High-speed rail distribution and storage day-ahead-intra-day two-stage optimization method considering emerging market

Abstract

The invention discloses a high-speed rail distribution and storage day-ahead-intra-day two-stage optimization method considering an emerging market, and relates to the technical field of power system optimization, and the method comprises the following steps: building a multi-element emerging market subject model according to a virtual power plant subject and a load aggregator subject; establishing a high-speed rail energy storage coupling rule based on the multi-element emerging market main body model in combination with a high-speed rail coupling energy storage main body; constructing a scheduling model by taking the minimization of the total operation cost of the high-speed rail as a target, and solving the scheduling model to obtain a day-ahead-intra-day two-stage scheduling scheme of the high-speed rail; by adding the emerging market main body for adjustment, the wind power consumption amount is greatly increased, smooth output of the thermal power generating unit is ensured, the peak regulation capacity of the thermal power generating unit is improved, meanwhile, after the high-speed rail coupling energy storage main body is added, the controllability of high-speed rail distribution and storage is enhanced, the energy utilization rate is increased, the method can better participate in power system optimization scheduling, and the energy utilization rate is increased. And the total operation cost of the system is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization, and in particular to a two-stage day-ahead and intraday optimization method for high-speed rail storage allocation considering emerging markets. Background Art

[0002] In the context of emerging markets, high-speed rail is experiencing unprecedented rapid development as an efficient and convenient mode of transportation. It not only greatly shortens the time and space distance between cities, but is also favored for its low-carbon and environmentally friendly characteristics. However, with the continuous expansion of the scale of high-speed rail operations, its energy consumption problem has become increasingly prominent. At present, the energy supply of high-speed rail mainly depends on the power system, but the stability and reliability of the power system are affected by many factors, such as the intermittent nature of renewable energy and load fluctuations. In order to improve the stability and reliability of high-speed rail operations, it is necessary to introduce energy storage systems.

[0003] In recent years, the electricity market mechanism in emerging markets has been undergoing profound changes, and the volatility of electricity prices has increased, which has further aggravated the difficulty of high-speed rail energy management. In this context, exploring high-speed rail energy optimization methods that adapt to the emerging market environment is not only related to the economic benefits of high-speed rail distribution and storage operations, but will also have a profound impact on the sustainable development of the entire transportation industry.

[0004] However, the existing methods for optimizing high-speed rail energy storage have some obvious shortcomings. First, most methods only consider the optimization of a single stage, ignoring the importance of the collaborative optimization of the day-ahead and intraday stages, which makes it difficult for the optimization results to adapt to the actual operational needs; second, the existing methods often do not fully consider the characteristics of emerging markets, such as price fluctuations, changes in market mechanisms and other factors, which may lead to large deviations in the optimization results in practical applications; in addition, the existing methods still have deficiencies in dealing with high-speed rail load characteristics, energy storage system configuration and renewable energy utilization, making it difficult to maximize the overall benefits of the system. These problems seriously restrict the optimization effect of the high-speed rail energy storage system in the emerging market environment, and a more comprehensive and efficient optimization method is urgently needed to solve them.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In response to the problems in the related technology, the present invention proposes a two-stage optimization method for high-speed rail storage allocation taking into account emerging markets, namely, day-ahead and day-intraday. The method has the advantages of optimizing high-speed rail coupled energy storage entities, improving the utilization rate of renewable energy, increasing the scheduling of emerging market entities and high-speed rail loads, and improving energy utilization efficiency and economic benefits, thereby solving the problem that the existing technology is unable to respond to the ever-changing electricity market mechanism in emerging markets, which directly affects the stability and reliability of high-speed rail energy management.

[0007] To this end, the specific technical solution adopted by the present invention is as follows:

[0008] A two-stage day-ahead and day-intraday optimization method for high-speed rail storage allocation considering emerging markets, the two-stage day-ahead and day-intraday optimization method for high-speed rail storage allocation considering emerging markets comprises the following steps:

[0009] S1. Establish a multi-element emerging market subject model based on virtual power plant subjects and load aggregator subjects;

[0010] S2. Based on the model of multiple emerging market entities and in combination with high-speed rail coupled energy storage entities, establish high-speed rail energy storage coupling rules;

[0011] S3. With the goal of minimizing the total operating cost of high-speed rail, a scheduling model is constructed and solved to obtain a two-stage scheduling plan for high-speed rail, namely, day-ahead and day-intraday.

[0012] Furthermore, based on the virtual power plant entity and the load aggregator entity, a multi-element emerging market entity model is established, including the following steps:

[0013] S11. Establish a virtual power plant model based on the virtual power plant entity;

[0014] S12. Establishing a load aggregator model based on the load aggregator entity;

[0015] S13. Based on the virtual power plant model and the load aggregator model, a diversified emerging market entity model is constructed.

[0016] Furthermore, based on the virtual power plant entity, establishing a virtual power plant model includes the following steps:

[0017] S111. Using Boolean variables, set the power purchasing state and power selling state of the virtual power plant;

[0018] S112, determining a first constraint of the virtual power plant according to the power purchase power and power purchase status of the virtual power plant;

[0019] S113. According to the power sales power and power sales status of the virtual power plant, the second constraint of the virtual power plant is set, and the virtual power plant model is constructed in combination with the first constraint of the virtual power plant.

[0020] Furthermore, the expression for constructing the virtual power plant model is:

[0021]

[0022] Where P buy,t With P sell,t They represent the power purchased and sold by the virtual power plant at time t respectively; P buy,max With P sell,max They represent the maximum power of electricity purchased and sold by the virtual power plant respectively; δbuy,t With δ sell,t Respectively represent the power purchasing and power selling states of the virtual power plant; when δ buy,t =1,δ sell,t =0, the virtual power plant purchases electricity; when δ buy,t =0,δ sell,t =1, the virtual power plant sells electricity.

[0023] Furthermore, based on the load aggregator entity, the expression of the load aggregator model is established as follows:

[0024]

[0025] Where P LA,t is the load dispatching amount called by the load aggregator at time t; P LA,max is the maximum dispatching capacity that the contract can provide; α is the load dispatching ratio; P load,t is the original electrical load at time t; μ LA,t is the state variable of the contract called at time t; LA,max is the maximum scheduling time of the contract; t on LA,t is the length of time the contract is continuously scheduled before time t.

[0026] Furthermore, based on the multiple emerging market subject model and combined with the high-speed rail coupled energy storage subject, the establishment of high-speed rail energy storage coupling rules includes the following steps:

[0027] S21. Establishing high-speed rail load constraints based on high-speed rail traction power and climbing rate;

[0028] S22. Establish high-speed rail energy storage coupling constraints based on adjustable power upper and lower limits;

[0029] S23. Determine the high-speed rail energy storage coupling rules based on the high-speed rail load constraints and the high-speed rail energy storage coupling constraints.

[0030] Furthermore, based on the high-speed rail traction power and the climbing rate, determining the high-speed rail load constraint includes the following steps:

[0031] S211. Based on the high-speed rail traction power, set the high-speed rail load traction power constraint condition;

[0032] S212. Based on the high-speed rail climbing rate, set the high-speed rail load climbing rate constraint condition;

[0033] S213. According to the high-speed rail load, set the high-speed rail load capacity constraint condition, and establish the high-speed rail load constraint by combining the high-speed rail load traction power constraint condition and the high-speed rail load climbing rate constraint condition.

[0034] Furthermore, with the goal of minimizing the total operating cost of high-speed rail, a scheduling model is constructed and solved to obtain a two-stage scheduling plan for high-speed rail, including the following steps:

[0035] S31. Based on the multi-element emerging market subject model, the day-ahead scheduling objective function is established with the goal of minimizing the total operating cost of high-speed rail;

[0036] S32. Based on the high-speed rail energy storage coupling rules, establish the intraday scheduling objective function and set the electrochemical energy storage constraints;

[0037] S33. Based on the day-ahead scheduling objective function and the intraday scheduling objective function, a scheduling model is established, and the day-ahead and intraday two-stage scheduling plan for high-speed rail is obtained.

[0038] Furthermore, based on the multi-element emerging market subject model, with the goal of minimizing the total operating cost of high-speed rail, the day-ahead scheduling objective function is established, which includes the following steps:

[0039] S311. Based on thermal power units, wind power units, virtual power plants, load aggregators and high-speed rail energy feed systems, a day-ahead dispatch objective function is established with the goal of minimizing the total operating cost of high-speed rail;

[0040] S312. Determine power balance constraints based on a multi-element emerging market subject model;

[0041] S313. Based on the day-ahead dispatch objective function, set wind power constraints and thermal power operation constraints.

[0042] Furthermore, based on the high-speed rail energy storage coupling rule, the intraday scheduling objective function is established, and the electrochemical energy storage constraints are set, including the following steps:

[0043] S321. Based on thermal power units, wind power units, high-speed rail energy feed system and electrochemical energy storage, the intraday scheduling objective function is established with the goal of minimizing the total operating cost of high-speed rail;

[0044] S322, setting charging and discharging constraint conditions based on the charging power and discharging power of the battery;

[0045] S323. Based on the battery storage capacity and response rate, set capacity constraints and response time constraints, and combine the charge and discharge constraints to establish electrochemical energy storage constraints.

[0046] The beneficial effects of the present invention are:

[0047] (1) The present invention greatly increases the wind power consumption by adding the regulation of emerging market entities, ensures the smooth output of thermal power units, and increases the peak-shaving capacity of thermal power units; at the same time, after adding the high-speed rail coupled energy storage entity, the controllability of high-speed rail storage is enhanced, the energy utilization rate is improved, and it can better participate in the optimization and scheduling of the power system; in addition, the present invention provides a strong guarantee for the stable operation of the high-speed rail, so that the train can still maintain efficient power output in various complex power consumption environments, thereby reducing the occurrence of unstable power supply, enhancing the flexibility of high-speed rail power supply, and reducing the total operating cost of the system.

[0048] (2) The present invention addresses the problems of energy supply uncertainty and uncontrollable high-speed rail load operation, and provides a new optimization strategy for emerging market players and new power systems with sudden increases in high-speed rail load, further improving the flexibility and economy of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 It is a flow chart of a two-stage optimization method for high-speed rail storage allocation considering the emerging market, based on an embodiment of the present invention;

[0051] Figure 2 This is a specific implementation diagram of a two-stage optimization method for high-speed rail storage allocation considering emerging markets according to an embodiment of the present invention;

[0052] Figure 3 This is a schematic diagram of the coupling principle of high-speed rail and energy storage in a two-stage day-ahead and intraday optimization method for high-speed rail storage allocation considering emerging markets according to an embodiment of the present invention;

[0053] Figure 4 It is a two-stage day-ahead and day-intraday scheduling framework diagram containing emerging market entities in a two-stage day-ahead and day-intraday optimization method for high-speed rail storage allocation considering emerging markets according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0055] According to an embodiment of the present invention, a two-stage optimization method for high-speed rail storage allocation considering emerging markets, namely, day-ahead and day-intraday, is provided.

[0056] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1-Figure 4 As shown, according to one embodiment of the present invention, a two-stage optimization method for high-speed rail storage allocation before and after the day considering emerging markets is provided, and the two-stage optimization method for high-speed rail storage allocation before and after the day considering emerging markets includes the following steps:

[0057] S1. Establish a multi-element emerging market subject model based on virtual power plant subjects and load aggregator subjects;

[0058] S2. Based on the multiple emerging market subject model and combined with the high-speed rail coupled energy storage subject, establish the high-speed rail energy storage coupling rules;

[0059] S3. With the goal of minimizing the total operating cost of high-speed rail, a scheduling model is constructed and solved to obtain a two-stage scheduling plan for high-speed rail, namely, day-ahead and day-intraday.

[0060] Specifically, the present invention provides a two-stage day-ahead and intraday optimization strategy considering high-speed rail storage under the background of emerging markets, which is implemented in accordance with the following steps: Step S1 establishes a model of multiple emerging market entities including virtual power plants and load aggregators; Step S2, based on Step S1, proposes a high-speed rail energy storage coupling rule to improve the feasibility of high-speed rail regulation; Step S3 considers the regulation flexibility of multiple emerging market entities such as storage high-speed rail, virtual power plants, load aggregators, etc., takes the system operation economy as the goal, proposes a two-stage day-ahead and intraday optimization operation strategy, and solves it.

[0061] Specifically, the present invention is a high-speed rail storage technology studied in the context of emerging markets. Virtual power plants and load aggregators are currently relatively mature emerging market entities, which are reflected in the S1 multiple emerging market entity model. The multiple emerging market entity model is also one of the constraints in the S3 scheduling model. The model construction in S1 only explains its port characteristics, and the cost is calculated in the S3 scheduling model.

[0062] In one embodiment, establishing a multi-element emerging market subject model based on a virtual power plant subject and a load aggregator subject includes the following steps:

[0063] S11. Establish a virtual power plant model based on the virtual power plant entity;

[0064] S12. Establishing a load aggregator model based on the load aggregator entity;

[0065] S13. Based on the virtual power plant model and the load aggregator model, a diversified emerging market entity model is constructed.

[0066] Specifically, on the basis that each entity satisfies the energy exchange with the new power system, a virtual power plant model is established by limiting the upper and lower limit constraints and climbing constraints of the virtual power plant and load aggregators.

[0067] In one embodiment, based on the virtual power plant entity, establishing a virtual power plant model includes the following steps:

[0068] S111. Using Boolean variables, set the power purchasing state and power selling state of the virtual power plant;

[0069] S112, determining a first constraint of the virtual power plant according to the power purchase power and power purchase status of the virtual power plant;

[0070] S113. According to the power sales power and power sales status of the virtual power plant, the second constraint of the virtual power plant is set, and the virtual power plant model is constructed in combination with the first constraint of the virtual power plant.

[0071] In one embodiment, the expression for constructing a virtual power plant model is:

[0072]

[0073] Where P buy,t With P sell,t They represent the power purchased and sold by the virtual power plant at time t respectively; P buy,max With P sell,max They represent the maximum power of electricity purchased and sold by the virtual power plant respectively; δ buy,t With δ sell,t Respectively represent the power purchasing state and power selling state of the virtual power plant; in this embodiment, δ buy,t , δ sell,t is a Boolean variable, indicating the state of the virtual power plant at time t; when δ buy,t =1,δ sell,t =0, the virtual power plant purchases electricity; when δ buy,t =0,δ sell,t =1, the virtual power plant sells electricity.

[0074] Specifically, the first row in formula (1) represents the first constraint of the virtual power plant, the second row represents the second constraint of the virtual power plant, and the third row represents the state of the virtual power plant, which includes the power purchasing state and the power selling state of the virtual power plant.

[0075] In one embodiment, based on the load aggregator subject, the expression for establishing the load aggregator model is:

[0076]

[0077] Where P LA,t is the load dispatching amount called by the load aggregator at time t; P LA,max is the maximum dispatching capacity that the contract can provide; α is the load dispatching ratio; P load,t is the original electrical load at time t; μ LA,t is the state variable of the contract called at time t; LA,max is the maximum scheduling time of the contract; t on LA,t is the length of time the contract is continuously scheduled before time t.

[0078] In one embodiment, based on the multiple emerging market subject model and in combination with the high-speed rail coupled energy storage subject, establishing the high-speed rail energy storage coupling rules includes the following steps:

[0079] S21. Establishing high-speed rail load constraints based on high-speed rail traction power and climbing rate;

[0080] Specifically, the high-speed rail load constraints include high-speed rail load traction power constraints, high-speed rail load climbing rate constraints and high-speed rail load capacity constraints.

[0081] S22. Establish high-speed rail energy storage coupling constraints based on adjustable power upper and lower limits;

[0082] Specifically, the high-speed rail energy storage coupling constraints include adjustable power upper and lower limit constraints, expressed as:

[0083]

[0084] Where P ch-bark,t is the regenerative braking power absorbed by the energy storage device at time t; P dis-h,t P is the power released by the energy storage device at time t for high-speed rail operation; ch,max , P dis,max They are the maximum power of energy storage charging and the maximum power of energy storage discharging respectively; u ch,t 、u dis,t They are respectively the energy storage charging flag and the energy storage discharging flag.

[0085] Specifically, ch,t 、u dis,t is a Boolean variable, indicating the energy storage charging and discharging state at time t; u ch,t =1,u dis,t =0, energy storage charging; u ch,t =0,udis,t =1, the energy storage is discharged.

[0086] S23. Determine the high-speed rail energy storage coupling rules based on the high-speed rail load constraints and the high-speed rail energy storage coupling constraints.

[0087] Specifically, the high-speed rail energy storage coupling rules include high-speed rail load constraints and high-speed rail energy storage coupling constraints.

[0088] Specifically, virtual power plants and load aggregators only represent the relatively mature emerging market entities, and the high-speed rail-coupled energy storage entity is the emerging market entity proposed by the present invention. The three are all dispatching objects.

[0089] Specifically, step S2 proposes a high-speed rail energy storage coupling rule to improve the feasibility of high-speed rail regulation based on the multi-emerging market subject model in step S1, including:

[0090] When the high-speed rail is in braking state, the electrochemical energy storage capacity is less than the maximum capacity, and the battery recovers the regenerative braking energy generated by the high-speed rail; if the high-speed rail is in normal operation, the system's wind power output power is much greater than the system load demand power, and the battery is charged from the abandoned wind. When the high-speed rail load is at its peak operation, the electrochemical energy storage capacity is greater than its minimum discharge capacity, and the battery discharges to the high-speed rail load to ensure the safe and stable operation of the high-speed rail. When the high-speed rail load is operating normally and has not reached its peak period, the battery discharges to the power grid.

[0091] In one embodiment, determining the high-speed rail load constraint based on the high-speed rail traction power and the climbing rate includes the following steps:

[0092] S211. Based on the high-speed rail traction power, set the high-speed rail load traction power constraint condition;

[0093] Specifically, the traction power constraint condition expression of the high-speed rail load is:

[0094] P h,min ≤P h,t ≤P h,max (3)

[0095] Where P h,t is the traction power of the high-speed rail at time t; P h,min , P h,max They are respectively the maximum and minimum values ​​of high-speed rail traction power.

[0096] S212. Based on the high-speed rail climbing rate, set the high-speed rail load climbing rate constraint condition;

[0097] Specifically, the high-speed rail load climbing rate constraint condition includes the high-speed rail load departure climbing rate constraint condition and the high-speed rail load parking climbing rate constraint condition, and the expression is:

[0098]

[0099] In the formula, ΔP h,0 , ΔP h,T are the starting climbing rate and the stopping climbing rate respectively; P h,0 , P h,1 are the rates of the high-speed rail load at the previous moment and the next moment respectively; P h,T-1 , P h,T are the speeds of the high-speed rail load before and after stopping, respectively; They are the maximum departure and stopping climbing speeds of high-speed rail respectively.

[0100] S213. According to the high-speed rail load, set the high-speed rail load capacity constraint condition, and establish the high-speed rail load constraint by combining the high-speed rail load traction power constraint condition and the high-speed rail load climbing rate constraint condition.

[0101] Specifically, the expression of the high-speed rail load capacity constraint is:

[0102] ∫P h，min dt≤∫P h,t dt≤∫P h，max dt (5)

[0103] Where P h,t is the traction power of the high-speed rail at time t; P h,min , P h,max They are respectively the maximum and minimum values ​​of high-speed rail traction power.

[0104] In one embodiment, with the goal of minimizing the total operating cost of high-speed rail, a scheduling model is constructed, and the scheduling model is solved to obtain a two-stage scheduling plan for high-speed rail before and during the day, including the following steps:

[0105] S31. Based on the multi-element emerging market subject model, the day-ahead scheduling objective function is established with the goal of minimizing the total operating cost of high-speed rail;

[0106] S32. Based on the high-speed rail energy storage coupling rules, establish the intraday scheduling objective function and set the electrochemical energy storage constraints;

[0107] S33. Based on the day-ahead scheduling objective function and the intraday scheduling objective function, a scheduling model is established, and the day-ahead and intraday two-stage scheduling plan for high-speed rail is obtained.

[0108] Specifically, in step S3, the regulatory flexibility of multiple emerging market players such as virtual power plants, load aggregators, and high-speed rail energy storage coupling is taken into account, and a scheduling model is established and solved with the goal of system operation economy to output a two-stage optimization operation strategy of day-ahead and day-intraday.

[0109] Specifically, the scheduling model finds the optimal high-speed rail storage scheduling plan by solving equality constraints, inequality constraints and objective functions to optimize the efficiency and economy of system operation.

[0110] In one embodiment, based on the multi-element emerging market subject model, with the goal of minimizing the total operating cost of high-speed rail, establishing a day-ahead scheduling objective function includes the following steps:

[0111] S311. Based on thermal power units, wind power units, virtual power plants, load aggregators and high-speed rail energy feed systems, a day-ahead dispatch objective function is established with the goal of minimizing the total operating cost of high-speed rail;

[0112] Specifically, the expression of the day-ahead scheduling objective function is:

[0113]

[0114]

[0115]

[0116]

[0117] It should be noted that the high-speed rail energy feedback system is an energy management system used for high-speed railways. Its main function is to feed back the electric energy generated by the train motor to the traction power supply network when the high-speed train brakes, rather than converting the braking energy into heat energy through braking resistors and consuming it as in the traditional way.

[0118] In the formula, F 1 is the day-ahead scheduling objective function; F k F is the operating cost of thermal power units; q Penalty cost for wind curtailment; F y is the maintenance cost of wind turbines; F VPP F is the cost of electricity purchase and sale of the virtual power plant; LA dispatch costs for load aggregators; is the carbon trading cost of thermal power units; h Cost of purchasing electricity for high-speed rail operation; F cap is the cost of the high-speed rail energy feedback system; N is the number of thermal power units; a i 、b i 、c i are the coal consumption cost coefficients of unit i; K q is the wind curtailment penalty coefficient, which is obtained from the power plant operation data; P is the predicted wind power at time t; w,t is the wind power grid-connected power at time t; K y is the wind turbine operation and maintenance cost coefficient; C buy,t , C sell,tare the electricity purchase price and electricity sales price of the virtual power plant at time t; C LA is the compensation unit price for the load aggregator to be dispatched; τ is the unit carbon trading price; E p is the total carbon emissions of the system in a scheduling cycle; E l is the total carbon emission quota of the system; σ gi is the carbon emission intensity of the i-th conventional unit; C price C is the unit price of electricity purchased by the high-speed railway from the power grid; bark It is the unit price of high-speed rail energy feedback system capacity.

[0119] S312. Determine power balance constraints based on a multi-element emerging market subject model;

[0120] Specifically, the expression of the power balance constraint is:

[0121]

[0122] Where P load,t is the day-ahead load power forecast at time t.

[0123] S313. Based on the day-ahead dispatch objective function, set wind power constraints and thermal power operation constraints.

[0124] Specifically, the expression of wind power constraint is:

[0125]

[0126] Specifically, the expression of thermal power operation constraint condition is:

[0127]

[0128] Where P gi,min is the minimum output value allowed for unit i at time t; P gi,max is the maximum output value allowed for unit i at time t; R i up is the maximum upward ramp rate of thermal power unit i in the system; R i down is the maximum downward ramp rate of thermal power unit i in the system; T is the duration of the statistical period.

[0129] Specifically, a scheduling model is established with the economic efficiency of system operation as the goal, and the objective functions of the scheduling model include the day-ahead scheduling objective function and the intra-day scheduling objective function.

[0130] Specifically, due to the fast regulation rate of energy storage, the day-ahead scheduling objective function does not include scheduling energy storage, nor does it include the main cost of high-speed rail coupled energy storage. Only the intraday scheduling objective function has it. In this embodiment, the day-ahead scheduling is based on a time scale of 1 hour, and the intraday scheduling is based on a time scale of 15 minutes.

[0131] In one embodiment, based on the high-speed rail energy storage coupling rule, establishing the intraday scheduling objective function and setting the electrochemical energy storage constraints include the following steps:

[0132] S321. Based on thermal power units, wind power units, high-speed rail energy feed system and electrochemical energy storage, the intraday scheduling objective function is established with the goal of minimizing the total operating cost of high-speed rail;

[0133] Specifically, the expression of the intraday scheduling objective function is:

[0134]

[0135]

[0136]

[0137] In the formula, F 2 is the intraday scheduling objective function; F k F is the operating cost of thermal power units; q Penalty cost for wind curtailment; F y is the maintenance cost of wind turbines; F VPP F is the cost of electricity purchase and sale of the virtual power plant; LA dispatch costs for load aggregators; is the carbon trading cost of thermal power units; h Cost of purchasing electricity for high-speed rail operation; F cap-lea is the cost of the high-speed rail energy feedback system after energy storage leasing; F s F is the life loss cost of electrochemical energy storage; P is the maintenance cost of electrochemical energy storage; K in is the investment cost of electrochemical energy storage; K es It is the charge and discharge cost per unit time of electrochemical energy storage.

[0138] Specifically, the difference between the day-ahead scheduling objective function and the intraday scheduling objective function is as follows: 1 The medium and high-speed rail load is not equipped with energy storage devices, and the intraday dispatch objective function F 2 The medium and high-speed rail load is equipped with an energy storage system; secondly, the intraday scheduling objective function F 2 Taking 15 minutes as the time scale, the day-ahead scheduling objective function F 1 The time scale is 1h.

[0139] S322, setting charging and discharging constraint conditions based on the charging power and discharging power of the battery;

[0140] Specifically, the expression of the charge and discharge constraint condition is:

[0141]

[0142] Where P ch-gard,t , P dis-gard,t are the charging and discharging power of the battery from the grid at time t, P ch,max , P dis,max They are the battery charge and discharge limits respectively.

[0143] S323. Based on the battery storage capacity and response rate, set capacity constraints and response time constraints, and combine the charge and discharge constraints to establish electrochemical energy storage constraints.

[0144] Specifically, the capacity constraint is expressed as:

[0145]

[0146] In the formula, S t is the storage capacity of the battery at time t, θ i is the self-discharge rate of the battery, are the charging and discharging efficiency of the battery, E max is the maximum capacity of the battery, S min , S max are the upper and lower limits of battery storage capacity, S 1 , S T They are the initial and final storage capacities of the battery respectively.

[0147] Specifically, the expression of the response time constraint is:

[0148]

[0149] Where β is the charge and discharge response rate of the battery.

[0150] Specifically, electrochemical energy storage constraints include charge and discharge constraints, capacity constraints, and response time constraints.

[0151] Specifically, the optimization problem of the present invention is subject to a series of equality constraints, including system power balance constraints; in addition, inequality constraints also need to be considered, such as wind power output constraints, thermal power unit operation constraints, and power regulation constraints of emerging market players, etc. By solving these constraints and objective functions, the present invention aims to find the optimal scheduling solution to optimize the efficiency and economy of system operation.

[0152] In order to facilitate understanding of the above technical solution of the present invention, the following takes a high-speed rail storage system in a certain place as an example to compare and analyze the effects before and after adopting this solution, and the specific description is as follows:

[0153] In order to comprehensively evaluate the performance of the method of the present invention, we designed two schemes for comparison: Scheme 1 does not consider the regulation function of emerging market entities and sets its regulation power to 0; Scheme 2 adopts the optimization method proposed by the present invention that considers the regulation of emerging market entities. In addition, the other conditions of the two schemes remain the same. By comparing and analyzing the calculation results of the two schemes, we can clearly evaluate the advantages of the method of the present invention.

[0154] In Scheme 1, the predicted output of wind power in the day is limited in 46 periods out of 96 periods in a day, the maximum blocked power in a single period is 264.2MW, and the total abandoned wind power is 3920.7MW·h; in Scheme 2, the predicted output of wind power in the day is limited in 13 periods out of 96 periods in a day, the maximum blocked power in a single period is 165.8MW, and the total abandoned wind power is 1339.2MW·h. Comparing the intraday abandoned wind situation of Scheme 1 and Scheme 2, the predicted output of wind power in the day is limited in 23 periods out of 96 periods in a day, the maximum blocked power in a single period is 203.7MW, and the total abandoned wind power is 1850.3MW·h; in Scheme 2, the predicted output of wind power in the day is limited in 12 periods out of 96 periods in a day, the maximum blocked power in a single period is 180.5MW, and the total abandoned wind power is 1077.2MW·h. Since the second option involves the participation of emerging market players in regulation, the system wind curtailment in the second option is reduced by 2581.5MW compared with the wind curtailment in the first option, and the wind curtailment during the day is reduced by 773.1MW. This is because the first option does not consider the regulation capacity of emerging market players, but only relies on the regulation capacity of thermal power plants to regulate the fluctuation of wind power. However, due to the limited regulation capacity of thermal power plants, the system wind curtailment is serious. In the second option, it not only relies on the regulation capacity of thermal power plants, but also considers the regulation capacity of emerging market players, which improves the flexibility of system regulation. Therefore, the method of considering emerging market players can meet the impact of load and renewable energy fluctuations on the power system by coordinating the output of various equipment, thereby improving the flexibility of the system.

[0155] In the day-ahead dispatch, the peak value of the load curve of Scheme 2, in which emerging market players participate in the regulation, becomes lower (periods 23-51, 64-91), and the valley value increases (periods 1-23, 51-64). The maximum peak-to-valley difference of Scheme 1 is 325.7MW, while the maximum peak-to-valley difference of Scheme 2 is 234.5MW, which is a decrease of 91.2MW. This means that the power demand of the power system in which emerging market players participate is more balanced at different times, thereby improving the overall flexibility of the system. The results of Scheme 2's day-ahead dispatch introduce the effect of independent energy storage in the intraday stage. Due to the faster response rate of independent energy storage, the power fluctuation is further smoothed, and the peak-to-valley load difference is further reduced. If the intraday dispatch follows the results of Scheme 1 in the day-ahead dispatch, the traditional dispatching means cannot smooth the power fluctuation, and the load peak-to-valley difference does not change much. In summary, emerging market players can effectively adjust the load curve, smooth the power fluctuation, and contribute to improving the flexibility of the new power system.

[0156] The moments when the high-speed rail generates regenerative braking energy are 30, 32, 38, 41, 46, 51, 57, 59, 61, 64, 70, 71, 78, and 84 respectively. In Scheme 1, the energy storage charging periods without coupling in the system are 2-6, 12-20, 28-34, 42-49, 59-66, and 75-80 respectively. Combined with the fact that 12-20, 28-34, 42-49, and 75-80 before Scheme 1 are in the low load valley period, the energy storage charging achieves an effective "valley filling" effect. 2-6 and 59-66 before Scheme 1 are the serious wind abandonment periods, and the energy storage charging effectively reduces the amount of wind abandonment. After the coupling of Scheme 2, the energy storage charging periods in the system are 2-6, 19-25, 30-33, 38-41, 46-51, 57-61, 64-65, 69-71, 77-80, and 84. Compared with Scheme 2, the ability to recover high-speed rail regenerative braking energy is significantly improved, and the energy storage charging and discharging frequency is increased, which improves the energy storage utilization rate.

[0157] The operating costs of thermal power units in the two different schemes are slightly different. However, through the implementation of scheme 2, the regulation capacity of the system has been improved, and the amount of wind power received has increased significantly, resulting in a significant reduction in the cost of wind abandonment in the system and improving the flexibility of the system. High-speed rail coupled energy storage entities can not only reduce the operating costs of high-speed rail and reduce energy waste, but also reduce the amount of wind abandonment to a certain extent, reduce the peak-to-valley difference of high-speed rail load, and alleviate the pressure on power quality of the power system, thereby improving the flexibility of the power system. In this process, the utilization rate of energy storage is greatly improved, the cost of energy storage charging and discharging is reduced, and the life of energy storage is not greatly affected. Therefore, compared with scheme 1, the total operating cost of the system is lower under scheme 2, and due to the use of the regulation capacity of emerging market entities for peak regulation and the absorption of limited wind power, this also brings significant economic benefits.

[0158] By comparing and analyzing the performance of Scheme 1 and Scheme 2 in terms of wind curtailment, load curve, energy storage utilization, system operating costs, etc., the effectiveness of the technical solution of the present invention is fully verified. The results show that the optimization method considering the regulation of emerging market entities can significantly improve system flexibility, reduce wind curtailment, flatten the load curve, improve energy storage utilization, and ultimately reduce the total operating cost of the system. This not only provides new ideas for the optimization of high-speed rail storage systems, but also provides strong support for the flexibility management of new power systems.

[0159] In summary, with the help of the above technical solution of the present invention, by facing the problems of energy supply uncertainty and uncontrollable high-speed rail load operation, a new optimization strategy is provided for emerging market entities and new power systems with sudden increase in high-speed rail load, further improving the flexibility and economy of the power system. After the addition of emerging market entities for regulation, the wind power consumption is greatly increased, and the output of thermal power units is smooth, which is equivalent to increasing the peak-shaving capacity of thermal power units; after the addition of high-speed rail energy storage coupling rules, the controllability of high-speed rail storage is enhanced, and the energy utilization rate is improved, making it possible to participate in the optimization and scheduling of the power system. At the same time, this also provides a strong guarantee for the stable operation of high-speed rail, so that the train can still maintain efficient power output in various complex power consumption environments, and reduce the occurrence of unstable power supply, enhance the flexibility of high-speed rail power supply, and reduce the total operating cost of the system.

[0160] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A two-stage optimization method for high-speed rail storage allocation considering emerging markets, characterized in that: The two-stage optimization method of high-speed rail allocation and storage considering emerging markets includes the following steps: S1. Establish a multi-element emerging market subject model based on virtual power plant subjects and load aggregator subjects; S2. Based on the model of multiple emerging market entities and in combination with high-speed rail coupled energy storage entities, establish high-speed rail energy storage coupling rules; S3. With the goal of minimizing the total operating cost of high-speed rail, a scheduling model is constructed and solved to obtain a two-stage scheduling plan for high-speed rail, namely, day-ahead and day-intraday.

2. The two-stage optimization method for high-speed rail storage allocation considering emerging markets according to claim 1, characterized in that: The establishment of a multi-element emerging market subject model based on the virtual power plant subject and the load aggregator subject includes the following steps: S11. Establish a virtual power plant model based on the virtual power plant entity; S12. Establishing a load aggregator model based on the load aggregator entity; S13. Based on the virtual power plant model and the load aggregator model, a diversified emerging market entity model is constructed.

3. The two-stage optimization method for high-speed rail storage allocation considering emerging markets according to claim 2, characterized in that: The establishment of a virtual power plant model based on the virtual power plant entity includes the following steps: S111. Using Boolean variables, set the power purchasing state and power selling state of the virtual power plant; S112, determining a first constraint of the virtual power plant according to the power purchase power and power purchase status of the virtual power plant; S113. According to the power sales power and power sales status of the virtual power plant, the second constraint of the virtual power plant is set, and the virtual power plant model is constructed in combination with the first constraint of the virtual power plant.

4. The two-stage optimization method for high-speed rail storage allocation considering emerging markets according to claim 3, characterized in that: The expression for constructing the virtual power plant model is: Where P buy,t With P sell,t They represent the power purchased and sold by the virtual power plant at time t respectively; P buy,max With P sell,max They represent the maximum power of electricity purchased and sold by the virtual power plant respectively; δ buy,t With δ sell,t Respectively represent the power purchasing and power selling states of the virtual power plant; when δ buy,t =1,δ sell,t =0, the virtual power plant purchases electricity; when δ buy,t =0,δ sell,t =1, the virtual power plant sells electricity.

5. The two-stage optimization method for high-speed rail storage allocation considering emerging markets according to claim 2, characterized in that: The expression for establishing the load aggregator model based on the load aggregator entity is: Where P LA,t is the load dispatching amount called by the load aggregator at time t; P LA,max is the maximum dispatching capacity that the contract can provide; α is the load dispatching ratio; P load,t is the original electrical load at time t; μ LA,t is the state variable of the contract called at time t; LA,max is the maximum scheduling time of the contract; t on LA,t is the length of time the contract is continuously scheduled before time t.

6. The two-stage optimization method for high-speed rail storage allocation considering emerging markets according to claim 1, characterized in that: The establishment of high-speed rail energy storage coupling rules based on the multiple emerging market subject model and combined with the high-speed rail coupled energy storage subject includes the following steps: S21. Establishing high-speed rail load constraints based on high-speed rail traction power and climbing rate; S22. Establish high-speed rail energy storage coupling constraints based on adjustable power upper and lower limits; S23. Determine the high-speed rail energy storage coupling rules based on the high-speed rail load constraints and the high-speed rail energy storage coupling constraints.

7. The two-stage optimization method for high-speed rail storage allocation considering emerging markets according to claim 6, characterized in that: Determining the high-speed rail load constraint based on the high-speed rail traction power and the climbing rate includes the following steps: S211. Based on the high-speed rail traction power, set the high-speed rail load traction power constraint condition; S212. Based on the high-speed rail climbing rate, set the high-speed rail load climbing rate constraint condition; S213. According to the high-speed rail load, set the high-speed rail load capacity constraint condition, and establish the high-speed rail load constraint by combining the high-speed rail load traction power constraint condition and the high-speed rail load climbing rate constraint condition.

8. The two-stage optimization method for high-speed rail storage allocation considering emerging markets according to claim 1, characterized in that: The method of constructing a scheduling model with the goal of minimizing the total operating cost of the high-speed rail, and solving the scheduling model to obtain a two-stage scheduling plan for the high-speed rail day-ahead and day-intraday includes the following steps: S31. Based on the multi-element emerging market subject model, the day-ahead scheduling objective function is established with the goal of minimizing the total operating cost of high-speed rail; S32. Based on the high-speed rail energy storage coupling rules, establish the intraday scheduling objective function and set the electrochemical energy storage constraints; S33. Based on the day-ahead scheduling objective function and the intraday scheduling objective function, a scheduling model is established, and the day-ahead and intraday two-stage scheduling plan for high-speed rail is obtained.

9. The two-stage optimization method for high-speed rail storage allocation considering emerging markets according to claim 8, characterized in that: The method of establishing a day-ahead scheduling objective function based on a multi-element emerging market subject model and aiming at minimizing the total operating cost of high-speed rail includes the following steps: S311. Based on thermal power units, wind power units, virtual power plants, load aggregators and high-speed rail energy feed systems, a day-ahead dispatch objective function is established with the goal of minimizing the total operating cost of high-speed rail; S312. Determine power balance constraints based on a multi-element emerging market subject model; S313. Based on the day-ahead dispatch objective function, set wind power constraints and thermal power operation constraints.

10. The two-stage optimization method for high-speed rail storage allocation considering emerging markets according to claim 8, characterized in that: The method of establishing a daily scheduling objective function based on the high-speed rail energy storage coupling rule and setting the electrochemical energy storage constraints includes the following steps: S321. Based on thermal power units, wind power units, high-speed rail energy feed system and electrochemical energy storage, the intraday scheduling objective function is established with the goal of minimizing the total operating cost of high-speed rail; S322, setting charging and discharging constraint conditions based on the charging power and discharging power of the battery; S323. Based on the battery storage capacity and response rate, set capacity constraints and response time constraints, and combine the charge and discharge constraints to establish electrochemical energy storage constraints.

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