Unit spot goods electric energy and frequency modulation combined optimization clearing method

Abstract

The invention discloses a unit spot electric energy and frequency modulation joint optimization clearing method, which belongs to the technical field of power market operation and comprises the following steps: acquiring unit basic parameters and spot electric energy market demand data, and calculating to obtain an electric energy clearing theoretical optimization value; based on the electric energy clearing theoretical optimization value, judging the electric energy clearing feasibility of each unit, and generating an electric energy clearing feasibility signal; obtaining frequency modulation service demand data based on the electric energy clearing feasible signal, and calculating to obtain a frequency modulation service optimization theoretical value; based on the frequency modulation service optimization theoretical value, judging the frequency modulation service capability of the unit, and generating a frequency modulation service capability signal; on the basis of theoretical optimization data, electric energy and frequency modulation combined operation constraint data of each unit are obtained, and a combined optimization clearing target value is obtained through calculation; and based on the joint optimization clearing target value, generating a joint clearing decision signal, and realizing collaborative optimization of the electric energy and the frequency modulation service.

Description

Technical Field

[0001] This invention relates to the field of power market operation technology, specifically to a method for joint optimization and clearing of spot electricity from generating units and frequency regulation. Background Technology

[0002] With the trend of coordinated development of the electricity spot market and the ancillary services market, the synergy between the clearing of generating unit power and the optimization of frequency regulation services is crucial to ensuring the safe and stable operation of the power system and improving the efficiency of resource allocation.

[0003] Currently, existing technologies often separate energy clearing from frequency regulation service optimization: they calculate energy clearing results solely based on load forecasts from the energy market and network constraint parameters, or configure frequency regulation services based solely on the total frequency regulation capacity required by the system and response time requirements, without fully considering basic parameters such as unit generation cost coefficients and output limits. This leads to constraint conflicts between the two, such as the energy clearing result potentially exceeding the unit output limit, or the frequency regulation service configuration failing to meet the energy supply demand, resulting in poor clearing feasibility.

[0004] Meanwhile, existing methods do not integrate the electricity market clearing price and frequency regulation service price for joint target optimization, making it difficult to balance economy and service quality. This results in problems such as high system operating costs and low resource utilization, and cannot meet the needs of refined operation of the electricity market. There is an urgent need for a joint optimization clearing scheme that takes into account both feasibility and economy. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for joint optimization and clearing of spot electrical energy and frequency regulation of generating units, so as to solve the problems mentioned in the background art.

[0006] The objective of this invention can be achieved through the following technical solution: a method for joint optimization and clearing of spot electrical energy and frequency regulation of generating units, comprising: Step 1: Obtain the basic parameters of the generating unit and the spot market demand data for electricity, and calculate the theoretical optimization value for electricity clearing; among which, the basic parameters of the generating unit include the generation cost coefficient, the upper and lower limits of the output limit; the spot market demand data for electricity includes the load forecast value and network constraint parameters; Step 2: Based on the theoretical optimization value of energy clearing, determine the feasibility of energy clearing for each unit and generate an energy clearing feasibility signal; the energy clearing feasibility signal includes an energy clearing feasible signal and an energy clearing infeasibility signal. Step 3: Based on the feasibility signal of energy clearing, obtain frequency modulation service demand data and calculate the theoretical value of frequency modulation service optimization; among which, the frequency modulation service demand data includes the total frequency modulation capacity required by the system, the frequency modulation response time requirement, and the frequency modulation accuracy; Step 4: Based on the theoretical value of frequency regulation service optimization, determine the unit's frequency regulation service capability and generate a frequency regulation service capability signal; among which, the frequency regulation service capability signal includes a normal frequency regulation service capability signal and an abnormal frequency regulation service capability signal; Step 5: Based on the theoretical optimization data, obtain the joint operation constraint data of electrical energy and frequency regulation for each unit, and calculate the joint optimization clearing target value; among which, the theoretical optimization data includes the theoretical optimization value of electrical energy clearing, the electrical energy market clearing price, the theoretical optimization value of frequency regulation service, and the frequency regulation service price; Step 6: Generate a joint clearing decision signal based on the joint optimization clearing target value.

[0007] Preferably, step one includes the following: S11: Set the clearing cycle and the total number of cycles, determine the total optimization duration, and form a cycle sequence; where the clearing cycle is the time granularity of the electricity market; S12: Collect basic unit parameters from the unit ledger, including power generation cost coefficient, upper and lower limits of output limit; The power generation cost is calculated using a quadratic function Ci(pi,t)=ai×pi(t)²+bi×pi(t)+ci, where i represents different units, Ci(pi,t) is the total output cost of unit i in period t, ai, bi, and ci are the power generation cost parameters of unit i, and pi(t) is the output of unit i in period t. The upper limit of the output limit is the maximum rated output of the unit, and the lower limit of the output limit is the minimum technical output of the unit. S13: Obtain spot electricity market demand data for each period, including load forecasts and network constraint parameters; S14: Construct an energy optimization clearing model and analyze each of the generating units participating in the market; the objective function of the energy optimization clearing model is to minimize the sum of the power generation costs of each unit in all cycles, including the start-up and shutdown costs of each unit; the energy constraints are power balance constraints, upper and lower limits of unit output limits, and network security constraints. S15: Solve the above optimization model using a quadratic programming algorithm to obtain the optimal output and minimum power generation cost of each unit in each cycle, which are denoted as the theoretical optimization value of power energy clearing and the power energy market clearing price, respectively, i.e., the power energy optimization clearing result.

[0008] Preferably, the method for determining the feasibility of clearing electrical energy for each unit is as follows: Obtain the theoretically optimized value of the power clearing for each unit in each cycle, denoted as {Pi(t)|t=1,2,...,T}, where T is the total number of cycles; where if Pi(t)>0, then unit i is in the operating state, and if Pi(t)=0, then unit i is in the shutdown state; The maximum ramp rate of each unit is obtained. Based on the operating status, the absolute value of the output change of the theoretical optimization value of the power energy clearing of each unit in adjacent cycles is calculated and compared with the maximum ramp rate of each unit to determine the feasibility of power energy clearing of each unit. If the absolute value of the output change of the theoretically optimized value of power energy clearing in adjacent cycles of each unit is less than or equal to its maximum ramp rate, a power energy clearing feasible signal is generated; otherwise, a power energy clearing infeasible signal is generated.

[0009] Preferably, the method for obtaining the theoretical value of frequency modulation service optimization is as follows: Obtain frequency regulation service demand data and frequency regulation parameters of each generating unit, and construct a frequency regulation service optimization model; wherein, the frequency regulation parameters of each generating unit are the maximum frequency regulation capacity that each generating unit can provide; The objective function for optimizing the frequency regulation service cost is to minimize the total frequency regulation cost; the frequency regulation constraints are the frequency regulation parameter constraints and frequency regulation response speed constraints for each unit; the frequency regulation parameter constraints for each unit are that the actual frequency regulation capacity of each unit is less than the corresponding unit's frequency regulation parameter, and the frequency regulation response speed is that the response time of each unit is less than or equal to the system's required response time; The frequency regulation service optimization model is solved by linear programming or mixed integer programming methods to obtain the theoretical frequency regulation capacity and theoretical frequency regulation cost of each unit in each cycle. These are denoted as the theoretical value and price of frequency regulation service optimization, respectively, which are the clearing results of frequency regulation service optimization.

[0010] Preferably, the method for determining the frequency regulation service capability of a generating unit is as follows: The theoretical values ​​for frequency regulation service optimization of each unit are obtained and variance is calculated to obtain the frequency regulation capacity fluctuation value; the formula for calculating the frequency regulation capacity fluctuation value is as follows: ; In the formula, R(t) represents the frequency regulation capacity fluctuation value, and R(t) represents the theoretical value of frequency regulation service optimization for the corresponding unit in period t. By setting a frequency regulation capacity fluctuation threshold, the frequency regulation capacity fluctuation value calculated for each unit is compared with the frequency regulation capacity fluctuation threshold to determine the frequency regulation service capability of the corresponding unit. If the frequency modulation capacity fluctuation value is less than the frequency modulation capacity fluctuation threshold, a normal frequency modulation service capability signal is generated; if the frequency modulation capacity fluctuation value is greater than or equal to the frequency modulation capacity fluctuation threshold, an abnormal frequency modulation service capability signal is generated. The cause of the fluctuation needs to be investigated and the frequency modulation strategy adjusted until the frequency modulation capacity fluctuation value is less than the frequency modulation capacity fluctuation threshold.

[0011] Preferably, the method for obtaining the joint optimization clearing target value is as follows: Obtain theoretical optimization data for each unit in each cycle, including theoretical optimization values ​​for energy clearing, energy clearing prices, theoretical optimization values ​​for frequency regulation services, and frequency regulation service prices; A coupled analysis was performed on the energy optimization clearing results and frequency regulation service optimization clearing results of each unit in each cycle, and the coupling influence coefficient G between the two was calculated. By setting a coupling threshold, the calculated coupling coefficient is compared with the coupling threshold. If the coupling coefficient G is less than the coupling threshold, a weak coupling signal is generated; if the coupling coefficient G is greater than or equal to the coupling threshold, a strong coupling signal is generated.

[0012] Preferably, the formula for calculating the coupling influence coefficient G is: ; In the formula, G is the coupling influence coefficient, P and R are the theoretical optimization values ​​of energy clearing and frequency regulation service optimization in the target cycle of the target unit, λe is the corresponding energy clearing price, and λf is the corresponding frequency regulation service price. and These are the benchmark values ​​for electricity prices and frequency regulation prices, respectively. This is the corresponding proportionality coefficient.

[0013] Preferably, based on the obtained strongly coupled signal, a joint optimization clearing model is constructed to synergistically optimize the electrical energy and frequency modulation clearing results; The joint optimization clearing model consists of an energy optimization clearing model and a frequency regulation service optimization model. Its objective function is to minimize the total cost of energy and frequency regulation, and the constraints include energy constraints and frequency regulation constraints. By employing mixed integer programming or nonlinear programming algorithms, the jointly optimized energy clearing value, energy clearing price, frequency regulation capacity value, and frequency regulation service price of each unit are obtained, which are the jointly optimized clearing target values.

[0014] Compared to existing solutions, the beneficial effects achieved by this invention are: This invention achieves progressive collaborative optimization of electrical energy and frequency regulation services through a progressive logic of electrical energy feasibility assessment → frequency regulation optimization → joint constraint calculation. This avoids the conflict between frequency regulation services and electrical energy supply caused by the fragmented configuration of existing technologies, and ensures the safe and stable operation of the power system. This invention recalculates the frequency regulation optimization value based on the feasibility signal of energy clearing and judges the frequency regulation service capability, and adjusts the spot energy and frequency regulation of each unit to reduce operational risks and adjustment losses. This invention integrates multi-dimensional data such as power generation cost coefficient, load forecast, frequency regulation response time, and accuracy to calculate the joint optimization clearing target value while taking into account the economics of the power market and the quality of frequency regulation services, thereby achieving efficient resource allocation and improving overall operational efficiency. Attached Figure Description

[0015] The invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of the combined optimization and clearing method for spot electrical energy and frequency regulation of generating units proposed in this invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] Example 1, such as Figure 1 As shown, this invention is a method for jointly optimizing and clearing the spot electrical energy and frequency regulation of generating units, including the following steps: Step 1: Obtain the basic parameters of the generating unit and the spot market demand data for electricity, and calculate the theoretical optimization value for electricity clearing; Among them, the basic parameters of the generating unit include the power generation cost coefficient, the upper limit and the lower limit of the output limit; Spot electricity market demand data includes load forecasts and network constraint parameters; It should be further explained that the implementation of step one includes: S11: Set the clearing cycle and the total number of cycles, determine the total optimization time, and form a cycle sequence; where the clearing cycle is the time granularity of the electricity market, usually 15 minutes or 30 minutes as one clearing cycle; if the total optimization time is 1 day, that is, 96 clearing cycles, then the cycle sequence is {t|t=1,2,...,96}, where t is a different cycle; S12: Collect basic unit parameters from the unit ledger, including power generation cost coefficient, upper and lower limits of output limit; The power generation cost of each unit is calculated using a quadratic function Ci(pi,t)=ai×pi(t)²+bi×pi(t)+ci; where i represents different units, Ci(pi,t) is the total output cost of unit i in period t, ai, bi, and ci are the power generation cost parameters of unit i, used to reflect the nonlinear change of cost with output, and pi(t) is the output of unit i in period t. The upper limit of the output limit is the maximum rated output of the unit, and the lower limit of the output limit is the minimum technical output of the unit. S13: Obtain spot electricity market demand data for each period, including load forecasts and network constraint parameters; Obtain the load forecast value for period t from the load forecasting system of the dispatch center; Obtain network constraint parameters from the power grid topology database, including but not limited to: line transmission limits, i.e., the maximum transmission power of each line; and node power constraints, i.e., the upper and lower limits of the injected power of each node, used to maintain node voltage stability. S14: Construct an energy optimization clearing model and analyze each of the generating units participating in the market; the objective function of the energy optimization clearing model is to minimize the sum of the power generation costs of each unit in all cycles, including the start-up and shutdown costs of each unit; the energy constraints are power balance constraints, upper and lower limits of unit output limits, and network security constraints. It should be noted that the power balance constraint is that the sum of the output of all generating units is greater than or equal to the system load; the network security constraint is that the power flow of the line does not exceed the line transmission limit. The above optimization model is solved by quadratic programming algorithm to obtain the optimal output and minimum power generation cost of each unit in each cycle, which are denoted as the theoretical optimization value of power energy clearing and the power energy market clearing price, respectively, i.e. the power energy optimization clearing result.

[0019] Step 2: Based on the theoretical optimization value of energy clearing, determine the feasibility of energy clearing for each unit and generate an energy clearing feasibility signal; the energy clearing feasibility signal includes an energy clearing feasible signal and an energy clearing infeasibility signal. It should be further explained that the method for determining the feasibility of clearing electrical energy for each unit is as follows: Obtain the theoretically optimized value of the power clearing for each unit in each cycle, denoted as {Pi(t)|t=1,2,...,T}, where T is the total number of cycles; where if Pi(t)>0, then unit i is in the operating state, and if Pi(t)=0, then unit i is in the shutdown state; The maximum ramp rate of each unit is obtained. Based on the operating status, the absolute value of the output change of the theoretical optimization value of the power energy clearing of each unit in adjacent cycles is calculated and compared with the maximum ramp rate of each unit to determine the feasibility of power energy clearing of each unit. If the absolute value of the output change of the theoretically optimized value of power energy clearing in adjacent cycles of each unit is less than or equal to its maximum ramp rate, a power energy clearing feasible signal is generated; otherwise, a power energy clearing infeasible signal is generated.

[0020] Step 3: Based on the feasibility signal of energy clearing, obtain frequency modulation service demand data and calculate the theoretical value of frequency modulation service optimization; The frequency modulation service requirements data include the total frequency modulation capacity required by the system, the frequency modulation response time requirements, and the frequency modulation accuracy. It should be further explained that the method for obtaining the theoretical value of frequency modulation service optimization is as follows: Obtain frequency regulation service demand data and frequency regulation parameters of each generating unit, and construct a frequency regulation service optimization model; wherein, the frequency regulation parameters of each generating unit are the maximum frequency regulation capacity that each generating unit can provide; The objective function for optimizing the frequency regulation service cost is to minimize the total frequency regulation cost; the frequency regulation constraints are the frequency regulation parameter constraints and frequency regulation response speed constraints for each unit; the frequency regulation parameter constraints for each unit are that the actual frequency regulation capacity of each unit is less than the corresponding unit's frequency regulation parameter, and the frequency regulation response speed is that the response time of each unit is less than or equal to the system's required response time; The frequency regulation service optimization model is solved by linear programming or mixed integer programming methods to obtain the theoretical frequency regulation capacity and theoretical frequency regulation cost of each unit in each cycle. These are denoted as the theoretical value and price of frequency regulation service optimization, respectively, which are the clearing results of frequency regulation service optimization.

[0021] Step 4: Based on the theoretical value of frequency regulation service optimization, determine the unit's frequency regulation service capability and generate a frequency regulation service capability signal; among which, the frequency regulation service carrying capacity signal includes a normal frequency regulation service capability signal and an abnormal frequency regulation service capability signal; It should be further explained that the method for determining the frequency regulation service capability of a generating unit is as follows: The theoretical values ​​for frequency regulation service optimization of each unit are obtained and variance is calculated to obtain the frequency regulation capacity fluctuation value; the formula for calculating the frequency regulation capacity fluctuation value is as follows: ; In the formula, R(t) represents the frequency regulation capacity fluctuation value, and R(t) represents the theoretical value of frequency regulation service optimization for the corresponding unit in period t. By setting a frequency regulation capacity fluctuation threshold, the frequency regulation capacity fluctuation value calculated for each unit is compared with the frequency regulation capacity fluctuation threshold to determine the frequency regulation service capability of the corresponding unit. If the frequency modulation capacity fluctuation value is less than the frequency modulation capacity fluctuation threshold, a normal frequency modulation service capability signal is generated; if the frequency modulation capacity fluctuation value is greater than or equal to the frequency modulation capacity fluctuation threshold, an abnormal frequency modulation service capability signal is generated. The cause of the fluctuation needs to be investigated, such as unit mechanical failure, control parameter deviation, etc., and the frequency modulation strategy should be adjusted until the frequency modulation capacity fluctuation value is less than the frequency modulation capacity fluctuation threshold.

[0022] Step 5: Based on the theoretical optimization data, obtain the joint operation constraint data of electrical energy and frequency regulation for each unit, and calculate the joint optimization clearing target value; among which, the theoretical optimization data includes the theoretical optimization value of electrical energy clearing, the electrical energy market clearing price, the theoretical optimization value of frequency regulation service, and the frequency regulation service price; It should be further explained that the method for obtaining the joint optimization clearing target value is as follows: Obtain the theoretical optimization data of each unit in each cycle, including the theoretical optimization value P of energy clearing, the energy clearing price λe, the theoretical optimization value R of frequency regulation service, and the frequency regulation service price λf; A coupled analysis was performed on the energy optimization clearing results and frequency regulation service optimization clearing results of each unit in each cycle, and the coupling influence coefficient G between the two was calculated. The formula for calculating the coupling influence coefficient G is as follows: ; In the formula, G is the coupling influence coefficient, P and R are the theoretical optimization values ​​of energy clearing and frequency regulation service optimization in the target cycle of the target unit, λe is the corresponding energy clearing price, and λf is the corresponding frequency regulation service price. and These are the benchmark value for electricity price and the benchmark value for frequency regulation price, respectively, and the specific values ​​are the historical frequency regulation service price data and the average value of the historical frequency regulation service price data, respectively; This is the corresponding scaling factor, which was set by experts in the field through multiple big data experiments; By setting a coupling threshold, the calculated coupling coefficient is compared with the coupling threshold. If the coupling coefficient G is less than the coupling threshold, a weak coupling signal is generated; if the coupling coefficient G is greater than or equal to the coupling threshold, a strong coupling signal is generated. In this embodiment, the coupling threshold is set to 0.6 based on historical data. Based on the obtained strongly coupled signal, a joint optimization clearing model is constructed to synergistically optimize the electrical energy and frequency modulation clearing results. The joint optimization clearing model consists of an energy optimization clearing model and a frequency regulation service optimization model. Its objective function is to minimize the total cost of energy and frequency regulation, and the constraints include energy constraints and frequency regulation constraints. By employing mixed integer programming or nonlinear programming algorithms, the jointly optimized energy clearing value, energy clearing price, frequency regulation capacity value, and frequency regulation service price of each unit are obtained, which are the jointly optimized clearing target values.

[0023] Step 6: Generate a joint clearing decision signal based on the joint optimization clearing target value.

[0024] It should be noted that the quadratic programming algorithm, linear programming or mixed integer programming method, and nonlinear programming algorithm in this embodiment are all existing algorithm technologies, and the solution process can be implemented through corresponding programming software. The specific calculation formulas are not described in detail here.

[0025] In the several embodiments provided by this invention, it should be understood that the disclosed system can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative; for example, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0026] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0027] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0028] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0029] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing and clearing out the spot electrical energy and frequency regulation of generating units, characterized in that, include: Step 1: Obtain the basic parameters of the generating unit and the spot market demand data for electricity, and calculate the theoretical optimization value for electricity clearing; among which, the basic parameters of the generating unit include the generation cost coefficient, the upper and lower limits of the output limit; the spot market demand data for electricity includes the load forecast value and network constraint parameters; Step 2: Based on the theoretical optimization value of energy clearing, determine the feasibility of energy clearing for each unit and generate an energy clearing feasibility signal; the energy clearing feasibility signal includes an energy clearing feasible signal and an energy clearing infeasibility signal. Step 3: Based on the feasibility signal of energy clearing, obtain frequency modulation service demand data and calculate the theoretical value of frequency modulation service optimization; among which, the frequency modulation service demand data includes the total frequency modulation capacity required by the system, the frequency modulation response time requirement, and the frequency modulation accuracy; Step 4: Based on the theoretical value of frequency regulation service optimization, determine the unit's frequency regulation service capability and generate a frequency regulation service capability signal; among which, the frequency regulation service capability signal includes a normal frequency regulation service capability signal and an abnormal frequency regulation service capability signal; Step 5: Based on the theoretical optimization data, obtain the joint operation constraint data of electrical energy and frequency regulation for each unit, and calculate the joint optimization clearing target value; among which, the theoretical optimization data includes the theoretical optimization value of electrical energy clearing, the electrical energy market clearing price, the theoretical optimization value of frequency regulation service, and the frequency regulation service price; Step 6: Generate a joint clearing decision signal based on the joint optimization clearing target value.

2. The method for joint optimization and clearing of unit spot electrical energy and frequency regulation according to claim 1, characterized in that, Step one includes the following: S11: Set the clearing cycle and the total number of cycles, determine the total optimization duration, and form a cycle sequence; where the clearing cycle is the time granularity of the electricity market; S12: Collect basic unit parameters from the unit ledger, including power generation cost coefficient, upper and lower limits of output limit; The power generation cost of each unit is calculated using a quadratic function Ci(pi,t)=ai×pi(t)²+bi×pi(t)+ci; where i represents different units, Ci(pi,t) is the total output cost of unit i in period t, ai, bi, and ci are the power generation cost parameters of unit i, and pi(t) is the output of unit i in period t. The upper limit of the output limit is the maximum rated output of the unit, and the lower limit of the output limit is the minimum technical output of the unit. S13: Obtain spot electricity market demand data for each period, including load forecasts and network constraint parameters; S14: Construct an energy optimization clearing model and analyze each of the generating units participating in the market; the objective function of the energy optimization clearing model is to minimize the sum of the power generation costs of each unit in all cycles, including the start-up and shutdown costs of each unit; the energy constraints are power balance constraints, upper and lower limits of unit output limits, and network security constraints. S15: Solve the above optimization model using a quadratic programming algorithm to obtain the optimal output and minimum power generation cost of each unit in each cycle, which are denoted as the theoretical optimization value of power energy clearing and the power energy market clearing price, respectively, i.e., the power energy optimization clearing result.

3. The method for joint optimization and clearing of unit spot electrical energy and frequency regulation according to claim 2, characterized in that, The methods for determining the feasibility of clearing electrical energy for each generating unit are as follows: Obtain the theoretically optimized value of the power clearing for each unit in each cycle, denoted as {Pi(t)|t=1,2,...,T}, where T is the total number of cycles; where if Pi(t)>0, then unit i is in the operating state, and if Pi(t)=0, then unit i is in the shutdown state; The maximum ramp rate of each unit is obtained. Based on the operating status, the absolute value of the output change of the theoretical optimization value of the power energy clearing of each unit in adjacent cycles is calculated and compared with the maximum ramp rate of each unit to determine the feasibility of power energy clearing of each unit. If the absolute value of the output change of the theoretically optimized value of power energy clearing in adjacent cycles of each unit is less than or equal to its maximum ramp rate, a power energy clearing feasible signal is generated; otherwise, a power energy clearing infeasible signal is generated.

4. The method for joint optimization and clearing of unit spot electrical energy and frequency regulation according to claim 1, characterized in that, The method for obtaining the theoretical value of frequency modulation service optimization is as follows: Obtain frequency regulation service demand data and frequency regulation parameters of each generating unit, and construct a frequency regulation service optimization model; wherein, the frequency regulation parameters of each generating unit are the maximum frequency regulation capacity that each generating unit can provide; The objective function for optimizing the frequency regulation service cost is to minimize the total frequency regulation cost; the frequency regulation constraints are the frequency regulation parameter constraints and frequency regulation response speed constraints for each unit; the frequency regulation parameter constraints for each unit are that the actual frequency regulation capacity of each unit is less than the corresponding unit's frequency regulation parameter, and the frequency regulation response speed is that the response time of each unit is less than or equal to the system's required response time; The frequency regulation service optimization model is solved by linear programming or mixed integer programming methods to obtain the theoretical frequency regulation capacity and theoretical frequency regulation cost of each unit in each cycle. These are denoted as the theoretical value and price of frequency regulation service optimization, respectively, which are the clearing results of frequency regulation service optimization.

5. The method for joint optimization and clearing of unit spot electrical energy and frequency regulation according to claim 4, characterized in that, The methods for determining the frequency regulation service capability of a generating unit are as follows: The theoretical values ​​for frequency regulation service optimization of each unit are obtained and variance is calculated to obtain the frequency regulation capacity fluctuation value; the formula for calculating the frequency regulation capacity fluctuation value is as follows: ； In the formula, R(t) represents the frequency regulation capacity fluctuation value, and R(t) represents the theoretical value of frequency regulation service optimization for the corresponding unit in period t. By setting a frequency regulation capacity fluctuation threshold, the frequency regulation capacity fluctuation value calculated for each unit is compared with the frequency regulation capacity fluctuation threshold to determine the frequency regulation service capability of the corresponding unit. If the frequency modulation capacity fluctuation value is less than the frequency modulation capacity fluctuation threshold, a normal frequency modulation service capability signal is generated; if the frequency modulation capacity fluctuation value is greater than or equal to the frequency modulation capacity fluctuation threshold, an abnormal frequency modulation service capability signal is generated. The cause of the fluctuation needs to be investigated and the frequency modulation strategy adjusted until the frequency modulation capacity fluctuation value is less than the frequency modulation capacity fluctuation threshold.

6. The method for joint optimization and clearing of unit spot electrical energy and frequency regulation according to claim 5, characterized in that, The method for obtaining the joint optimization clearing target value is as follows: Obtain theoretical optimization data for each unit in each cycle, including theoretical optimization values ​​for energy clearing, energy clearing prices, theoretical optimization values ​​for frequency regulation services, and frequency regulation service prices; A coupled analysis was performed on the energy optimization clearing results and frequency regulation service optimization clearing results of each unit in each cycle, and the coupling influence coefficient G between the two was calculated. By setting a coupling threshold, the calculated coupling coefficient is compared with the coupling threshold. If the coupling coefficient G is less than the coupling threshold, a weak coupling signal is generated. If the coupling coefficient G is greater than or equal to the coupling threshold, a strongly coupled signal is generated.

7. The method for joint optimization and clearing of unit spot electrical energy and frequency regulation according to claim 6, characterized in that, The formula for calculating the coupling influence coefficient G is as follows: ； In the formula, G is the coupling influence coefficient, P and R are the theoretical optimization values ​​of energy clearing and frequency regulation service optimization in the target cycle of the target unit, λe is the corresponding energy clearing price, and λf is the corresponding frequency regulation service price. and These are the benchmark values ​​for electricity prices and frequency regulation prices, respectively. This is the corresponding proportionality coefficient.

8. The method for joint optimization and clearing of unit spot electrical energy and frequency regulation according to claim 7, characterized in that, Also includes: Based on the obtained strongly coupled signal, a joint optimization clearing model is constructed to synergistically optimize the electrical energy and frequency modulation clearing results. The joint optimization clearing model consists of an energy optimization clearing model and a frequency regulation service optimization model. Its objective function is to minimize the total cost of energy and frequency regulation, and the constraints include energy constraints and frequency regulation constraints. By employing mixed integer programming or nonlinear programming algorithms, the jointly optimized energy clearing value, energy clearing price, frequency regulation capacity value, and frequency regulation service price of each unit are obtained, which are the jointly optimized clearing target values.

Images