Electric energy and flexible climbing collaborative optimization method considering energy storage

Abstract

The invention relates to an electric energy and flexible climbing collaborative optimization method considering energy storage, belongs to the field of power system optimization, and solves the problems that energy storage is difficult to effectively participate in flexible climbing of a power system, and the requirement of the system for flexible adjustment capability under the background of new energy permeability improvement cannot be met in the existing method. And the wind and light abandoning and load shedding electric quantity is large. Comprises: obtaining day-ahead power system operation data of a target power grid; based on the day-ahead operation data of the power system, the deterministic and non-deterministic climbing requirements of the power system are calculated; based on the deterministic and non-deterministic climbing demands of the power system, calculating to obtain a flexible climbing demand of the power system; based on the flexible climbing requirement of the power system, an electric energy and flexible climbing collaborative optimization model with the goal of minimizing the total loss of the coal consumption and the energy storage operation loss of the thermal power generating unit is constructed; and solving the electric energy and flexible climbing collaborative optimization model based on a set constraint condition by using a solver to obtain a thermal power generating unit output plan and an energy storage charging and discharging scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization, and in particular to a method for collaborative optimization of electric energy and flexible ramping considering energy storage. Background Art

[0002] Renewable energy sources such as wind and solar are increasingly being integrated into the power system due to their clean, low-carbon, and renewable characteristics. However, the volatility and uncertainty of renewable energy generation pose significant challenges to the stable operation of the power system.

[0003] Existing approaches primarily rely on thermal power generation to provide flexible ramping services, with minimal involvement of energy storage. This limits the potential of energy storage technology to enhance power system flexibility and prevents the full utilization of energy storage's rapid response and bidirectional regulation capabilities. This issue directly impacts the power system's ability to meet the demand for flexible regulation as renewable energy penetration increases.

[0004] Existing technologies lack a mechanism for the coordinated optimization of thermal power plants and energy storage. Operational plans for thermal power plants and energy storage are typically developed independently, failing to achieve coordinated optimization. This reduces the overall regulation capacity of the power system and prevents optimal utilization of the regulation capabilities of thermal power plants and energy storage. Summary of the Invention

[0005] In view of the above analysis, the embodiments of the present invention aim to provide a method for collaboratively optimizing electric energy and flexible ramping that takes energy storage into account. This method is intended to address the technical issues that existing methods make it difficult for energy storage to effectively participate in the flexible ramping of the power system, and are unable to meet the power system's demand for flexible adjustment capabilities in the context of increasing new energy penetration, resulting in a large amount of wind and solar power curtailment and load shedding.

[0006] The purpose of the present invention is mainly achieved through the following technical solutions:

[0007] The present invention provides a method for collaborative optimization of electric energy and flexible ramping considering energy storage, comprising the following steps:

[0008] Obtaining day-ahead power system operation data of the target power grid; calculating deterministic and non-deterministic ramping demands of the power system based on the day-ahead power system operation data; and calculating flexible ramping demands of the power system based on the deterministic and non-deterministic ramping demands of the power system;

[0009] Based on the flexible ramping requirements of the power system, a coordinated optimization model of electric energy and flexible ramping is constructed with the goal of minimizing the total loss of coal consumption of thermal power units and energy storage operation losses;

[0010] A solver is used to solve the electric energy and flexible ramping collaborative optimization model based on set constraints to obtain a thermal power unit output plan and an energy storage charging and discharging plan.

[0011] Furthermore, the power system deterministic and non-deterministic ramping requirements include corresponding up-ramp requirements and down-ramp requirements respectively; the power system flexible ramping requirements are as follows:

[0012]

[0013] in, are the up-climbing demand and down-climbing demand in period t respectively; are the deterministic up-ramp demand and down-ramp demand in period t respectively; and are the uncertain up-ramp demand and down-ramp demand of the system in time period t, and T is the time period set.

[0014] Furthermore, the deterministic ramp-up requirement is as follows:

[0015]

[0016] in, and are the deterministic up-ramp demand and down-ramp demand of the system in period t, respectively; L t , L t+1 are the predicted net loads of the system in time periods t and t+1 respectively; J is the set of loads; P j,t is the predicted value of load j in period T; V is the set of new energy sources; P v,t is the predicted output value of the new energy v in period t.

[0017] Furthermore, the uncertain climbing requirement is constructed based on the deterministic climbing requirement as follows:

[0018]

[0019] in, and are the uncertain up-ramp demand and down-ramp demand of the power system in time period t, respectively; and are the upward safety margin and downward safety margin reserved for the system period t respectively; and are the contribution values ​​of the upward safety margin reserved by the power system for load and renewable energy in time period t respectively; and are the contribution values ​​of the downward safety margin reserved by the power system for load and renewable energy in period t; P j,t+1 is the predicted value of load j in period t+1; P v,t+1 is the output forecast value of the new energy source v in the period t+1; and are the upper and lower limits of the load j prediction error rate under the preset confidence level; and It is the upper and lower limits of the error rate of the new energy V output prediction under the preset confidence level.

[0020] Furthermore, the objective function is established with the goal of minimizing the total loss of coal consumption of thermal power units and energy storage operation loss, which is expressed as follows:

[0021]

[0022] Among them, G is the set of thermal power units; B is the set of output stages of thermal power units; T is the set of time periods; λ i,b P is the coal consumption of thermal power unit i in the output section b; i,t,b is the power generation power of thermal power unit i in the bth output interval of time period t; c ess is the unit operating loss of energy storage; P t,dis 、P t,ch are the discharge power and charging power of the energy storage in time period t respectively.

[0023] Furthermore, the set constraints include energy storage operation constraints, system ramping demand constraints, system power balance constraints, thermal power unit operation constraints, thermal power unit flexible ramping capacity constraints, and energy storage flexible ramping capacity constraints.

[0024] Furthermore, the energy storage operation constraints are as follows:

[0025]

[0026] Among them, P ess,max is the rated power of energy storage; u t,dis and u t,ch is the discharge and charge state of the energy storage in time period t, which is a 0-1 variable; E t 、E t+1 are the amount of energy stored in time periods t and t+1 respectively; η dis and η ch are the discharge efficiency and charging efficiency of energy storage respectively; E min and E max The lower and upper limits of energy storage capacity.

[0027] Furthermore, the system climbing requirement constraints are as follows:

[0028]

[0029] in, and The ramp-up capacity and ramp-down capacity provided for thermal power unit i; and Provides both ramp-up and ramp-down capacity for energy storage.

[0030] Furthermore, the output plan of the thermal power unit includes the power generation power P of the thermal power unit i in the output interval b of the time period t. i,t,b ;

[0031] The energy storage charging and discharging scheme includes the energy storage unit operating loss c ess , the discharge power P of the energy storage in time period t t,dis and charging power P t,ch , the discharge state u of the energy storage in time period t t,dis and charging status u t,ch ; The amount of energy stored in time period t is E t .

[0032] Furthermore, the target power grid's day-ahead power system operation data includes new energy and load data, thermal power unit data, and energy storage data;

[0033] The new energy and load data include new energy output forecast value, load forecast value, new energy output forecast error rate and load forecast error rate.

[0034] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0035] 1. The present invention comprehensively considers the participation of thermal power units and energy storage in providing flexible ramping services for the power system. By considering the electric energy of energy storage and the flexible ramping collaborative optimization method, it can effectively integrate thermal power units and energy storage resources, enhance the flexible regulation capability of the power system, better cope with the volatility and uncertainty of renewable energy power generation, reduce the amount of wind and solar power curtailment and load shedding, and improve the flexibility of the power system.

[0036] 2. The present invention constructs an optimization model with the goal of minimizing the total loss of coal consumption of thermal power units and energy storage operation losses, thereby achieving minimum loss operation of thermal power units and energy storage;

[0037] 3. This invention fully considers the volatility and uncertainty of new energy. By optimizing the scheduling of thermal power units and energy storage, it provides more space for the consumption of new energy and promotes the consumption of new energy.

[0038] 4. The present invention ensures the power balance and frequency stability of the power system by rationally arranging the output plan and charging and discharging scheme of thermal power units and energy storage, thereby improving the safety and reliability of the power system and enhancing the stability of the power system.

[0039] In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of the present invention will be described in the following description, and some advantages will become apparent from the description or be learned through practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like parts throughout the drawings.

[0041] Figure 1 This is a flow chart of a method for collaborative optimization of electric energy and flexible ramping considering energy storage in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0043] As the construction of new power systems continues to advance, renewable energy sources, such as wind and solar, are increasingly connected to the grid. Due to the anti-peaking properties of wind and solar, the daily net load of the power system exhibits a "duck-shaped curve," with net load rapidly rising or falling in a short period of time. This situation worsens as the proportion of wind and solar power increases. To address this dynamic, flexible ramping services have emerged. Currently, these services are primarily provided by thermal power units, with less involvement from energy storage, which offers greater adjustability.

[0044] To address the above issues, this paper proposes a method for collaboratively optimizing electric energy and flexible ramping, taking energy storage into account, for thermal power generation units and energy storage. Starting from the source of flexible ramping services, the ramping requirements of the new power system are calculated using day-ahead power system operation information. Then, based on the operating characteristics of the thermal power generation units and energy storage, a collaborative optimization model for electric energy and flexible ramping involving the thermal power generation units and energy storage is constructed. Finally, the model is solved to obtain an operating plan for the thermal power generation units and energy storage, which is suitable for the flexible ramping requirements of the new power system with increasing penetration of new energy sources.

[0045] The present invention is used to provide a flexible ramping service provision solution involving multiple types of dispatchable resources in a new power system with an increasing penetration rate of new energy. By embedding thermal power units and energy storage into the power system's advance dispatch plan, the new power system can reduce the amount of wind and solar power curtailment and load shedding through flexible ramping services.

[0046] A specific embodiment of the present invention discloses a method for collaborative optimization of electric energy and flexible ramping considering energy storage, such as Figure 1 As shown, the following steps are included:

[0047] Step S1: acquiring day-ahead power system operation data of a target power grid; calculating deterministic and non-deterministic ramping demands of the power system based on the day-ahead power system operation data; and calculating flexible ramping demands of the power system based on the deterministic and non-deterministic ramping demands of the power system;

[0048] Step S2: Based on the flexible ramping requirements of the power system, a coordinated optimization model for electric energy and flexible ramping is constructed with the goal of minimizing the total loss of coal consumption of thermal power units and energy storage operation losses;

[0049] Step S3: using a solver to solve the electric energy and flexible ramping collaborative optimization model based on set constraints to obtain a thermal power unit output plan and an energy storage charging and discharging plan.

[0050] The target grid refers to the specific power system to be optimized and managed. It can be flexibly defined based on specific application scenarios and requirements. For example, the target grid can be a local grid, a regional grid, or any other type of power system, as long as it is the subject of the present invention's attention and optimization. The technical solutions of the present invention can be flexibly applied to power systems of different sizes and types.

[0051] Step S1 includes steps S11-S13.

[0052] Step S11: Acquire the day-ahead power system operation data of the target power grid.

[0053] Obtain the target power grid's day-ahead power system operation data for multiple time periods.

[0054] The target power grid's day-ahead power system operation data includes new energy and load data, thermal power unit data, and energy storage data.

[0055] (1) Obtain new energy and load data.

[0056] The renewable energy and load data specifically include: the output forecast value of renewable energy in each time period, the forecast value of each load in each time period, the output forecast error rate of each renewable energy source, the forecast error rate of each load; the predicted net load of the target power grid system;

[0057] For example, new energy and load data can be obtained from the dispatching agency, and the time resolution is generally 15 minutes.

[0058] The new energy output forecast value, load forecast value, new energy output forecast error rate, and load forecast error rate are generated by the dispatching agency based on the forecast algorithm and historical data, and are used to reflect the expected new energy output and load in each future time period.

[0059] For example, the new energy set V includes renewable energy sources such as solar power generation, wind power generation (onshore wind power, offshore wind power), biomass power generation, ocean tidal power generation and ocean wave power generation; the load set J includes residential loads, commercial loads, industrial loads and public service loads, etc.

[0060] (2) Obtain data on thermal power units.

[0061] The thermal power unit data specifically include: coal consumption of each unit's segmented output, power generation power in each unit's segmented output range, unit output upper limit, unit output lower limit, unit segmented up-climbing and down-climbing capacity, unit up-climbing rate upper limit, unit down-climbing rate upper limit, unit output capacity upper limit, and unit total output.

[0062] For example, the thermal power unit data may be obtained from a power plant.

[0063] (3) Obtain energy storage data.

[0064] The energy storage data specifically includes: energy storage charging power upper limit, energy storage discharging power upper limit, energy storage charging efficiency, energy storage discharging efficiency, energy storage capacity upper limit, energy storage capacity lower limit, energy storage unit operating loss, energy storage rated power, energy storage ramp-up capacity and ramp-down capacity, and energy storage capacity.

[0065] and obtaining the upward and downward safety margin contribution values ​​reserved by the target power grid power system for new energy and loads;

[0066] Exemplarily, the energy storage data may be obtained from an energy storage power station.

[0067] Through step S11, the data required for calculating the system ramp demand and the parameter data required for constructing the electric energy and flexible ramp-up collaborative optimization model can be obtained.

[0068] Step S12: Calculate the deterministic and non-deterministic ramping requirements of the power system based on the day-ahead power system operation data.

[0069] Flexible ramping service refers to the provision of active power output services with a high up and down adjustment rate by dispatchable resources that quickly respond to short-term and large changes in system load or renewable energy output according to dispatch instructions. Dispatched resources such as thermal power units and energy storage provide flexible ramping services by pre-reserving ramping capacity, and calculating the ramping demand to determine how much climbing capacity the system needs.

[0070] The system climbing demand is divided into upward climbing demand and downward climbing demand, both of which are composed of deterministic climbing demand and uncertain climbing demand.

[0071] The challenges that renewable energy sources or loads pose to power system operations primarily stem from their volatility and uncertainty. Volatility refers to the random variations in renewable energy output or load over time, while uncertainty refers to the discrepancy between the predicted and actual values ​​of renewable energy output or load. Deterministic ramping addresses volatility, while uncertain ramping addresses uncertainty.

[0072] (1) Calculate the system's deterministic ramping requirements.

[0073] Deterministic ramping demand is the power regulation capability that the power system needs to reserve to cope with foreseeable net load fluctuations.

[0074] The deterministic ramp-up requirements are as follows:

[0075]

[0076] in, and are the deterministic up-ramp demand and down-ramp demand of the system in period t, respectively; L t , Lt+1 are the predicted net loads of the system in time periods t and t+1 respectively; J is the set of loads; P j,t is the predicted value of load j in period t; V is the set of new energy sources; P v,t is the predicted output value of the new energy v in period t.

[0077] t∈T, T is the set of time periods; formula and The deterministic ramping demand is the change in the system's predicted net load in the next period compared to the current period, that is, the first-order difference of the system's predicted net load in adjacent periods.

[0078] (2) Calculate the system uncertainty ramping requirements.

[0079] The uncertain ramping requirement is constructed based on the deterministic ramping requirement as follows:

[0080]

[0081] in, and are the uncertain up-ramp demand and down-ramp demand of the power system in time period t, respectively; and are the upward safety margin and downward safety margin reserved for the system period t respectively; and are the contribution values ​​of the upward safety margin reserved by the power system for load and renewable energy in time period t respectively; and are the contribution values ​​of the downward safety margin reserved by the power system for load and renewable energy in period t; Pj,t+1 is the predicted value of load j in period t+1; P v,t+1 is the output forecast value of the new energy source v in the period t+1; and are the upper and lower limits of the load j prediction error rate under the preset confidence level; and It is the upper and lower limits of the error rate of the new energy V output prediction under the preset confidence level.

[0082] The upper and lower safety margin contributions reserved by the power system for new energy and loads are the upper and lower safety margin contributions reserved by thermal power units and energy storage for new energy and loads. There are certain errors in the prediction of new energy power generation and load power consumption, so the power system must arrange for thermal power units and energy storage to reserve a part to deal with the prediction error.

[0083] For example, the upper and lower limits of the forecast error rate of load and renewable energy output are determined based on the upper and lower limits of the average error of load forecast and renewable energy output forecast in period t in the same month of the previous year and within 15 days before the operation day (the error range with a confidence level between 2.5% and 97.5% is selected, covering the error distribution with a confidence level of 95%).

[0084] The uncertain ramping demand takes into account the forecast errors of renewable energy output and load.

[0085] Step S13: Calculate the power system flexible ramping demand based on the power system deterministic and non-deterministic ramping demands.

[0086] The power system deterministic and non-deterministic ramping requirements include corresponding up-ramp requirements and down-ramp requirements respectively; the power system flexible ramping requirements are as follows:

[0087]

[0088] in, are the up-climbing demand and down-climbing demand in period t respectively; are the deterministic up-ramp demand and down-ramp demand in period t respectively; and are the uncertain up-ramp demand and down-ramp demand of the system in time period t, and T is the time period set.

[0089] The power system flexibility ramping demand is the sum of the deterministic ramping demand and the uncertain ramping demand.

[0090] Uphill climbing demand during period t and downhill climbing requirements It is used as a parameter in the system ramping demand constraint in the electric energy and flexible ramping collaborative optimization model constructed in step S2.

[0091] The function of step S1 is to obtain the day-ahead power system operation data of the target power grid, and calculate the deterministic and non-deterministic ramping requirements based on these data, and then obtain the flexible ramping requirements of the power system.

[0092] Step S2 includes steps S21-S22.

[0093] The coordinated optimization model of electric energy and flexible ramping consists of two parts: the constructed objective function and the set constraints.

[0094] The objective function is the technical goal to be achieved through optimization, usually maximizing or minimizing the target expression. Constraints are the physical or resource limitations that need to be met during the optimization process, usually expressed as equations or inequalities.

[0095] Step S21: constructing a coordinated optimization model for electric energy and flexible ramping with the goal of minimizing the total loss of coal consumption of thermal power units and energy storage operation losses.

[0096] The objective function is established with the goal of minimizing the total loss of coal consumption of thermal power units and energy storage operation loss, which is expressed as follows:

[0097]

[0098] Among them, G is the set of thermal power units; B is the set of output stages of thermal power units; T is the set of time periods; λ i,b P is the coal consumption of thermal power unit i in the output section b; i,t,b is the power generation power of thermal power unit i in the bth output interval of time period t; c ess is the unit operating loss of energy storage; P t,dis 、P t,ch are the discharge power and charging power of the energy storage in time period t respectively.

[0099] Step S22: setting the constraint conditions of the electric energy and flexible ramp-up collaborative optimization model.

[0100] The set constraints include energy storage operation constraints, system ramping demand constraints, system power balance constraints, thermal power unit operation constraints, thermal power unit flexible ramping capacity constraints, and energy storage flexible ramping capacity constraints.

[0101] The details are as follows:

[0102] (1) Energy storage operation constraints.

[0103] The energy storage operation constraints are as follows:

[0104]

[0105] Among them, P ess,max is the rated power of energy storage; u t,disand u t,ch is the discharge and charge state of the energy storage in time period t, which is a 0-1 variable; E t 、E T+1 are the amount of energy stored in time periods t and t+1 respectively; η dis and η ch are the discharge efficiency and charging efficiency of energy storage respectively; E min and E max The lower and upper limits of energy storage capacity.

[0106] u t,dis +u t,ch ≤1 indicates that the energy storage can only be in one state of charging or discharging at the same time.

[0107] For example, the discharge efficiency and charge efficiency of energy storage are generally taken as 0.95.

[0108] (2) System climbing demand constraints.

[0109] The system ramp requirement constraints are as follows:

[0110]

[0111] in, and The ramp-up capacity and ramp-down capacity provided for thermal power unit i; and Provides both ramp-up and ramp-down capacity for energy storage.

[0112] (3) System power balance constraints, as shown below:

[0113]

[0114] Among them, P v,t is the output forecast value of new energy v in period t; P j,t is the predicted value of load j in time period t+1.

[0115] The system power balance constraint indicates that the power system must maintain power balance during operation, that is, the power generation is equal to the power consumption.

[0116] (4) Operation constraints of thermal power units are as follows:

[0117]

[0118] in, and is the upper and lower output limits of unit i; is the upper limit of the output capacity of unit i in section b; and are the upper and lower ramp rate limits of unit i respectively; P i,t 、P i,t-1 are the total outputs of unit i in time periods t and t-1 respectively.

[0119] (5) The thermal power generation unit provides flexible ramp capacity constraints as follows:

[0120]

[0121] in, and The upward and downward climbing capacity provided for thermal power unit i; ΔP i U , ΔP i D are the upper limit of the ramp-up rate and the lower limit of the ramp-down rate of thermal power unit i respectively.

[0122] (6) Energy storage provides flexible ramp capacity constraints, as shown below:

[0123]

[0124] in, and The up-climbing capacity and down-climbing capacity provided for energy storage; E t is the amount of energy stored in time period t; E min The lower limit of energy storage capacity.

[0125] The purpose of step S2 is to construct an optimization model based on the flexible ramping requirements of the power system, with the goal of minimizing the total loss of coal consumption of thermal power units and energy storage operation losses, and set corresponding constraints.

[0126] Step S3, specifically.

[0127] The electric energy and flexible ramping collaborative optimization model is solved by a solver. The electric energy and flexible ramping collaborative optimization model is solved based on set constraints to obtain a thermal power unit output plan and an energy storage charging and discharging plan.

[0128] The output plan of the thermal power unit includes the generated power P of the thermal power unit i in the output interval b of the time period t. i,t,b ;

[0129] The energy storage charging and discharging scheme includes the energy storage unit operating loss c ess , the discharge power P of the energy storage in time period t t,dis and charging power P t,ch , the discharge state u of the energy storage in time period t t,dis and charging status u t,ch ; The amount of energy stored in time period t is Et .

[0130] Exemplarily, a solver such as Gurobi, CPLEX, MOSEK or Firtree COPT is used to solve the electric energy and flexible ramping collaborative optimization model.

[0131] The purpose of step S3 is to solve the electric energy and flexible ramping collaborative optimization model based on the set constraints through solvers such as Gurobi, obtain the thermal power unit output plan and energy storage charging and discharging plan, and provide guidance for the operation of the target power grid.

[0132] In summary, the method for collaboratively optimizing electric energy and flexible ramping with consideration of energy storage according to an embodiment of the present invention has the following beneficial effects:

[0133] 1. The present invention comprehensively considers the participation of thermal power units and energy storage in providing flexible ramping services for the power system. By considering the electric energy of energy storage and the flexible ramping collaborative optimization method, it can effectively integrate thermal power units and energy storage resources, enhance the flexible regulation capability of the power system, better cope with the volatility and uncertainty of renewable energy power generation, reduce the amount of wind and solar power curtailment and load shedding, and improve the flexibility of the power system.

[0134] 2. The present invention constructs an optimization model with the goal of minimizing the total loss of coal consumption of thermal power units and energy storage operation losses, thereby achieving minimum loss operation of thermal power units and energy storage;

[0135] 3. This invention fully considers the volatility and uncertainty of new energy. By optimizing the scheduling of thermal power units and energy storage, it provides more space for the consumption of new energy and promotes the consumption of new energy.

[0136] 4. The present invention ensures the power balance and frequency stability of the power system by rationally arranging the output plan and charging and discharging scheme of thermal power units and energy storage, thereby improving the safety and reliability of the power system and enhancing the stability of the power system.

[0137] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0138] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for collaborative optimization of electric energy and flexible ramping considering energy storage, characterized in that: include: Obtain the day-ahead power system operation data of the target power grid; Calculating deterministic and non-deterministic ramping demands of the power system based on the day-ahead power system operation data; Calculating the flexible ramping demand of the power system based on the deterministic and non-deterministic ramping demand of the power system; Based on the flexible ramping requirements of the power system, a coordinated optimization model of electric energy and flexible ramping is constructed with the goal of minimizing the total loss of coal consumption of thermal power units and energy storage operation losses; A solver is used to solve the electric energy and flexible ramping collaborative optimization model based on set constraints to obtain a thermal power unit output plan and an energy storage charging and discharging plan.

2. The method for collaborative optimization of electric energy and flexible ramping considering energy storage according to claim 1, characterized in that: The power system deterministic and non-deterministic ramping requirements include corresponding up-ramp requirements and down-ramp requirements respectively; the power system flexible ramping requirements are as follows: in, are the up-climbing demand and down-climbing demand in period t respectively; are the deterministic up-ramp demand and down-ramp demand in period t respectively; and are the uncertain up-ramp demand and down-ramp demand of the system in time period t, and T is the time period set.

3. The method for collaborative optimization of electric energy and flexible ramping considering energy storage according to claim 2, characterized in that: The deterministic ramp-up requirements are as follows: in, and are the deterministic up-ramp demand and down-ramp demand of the system in period t, respectively; L t 、L t+1 are the predicted net loads of the system in time periods t and t+1 respectively; J is the set of loads; P j,t is the predicted value of load j in period t; V is the set of new energy sources; P v,t is the predicted output value of the new energy v in period t.

4. The method for collaborative optimization of electric energy and flexible ramping considering energy storage according to claim 3 is characterized in that: The uncertain ramping requirement is constructed based on the deterministic ramping requirement as follows: in, and are the uncertain up-ramp demand and down-ramp demand of the power system in time period t, respectively; and are the upward safety margin and downward safety margin reserved for the system period t respectively; and are the contribution values ​​of the upward safety margin reserved by the power system for load and renewable energy in time period t respectively; and are the contribution values ​​of the downward safety margin reserved by the power system for load and renewable energy in period t; P j,t+1 is the predicted value of load j in period t+1; P v,t+1 is the output forecast value of the new energy source v in the period t+1; and are the upper and lower limits of the load j prediction error rate under the preset confidence level; and It is the upper and lower limits of the error rate of the new energy V output prediction under the preset confidence level.

5. The method for collaborative optimization of electric energy and flexible ramping considering energy storage according to claim 1, characterized in that: The objective function is established with the goal of minimizing the total loss of coal consumption of thermal power units and energy storage operation loss, which is expressed as follows: Among them, G is the set of thermal power units; B is the set of output stages of thermal power units; T is the set of time periods; λ i,b P is the coal consumption of thermal power unit i in the output section b; i,t,b is the power generation power of thermal power unit i in the bth output interval of time period t; c ess is the unit operating loss of energy storage; P t,dis 、P t,ch are the discharge power and charging power of the energy storage in time period t respectively.

6. The method for collaborative optimization of electric energy and flexible ramping considering energy storage according to claim 5, characterized in that: The set constraints include energy storage operation constraints, system ramping demand constraints, system power balance constraints, thermal power unit operation constraints, thermal power unit flexible ramping capacity constraints, and energy storage flexible ramping capacity constraints.

7. The method for collaborative optimization of electric energy and flexible ramping considering energy storage according to claim 6, characterized in that: The energy storage operation constraints are as follows: Among them, P ess,max is the rated power of energy storage; u t,dis and u t,ch is the discharge and charge state of the energy storage in time period t, which is a 0-1 variable; E t 、E t+1 are the amount of energy stored in time periods t and t+1 respectively; η dis and η ch are the discharge efficiency and charging efficiency of energy storage respectively; E min and E max The lower and upper limits of energy storage capacity.

8. The method for collaborative optimization of electric energy and flexible ramping considering energy storage according to claim 6, characterized in that: The system ramp requirement constraints are as follows: in, and The ramp-up capacity and ramp-down capacity provided for thermal power unit i; and Provides both ramp-up and ramp-down capacity for energy storage.

9. The method for collaborative optimization of electric energy and flexible ramping considering energy storage according to any one of claims 1 to 8, characterized in that: The output plan of the thermal power unit includes the generated power P of the thermal power unit i in the output interval b of the time period t. i,t,b ; The energy storage charging and discharging scheme includes the energy storage unit operating loss c ess , the discharge power P of the energy storage in time period t t,dis and charging power P t,ch , the discharge state u of the energy storage in time period t t,dis and charging status u t,ch ; The amount of energy stored in time period t is E t .

10. The method for collaborative optimization of electric energy and flexible ramping considering energy storage according to any one of claims 1 to 8, characterized in that: The target power grid's day-ahead power system operation data includes new energy and load data, thermal power unit data, and energy storage data; The new energy and load data include new energy output forecast value, load forecast value, new energy output forecast error rate and load forecast error rate.

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