Power system section regulation and control method based on new energy abandoned electricity refined cost

Abstract

The invention relates to a power system section regulation and control method based on new energy power abandoning refined cost. The method comprises the steps of new energy generator set modeling, new energy power abandoning refined modeling, objective function construction, constraint condition establishment and optimal value solving through a gurobi solver. According to the refined new energy power abandoning cost modeling method, the output probability of a new energy unit is described by adopting truncated normal distribution, a segmented gradient curve is obtained through an integration method, and the randomness and the power abandoning cost of new energy output are described more accurately. By constructing the new energy electricity abandoning refined model, the problem that the contribution rate of the new energy unit at different power points is difficult to describe by a traditional method is solved.

Description

Technical Field

[0001] The present invention relates to the field of power grid regulation, and specifically to a power system section regulation method based on the refined cost of new energy curtailment. Background Art

[0002] Traditional new energy output models usually adopt simple linear approximations, which cannot accurately describe the probability distribution of new energy unit output, resulting in inaccurate curtailment cost estimation, and further affecting the economy and reliability of power system regulation. The calculation of new energy curtailment cost is rough, and the dispatching strategy is not refined. Existing methods usually calculate the curtailment amount by simple power difference, ignoring the cumulative effect of curtailment at different power points, resulting in insufficient accuracy of new energy dispatching strategies and affecting the optimization effect of section power control.

[0003] The section overload accommodation plan lacks a refined new energy dispatching strategy: existing section power regulation methods mainly rely on the regulation of conventional units and energy storage, without fully considering the adjustable nature of new energy units, and cannot maximize new energy accommodation and reduce the total system operation cost.

[0004] There is a lack of coordinated optimization of new energy units, conventional units, and energy storage: traditional optimization methods often adopt step-by-step dispatching strategies when adjusting section power, resulting in uncoordinated optimal dispatching of new energy, energy storage, and thermal power units, reducing the economy and feasibility of overall dispatching. Summary of the Invention

[0005] The present invention precisely aims at the deficiencies existing in the prior art and provides a power system section regulation method based on the refined cost of new energy curtailment.

[0006] To solve the above problems, the technical solutions adopted by the present invention are as follows:

[0007] A power system section regulation method considering the refined cost of new energy curtailment, comprising the following steps:

[0008] Step 1: Modeling of new energy generators.

[0009] Step 2: Refined modeling of new energy curtailment.

[0010] Step 3: Constructing the objective function.

[0011] Step 4: Establishing constraint conditions.

[0012] Step 5: Using the gurobi solver to solve for the optimal value.

[0013] Further, in Step 1, the modeling of new energy generators includes:

[0014] Construct a mathematical model for the refined cost of new - energy curtailment according to the probability of each power point of the unit, and define a normal - distribution formula:

[0015]

[0016] In the formula, μ is the mean of the normal distribution, which determines the central position of the distribution, and σ is the standard deviation of the normal distribution, representing the degree of data dispersion. σ 2 is the variance, representing the degree of data fluctuation;

[0017] By the formula:

[0018]

[0019] Truncate and normalize the standard normal - distribution curve in the interval [P′ min , P′ max , so that the sum of the output - probability integrals of the new - energy unit is still 1 within the range of the interval [P′ min , P′ max , which conforms to the definition of the probability - density function.

[0020] In the formula, Z is the integral of the standard normal distribution f(x) in the interval [P′ min , P′ max , P′ min is the minimum power of the new - energy unit, P′ max is the maximum power of the new - energy unit, f norm (x) is the truncated normal distribution after normalization, and the abscissas of the left - and right - hand endpoints of f norm (x) are P′ min and P′ max respectively. Equation (3) represents the probability of each output probability occurring within the output range [P′ min , P′ max of the new - energy unit, fully considering the unpredictability of the output of the new - energy unit.

[0021] Furthermore, in step two, the refined modeling of new - energy curtailment includes:

[0022]

[0023] Integrate each power point P′ (P′ norm ≤P′≤P′ min ) in f max (x) using Equation (4), and draw a piece - wise gradient curve according to the integral value corresponding to each power point.

[0024] In the formula, G(P′) is the integral of each power point P′ (P′ norm ) in f min≤P′≤P′ max ) The value obtained by integration, which represents the expected value of new - energy curtailment. It represents the area value of the set P′ in the probability density function of new - energy output, and is the expected value that can be used for curtailment.

[0025] Furthermore, the refined modeling of new - energy curtailment also includes:

[0026] Construct an output model for a conventional new - energy unit. Substitute the wind speed of the wind turbine in the new energy into the model to obtain the output of the wind power. The output model of the conventional wind turbine is constructed as follows:

[0027]

[0028] In the formula, P t wind is the output of the wind turbine, v t is the natural wind speed at time t, v in is the cut - in wind speed, v out is the cut - out wind speed, v r is the rated wind speed, P r wind is the rated output of the wind turbine. The output of the wind turbine is constructed through the wind speed.

[0029] Furthermore, in step three, the construction of the objective function includes:

[0030] By minimizing the regulation power of the conventional unit, new - energy unit, and energy storage, the overloaded section power in the power grid is adjusted with the smallest adjustment amount. According to the above requirements, the day - ahead output curves of the conventional unit and energy storage need to be obtained first. To construct the minimization objective function, the formula is constructed as follows:

[0031]

[0032] The objective function composed of the conventional unit, new - energy unit, and energy storage is constructed through the above formula (6).

[0033] In the formula, P i,t is the power of the i - th conventional unit at time t. Among them, N0 represents the total number of time periods, and N1, N2, N3 respectively represent the total numbers of conventional units, energy storage, and new - energy units. is the day - ahead power of the i - th conventional unit at time t, R j,t is the charge - discharge power of the j - th energy storage at time t, where j = 1, 2, 3, that is, there are three energy storages. is the day - ahead power of the j - th energy storage at time t, G(P′ k,t) is the expected value of new energy curtailment of the k-th new energy unit at the t-th moment, and α, β, and λ are the weight coefficients of conventional units, energy storage, and new energy units respectively.

[0034] Further, in step four, the establishment of the constraint conditions includes:

[0035] Constraints are imposed on the power balance at the section, conventional units, new energy units, and energy storage to establish constraint conditions:

[0036]

[0037] According to Equation (7), a section power flow constraint is established, where F max is the maximum section power flow, and α1, β1, and λ1 represent the sensitivity coefficients of conventional units, energy storage, and new energy units respectively.

[0038]

[0039] According to Equation (8), a unit output constraint is established, where P min is the minimum output of the conventional unit, and P max is the maximum output of the conventional unit.

[0040]

[0041] According to Equation (9), a charge and discharge constraint of the energy storage is established, where R min is the minimum charge and discharge power of the energy storage, and R max is the maximum charge and discharge power of the energy storage.

[0042]

[0043] According to Equation (10), an SOC constraint of the energy storage is established, where E j,t is the SOC of the j-th energy storage at the t-th moment, E j,t-1 is the SOC of the j-th energy storage at the (t - 1)-th moment, η c and η d are the charging and discharging efficiencies respectively, and is the discharging power of the j-th energy storage at the t-th moment.

[0044]

[0045] According to Equation (11), an output constraint of the new energy unit is established, where P′ min represents the minimum output of the new energy unit, and P′ max represents the maximum output of the new energy unit.

[0046] Further, in step five, the process of solving the optimal value by using the Gurobi solver includes: taking the minimum operation cost of conventional units and the minimum curtailment of wind and light as the optimization objectives, combining the curtailment penalty costs of wind and light, the peak regulation cost, and the operation characteristics of each thermal power unit, using the MATLAB platform to call Gurobi for solution, determining the economically optimal scheduling strategy, then adopting an alternating iterative solution method to conduct a proactive peak regulation verification of the system. After the proactive peak regulation constraints are satisfied, the iteration ends, the thermal power units participating in deep peak regulation are determined, and the final scheduling strategy is output.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] (1) A refined modeling method for the cost of new energy curtailment is proposed: the truncated normal distribution is used to describe the output probability of new energy units, and the piecewise gradient curve is obtained through the integral method, which more accurately depicts the randomness of new energy output and the curtailment cost. By constructing a refined model of new energy curtailment, the problem that it is difficult for traditional methods to describe the contribution rate of new energy units at different power points is solved.

[0049] (2) A more accurate objective function is constructed to improve the optimization effect: the objective function adopts a combined optimization method of minimizing the curtailment amount and the cross-section power adjustment amount. Compared with the traditional method that only considers minimizing curtailment, it can optimize both the economy and security of the system. The objective function considers the different adjustment costs of new energy, conventional units, and energy storage, improving the rationality of the scheduling optimization.

[0050] (3) A more scientific modeling method for new energy curtailment is adopted: the cumulative value of the new energy output probability is calculated through the integral formula to obtain the contribution value of the new energy unit at different power points, and a new energy curtailment gradient curve is constructed. This method avoids the defect of the traditional linear approximation calculation of the curtailment amount, making the new energy output modeling more in line with the actual situation.

[0051] (4) Combined with the Gurobi solver, fast solution is achieved: by using Gurobi for optimization and solution, the joint optimization scheduling problem of new energy, thermal power units, and energy storage can be efficiently solved, improving the calculation efficiency. Through mathematical optimization methods, the new energy scheduling is made more accurate, enhancing the feasibility of the scheduling scheme.

[0052] (5) A more reasonable cross-section power control strategy is proposed: by constructing multiple constraint conditions such as cross-section power flow constraints, unit output constraints, energy storage charge and discharge constraints, and new energy unit output constraints, the cross-section power control strategy is optimized. By setting the energy storage charge and discharge optimization strategy through the constraint conditions, the energy storage resources are maximally utilized to balance the new energy fluctuations, improving the flexibility of the power system.

[0053] (6) Applicable to scenarios with high - proportion new - energy access: This method can effectively improve the new - energy consumption level, is particularly applicable to scenarios where high - penetration wind power and photovoltaic power are connected to the power grid, can significantly improve the utilization rate of new energy, and reduce the cost of curtailment. By coordinately optimizing the dispatching of new - energy units, conventional units, and energy storage, the security and economy of the power grid are ensured. Brief Description of the Drawings

[0054] Figure 1 This is a flowchart of the power - system section - control method based on the refined cost of new - energy curtailment of the present invention. Detailed Embodiments

[0055] The following will describe the content of the present invention in combination with specific embodiments. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar components or components with the same or similar functions throughout.

[0056] The directional terms mentioned in the present invention, such as: up, down, left, right, front, back, inside, outside, front side, back side, side, etc., are only with reference to the directions in the drawings. The embodiments described below with reference to the drawings and the directional terms used are exemplary and are only used to explain the present invention and cannot be construed as a limitation of the present invention. In addition, for the various specific examples of processes and materials provided by the present invention, those of ordinary skill in the art can be aware of the application of other processes and / or the use of other materials.

[0057] Please refer to Figure 1 , Figure 1 This is a flowchart of the power - system section - control method based on the refined cost of new - energy curtailment of the present invention.

[0058] Considering the random volatility of new - energy units, assume that the probability curve of each power is a truncated normal - distribution curve in the interval [P′ min ,P′ max . Integrate for each power point P′(P′ min ≤P′≤P′ max ), and plot the obtained integral values to form a piece - wise gradient curve G(P′), which represents the expected value of new - energy curtailment at different power points P′, so as to construct a refined new - energy unit model. Combining with the gurobi solution algorithm, it effectively solves the comprehensive optimization problem of uncertainty, flexibility, and robustness scheduling in new - energy access, and provides a new technical path for efficient scheduling and renewable - energy consumption.

[0059] Considering the random volatility of new - energy units, in the range of new - energy output [P′ min ,P′ maxIn it, a probability density function is set to give the probabilities of different powers occurring, making the model for new energy processing more in line with reality, and an new energy unit model is established considering the refined cost of new energy curtailment.

[0060] An conventional new energy output model is established. Through the modeling of wind turbines, the advantages of refined new energy modeling are compared. A model for predicting the output of new energy units based on the wind speed of new energy units.

[0061] The mathematical models of conventional units and energy storage are established, and they are respectively applied to the objective function together with the output models of the two new energy units to compare their numerical values and reflect the superiority of considering the refined cost of new energy curtailment.

[0062] The constraints of conventional units, new energy units and energy storage are established. When adding the power generation of conventional units and new energy units on the day-ahead basis, a section value is set, and the excess part is absorbed by adjusting the output of conventional units, new energy units and energy storage, and the optimal value is solved through gurobi.

[0063] The power system section regulation method considering the refined cost of new energy curtailment provided by this application includes the following steps:

[0064] Step 1: Modeling of new energy generators;

[0065] According to the probability of each power point of the unit, a mathematical model of the refined cost of new energy curtailment is constructed, and a normal distribution formula is defined:

[0066]

[0067] In the formula, μ is the mean of the normal distribution, which determines the central position of the distribution, σ is the standard deviation of the normal distribution, representing the degree of data dispersion, and σ 2 is the variance, representing the degree of data fluctuation.

[0068] By the formula:

[0069]

[0070] The standard normal distribution curve is truncated and normalized in the interval [P′ min , P′ max , so that within the range of the interval [P′ min , P′ max , the sum of the output probabilities of the new energy unit is still 1, which conforms to the definition of the probability density function.

[0071] In the formula, Z is the integral of the standard normal distribution f(x) in the interval [P′ min , P′ max , and P′ minis the minimum power of the new energy unit, P′ max is the maximum power of the new energy unit, f norm (x) is the truncated normal distribution after normalization, and f norm (x) has the abscissas of the left and right endpoints as P′ min and P′ max , Equation (3) represents the probability of each output power occurring within the output range [P′ min , P′ max of the new energy unit, fully considering the unpredictability of the new energy unit's output.

[0072] Step 2: Fine-grained modeling of new energy curtailment;

[0073]

[0074] Use Equation (4) to integrate each power point P′ (P′ norm ) in f min ≤P′≤P′ max ), and draw a piecewise gradient curve according to the integral value corresponding to each power point.

[0075] In the formula, G(P′) is the value obtained by integrating each power point P′ (P′ norm ) in f min ≤P′≤P′ max ), which represents the expected value of new energy curtailment, the area value of the set P′ in the probability density function of new energy output, and is the expected value that can be used for curtailment.

[0076] Construct an output model of a conventional new energy unit. Substitute the wind speed of the wind turbine in the new energy into the model to obtain the output of the wind power, and construct the output model of the conventional wind turbine as follows:

[0077]

[0078] In the formula, P t wind is the output of the wind turbine, v t is the natural wind speed at time t, v in is the cut-in wind speed, v out is the cut-out wind speed, v r is the rated wind speed, is the rated output of the wind turbine, and the output of the wind turbine is constructed through the wind speed.

[0079] Step 3: Construct the objective function;

[0080] By minimizing the regulation power of conventional units, new energy units, and energy storage, the overloaded section power in the power grid is regulated with the smallest adjustment amount. According to the above requirements, the day-ahead output curves of conventional units and energy storage need to be obtained first. To construct the minimization objective function, the following formula is constructed:

[0081]

[0082] The objective function composed of conventional units, new energy units, and energy storage is constructed through the above formula (6).

[0083] In the formula, P i,t is the power of the i-th conventional unit at the t-th moment, where N0 represents the total number of moments, and N1, N2, and N3 represent the total numbers of conventional units, energy storage, and new energy units respectively. is the day-ahead power of the i-th conventional unit at the t-th moment, and R j,t is the charge and discharge power of the j-th energy storage at the t-th moment, where j = 1, 2, 3, that is, there are three energy storages. is the day-ahead power of the j-th energy storage at the t-th moment, and G(P′ k,t ) is the expected value of new energy abandonment of the k-th new energy unit at the t-th moment. α, β, and λ are the weight coefficients of conventional units, energy storage, and new energy units respectively.

[0084] Step 4: Establish constraint conditions;

[0085] Constraints are imposed on the power balance at the section, conventional units, new energy units, and energy storage to establish constraint conditions:

[0086]

[0087] According to Equation (7), the section power flow constraint is established. In the formula, F max is the maximum section power flow, and α1, β1, and λ1 represent the sensitivity coefficients of conventional units, energy storage, and new energy units respectively.

[0088]

[0089] According to Equation (8), the unit output constraint is established. In the formula, P min is the minimum output of the conventional unit, and P max is the maximum output of the conventional unit.

[0090]

[0091] According to Equation (9), the energy storage charge and discharge constraint is established. In the formula, R min is the minimum charge and discharge power of the energy storage, and R max is the maximum charge and discharge power of the energy storage.

[0092]

[0093] Establish the energy storage SOC constraint according to Equation (10), where E j,t is the SOC of the j-th energy storage at the t-th moment, and E j,t-1 is the SOC of the j-th energy storage at the (t-1)-th moment, and η c and η d are the charging and discharging efficiencies respectively, is the discharging power of the j-th energy storage at the t-th moment.

[0094]

[0095] Establish the output constraint of the new energy unit according to Equation (11), where P′ min represents the minimum output of the new energy unit, and P′ max represents the maximum output of the new energy unit.

[0096] Step Five: Use the gurobi solver to solve for the optimal value;

[0097] Taking the minimum operating cost of conventional units and the minimum curtailment of wind and light as the optimization objectives, combining the curtailment penalty cost of wind and light, the peak shaving cost, and the operating characteristics of each thermal power unit, use the MATLAB platform to call gurobi for solution, determine the economically optimal dispatching strategy, and then adopt an alternating iterative solution method to conduct a verification of the initiative of system peak shaving. After the peak shaving initiative constraint is satisfied, end the iteration, determine the thermal power units participating in deep peak shaving, and output the final dispatching strategy.

[0098] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0099] (1) Propose a refined modeling method for the new energy curtailment cost: Use the truncated normal distribution to describe the output probability of the new energy unit, and obtain the piecewise gradient curve through the integral method to more accurately depict the randomness of the new energy output and the curtailment cost. By constructing a refined model of new energy curtailment, the problem that it is difficult for traditional methods to describe the contribution rate of new energy units at different power points is solved.

[0100] (2) Construct a more accurate objective function to improve the optimization effect: The objective function adopts a combined optimization method of minimizing the curtailment amount and the section power adjustment amount. Compared with the traditional method that only considers minimizing curtailment, it can optimize the system economy and security at the same time. The objective function considers the different adjustment costs of new energy, conventional units, and energy storage, and improves the rationality of dispatching optimization.

[0101] (3) Adopt a more scientific new energy curtailment modeling method: Calculate the cumulative value of the new energy output probability through integral formulas, obtain the contribution values of new energy units at different power points, and construct a new energy curtailment gradient curve. This method avoids the defects of the traditional linear approximation for calculating curtailment electricity, making the new energy output modeling more in line with the actual situation.

[0102] (4) Combine with the Gurobi solver to achieve fast solution: Use Gurobi for optimization and solution, which can efficiently solve the joint optimal scheduling problems of new energy, thermal power units and energy storage, improving the calculation efficiency. Through mathematical optimization methods, the new energy scheduling is made more accurate, enhancing the feasibility of the scheduling plan.

[0103] (5) Propose a more reasonable cross-section power control strategy: Optimize the cross-section power control strategy by constructing multiple constraint conditions such as cross-section power flow constraints, unit output constraints, energy storage charge and discharge constraints, and new energy unit output constraints. Set the energy storage charge and discharge optimization strategy through the constraint conditions to maximize the use of energy storage resources to balance new energy fluctuations and improve the flexibility of the power system.

[0104] (6) Applicable to the scenario of high proportion of new energy access: This method can effectively improve the new energy consumption level, especially applicable to the scenario of high-penetration wind power and photovoltaic access to the power grid, which can significantly improve the utilization rate of new energy and reduce the curtailment cost. By coordinating and optimizing the scheduling of new energy units, conventional units and energy storage, the safety and economy of the power grid are ensured.

[0105] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A power system section regulation method considering the refined cost of new energy curtailment, characterized in that Including the following steps: Step 1: Modeling of new energy generator sets; Step 2: Fine-grained modeling of new energy curtailment; Step 3: Constructing the objective function; Step 4: Establishing the constraint conditions; Step 5: Using the Gurobi solver to solve for the optimal value.

2. The power system section regulation method considering the refined cost of new energy curtailment according to claim 1, wherein In Step 1, the modeling of the new energy generator sets includes: According to the probability of each power point of the unit, constructing a mathematical model of the fine-grained cost of new energy curtailment, and defining a normal distribution formula: where μ is the mean of the normal distribution, which determines the central position of the distribution, and σ is the standard deviation of the normal distribution, representing the degree of dispersion of the data. σ 2 is the variance, representing the degree of fluctuation of the data; By the formula: Truncate and normalize the standard normal distribution curve in the interval [P′ min , P′ max , so that within the range of the interval [P′ min , P′ max , the sum of the output probability integrals of the new energy generating units is still 1, which conforms to the definition of the probability density function; where Z is the integral of the standard normal distribution f(x) over the interval [P′ min , P′ max , P′ min is the minimum power of the new energy unit, P′ max is the maximum power of the new energy unit, f norm (x) is the normalized truncated normal distribution, and the abscissas of the left and right endpoints of f norm (x) are P′ min and P′ max respectively. Equation (3) represents the probability of each output probability occurring within the output range [P′ min , P′ max of the new energy unit, fully considering the unpredictability of the output of the new energy unit.

3. The power system section regulation method considering the refined cost of new energy curtailment according to claim 2, wherein, In Step 2, the fine-grained modeling of new energy curtailment includes: Integrate f using Equation (4) for each power point P′ in norm (x) where P′ min ≤P′≤P′ max ), and plot a piecewise gradient curve based on the integral value corresponding to each power point; where G(P′) is the value obtained by integrating each power point P′ (P′ norm (x) with P′ min ≤P′≤P′ max ), which represents the expected value of new energy curtailment, that is, the area value of the set P′ in the probability density function of new energy output, and is the expected value that can be used for curtailment.

4. The power system section regulation method considering the refined cost of new energy curtailment according to claim 3, characterized in that, The fine-grained modeling of new energy curtailment also includes: Constructing an output model of a conventional new energy unit, substituting the wind speed of the wind turbine in the new energy into the model to obtain the output of the wind power, and constructing the output model of the conventional wind turbine as the following formula: Wherein, is the output of the wind turbine, v t is the natural wind speed at time t, v in is the cut-in wind speed, v out is the cut-out wind speed, v r is the rated wind speed, is the rated output of the wind turbine, and the output of the wind turbine is constructed by the wind speed.

5. The power system section regulation method considering the refined cost of new energy curtailment according to claim 4, characterized in that, In Step 3, the constructing of the objective function includes: By minimizing the regulation power of the conventional unit, new energy unit, and energy storage, so as to regulate the overloaded section power in the power grid with the smallest regulation amount. According to the above requirements, first obtain the day-ahead output curves of the conventional unit and energy storage. In order to construct the minimization objective function, the formula is constructed as follows: Constructing the objective function composed of the conventional unit, new energy unit, and energy storage through the above formula (6); Where P i,t is the power of the i-th conventional unit at the t-th moment, where N0 represents the total number of moments, and N1, N2, and N3 represent the total numbers of conventional units, energy storage, and new energy units respectively. is the day-ahead power of the i-th conventional unit at the t-th moment, and R j,t is the charge and discharge power of the j-th energy storage at the t-th moment, where j = 1, 2, 3, that is, there are three energy storages. is the day-ahead power of the j-th energy storage at the t-th moment, and G(P′ k,t ) is the expected value of the new energy curtailment of the k-th new energy unit at the t-th moment. α, β, and λ are the weight coefficients of the conventional unit, energy storage, and new energy unit respectively.

6. The power system section regulation method considering the refined cost of new energy abandoned electricity according to claim 5, wherein In Step 4, the establishing of the constraint conditions includes: Constraining the power balance at the section, conventional unit, new energy unit, and energy storage, and establishing the constraint conditions: Establish the cross-section power flow constraint according to Equation (7), where F max is the maximum cross-section power flow, and α1, β1, and λ1 respectively represent the sensitivity coefficients of conventional units, energy storage, and new energy units; The output constraint of the unit is established according to Equation (8), where P min is the minimum output of the conventional unit, and P max is the maximum output of the conventional unit; Establish the energy storage charge and discharge constraints according to Equation (9), where R min is the minimum power of the energy storage charge and discharge, and R max is the maximum power of the energy storage charge and discharge; The energy storage SOC constraint is established according to Equation (10), where E j,t is the SOC of the jth energy storage at the tth moment, and E j,t-1 is the SOC of the jth energy storage at the (t-1)th moment. η c and η d are the charging and discharging efficiencies respectively. is the discharging power of the jth energy storage at the tth moment; The output constraint of the new energy unit is established according to Equation (11), where P′ min represents the minimum output of the new energy unit, and P′ max represents the maximum output of the new energy unit.

7. A power system section regulation method considering the refined cost of new energy curtailment according to claim 6, characterized in that In Step 5, the using of the Gurobi solver to solve for the optimal value includes: Taking the minimum operation cost of the conventional unit and the minimum curtailment of wind and light as the optimization objectives, combining the curtailment penalty cost of wind and light, peak shaving cost, and the operation characteristics of each thermal power unit, using the MATLAB platform to call Gurobi for solution, determining the economically optimal dispatching strategy, then adopting an alternating iterative solution method to conduct the initiative verification of system peak shaving. After the initiative constraint of peak shaving is satisfied, the iteration ends, determining the thermal power units participating in deep peak shaving, and outputting the final dispatching strategy.

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