating cost; mc F is the unit maintenance cost; pc Penalty costs for excess or deficit peaking.

[0038] 1) Unit operating cost:

[0039]

[0040] Where: a g 、a h and a k They represent the unit operating costs of thermal power unit g, hydropower unit h and pumped storage unit k respectively; P g,t is the active power output of thermal power unit g during period t; P h,t is the active power output of hydropower unit h in period t; P k,t is the active power output of pumped storage unit k in period t.

[0041] 2) Unit maintenance cost:

[0042]

[0043] Where: N is the total number of units that need to be overhauled; B n,t is the unit maintenance cost of the maintenance unit n in period t; X n,t It is the start of maintenance status of maintenance unit n in period t. 1 means the unit starts maintenance in period t, otherwise it is 0.

[0044] 3)Peak load excess or shortage penalty costs:

[0045] When the sum of the peak-shaving capabilities of each unit is greater than the system peak-shaving demand, the system peak-shaving is sufficient at this time, but when the peak-shaving surplus is too large, it will cause a huge waste of peak-shaving costs. The present invention sets the peak-shaving surplus exceeding 125% as the peak-shaving surplus period. When the sum of the peak-shaving capabilities of each unit is less than the system peak-shaving demand, the system peak-shaving shortage will lead to power shortage losses due to insufficient upward peak-shaving capacity of the system or wind curtailment due to insufficient downward peak-shaving capacity of the system. The penalty cost function for peak-shaving surplus or shortage is as follows:

[0046]

[0047] Where: E W,t and E L,t are the wind power abandonment and power shortage of the system at time t respectively; ρ W is the penalty fee per unit of wind power abandoned; ρ L E is the penalty fee for the unit's power shortage; S,t is the excess power of the system at time t; ρ S is the penalty fee for excess electricity per unit; β max is the peak load margin critical value; β P It is the peak-shaving margin of the system.

[0048] Build constraints:

[0049] 1) System power balance constraints:

[0050]

[0051] Where: N W is the number of wind turbines; P w,t is the active power output of wind turbine w in period t; P l,t is the total load of the system during period t; g is the number of thermal power units; h is the number of hydropower units; k is the number of pumped storage units; and w is the number of wind power units.

[0052] 2) Relevant constraints on unit maintenance:

[0053] Unit maintenance status constraints:

[0054]

[0055] Where: X n,t is the start of maintenance status of maintenance unit n in period t; d n The latest time allowed for the start of maintenance of unit n; z n is the earliest time allowed for the start of maintenance of unit n; N is the total number of units that need to be scheduled for maintenance.

[0056]

[0057] Where: t is the calculation period.

[0058]

[0059] Where: Y n,t is the state variable of the maintenance unit n in period t; l n is the maintenance duration of the overhauled unit n.

[0060] Unit maintenance time constraints:

[0061]

[0062] Unit maintenance continuity constraints:

[0063]

[0064] Where: Y n,t-1 is the state variable of the maintenance unit n in the period t-1.

[0065] Unit maintenance resource constraints:

[0066]

[0067] Where: Y n,t is the state variable of the maintenance unit n in period t, which is 1 if it is in the maintenance state, otherwise it is 0; S t The maximum number of units that can be overhauled simultaneously.

[0068] Unit mutually exclusive maintenance constraints:

[0069]

[0070] 3) Unit operation related constraints:

[0071] Output constraints of thermal power units:

[0072] P g,min U g,t ≤P g,t ≤P g,max U g,t (twenty one);

[0073] Where: P g,min is the minimum technical output of thermal power unit g; P g,max is the maximum technical output of thermal power unit g; U g,t is the operating status of thermal power unit g during period t; P g,t is the active power output of thermal power unit g during period t.

[0074] Thermal power unit climbing constraints:

[0075]

[0076] Where: P g,t-1 is the active power output of thermal power unit g in period t-1; is the lower limit of the climbing slope of the thermal power unit g; It is the upper limit of the climbing rate of thermal power unit g.

[0077] Constraints on continuous start and stop of thermal power units:

[0078]

[0079]

[0080] Where: and is the minimum continuous start time and minimum continuous shutdown time of thermal power unit g; is the continuous start-up time of thermal power unit g in period t-1; is the continuous shutdown time of thermal power unit g in period t-1; U g,t-1 is the operating status of thermal power unit g in period t-1.

[0081] Output constraints of hydropower units

[0082]

[0083] Where: and are the minimum and maximum output of the hydropower unit h respectively; P h,t is the active power output of hydropower unit h in period t.

[0084] Wind power output constraints:

[0085]

[0086] Where: P w,t is the active power output of wind turbine w in period t; The maximum predicted output of wind power.

[0087] Output constraints of pumped storage units:

[0088]

[0089] Where: P k,t is the active power output of pumped storage unit k in period t; if P k,t <0, the pumped storage unit will perform pumped storage, and is the upper limit of the power of the pumped storage unit for pumped storage; if P k,t >0, the pumped storage unit will release water for power generation, and The upper limit of the power for pumped storage units to release water for power generation.

[0090] In the step 5, the established multi-source system unit maintenance and unit commitment collaborative optimization model is a mixed integer linear programming model (MILP), which is solved using the CPLEX solver.

[0091] The present invention provides a method for coordinated optimization of unit maintenance and unit combination in a multi-source system taking into account the peak load margin, and the technical effects are as follows:

[0092] 1) In view of the integration of wind power with anti-peaking characteristics into the power grid, the present invention constructs a multi-source system consisting of a pumped storage power station, a wind farm, a thermal power plant, a hydropower station and user loads, analyzes the peak-shaving capacity of each unit in the multi-source system and the peak-shaving demand of the system, and on this basis constructs a peak-shaving margin to measure the peak-shaving state of the power system. First, according to the size of the system peak-shaving demand, the strong peak-shaving demand period and the weak peak-shaving demand period are distinguished, and the peak-shaving margin index is used to judge whether the system peak-shaving is sufficient or insufficient.

[0093] 2) Construct a collaborative optimization model for unit maintenance and unit combination in a multi-source system, minimize the unit maintenance cost and operating cost, and introduce a penalty cost part for excess or shortage of peak-shaving. Too much peak-shaving capacity will increase the operating cost of the system, and too little peak-shaving capacity will cause wind abandonment and load shedding. Coordinate the balance between peak-shaving capacity and peak-shaving demand during maintenance and combination.

[0094] 3) Since the established model is a mixed integer linear programming model, the CPLEX solver is used to solve it. Finally, by using a regional power system as a test system, it is verified that the model proposed in the present invention enhances the stability and economy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0096] Figure 1 It is a schematic diagram of the multi-source system structure;

[0097] Figure 2 It is the peak regulation margin variation curve;

[0098] Figure 3 This is a schematic diagram of the maintenance status and start-stop status of some units.

[0099] Figure 4 is a flow chart of the present invention;

[0100] Figure 5 Flowchart for optimization solution. DETAILED DESCRIPTION

[0101] The collaborative optimization method of multi-source system unit maintenance and unit combination taking into account the peak-shaving margin is established, and the collaborative optimization model of multi-source system unit maintenance and unit combination is established, with the goal of minimizing the unit maintenance cost and operating cost. In addition, the arrangement of maintenance plan and unit combination will affect the peak-shaving capacity of the system. Too much peak-shaving capacity will increase the operating cost of the system, and too little peak-shaving capacity will cause wind abandonment and load shedding. Therefore, the peak-shaving excess or shortage penalty cost part is introduced in the objective function to coordinate the balance between peak-shaving capacity and peak-shaving demand during maintenance and combination.

[0102] The specific method is as follows:

[0103] (I) Propose multi-source system structure and peak load margin:

[0104] Step 1: Propose the structure of the multi-source system:

[0105] The multi-source system consists of a pumped storage power station with energy storage and power generation functions, a wind farm, a thermal power plant, a hydropower station, and user loads.

[0106] The operating principle of the multi-source system is as follows: ① When the system load is a constant value, when the load forecast value of the power grid system is less than the sum of the minimum output power of the thermal power plant and the hydropower plant and the predicted wind power, the pumped storage power station is in the pumped storage condition, and the system converts and stores the extra electric energy; when the load forecast value of the power grid system is greater than the sum of the output power of the thermal power plant and the hydropower plant and the predicted wind power, the pumped storage power station is in the discharge power generation condition, and the stored electric energy is released to make up for the load gap and ensure that the power grid is in a balanced state. ② When the minimum output power of the thermal power plant is met, the wind power in the joint operation system is called for power generation first. When the load forecast value of the power grid system is less than the sum of the minimum output power of the thermal power plant and the predicted wind power, and the energy storage capacity of the pumped storage power station also reaches its peak, the optimized dispatching system begins to gradually abandon wind and water.

[0107] Step 2: Propose peak load margin:

[0108] Peak load margin is an indicator to measure whether the peak load of the power system is sufficient or insufficient. It is expressed as the relationship between the peak load capacity of the power system and the peak load demand, that is, the system has sufficient regulation capacity to maintain the stability and reliability of the power grid. The expression of peak load margin is as follows:

[0109]

[0110] Where: β P is the peak load margin of the system; ability With P need They are the peak-shaving capacity and peak-shaving demand of the system respectively.

[0111] 1) System peak load regulation capability:

[0112] The total peak-shaving capacity of the system is the sum of the peak-shaving capacities of different units in normal operation, including the following three parts:

[0113]

[0114] Where: It is the total peak load regulation capacity of thermal power units in operation; is the total peak load regulation capacity of the hydropower units in operation; It is the total peak-shaving capacity of the pumped-storage unit in operation.

[0115]

[0116]

[0117]

[0118] Where: T is the total duration of the calculation cycle, t is the time period; P g,max is the maximum technical output of thermal power unit g; P g,min is the minimum technical output of thermal power unit g; U g,t is the operating status of thermal power unit g in period t, which is a 0 / 1 variable. If it is running, it is 1, otherwise it is 0; N G N is the number of thermal power units. H is the number of hydropower units; P h,e is the rated power of the hydropower unit h; O h When it is 1, it indicates the dry season; h When it is 0, it indicates the flood season; h,t is the operating status of the hydropower unit h in period t, which is a 0 / 1 variable. If it is running, it is 1, otherwise it is 0. K is the number of pumped storage units; is the maximum power generation level of pumped storage unit k; is the maximum energy storage level of pumped storage unit k; U k,t is the operating status of pumped storage unit k in period t, which is a 0 / 1 variable. If it is operating, it is 1, otherwise it is 0.

[0119] 2) System peak load demand:

[0120] In the power system, the most important factors affecting the system peak load demand include two parts. The first is the change in power load. The greater the difference between the peak and valley values ​​of the system load, the greater the peak load demand. Secondly, wind power has obvious anti-peak load characteristics.

[0121] P need =D L +D W (6);

[0122] Where: D L Peak load demand caused by power load fluctuation; D W is the peak load demand caused by wind power output. Their expressions are as follows:

[0123] D L =M+ε M (7);

[0124] D W =(W F,t1 +ε w,t1 )-(W F,t2 +ε w,t2 )(8);

[0125] Where: M is the predicted value of the maximum peak-to-valley difference of the daily power load; ε M is the prediction error of the maximum peak-to-valley difference of the power load on a daily basis. F,t is the predicted value of wind farm output power in period t; w,t is the prediction error of wind farm output power in period t; t1 is the load valley moment; t2 is the load peak moment.

[0126] (II) Construction of collaborative optimization model for unit maintenance and unit combination in multi-source system

[0127] Step 3: Construction of objective function:

[0128] The goal of the multi-source system unit maintenance and unit combination collaborative optimization model is to minimize the unit maintenance cost and operating cost. In addition, the arrangement of maintenance plan and unit combination will affect the peak-shaving capacity of the system. Too much peak-shaving capacity will increase the operating cost of the system, and too little peak-shaving capacity will cause wind abandonment and load shedding. Therefore, the peak-shaving excess or shortage penalty cost part is introduced into the objective function to coordinate the balance between peak-shaving capacity and peak-shaving demand during maintenance and combination. The objective function is specifically expressed as follows:

[0129] F E =min(F run +F mc +F pc )(9);

[0130] Where: F E is the total cost of the system; F run F is the unit operating cost; mc F is the unit maintenance cost; pc Penalty costs for excess or deficit peaking.

[0131] 1) Unit operating cost

[0132]

[0133] Where: a g 、a h and a k They represent the unit operating costs of thermal power unit g, hydropower unit h and pumped storage unit k respectively; P g,t is the active power output of thermal power unit g during period t; P h,t is the active power output of hydropower unit h in period t; P k,t is the active power output of pumped storage unit k in period t.

[0134] 2) Unit maintenance cost:

[0135]

[0136] Where: N is the total number of units that need to be overhauled. n,t is the unit maintenance cost of the maintenance unit n in period t; X n,t It is the start of maintenance status of maintenance unit n in period t. 1 means the unit starts maintenance in period t, otherwise it is 0.

[0137] 3)Peak load excess or shortage penalty costs:

[0138] When the sum of the peak-shaving capabilities of each unit is greater than the system peak-shaving demand, the system peak-shaving is sufficient at this time, but when the peak-shaving surplus is too large, it will cause a huge waste of peak-shaving costs. The present invention sets the peak-shaving surplus exceeding 125% as the peak-shaving surplus period. When the sum of the peak-shaving capabilities of each unit is less than the system peak-shaving demand, the system peak-shaving shortage will lead to power shortage losses due to insufficient upward peak-shaving capacity of the system or wind curtailment due to insufficient downward peak-shaving capacity of the system. The penalty cost function for peak-shaving surplus or shortage is as follows:

[0139]

[0140] Where: E W,t and E L,t are the wind power abandonment and power shortage of the system at time t respectively; ρ W is the penalty fee per unit of wind power abandoned; ρ L E is the penalty fee for the unit's power shortage; S,t is the excess power of the system at time t; ρ S is the penalty fee for excess electricity per unit. max It is the critical value of peak regulation margin.

[0141] Step 4: Construction of constraints:

[0142] 1) System power balance constraints:

[0143]

[0144] Where: N Wis the number of wind turbines; P w,t is the active power output of wind turbine w in period t; P l,t is the total load of the system during period t.

[0145] 2) Relevant constraints on unit maintenance:

[0146] Unit maintenance status constraints:

[0147]

[0148]

[0149]

[0150] Unit maintenance time constraints:

[0151]

[0152] Unit maintenance continuity constraints:

[0153]

[0154] Unit maintenance resource constraints:

[0155]

[0156] Unit mutually exclusive maintenance constraints:

[0157]

[0158] Where: Y n,t is the state variable of the maintenance unit n in period t, which is 1 if it is in the maintenance state, otherwise it is 0; n The earliest time that maintenance of unit n is allowed to start; d n The latest time allowed for the start of maintenance of unit n; l n is the maintenance duration of the maintenance unit n; S t The maximum number of units that can be overhauled simultaneously.

[0159] 3) Unit operation related constraints:

[0160] Output constraints of thermal power units:

[0161] P g,min U g,t ≤P g,t ≤P g,max U g,t (twenty one);

[0162] Thermal power unit climbing constraints:

[0163]

[0164] Where: P g,t-1 is the active power output of thermal power unit g in period t-1; is the lower limit of the climbing slope of the thermal power unit g; It is the upper limit of the climbing rate of thermal power unit g.

[0165] Constraints on continuous start and stop of thermal power units:

[0166]

[0167]

[0168] Where: is the continuous start-up time of thermal power unit g in period t-1; is the continuous shutdown time of thermal power unit g in period t-1; and is the minimum continuous start-up time and minimum continuous shutdown time of thermal power unit g.

[0169] Output constraints of hydropower units:

[0170]

[0171] Where: and are the minimum and maximum output of the hydropower unit h respectively; P h,t is the active power output of hydropower unit h in period t.

[0172] Wind power output constraints:

[0173]

[0174] Where: P w,t is the active power output of wind turbine w in period t; The maximum predicted output of wind power.

[0175] Output constraints of pumped storage units

[0176]

[0177] Where: P k,t is the active power output of pumped storage unit k in period t; if P k,t <0, the pumped storage unit will perform pumped storage, and is the upper limit of the power of the pumped storage unit for pumped storage; if P k,t >0, the pumped storage unit will release water for power generation, and The upper limit of the power for pumped storage units to release water for power generation.

[0178] Step 5: Solve the model:

[0179] The established multi-source system unit maintenance and unit commitment collaborative optimization model is a mixed integer linear programming model (MILP), which is solved using the CPLEX solver. Figure 5 It can be seen that the optimization calculation process is as follows:

[0180] S1: Input load, wind power output data and parameters of each unit;

[0181] S2: Build a collaborative optimization model for unit maintenance and unit combination in a multi-source system;

[0182] S3: Based on the established objective function and constraints, the maintenance plan of the system under the optimal allocation of units is optimized to minimize the sum of the costs in the objective function;

[0183] S4: Determine whether the system conditions are met. If they are met, output the final system unit maintenance scheduling plan. If not, re-optimize the calculation.

[0184] Verification example:

[0185] Table 1 Comparison of various costs

[0186]

[0187] As shown in Table 1, the operating cost and the penalty cost of excess or shortage of peak load in Scheme 1 are lower than those in Scheme 2, and the total cost is 11.148% lower than that in Scheme 2, which reflects the advantages of collaborative optimization in terms of economic benefits for unit maintenance and unit combination. Under Scheme 3, the operating cost and maintenance cost of the system are lower than those in Scheme 1, but the penalty cost of excess or shortage of peak load increases significantly, which increases the total cost by 1.959% compared with the model considering the peak load sufficiency. Because Scheme 3 ignores the peak load balance problem of the system, it leads to insufficient peak load of the system, increased wind and load abandonment, and may endanger the safety and stability of the system; the system has excessive peak load, and the operating cost increases, which greatly increases the penalty cost of excess or shortage of peak load.

[0188] exist Figure 2 In the figure, the blue dot indicates that the peak-shaving margin is between 100% and 125%, which is within the normal range and will not generate peak-shaving excess or shortage penalty fees. The red dot indicates that the peak-shaving margin is greater than 125%, which is a peak-shaving resource surplus, resulting in a huge waste of peak-shaving costs. Figure 2It can be seen that the model proposed in the present invention is solved to obtain that the system's peak load margin is not less than 100%, indicating that the system does not have insufficient peak load regulation resulting in wind and load abandonment, reflecting the reliability of the model. However, the system's peak load margin exceeds 125% for 89 days, indicating that the unit maintenance and unit combination plans arranged during these periods lead to excessive system peak load capacity, which significantly increases the penalty cost of excessive peak load regulation.

Claims

1. A method for collaborative optimization of unit maintenance and unit commitment in a multi-source system taking into account the peak load margin, characterized in that The following steps are involved: Step 1: Establish a multi-source system consisting of pumped storage power stations, wind farms, thermal power plants, hydropower stations and user loads with energy storage and power generation functions, and calculate the total peak-shaving capacity of the multi-source system; Step 2: Calculate the peak load demand of the multi-source system; Step 3: Derive the peak-shaving margin, which represents the relationship between the peak-shaving capacity and peak-shaving demand of the power system; Step 4: Construct a collaborative optimization model of unit maintenance and unit combination in a multi-source system to minimize the unit maintenance cost, unit operation cost, and penalty cost for excess or shortage of system peak load regulation; Step 5: Obtain the optimal maintenance and operating status of each unit.

2. The method for coordinated optimization of unit maintenance and unit commitment in a multi-source system taking into account peak load margin according to claim 1, characterized in that: In step 1, the operating principle of the multi-source system is as follows: when the load forecast value of the power grid system is less than the sum of the minimum output power of the thermal power plant and the hydropower plant and the predicted wind power, the pumped storage power station will perform pumped storage, and the system will convert and store the excess electric energy; when the load forecast value of the power grid system is greater than the sum of the output power of the thermal power plant and the hydropower plant and the predicted wind power, the pumped storage power station will discharge water for power generation, release the stored electric energy to fill the lack of load power, and ensure that the power grid is in a balanced state.

3. The method for coordinated optimization of unit maintenance and unit commitment of a multi-source system taking into account peak load margin according to claim 2, characterized in that: In step 1, the total peak-shaving capacity of the multi-source system is the sum of the peak-shaving capacities of different units in normal operation, including the following three parts: Where: P ability is the total peak-shaving capacity of the system; It is the total peak load regulation capacity of thermal power units in operation; is the total peak load regulation capacity of the hydropower units in operation; It is the total peak-shaving capacity of the pumped-storage unit in operation; Where: T is the total duration of the calculation cycle, t is the time period; P g,max is the maximum technical output of thermal power unit g; P g,min is the minimum technical output of thermal power unit g; U g,t is the operating status of thermal power unit g in period t, which is a 0 / 1 variable. If it is running, it is 1, otherwise it is 0; N G is the number of thermal power units; N H is the number of hydropower units; P h,e is the rated power of the hydropower unit h; O h When it is 1, it indicates the dry season; h When it is 0, it indicates the flood season; h,t is the operating status of the hydropower unit h in period t, which is a 0 / 1 variable. If it is running, it is 1, otherwise it is 0; N K is the number of pumped storage units; is the maximum power generation level of pumped storage unit k; is the maximum energy storage level of pumped storage unit k; U k,t is the operating status of pumped storage unit k in period t, which is a 0 / 1 variable. If it is operating, it is 1, otherwise it is 0.

4. The method for coordinated optimization of unit maintenance and unit commitment in a multi-source system taking into account peak load margin according to claim 1, characterized in that: In step 2, the most important factors affecting the peak-shaving demand of the multi-source system include: first, the change in electricity load. The greater the difference between the peak and valley values ​​of the system load, the greater the peak-shaving demand; second, wind power has obvious anti-peak-shaving characteristics, and the peak-shaving demand is expressed as: the daily maximum peak-to-valley difference of the electricity load; the difference between the power generation level of wind power generation at the low load and the power generation level of wind power generation at the peak load.

5. The method for coordinated optimization of unit maintenance and unit commitment in a multi-source system taking into account peak load margin according to claim 4, characterized in that: In step 2, the most important factors affecting the system peak load demand include: the change in power load, the greater the difference between the peak and valley values ​​of the system load, the greater the peak load demand; and the anti-peak load characteristics of wind power; P need =D L +D W (6); Where: P need is the total peak load demand of the system; D L Peak load demand caused by power load fluctuation; D W is the peak load demand caused by wind power output; their expressions are as follows: D L =M+e M (7); D W =(W F,t1 +e w,t1 )-(W F,t2 +e w,t2 )(8); Where: M is the predicted value of the maximum peak-to-valley difference of the daily power load; ε M is the prediction error of the maximum peak-to-valley difference of the daily power load; W F,t is the predicted value of wind farm output power in period t; w,t is the prediction error of wind farm output power in period t; t1 is the load valley moment; t2 is the load peak moment.

6. The method for coordinated optimization of unit maintenance and unit commitment in a multi-source system taking into account peak load margin according to claim 1, characterized in that: In step 3, the expression of peak load margin is as follows: Where: β P is the peak load margin of the system; ability With P need They are the peak-shaving capacity and peak-shaving demand of the system respectively.

7. The method for coordinated optimization of unit maintenance and unit commitment in a multi-source system taking into account peak load margin according to claim 1, characterized in that: In step 4, the objective function of the multi-source system unit maintenance and unit combination collaborative optimization model includes the unit operation cost F run , unit maintenance cost F mc ; Peak load excess or shortage penalty cost F pc ; The constraints include: system power balance constraints, unit maintenance status constraints, unit maintenance time constraints, unit maintenance continuity constraints, unit maintenance resource constraints, unit mutually exclusive maintenance constraints, thermal power unit output constraints, thermal power unit climbing constraints, thermal power unit continuous start and stop constraints, hydropower unit output constraints, wind power output constraints, and pumped storage unit output constraints.

8. The method for coordinated optimization of unit maintenance and unit commitment in a multi-source system taking into account peak load margin according to claim 7, characterized in that: The objective function of the coordinated optimization model of unit maintenance and unit commitment in a multi-source system is expressed as follows: F E =min(F run +F mc +F pc )(9); Where: F E is the total cost of the system; F run F is the unit operating cost; mc F is the unit maintenance cost; pc Penalty costs for excess or deficit peaking; 1) Unit operating cost: Where: a g 、a h and a k They represent the unit operating costs of thermal power unit g, hydropower unit h and pumped storage unit k respectively; P g,t is the active power output of thermal power unit g during period t; P h,t is the active power output of hydropower unit h in period t; P k,t is the active power output of pumped storage unit k in period t; 2) Unit maintenance cost: Where: N is the total number of units that need to be overhauled; B n,t is the unit maintenance cost of the maintenance unit n in period t; X n,t is the start of maintenance status of maintenance unit n in period t, 1 means the unit starts maintenance in period t, otherwise it is 0; 3) The penalty cost function for excess or shortage of peak load is as follows: Where: E W,t and E L,t are the wind power abandonment and power shortage of the system at time t respectively; ρ W is the penalty fee per unit of wind power abandoned; ρ L E is the penalty fee for the unit's power shortage; S,t is the excess power of the system at time t; ρ S is the penalty fee for excess electricity per unit; β max is the peak load margin critical value; β P It is the peak-shaving margin of the system.

9. The method for coordinated optimization of unit maintenance and unit commitment in a multi-source system taking into account peak load margin according to claim 8, characterized in that: Constraints for constructing a collaborative optimization model for unit maintenance and unit commitment in a multi-source system: 1) System power balance constraints: Where: N W is the number of wind turbines; P w,t is the active power output of wind turbine w in period t; P l,t is the total load of the system during period t; g is the number of thermal power units; h is the number of hydropower units; k is the number of pumped storage units; w is the number of wind power units; 2) Relevant constraints on unit maintenance: Unit maintenance status constraints: Where: X n,t is the start of maintenance status of maintenance unit n in period t; d n The latest time allowed for the start of maintenance of unit n; z n is the earliest time that maintenance of unit n is allowed to start; N is the total number of units that need to be scheduled for maintenance; Where: t is the calculation period; Where: Y n,t is the state variable of the maintenance unit n in period t; l n is the maintenance duration of the overhauled unit n; Unit maintenance time constraints: Unit maintenance continuity constraints: Where: Y n,t-1 is the state variable of the maintenance unit n in the period t-1; Unit maintenance resource constraints: Where: Y n,t is the state variable of the maintenance unit n in period t, which is 1 if it is in the maintenance state, otherwise it is 0; S t is the maximum number of units to be overhauled simultaneously; Unit mutually exclusive maintenance constraints: 3) Unit operation related constraints: Output constraints of thermal power units: P g,min IN g,t ≤P g,t ≤P g,max IN g,t (21); Where: P g,min is the minimum technical output of thermal power unit g; P g,max is the maximum technical output of thermal power unit g; U g,t is the operating status of thermal power unit g during period t; P g,t is the active power output of thermal power unit g in period t; Thermal power unit climbing constraints: Where: P g,t-1 is the active power output of thermal power unit g in period t-1; is the lower limit of the climbing slope of the thermal power unit g; is the upper limit of the climbing slope of the thermal power unit g; Constraints on continuous start and stop of thermal power units: Where: and is the minimum continuous start time and minimum continuous shutdown time of thermal power unit g; is the continuous start-up time of thermal power unit g in period t-1; is the continuous shutdown time of thermal power unit g in period t-1; U g,t-1 is the operating status of thermal power unit g in period t-1; Output constraints of hydropower units Where: and are the minimum and maximum output of the hydropower unit h respectively; P h,t is the active power output of hydropower unit h in period t; Wind power output constraints: Where: P w,t is the active power output of wind turbine w in period t; Maximum predicted output for wind power; Output constraints of pumped storage units: Where: P k,t is the active power output of pumped storage unit k in period t; if P k,t <0, the pumped storage unit will perform pumped storage, and The upper power limit for pumped storage units; If P k,t >0, the pumped storage unit will discharge water for power generation, and The upper limit of the power for pumped storage units to release water for power generation.

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