System climbing demand evaluation and calling method for high-proportion new energy consumption

Abstract

The invention discloses a system climbing demand evaluation and calling method for high-proportion new energy consumption. The method comprises the following steps: step 1, determining a thermal power generating unit start-stop model based on a fan, photovoltaic and load prediction curve of a time period scale; 2, evaluating the output condition of the fire motor start-stop model in the sub-period based on the short-time scale prediction fan and photovoltaic data; 3, evaluating the climbing power vacancy of the system by calculating the climbing capacity and the climbing capacity based on the output data of the thermal power generating unit; 4, constructing an energy storage sequential constraint model based on the system climbing power vacancy; and step 5, evaluating the system power supply shortage, the wind and light discarding result and the calling cost based on the system climbing power vacancy. The multi-source cooperative climbing capacity evaluation model including thermal power, hydroelectric power and energy storage is constructed, and the deep peak regulation capability of the thermal power generating unit and the cooperative optimization mechanism of the energy storage rapid response characteristic are combined; and the extreme climbing event coping capability is obviously improved.

Description

Technical Field

[0001] The present invention belongs to the field of capacity assessment and coordinated optimization scheduling of power grids, and in particular relates to a system ramp demand assessment and calling method for accommodating a high proportion of new energy. Background Art

[0002] Against the backdrop of the "dual carbon" goals, China has proposed building a new power system with a gradually increasing proportion of renewable energy. The large-scale integration of renewable energy sources such as wind and solar power into the grid has led to increased volatility on the system's power generation side due to their difficulty in accurate prediction and regulation. Take wind power ramping events as an example: when long-term extreme weather events occur, wind farm power surges for a period of time, resulting in an up-ramp event. When a relative weather event occurs, or wind turbine speeds exceed the cut-out speed, causing some turbines to gradually shut down, wind farm power plummets for a period of time, resulting in a down-ramp event. Following a ramping event, the system experiences a short-term, severe imbalance in active power, which can easily lead to frequency violations and even load shedding or widespread power outages. The proper assessment and deployment of ramping capacity is crucial to the safe operation of the power grid.

[0003] Related studies have proposed the establishment of an online wind farm operating performance evaluation system based on a wide-area measurement system data platform to achieve online detection of wind farm grid access performance indicators. However, existing research rarely covers the risk assessment of ramping events caused by wind power output uncertainty. In addition, some studies have characterized the ramping capacity under different working conditions as an effective capacity based on probability distribution, and used this to evaluate the system's flexible ramping capability. Other studies have proposed a wind power time series scenario generation algorithm based on regular sampling that includes ramping events. A clustering algorithm is used to reduce the large number of generated wind power ramping scenarios, and ultimately obtain a typical scenario set that can reflect the probabilistic characteristics of wind power ramping events. In addition, some studies consider factors such as random shutdown of units, rated capacity, minimum technical output, and ramping rate, and use random production simulation methods to calculate the probability and expectation of insufficient ramping capacity of the unit, which is used to evaluate the system's flexible ramping capability.

[0004] Ramp capacity deployment is widely used in power systems, particularly for addressing renewable energy volatility, optimizing dispatch, and improving system flexibility and economic efficiency. One study optimized the dispatch of flexible ramp capacity on an hourly basis and proposed a probabilistic method for quantifying flexible ramping demand. Another study established a model for providing upward and downward flexible ramping capacity for thermal power units, effectively improving the system's wind power absorption capacity. Another study incorporated flexible ramping of photovoltaic output into power system economic dispatch models, accounting for the extreme uncertainty introduced by photovoltaics. Another study modeled the uncertainty range of wind power using an adjustable polygon approach, effectively adapting to the system's ramping needs.

[0005] Currently, flexible ramping resources include traditional thermal power units, microgrids, electric vehicles, demand response, energy storage, and new energy sources. In my country's energy system, traditional thermal power generation remains the primary production resource and has great potential to become a flexible ramping product. However, due to the current insufficient deep peak-shaving capability of traditional thermal power units and issues with combustion stability during peak-shaving, they still lack flexibility to a certain extent. Microgrids are seen as an effective organizational form for large-scale renewable energy access in the future, offering low costs, low emissions, and high flexibility. In the microgrid concept, through the collaboration of different energy sources, renewable energy generators and controllable energy sources jointly provide energy and ancillary services to large power plants. Therefore, microgrids are expected to play a more important role in improving system flexibility in the future.

[0006] Due to worsening environmental issues and the application of related technologies, the widespread adoption of electric vehicles is inevitable. Electric vehicles offer controllable charging and discharging times, locations, and power consumption. These resources can be effectively utilized through demand response and other methods, improving the flexibility and reliability of the power system. Demand response resources refer to demand-side loads, distributed generation (DGs), electric vehicles, and energy storage systems that can adjust their operating status according to demand response events. However, utilizing demand response comes at a cost and places great emphasis on whether policymakers have a deep understanding of user electricity usage habits. This ultimately determines whether a win-win situation can be achieved for both the grid and the user.

[0007] Compared to traditional units, energy storage units have attracted widespread attention due to their faster charging and discharging speeds, greater start-stop capabilities, and ability to serve as both a power source and a load. However, energy storage technology is still immature and investment costs are high. Therefore, using energy storage as a flexible ramping resource requires considering its advantages and disadvantages to ensure a rational allocation of storage resources.

[0008] One of the security risks that renewable energy poses to the power grid is its high uncertainty. Taking wind power as a typical example, despite its flexible adjustment capabilities, its opportunity cost as a flexible ramping product is relatively high. Because its availability cannot be accurately predicted, it can lead to further wind curtailment and losses. However, in areas with abundant wind resources, using wind power as a flexible ramping resource can replace other types of ramping resources at a very low cost. Summary of the Invention

[0009] The purpose of the present invention is to propose a method for optimizing the call of system ramp capacity for a high proportion of new energy consumption. This method effectively addresses the shortage of ramp capacity caused by the volatility of new energy by finely evaluating the system's dynamic ramp capacity and optimizing the call of various flexibility resources, thereby ensuring the safe and stable operation of the power grid and promoting the consumption of new energy.

[0010] In order to solve the problems of the prior art, the present invention adopts the following technical solutions:

[0011] A system ramp demand assessment and invocation method for high-proportion new energy consumption includes the following steps:

[0012] Step 1: Determine the start-stop model of thermal power units based on the wind turbine, photovoltaic and load forecast curves at the time scale;

[0013] Step 2: Based on the short-term predicted wind turbine and photovoltaic data, evaluate the output status of the thermal power start-stop model in the sub-period;

[0014] Step 3: Evaluate the system's ramp power shortage by calculating the ramp-up and ramp-down capacities based on the thermal power unit output data;

[0015] Step 4: Construct an energy storage sequential constraint model based on the system ramp power shortage;

[0016] Step 5: Evaluate the system power shortage, wind and solar curtailment results, and call costs based on the system ramp power shortage.

[0017] Furthermore, the process of determining the start-stop model of the thermal power unit based on the hourly scale wind turbine, photovoltaic and load forecast curves in step 1 includes:

[0018] The operating cost of a thermal power unit is calculated using the following formula:

[0019] ;

[0020] in, Represents the coal consumption cost, which is based on the active power output P of the unit in time period i i,t The fuel consumption cost function Cf i (.) is usually divided into smaller time intervals t within i and then summed up. CR i represents the ramping cost, which reflects the additional wear and efficiency loss costs caused by frequent or large changes in the unit's output, and is related to the ramping rate and power change. CU i represents the startup cost, which is the cost incurred from starting the unit from a shutdown state to grid-connected operation. CD i represents the shutdown cost, which is the cost incurred from decoupling the unit from an operating state to shutdown.

[0021] If the thermal power unit is running at time t-1 and shut down at time t, then the time constraint of the thermal power unit must be constructed according to the following formula to ensure the shutdown state within TS consecutive time periods starting from time t and ending at time t+TS-1;

[0022] ;

[0023] If the thermal power unit is shut down at time t-1 and started at time t, then the time constraint of the thermal power unit must be constructed according to the following formula to ensure the running state within TO consecutive time periods starting from time t and ending at time t+TO-1;

[0024] ;

[0025] Among them, u i,k represents the start and stop state variable of the unit in time period k; u i,k = 1 means running, u i,k = 0 means shutdown; TS means the minimum continuous shutdown time of the unit, TO means the minimum continuous operation time of the unit;

[0026] The start-up and shutdown costs of thermal power plants are constrained according to the following formula:

[0027] ;

[0028] ;

[0029] Among them, CU i,t represents the startup cost of the unit in period t, CD i,t represents the shutdown cost of the unit in period t, and H i Represents the one-time startup cost of the unit; if startup occurs, CU i,t is at least H i ; If not started, it is 0; J i represents the cost of a shutdown of the unit; if a shutdown occurs, CD i,t is at least J i ; If not shut down, it is 0.

[0030] Furthermore, in step 2, the output status of the thermal power generator start-stop model in the sub-period is evaluated based on the short-term predicted wind turbine and photovoltaic data; the steps include:

[0031] The output of the running unit is evaluated by the following formula:

[0032] ;

[0033] Among them, P i represents the planned active power output of thermal power unit i; P i,min represents the actual minimum technical output of thermal power unit i under the current operating conditions, reflecting its deep peak regulation capability; P i,max It represents the actual maximum technical output of thermal power unit i under the current operating conditions;

[0034] The following formula is used to limit the sudden power change of the unit at the moment of startup or shutdown:

[0035] ;

[0036] ;

[0037] ;

[0038] Among them, P i,t represents the active power output of thermal power unit i in sub-period t; R u Indicates the maximum ramp rate of thermal power unit i; indicates the maximum power increase allowed per unit time; R d Indicates the maximum ramp rate of thermal power unit i; indicates the maximum power reduction allowed per unit time; S i,u It indicates the maximum power jump allowed for thermal power unit i at the moment of startup, which is usually allowed to rise rapidly from 0 to a certain value within the first period after the startup command is issued; S i,d It indicates the maximum power jump allowed for thermal power unit i at the moment of shutdown. It is usually allowed to drop quickly from a certain value to 0 within the first period after the shutdown command is issued.

[0039] The system power flow safety is estimated by the following formula:

[0040] ;

[0041] Among them, P l,t represents the actual active power flow of transmission line l in sub-period t; P l,min It represents the minimum active power flow allowed by the transmission line l, usually referring to the limit of reverse power flow or stability limit; P l,max It represents the maximum active power flow allowed by the transmission line l, that is, its thermal stability or transient stability limit;

[0042] The system power balance is estimated using the following formula:

[0043]

[0044] Where PT i,t represents the active power output of thermal power unit i in sub-period t; PW i,t represents the actual available active power output of wind farm i in sub-period t; PPV i,t represents the actual available active power output of photovoltaic power station i in sub-period t; PPH i,t represents the active power output of hydropower station / pumped storage power station i in sub-period t; PPC i,t represents the total wind and solar power curtailment caused by insufficient system regulation capacity in sub-period t; PLS i,t represents the total load shedding power caused by user load i in sub-period t due to system power shortage. It represents the total active load demand of the system in sub-period t, which is the sum of the power demands of all load points i.

[0045] Furthermore, the step three is to evaluate the system-level ramp power shortage process by calculating the ramp-up and ramp-down capacities based on the thermal power unit output data, including:

[0046] The ramp capacity model is constructed by the following formula:

[0047] ;

[0048] Among them, URC avail It represents the available ramp capacity of the thermal power unit at the current operating point and within the evaluation time window; Rupmax represents the theoretical maximum ramp rate of the thermal power unit; it is determined by the physical characteristics of the main equipment such as the boiler and steam turbine and is an inherent property of the unit; P max Indicates the actual maximum technical output of the thermal power unit under the current operating conditions; affected by factors such as ambient temperature and auxiliary equipment status, it may be lower than the nameplate rated capacity; P current Indicates the current active power output of the thermal power unit; ∆t represents the ramp evaluation / dispatch time interval, which is usually consistent with the time resolution of system dispatch; this parameter defines the response time window for ramp capacity calculation;

[0049] The down-slope capacity model is constructed by the formula:

[0050] ;

[0051] Among them, DRC avail It represents the available down-ramp capacity of the thermal power unit at the current operating point and within the evaluation time window; Rdown max represents the theoretical maximum down-ramp rate of the thermal power unit, which is also determined by the physical limit of the equipment; P min Indicates the actual minimum technical output of the thermal power unit under the current operating conditions;

[0052] The total ramp capacity of the system is constructed according to the following formula

[0053]

[0054] in, Represents the total available ramp capacity of the system in sub-period t; it is composed of the sum of the ramp capacities of all available thermal power units; Represents the total available ramp-down capacity of the system in sub-period t; it is composed of the sum of the ramp-down capacities of all available thermal power units; Represents the set of available units; refers to the online thermal power units that are in stable operation during sub-period t and meet the minimum stable operation time requirement. ,i represents the available ramp capacity of available unit i, ,i represents the available down-ramp capacity of available unit i.

[0055] Furthermore, a sequential energy storage constraint model is constructed based on the system-level ramp power shortage, including the following steps:

[0056] The system energy storage loss cost is estimated using the following formula:

[0057] ;

[0058] ;

[0059] Where W represents the total discharge of the energy storage device during the scheduling period; represents the total dischargeable amount of energy storage device i during its entire life cycle, represents the rated charge and discharge efficiency of energy storage device i, represents the average discharge depth of energy storage device i, represents the rated cycle number of energy storage device i, represents the rated ampere-hour capacity of energy storage device i, and V represents the rated voltage of energy storage device i;

[0060] The system energy storage is constrained by the following formula:

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] in, represents the amount of energy stored at the end of time period t; represents the planned charging power of the energy storage at time period t; represents the planned discharge power of the energy storage in time period t; η represents the charging and discharging efficiency of the energy storage; Indicates the initial amount of energy storage at the beginning of the dispatch cycle; Indicates the target energy storage capacity at the end of the dispatch cycle. The information in brackets refers to the energy storage capacity for 1 hour and 24 hours when calculated on a daily basis. and represents the planned charge / discharge power of the energy storage at time period t; and Indicates the maximum allowable charge / discharge power of energy storage; represents the discharge capacity of battery i, and Respectively represent the lower and upper limits of battery discharge capacity;

[0066] The system's new energy output is constrained by the following formula:

[0067]

[0068]

[0069] in, Indicates the maximum wind power output, Indicates the maximum photovoltaic output.

[0070] Furthermore, the process of evaluating the system power shortage, wind and solar curtailment results, and call costs based on the system ramp power shortage in step 5 includes:

[0071] The penalty cost for curtailing wind and solar power is constructed according to the following formula:

[0072]

[0073] Where λ represents the penalty cost factor for wind and solar power curtailment; this factor is set by government policies or market rules and is used to quantify the economic losses caused by the abandonment of renewable energy power generation;

[0074] The load shedding penalty cost is constructed according to the following formula:

[0075]

[0076] Among them, θ represents the load shedding penalty cost factor, which is used to characterize the huge economic losses and social impacts caused by power outages to users.

[0077] Beneficial effects

[0078] To address the system ramping capacity shortage caused by the integration of a high proportion of renewable energy, this paper proposes a refined assessment method for grid ramping capacity and multi-source collaborative optimization scheduling. By constructing a multi-source collaborative ramping capacity assessment model encompassing thermal power, hydropower, and energy storage, this method achieves the first quantitative characterization of the system's dynamic ramping capacity. Combining the deep peak-shaving capabilities of thermal power units with the rapid response characteristics of energy storage, this collaborative optimization mechanism significantly enhances the ability to respond to extreme ramping events. In practical applications, only conventional system parameters (load curve, predicted renewable energy output, unit ramping rate, and energy storage characteristics) need to be input to generate a dispatch plan that balances safety and economy. This method can effectively reduce the risk of ramping shortages and increase renewable energy absorption in high-penetration wind power systems. Its core advantages include: 1) transcending the limitations of traditional static ramping assessments by establishing a dynamic capacity calculation model that adapts to unit operating points; 2) achieving efficient utilization of ramping resources through the collaborative optimization of demand response, inter-provincial mutual assistance, and energy storage sequential constraints; and 3) quantifying the penalty costs for wind and solar curtailment and load shedding, thereby improving operational economy while ensuring system safety. The present invention provides theoretical support for flexible resource planning and real-time scheduling of new power systems and has significant engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a flow chart of the system ramp capacity optimization calling method for accommodating a high proportion of new energy. DETAILED DESCRIPTION

[0080] The following is combined with Figure 1 The present invention is described as follows:

[0081] The present invention provides a system ramp demand assessment and calling method for high-proportion new energy consumption, comprising:

[0082] ① A multi-source coordinated ramping capacity assessment model, encompassing thermal power, hydropower, and energy storage, was established to quantify the system's dynamic ramping capability in response to power fluctuations at specific moments and within specific time windows, providing core input parameters for optimized scheduling.

[0083] ② A ramp capacity optimization method based on the evaluation results is proposed. This method comprehensively considers the characteristics and constraints of various ramp resources and develops an economical and efficient scheduling plan to deal with ramp events.

[0084] ③ Taking into account the key physical constraints of system operation, including unit output boundaries, ramp rate limits, start-stop times, power balance, power flow safety, and energy storage sequence constraints, to ensure the feasibility and safety of the scheduling plan. This invention will dynamically determine the system ramp capacity optimization call method for high-proportion new energy consumption through the following five steps: including:

[0085] Step 1: Determine the start-stop model of thermal power units based on the wind turbine, photovoltaic and load forecast curves at the time scale;

[0086] 1. Objective function:

[0087] The objective function is to minimize the comprehensive operating cost of the system during the total operating period. This cost mainly includes the following parts:

[0088]

[0089] Where CTi represents the operating cost of the thermal power unit. CBi represents the energy storage equipment loss cost, reflecting the aging loss of batteries during the charge and discharge cycle. CPCi represents the wind and solar curtailment penalty cost, which is the penalty for forced reduction of renewable energy (wind power, photovoltaic) power generation due to insufficient system regulation capacity or scheduling strategies. CLSi represents the load shedding penalty cost, which is the penalty for forced reduction of user load due to system power shortages. N represents the total operating period.

[0090] 2. Decomposition of operating costs of thermal power units:

[0091] The operating cost of thermal power units is the main component of system costs, and its calculation needs to consider:

[0092]

[0093] in, Represents the coal consumption cost, which is based on the active power output P of the unit in time period i i,t The fuel consumption cost function Cf i (.) is typically calculated and summed over time intervals t within the time interval i. CR i represents the ramping cost, reflecting the additional wear and efficiency losses incurred by frequent or drastic changes in the unit's output. This cost is related to the ramping rate and power change. CU i represents the startup cost, which is the cost of starting a unit from a shutdown state to grid-connected operation. CD i represents the shutdown cost, which is the cost of de-energizing a unit from an operating state to shutdown.

[0094] Based on hourly wind turbine output forecast curves, PV output forecast curves, and system load forecast curves, combined with the operating constraints of the thermal power units (described below), the start and stop status of each thermal power unit in each time period i is optimized. This step is typically completed during day-ahead or intraday rolling scheduling, laying the foundation for more refined output optimization later.

[0095] 3. Unit start and stop time constraints:

[0096]

[0097]

[0098] Among them, u i,k Indicates the start and stop state variables of the unit in time period k. i,k = 1 means running, u i,k = 0 means shutdown. TS represents the minimum continuous shutdown time of the unit, and TO represents the minimum continuous operation time of the unit. This means that if the unit is running at time t-1 and shut down at time t, then the unit must be shut down for TS consecutive periods starting from time t and ending at time t+TS-1. The actual number of shutdown periods calculated on the left side of the inequality must be greater than or equal to the required TS. This means that if the unit is shut down at time t-1 and started up at time t, then the unit must be running for TO consecutive time periods, starting at time t and ending at time t+TO-1. The actual number of operating hours within this time period, calculated on the left side of the inequality, must be greater than or equal to the required TO.

[0099] 4. Start-stop fee constraints:

[0100]

[0101]

[0102] Among them, CU i,t represents the startup cost of the unit in period t, CD i,t represents the shutdown cost of the unit in period t, and H i Represents the startup cost of the unit. If startup occurs, CU i,t is at least H i ; If not started, it is 0. J i represents the cost of a shutdown of the unit. If a shutdown occurs, CD i,t is at least J i ; If not shut down, it is 0.

[0103] Step 2: Evaluate the output status of the thermal power generation start-stop model in the sub-period based on the short-time scale predicted wind turbine and photovoltaic data;

[0104] Based on the start / stop status of each thermal power unit in each time period i determined in step 1, this step utilizes more precise 15-minute wind and photovoltaic power forecast data to optimize the active power output plan for each operational thermal power unit within each 15-minute sub-period t. This step must meet key constraints such as unit operating boundaries, ramp rates, system power balance, and power flow safety, and consider that the start / stop status is fixed.

[0105] 1. Unit output constraints:

[0106] Ensure that the output of the running unit is between the minimum output and the maximum output allowed by technology. The output of the shut-down unit is 0.

[0107]

[0108] Among them, P i Represents the planned active power output of thermal power unit i. i,min It represents the actual minimum technical output of thermal power unit i under the current operating conditions, reflecting its deep peak regulation capability. i,max It represents the actual maximum technical output of thermal power unit i under the current operating conditions.

[0109] 2. Unit climbing constraints:

[0110] Limit the power change of the unit between two consecutive sub-periods t-1 and t, ensuring that its rate of change does not exceed the maximum ramp-up and ramp-down rates allowed by the physical device. This constraint also considers the power surge limit at the moment of startup or shutdown (although the startup and shutdown states are fixed in this step, the power change still needs to meet the rate constraint).

[0111]

[0112]

[0113]

[0114] Among them, P i,t Represents the active power output of thermal power unit i in sub-period t. u Indicates the maximum ramp rate of thermal power unit i. Indicates the maximum power increase allowed per unit time (a 15-minute sub-period in this model). R d Indicates the maximum ramp rate of thermal power unit i. Indicates the maximum power reduction allowed per unit time. S i,u It indicates the maximum power jump allowed for thermal power unit i at the moment of startup, usually allowed to rise rapidly from 0 to a certain value within the first period after the startup command is issued. i,d It represents the maximum power jump allowed for thermal power unit i at the moment of shutdown. It is usually allowed to drop quickly from a certain value to 0 within the first period after the shutdown command is issued.

[0115] 3. Current safety constraints:

[0116] Ensure that the system power flow distribution after optimized scheduling will not cause the transmission power of any transmission line to exceed its safety limit, preventing line overload.

[0117]

[0118] Among them, P l,t P represents the actual active power flow of transmission line l in sub-period t. l,min Indicates the minimum active power flow allowed on the transmission line l, usually referring to the limit of reverse power flow or stability limit. l,max It represents the maximum active power flow allowed for transmission line l, that is, its thermal stability or transient stability limit.

[0119] 4. System power balance:

[0120] This is the core equality constraint for optimal scheduling, ensuring that in any sub-period, the total generated power of all power sources in the system (thermal power, wind power, photovoltaic power, hydropower / pumped storage), minus the wind and solar power curtailment and load shedding power caused by insufficient regulation capacity, must be equal to the total load demand of the system.

[0121]

[0122] Where PT i,t represents the active power output of thermal power unit i in subperiod t. PW i,t represents the actual available active power output of wind farm i in subperiod t. PPV i,t represents the actual available active power output of photovoltaic power station i in subperiod t. PPH i,t represents the active power output of hydropower station / pumped-storage power station i in subperiod t (for pumped-storage, power generation is positive and pumping is negative. This model assumes that its output plan may also be an optimization variable or given value in this step). PPC i,t represents the total wind and solar power curtailment caused by insufficient system regulation (lack of ramping capacity or congestion) in subperiod t. PLSi,t represents the total load shedding caused by user load i in subperiod t due to insufficient system power. It represents the total active load demand of the system in sub-period t, which is the sum of the power demands of all load points i.

[0123] Step 3: Evaluate the system's ramp power shortage by calculating the ramp-up and ramp-down capacities based on the thermal power unit output data;

[0124] Based on the active power output plan for each sub-period determined for all online thermal power units in Step 2, the core task of this step is to calculate the dynamic available ramping capacity of each thermal power unit at its current operating point. This capacity is limited by the unit's physical ramping rate and the power variation space from the current output point to its technical output boundary. The total up-ramp capacity and total down-ramp capacity in the sub-period are calculated. The 15-minute forecast data (wind power, photovoltaic power, and load) used in Step 2 are compared with the current system net load change demand (ramp events). Combined with the calculated total system thermal power ramping capacity, the system is assessed to determine whether it faces the risk of insufficient up-ramp capacity or insufficient down-ramp capacity in the next period.

[0125] 1. Up-scaling capacity model:

[0126]

[0127] Among them, URC avail Indicates the available ramp capacity of the thermal power unit at the current operating point and within the evaluation time window. Rupmax represents the theoretical maximum ramp rate of the thermal power unit. It is determined by the physical characteristics of the main equipment such as the boiler and steam turbine (such as thermal stress limit) and is an inherent property of the unit. max Indicates the actual maximum technical output of the thermal power unit under the current operating conditions. Affected by factors such as ambient temperature and auxiliary equipment status, it may be lower than the nameplate rated capacity. current Indicates the current active power output of the thermal power unit. ∆t represents the ramp evaluation / dispatch interval. This is typically consistent with the time resolution of system dispatch. This parameter defines the response time window for ramp capacity calculations.

[0128] 2. Downhill capacity model:

[0129]

[0130] Among them, DRC avail Indicates the available ramp-down capacity of the thermal power unit at the current operating point and within the evaluation time window. Rdown max indicates the theoretical maximum ramp-down rate of the thermal power unit. This is also determined by the physical limits of the equipment. min This represents the actual minimum technical output of a thermal power unit under current operating conditions. Especially in scenarios with a high proportion of renewable energy, it is crucial to determine how low its deep peak-shaving capacity can be reduced.

[0131] 3. System total climbing capacity

[0132]

[0133] Where URCthermalsys represents the total available ramp-up capacity of the system in sub-period t. It is composed of the sum of the ramp-up capacities of all available thermal power units. DRCthermalsys represents the total available ramp-down capacity of the system in sub-period t. It is composed of the sum of the ramp-down capacities of all available thermal power units. on Represents the set of available units. Refers to the online thermal power units that are in stable operation (not starting, stopping, or warming up) during sub-period t and meet the minimum stable operation time requirement. URC avail,i It represents the available ramp capacity of the available unit i. avail,i represents the available down-ramp capacity of the available unit i.

[0134] Step 4: Construct an energy storage sequential constraint model based on the system ramp power shortage;

[0135] When the system is assessed as having a ramp power shortfall in step three, the core task of this step is to develop a cost-effective scheduling plan to compensate for the ramp power shortfall based on the technical characteristics of various flexible resources, available capacity quota information, and call priority rules. This call process must strictly adhere to the physical constraints of various resources, especially the sequential state of charge constraints of energy storage, to ensure safe, reliable, and economical access to ramp capacity.

[0136] 1. Energy storage loss cost:

[0137]

[0138]

[0139] Where W represents the total discharge of the energy storage device during the scheduling period; represents the total dischargeable amount of energy storage device i during its entire life cycle, represents the rated charge and discharge efficiency of energy storage device i, represents the average discharge depth of energy storage device i, represents the rated cycle number of energy storage device i, represents the rated ampere-hour capacity of energy storage device i, and V represents the rated voltage of energy storage device i.

[0140] 2. Energy storage constraints:

[0141] ;

[0142]

[0143]

[0144]

[0145] Among them, W t represents the amount of energy stored at the end of time period t. PCt represents the planned charging power of the energy stored in time period t. PBt represents the planned discharge power of the energy stored in time period t. η represents the charging and discharging efficiency of the energy stored. W begin Indicates the initial amount of energy stored at the beginning of the scheduling period. W end The target energy storage capacity at the end of the scheduling period is represented by the target energy storage capacity for 1 hour and 24 hours when calculated on a daily basis. PCi and PBi represent the planned charge / discharge power of the energy storage during time period t. PCmax and PBmax represent the maximum allowable charge / discharge power of the energy storage. WBi represents the discharge capacity of battery i, and WBmin and WBmax represent the lower and upper limits of the battery discharge capacity, respectively.

[0146] 3. Constraints on new energy output

[0147]

[0148]

[0149] Among them, Pw max represents the maximum wind power output, and PPV max represents the maximum photovoltaic output.

[0150] Step 5: Evaluate the system power shortage, wind and solar curtailment results, and deployment costs based on the system ramp power shortage:

[0151] After completing the multi-source ramping optimization in step 4, the core task of this step is to quantitatively evaluate the effectiveness of the entire scheduling plan. This evaluation focuses on three key aspects: system power supply reliability; renewable energy absorption level; and comprehensive economic costs. This evaluation not only verifies the effectiveness of the scheduling plan but also provides key performance indicators for system operational decision-making and feedback for subsequent scheduling cycles or resource planning.

[0152] 1. Penalty costs for curtailing wind and solar power

[0153]

[0154] Where λ represents the penalty cost factor for curtailed wind and solar power generation. This factor, set by government policies or market rules, quantifies the economic losses from abandoned renewable energy generation.

[0155] 2. Load Shedding Penalty Cost

[0156]

[0157] Here, θ represents the load shedding penalty cost factor. This factor is usually set very high (much higher than the electricity price) to reflect the huge economic losses and social impact caused by power outages to users.

[0158] Although the present invention has been described above, the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can make many variations without departing from the purpose of the present invention, and these are all protected by the present invention.

Claims

1. A system ramp demand assessment and call method for high-proportion new energy consumption, characterized in that: The steps include: Step 1: Determine the start-stop model of thermal power units based on the wind turbine, photovoltaic and load forecast curves at the time scale; Step 2: Based on the short-term predicted wind turbine and photovoltaic data, evaluate the output status of the thermal power start-stop model in the sub-period; Step 3: Evaluate the system's ramp power shortage by calculating the ramp-up and ramp-down capacities based on the thermal power unit output data; Step 4: Construct an energy storage sequential constraint model based on the system ramp power shortage; Step 5: Evaluate the system power shortage, wind and solar curtailment results, and call costs based on the system ramp power shortage.

2. According to claim 1, a system ramp demand assessment and calling method for high-proportion new energy consumption is characterized in that: The method comprises the following steps: the process of determining the start-stop model of the thermal power unit based on the hourly scale wind turbine, photovoltaic and load forecast curves in step 1 includes: The operating cost of a thermal power unit is calculated using the following formula: ； in, Represents the coal consumption cost, which is based on the active power output P of the unit in time period i i,t The fuel consumption cost function Cf i (.) is usually divided into smaller time intervals t within i and then summed up. CR i represents the ramping cost, which reflects the additional wear and efficiency loss costs caused by frequent or large changes in the unit's output, and is related to the ramping rate and power change. CUi represents the startup cost, which is the cost incurred from starting the unit from a shutdown state to grid-connected operation. CD i represents the shutdown cost, which is the cost incurred from decoupling the unit from an operating state to shutdown. If the thermal power unit is running at time t-1 and shut down at time t, then the time constraint of the thermal power unit must be constructed according to the following formula to ensure the shutdown state within TS consecutive time periods starting from time t and ending at time t+TS-1; ； If the thermal power unit is shut down at time t-1 and started at time t, then the time constraint of the thermal power unit must be constructed according to the following formula to ensure the running state within TO consecutive time periods starting from time t and ending at time t+TO-1; ； Among them, u i,k represents the start and stop state variable of the unit in time period k; u i,k = 1 means running, u i,k = 0 means shutdown; TS means the minimum continuous shutdown time of the unit, TO means the minimum continuous operation time of the unit; The start-up and shutdown costs of thermal power plants are constrained according to the following formula: ； ； Among them, CU i,t represents the startup cost of the unit in period t, CD i,t represents the shutdown cost of the unit in period t, and H i Represents the one-time startup cost of the unit; if startup occurs, CU i,t is at least H i ; If not started, it is 0; J i represents the cost of a shutdown of the unit; if a shutdown occurs, CD i,t is at least J i ; If not shut down, it is 0.

3. The system ramp demand assessment and call method for high-proportion new energy consumption according to claim 1 is characterized in that: The step 2 of evaluating the output status of the thermal power generator start-stop model in the sub-period based on the short-time scale predicted wind turbine and photovoltaic data includes the following steps: The output of the running unit is evaluated by the following formula: ； Among them, P i represents the planned active power output of thermal power unit i; P i,min represents the actual minimum technical output of thermal power unit i under the current operating conditions, reflecting its deep peak regulation capability; P i,max It represents the actual maximum technical output of thermal power unit i under the current operating conditions; The following formula is used to limit the sudden power change of the unit at the moment of startup or shutdown: ； ； ； Among them, P i,t represents the active power output of thermal power unit i in sub-period t; R u Indicates the maximum ramp rate of thermal power unit i; indicates the maximum power increase allowed per unit time; R d Indicates the maximum ramp rate of thermal power unit i; indicates the maximum power reduction allowed per unit time; S i,u It indicates the maximum power jump allowed for thermal power unit i at the moment of startup, which is usually allowed to rise rapidly from 0 to a certain value within the first period after the startup command is issued; S i,d It indicates the maximum power jump allowed for thermal power unit i at the moment of shutdown. It is usually allowed to drop quickly from a certain value to 0 within the first period after the shutdown command is issued. The system power flow safety is estimated by the following formula: ； Among them, P l,t represents the actual active power flow of transmission line l in sub-period t; P l,min It represents the minimum active power flow allowed by the transmission line l, usually referring to the limit of reverse power flow or stability limit; P l,max It represents the maximum active power flow allowed by the transmission line l, that is, its thermal stability or transient stability limit; The system power balance is estimated using the following formula: ； Where PT i,t represents the active power output of thermal power unit i in sub-period t; PW i,t represents the actual available active power output of wind farm i in sub-period t; PPV i,t represents the actual available active power output of photovoltaic power station i in sub-period t; PPH i,t represents the active power output of hydropower station / pumped storage power station i in sub-period t; PPC i,t represents the total wind and solar power curtailment caused by insufficient system regulation capacity in sub-period t; PLS i,t represents the total load shedding power caused by user load i in sub-period t due to system power shortage. It represents the total active load demand of the system in sub-period t, which is the sum of the power demands of all load points i.

4. The system ramp demand assessment and call method for high-proportion new energy consumption according to claim 1 is characterized in that: The step 3 is a process of evaluating the system-level ramp power shortage by calculating the ramp-up and ramp-down capacities based on the thermal power unit output data, including: The ramp capacity model is constructed by the following formula: ； Among them, URC avail It represents the available ramp capacity of the thermal power unit at the current operating point and within the evaluation time window; Rup max represents the theoretical maximum ramp rate of the thermal power unit; it is determined by the physical characteristics of the main equipment such as the boiler and steam turbine and is an inherent property of the unit; P max Indicates the actual maximum technical output of the thermal power unit under the current operating conditions; affected by factors such as ambient temperature and auxiliary equipment status, it may be lower than the nameplate rated capacity; P current Indicates the current active power output of the thermal power unit; ∆t represents the ramp evaluation / dispatch time interval, which is usually consistent with the time resolution of system dispatch; this parameter defines the response time window for ramp capacity calculation; The down-slope capacity model is constructed by the formula: ; Among them, DRC avail It represents the available down-ramp capacity of the thermal power unit at the current operating point and within the evaluation time window; Rdownmax represents the theoretical maximum down-ramp rate of the thermal power unit, which is also determined by the physical limit of the equipment; P min Indicates the actual minimum technical output of the thermal power unit under the current operating conditions; The total ramp capacity of the system is constructed according to the following formula ； in, Represents the total available ramp capacity of the system in sub-period t; it is composed of the sum of the ramp capacities of all available thermal power units; Represents the total available ramp-down capacity of the system in sub-period t; it is composed of the sum of the ramp-down capacities of all available thermal power units; Represents the set of available units; refers to the online thermal power units that are in stable operation during sub-period t and meet the minimum stable operation time requirement. ,i represents the available ramp capacity of available unit i, ,i represents the available down-ramp capacity of available unit i.

5. The system ramp demand assessment and invocation method for high-proportion new energy consumption according to claim 1 is characterized by: The energy storage sequential constraint model is constructed based on the system-level ramp power shortage, including the following steps: The system energy storage loss cost is estimated using the following formula: ; ; Where W represents the total discharge of the energy storage device during the scheduling period; represents the total dischargeable amount of energy storage device i during its entire life cycle, represents the rated charge and discharge efficiency of energy storage device i, represents the average discharge depth of energy storage device i, represents the rated cycle number of energy storage device i, represents the rated ampere-hour capacity of energy storage device i, and V represents the rated voltage of energy storage device i; The system energy storage is constrained by the following formula: ; ; ; ; in, represents the amount of energy stored at the end of time period t; represents the planned charging power of the energy storage at time period t; represents the planned discharge power of the energy storage in time period t; η represents the charging and discharging efficiency of the energy storage; Indicates the initial amount of energy storage at the beginning of the dispatch cycle; Indicates the target energy storage capacity at the end of the dispatch cycle. The information in brackets refers to the energy storage capacity for 1 hour and 24 hours when calculated on a daily basis. and represents the planned charge / discharge power of the energy storage at time period t; and Indicates the maximum allowable charge / discharge power of energy storage; represents the discharge capacity of battery i, and Respectively represent the lower and upper limits of battery discharge capacity; The system's new energy output is constrained by the following formula: ； ； in, Indicates the maximum wind power output, Indicates the maximum photovoltaic output.

6. The system ramp demand assessment and invocation method for high-proportion new energy consumption according to claim 1 is characterized by: The process of evaluating the system power shortage, wind and solar curtailment results, and call costs based on the system ramp power shortage in step 5 includes: The penalty cost for curtailing wind and solar power is constructed according to the following formula: ； Where λ represents the penalty cost factor for wind and solar power curtailment; this factor is set by government policies or market rules and is used to quantify the economic losses caused by the abandonment of renewable energy power generation; The load shedding penalty cost is constructed according to the following formula: ； Among them, θ represents the load shedding penalty cost factor, which is used to characterize the huge economic losses and social impacts caused by power outages to users.

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