A two-stage optimization method for high-speed rail storage allocation considering emerging markets

Abstract

The present invention discloses a two-stage day-ahead and day-intraday optimization method for high-speed rail allocation and storage considering emerging markets, and relates to the technical field of power system optimization. The method comprises the following steps: establishing a multiple emerging market subject model based on a virtual power plant subject and a load aggregator subject; establishing a high-speed rail energy storage coupling rule based on the multiple emerging market subject model in combination with a high-speed rail coupled energy storage subject; 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 day-ahead and day-intraday scheduling plan for the high-speed rail; the present invention greatly increases the wind power consumption by adding the regulation of the emerging market subject, ensures the smooth output of the thermal power unit, and increases the peak-shaving capacity of the thermal power unit. At the same time, after adding the high-speed rail coupled energy storage subject, the controllability of the high-speed rail allocation and storage is enhanced, the energy utilization rate is improved, and it can better participate in the optimization and scheduling of the power system, thereby reducing the total operating cost of the system.

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, as an efficient and convenient mode of transportation, is experiencing unprecedented rapid development. It not only significantly shortens the spatial and temporal distances between cities but is also highly sought after for its low-carbon and environmentally friendly characteristics. However, as high-speed rail operations continue to expand, energy consumption issues are becoming increasingly prominent. Currently, high-speed rail's energy supply primarily relies on the power system, but the stability and reliability of this power system are affected by a variety of factors, such as the intermittent nature of renewable energy and load fluctuations. 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 mechanisms in emerging markets have been undergoing profound changes, and the volatility of electricity prices has increased, which has further exacerbated 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, existing methods for optimizing HSR energy storage have some significant shortcomings. First, most methods only consider a single-stage optimization, ignoring the importance of collaborative optimization in both the day-ahead and intraday stages. This makes the optimization results difficult to adapt to actual operational needs. Second, existing methods often fail to fully consider the characteristics of emerging markets, such as price fluctuations and changes in market mechanisms, which can lead to significant deviations in optimization results in practical applications. Furthermore, existing methods still have shortcomings in handling HSR 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 HSR energy storage systems in emerging market environments, and a more comprehensive and efficient optimization method is urgently needed to address them.

[0005] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0006] In response to the problems in related technologies, 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 optimization. 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. It thus solves the problem that existing technologies are unable to respond to the ever-changing electricity market mechanisms in emerging markets, which directly affects the stability and reliability of high-speed rail energy management.

[0007] To this end, the specific technical solutions adopted in the present invention are as follows:

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

[0009] S1. Establish a multi-faceted emerging market entity model based on virtual power plant entities and load aggregator entities;

[0010] S2. Based on the multiple emerging market subject model and combined with the high-speed rail coupled energy storage subject, 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, establishing a multi-element emerging market entity model includes the following steps:

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

[0014] S12. Establish 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 virtual power plant's electricity purchasing and selling states;

[0018] S112. Determine a first constraint of the virtual power plant based on 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, set the second constraint of the virtual power plant, and build a virtual power plant model 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 and δ sell,t Respectively represent the power purchasing state and power selling state 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 when it is 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 has been 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 a high-speed rail load constraint based on the 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 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 combine the high-speed rail load traction power constraint condition and the high-speed rail load climbing rate constraint condition to establish the high-speed rail load constraint.

[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 player model and aiming to minimize 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 turbines, virtual power plants, load aggregators, and high-speed rail energy feed systems, establish a day-ahead scheduling objective function 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 entity 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, establishing the intraday scheduling objective function and setting the electrochemical energy storage constraints include the following steps:

[0043] S321. Based on thermal power units, wind turbines, high-speed rail energy feed systems, and electrochemical energy storage, establish a daily scheduling objective function with the goal of minimizing the total operating cost of high-speed rail;

[0044] S322. Setting charge and discharge constraints based on the battery charging power and discharging power;

[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, it enhances the controllability of high-speed rail storage, improves energy utilization, and 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 usage 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 following briefly introduces the drawings required for use in the embodiments. 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 any creative work.

[0050] Figure 1 1. It is a flowchart of 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;

[0051] Figure 2 This is a specific implementation diagram of 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;

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

[0053] Figure 4 This is a two-stage day-ahead and day-intraday scheduling framework diagram including 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. They 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. By referring to these contents, ordinary technicians in this field should be able to understand other possible implementation methods 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 is provided.

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

[0057] S1. Establish a multi-faceted emerging market entity model based on virtual power plant entities and load aggregator entities;

[0058] S2. Based on the multiple emerging market subject model and combined with the high-speed rail coupled energy storage subject, establish 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 allocation in the context of emerging markets, which is implemented specifically in 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, and load aggregators, takes 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 calculation is in the S3 scheduling model.

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

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

[0064] S12. Establish 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 of each subject satisfying 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 virtual power plant's electricity purchasing and selling states;

[0069] S112. Determine a first constraint of the virtual power plant based on 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, set the second constraint of the virtual power plant, and build a virtual power plant model in combination with the first constraint of the virtual power plant.

[0071] In one embodiment, the expression for constructing the 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 and 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 entity, 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 when it is 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 has been continuously scheduled before time t.

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

[0079] S21. Establishing a high-speed rail load constraint based on the 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 the upper and lower limits of adjustable power, which are expressed as follows:

[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 to supply the high-speed rail operation; ch,max 、P dis,max 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, u ch,t 、u dis,t is a Boolean variable, indicating the energy storage charge and discharge status at time t; u ch,t =1,u dis,t =0, energy storage charging; u ch,t =0,udis,t =1, the stored energy 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 high-speed rail coupled energy storage entities are the emerging market entities proposed in this invention. The three are all scheduling 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-element emerging market subject model in step S1, including:

[0090] When the high-speed train is braking, the electrochemical energy storage capacity is less than its maximum capacity, and the battery recovers the regenerative braking energy generated by the high-speed train. If the high-speed train is operating normally, and the system's wind power output far exceeds the system load's power demand, the battery will be charged from the curtailed wind power. When the high-speed train load is at its peak, the electrochemical energy storage capacity exceeds its minimum discharge capacity, and the battery discharges energy to the high-speed train load to ensure safe and stable operation. When the high-speed train load is operating normally and not at its peak, the battery discharges energy to the 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 the maximum and minimum values ​​of high-speed rail traction power respectively.

[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] Where 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 before and after the high-speed rail load respectively; P h,T-1 、P h,T are the rates before and after the high-speed rail load stops, 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 combine the high-speed rail load traction power constraint condition and the high-speed rail load climbing rate constraint condition to establish the high-speed rail load constraint.

[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 the maximum and minimum values ​​of high-speed rail traction power respectively.

[0104] In one embodiment, 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:

[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 optimized operation strategy of day-ahead and 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 a multi-element emerging market player model and 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 turbines, virtual power plants, load aggregators, and high-speed rail energy feed systems, establish a day-ahead scheduling objective function 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 electrical 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] Where, F1 is the day-ahead scheduling objective function; F k is the operating cost of thermal power units; F q Penalty cost for wind curtailment; F y is the maintenance cost of wind turbine; F VPP F is the cost of electricity purchase and sale for the virtual power plant; LA dispatch costs for load aggregators; is the carbon trading cost of thermal power units; F h Cost of electricity purchased 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, both 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 unit 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 The unit price of electricity purchased by the high-speed rail from the power grid; C bark It is the unit price of high-speed rail energy feed system capacity.

[0119] S312. Determine power balance constraints based on a multi-element emerging market entity 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 length of the statistical period.

[0129] Specifically, a scheduling model is established with the economic efficiency of system operation as the goal. The objective functions of the scheduling model include the day-ahead scheduling objective function and the intraday 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 uses a time scale of 1 hour, and the intraday scheduling uses a time scale of 15 minutes.

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

[0132] S321. Based on thermal power units, wind turbines, high-speed rail energy feed systems, and electrochemical energy storage, establish a daily scheduling objective function 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] Where, F2 is the intraday scheduling objective function; F k is the operating cost of thermal power units; F q Penalty cost for wind curtailment; F y is the maintenance cost of wind turbine; F VPP F is the cost of electricity purchase and sale for the virtual power plant; LA dispatch costs for load aggregators; is the carbon trading cost of thermal power units; F h Cost of electricity purchased for high-speed rail operation; F cap-lea F is the cost of the high-speed rail energy feedback system after energy storage leasing; 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 dispatching objective function and the intraday dispatching objective function is as follows: in the day-ahead dispatching objective function F1, the high-speed rail load is not equipped with an energy storage device, while in the intraday dispatching objective function F2, the high-speed rail load is equipped with an energy storage system; secondly, the intraday dispatching objective function F2 uses a 15-minute time scale, while the day-ahead dispatching objective function F1 uses a 1-hour time scale.

[0139] S322. Setting charge and discharge constraints based on the battery charging power and discharging power;

[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] Where S t is the battery storage capacity at time t, θ i is the self-discharge rate of the battery, are the charge and discharge efficiency of the battery, E max is the maximum capacity of the battery, S min 、S max S1 and S2 are the upper and lower limits of battery storage capacity, respectively. T They are the initial and final storage capacity 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 in this paper 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 restrictions imposed by emerging market players. By addressing these constraints and the objective function, this paper aims to find the optimal scheduling solution to optimize system efficiency and economic efficiency.

[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] To comprehensively evaluate the performance of our method, we designed two comparative scenarios: Scenario 1 disregards the regulation function of emerging market entities and sets their regulation power to 0; Scenario 2 utilizes the optimization method proposed in this invention that does consider regulation by emerging market entities. Other conditions remain the same for both scenarios. By comparing and analyzing the calculation results of these two scenarios, we can clearly assess the advantages of our method.

[0154] In Scheme 1, the day-ahead wind power output is restricted in 46 of the 96 time periods per day, with a maximum blocked power of 264.2 MW per period and a total of 3,920.7 MW·h of wind power curtailed. In Scheme 2, the day-ahead wind power output is restricted in 13 of the 96 time periods per day, with a maximum blocked power of 165.8 MW per period and a total of 1,339.2 MW·h of wind power curtailed. Comparing the intraday wind curtailment of Schemes 1 and 2, the day-ahead wind power output is restricted in 23 of the 96 time periods per day, with a maximum blocked power of 203.7 MW per period and a total of 1,850.3 MW·h of wind power curtailed. In Scheme 2, the day-ahead wind power output is restricted in 12 of the 96 time periods per day, with a maximum blocked power of 180.5 MW per period and a total of 1,077.2 MW·h of wind power curtailed. Because Option 2 incorporates the regulation of emerging market players, the system's day-ahead wind curtailment under Option 2 is 2581.5 MW lower than Option 1's day-ahead curtailment, and intraday curtailment is 773.1 MW lower. This is because Option 1 ignores the regulatory capacity of emerging market players and relies solely on the regulatory capacity of thermal power plants to adjust for wind power fluctuations. However, due to the limited regulatory capacity of thermal power plants, this leads to significant system curtailment. Option 2, by not solely relying on the regulatory capacity of thermal power plants but also considering the regulatory capacity of emerging market players, improves system regulatory flexibility. Therefore, the approach that considers emerging market players can coordinate the output of various devices to address the impact of load and renewable energy fluctuations on the power system, thereby improving system flexibility.

[0155] In the day-ahead dispatch scenario 2, which includes the participation of emerging market players, the load curve has lower peaks (periods 23-51 and 64-91) and larger valleys (periods 1-23 and 51-64). The maximum peak-to-valley difference in scenario 1 is 325.7 MW, while in scenario 2 it is 234.5 MW, a reduction of 91.2 MW. This means that the power system with the participation of emerging market players achieves more balanced power demand across different time periods, thereby improving overall system flexibility. The results of scenario 2, which includes the introduction of independent energy storage during the intraday phase, further smooth out power fluctuations due to its faster response rate, further reducing the peak-to-valley load difference. If intraday dispatch follows the results of scenario 1, traditional dispatching methods will be unable to smooth out power fluctuations, and the peak-to-valley load difference will not change much. In summary, emerging market players can effectively adjust the load curve, smooth out power fluctuations, and contribute to enhancing 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 without coupling, the energy storage charging periods are 2-6, 12-20, 28-34, 42-49, 59-66, and 75-80 respectively. Considering that the load is low at 12-20, 28-34, 42-49, and 75-80 before Scheme 1, energy storage charging achieves an effective "valley filling" effect. The wind curtailment is serious at 2-6 and 59-66 before Scheme 1, and energy storage charging effectively reduces the amount of wind curtailment. After coupling with 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, thereby improving the energy storage utilization rate.

[0157] The operating costs of thermal power units differed slightly between the two different options. However, through the implementation of Option 2, the system's regulation capacity was improved, and the amount of wind power received increased significantly, resulting in a significant reduction in the system's wind curtailment costs and increased system flexibility. High-speed rail coupled with energy storage entities not only reduces high-speed rail operating costs and reduces energy waste, but also reduces wind curtailment to a certain extent, reduces the peak-to-valley difference in high-speed rail load, alleviates power quality pressures in the power system, and thus improves power system flexibility. In this process, energy storage utilization is greatly improved, energy storage charging and discharging costs are reduced, and energy storage lifespan is not significantly affected. Therefore, compared with Option 1, the total system operating cost is lower under Option 2. Furthermore, the use of the regulation capabilities of emerging market entities for peak regulation and the absorption of limited wind power also brings significant economic benefits.

[0158] The effectiveness of the proposed technical solution was fully verified by comparing and analyzing the performance of Schemes 1 and 2 in terms of wind curtailment, load profile, energy storage utilization, and system operating costs. The results show that an optimization approach that considers the regulation of emerging market players can significantly improve system flexibility, reduce wind curtailment, flatten the load profile, increase energy storage utilization, and ultimately reduce total system operating costs. This not only provides new insights for optimizing 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 solutions of the present invention, by addressing 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 increases in high-speed rail loads, further improving the flexibility and economy of the power system. After the addition of the regulation of emerging market entities, the wind power consumption capacity is greatly increased, and the output of thermal power units is smoothed, 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 distribution is enhanced, the energy utilization rate is improved, and it has the possibility of participating in the optimization and scheduling of the power system. At the same time, this also 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, 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 principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A two-stage optimization method for high-speed rail storage allocation considering emerging markets, characterized by: The following steps are involved: S1. Establish a multi-faceted emerging market entity model based on virtual power plant entities and load aggregator entities; S2. Based on the multiple emerging market subject model and combined with the high-speed rail coupled energy storage subject, 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, both before and after the day. Step S1 includes: S11. Establish a virtual power plant model based on the virtual power plant entity; S12. Establish a load aggregator model based on the load aggregator entity; S13. Based on the virtual power plant model and the load aggregator model, a multi-faceted emerging market entity model is constructed; Step S2, based on the multi-element emerging market subject model in step S1, proposes a high-speed rail energy storage coupling rule to improve the feasibility of high-speed rail regulation, including: when the high-speed rail is operating in a 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; when the high-speed rail is operating in a normal state, the system's wind power output power is greater than the system load demand power, and the battery is charged from the abandoned wind; when the high-speed rail load is operating at a peak period, the electrochemical energy storage capacity is greater than the minimum discharge capacity, and the battery discharges to the high-speed rail load; when the high-speed rail load is operating normally and has not reached a peak period, the battery discharges to the grid; Step S2 includes: S21. Establishing high-speed rail load constraints based on high-speed rail traction power and climbing rate; specifically including: 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 combine the high-speed rail load traction power constraint condition and the high-speed rail load climbing rate constraint condition to establish the high-speed rail load constraint condition; S22. Establish high-speed rail energy storage coupling constraints based on adjustable power upper and lower limits; S23. Determine a high-speed rail energy storage coupling rule based on the high-speed rail load constraint and the high-speed rail energy storage coupling constraint; Among them, the high-speed rail energy storage coupling constraints include the upper and lower limits of adjustable power constraints, which are expressed as follows: 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 to supply the high-speed rail operation; ch,max 、P dis,max 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.

2. The two-stage optimization method for high-speed rail reserve allocation considering emerging markets according to claim 1, 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 virtual power plant's electricity purchasing and selling states; S112. Determine a first constraint of the virtual power plant based on 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, set the second constraint of the virtual power plant, and build a virtual power plant model in combination with the first constraint of the virtual power plant.

3. The two-stage optimization method for high-speed rail reserve allocation considering emerging markets according to claim 2, 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 and δ sell,t Respectively represent the power purchasing state and power selling state 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.

4. The two-stage optimization method for high-speed rail reserve allocation considering emerging markets according to claim 1, 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 L A ,t is the state variable of the contract called at time t; t LA,max is the maximum scheduling time of the contract; t on LA,t is the length of time the contract has been continuously scheduled before time t.

5. The two-stage optimization method for high-speed rail reserve 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 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.

6. The method of optimizing high-speed rail reserve allocation in emerging markets in two stages, namely, pre-day and intra-day, according to claim 5, characterized in that: The method of establishing a day-ahead scheduling objective function based on a multi-element emerging market entity model with the goal of minimizing the total operating cost of high-speed rail includes the following steps: S311. Based on thermal power units, wind turbines, virtual power plants, load aggregators, and high-speed rail energy feed systems, establish a day-ahead scheduling objective function 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 entity model; S313. Based on the day-ahead dispatch objective function, set wind power constraints and thermal power operation constraints.

7. The two-stage optimization method for high-speed rail reserve allocation considering emerging markets according to claim 5, characterized in that: The method of establishing a daily scheduling objective function based on the high-speed rail energy storage coupling rule and setting electrochemical energy storage constraints includes the following steps: S321. Based on thermal power units, wind turbines, high-speed rail energy feed systems, and electrochemical energy storage, establish a daily scheduling objective function with the goal of minimizing the total operating cost of high-speed rail; S322. Setting charge and discharge constraints based on the battery charging power and discharging power; 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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