Power power balance method based on fuzzy chance constraint conversion and computer equipment

Abstract

The application discloses a power and electricity balance method based on fuzzy opportunity constraint conversion and a computer device, and the method comprises the following steps: constructing a tolerance degree based on power shortage risk, taking a comprehensive objective function of conventional power generation cost, standby capacity cost, new energy abandoned electricity cost and uncertainty penalty term, taking system constraints, unit constraints, unit electricity constraints, cross-section safety constraints and energy storage constraints as constraint conditions, and improving the accuracy of a medium and long term power and electricity balance model under the consideration of single modeling of new energy uncertainty, thereby improving the economy and safety of the power system. Through the establishment of the uncertainty modeling mode of long-period power and electricity balance, the fuzzy opportunity constraint and the gradient penalty coefficient, the economy and reliability can be flexibly balanced, the redundant cost of standby capacity is reduced by converting the fuzzy boundary into a deterministic boundary, and through risk quantification, the decision transparency can be supported and the interpretability can be ensured.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power balance, in particular to a power balance method based on fuzzy opportunity constraint conversion and a computer device. BACKGROUND

[0002] With large-scale development and high proportion of grid connection of new energy, the seasonal fluctuation characteristics and extreme weather scenarios are prominent, and the long-period supply-demand imbalance risk of the power system is increasingly prominent, which poses new challenges to power balance. SUMMARY

[0003] Therefore, the application provides a power balance method based on fuzzy opportunity constraint conversion and a computer device, constructs a tolerance degree based on power shortage risk, and takes the comprehensive objective function of the conventional power generation cost, the standby capacity cost, the new energy curtailment cost and the uncertainty penalty term as the constraint condition, taking the system constraint (system balance and standby constraint), unit constraint (unit output, unit start-stop time and climbing constraint), unit power constraint, cross-section safety constraint and energy storage constraint as the constraint condition, and improving the accuracy of the medium and long-term power balance model under the consideration of the uncertainty of new energy, thereby improving the economy and safety of the power system.

[0004] According to one aspect of the application, a power balance method based on fuzzy opportunity constraint conversion is provided, which is applied to a power system connected with new energy, the power system comprising units, power plants, power grids and energy storage batteries, the units being used to convert various energies into electric energy, the power plants being used to deliver the electric energy generated by the units to the power grids, and the energy storage batteries being used to provide flexible adjustment resources for the power grids, when the output of the units is balanced with the load of the power grids, the power system reaches power balance, and the method comprises the following steps:

[0005] For a target balance year including a plurality of continuous preset balance periods, the conventional power generation cost, the standby capacity cost and the new energy curtailment cost declared by the power system in the first preset balance period in the target balance year are obtained, wherein each preset balance period comprises a plurality of continuous output time points, and the adjacent output time points are output time periods;

[0006] The output of the new energy with uncertainty is taken as a fuzzy variable, a gradient penalty coefficient for punishing the expected value of the power shortage of the power system is introduced to the fuzzy variable, an uncertainty penalty term is obtained, and the conventional power generation cost, the standby capacity cost, the new energy curtailment cost and the uncertainty penalty term are combined to construct a power system power balance objective function containing the uncertainty penalty term;

[0007] The fuzzy variable corresponding to the converted fuzzy opportunity constraint is taken as a first constraint condition, the load balance constraint is taken as a second constraint condition, the power system positive reserve capacity constraint is taken as a third constraint condition, the power system negative reserve capacity constraint is taken as a fourth constraint condition, the special unit state constraint is taken as a fifth constraint condition, the unit output upper and lower limit constraint is taken as a sixth constraint condition, the unit group output upper and lower limit constraint is taken as a seventh constraint condition, the unit climbing constraint is taken as an eighth constraint condition, the unit minimum continuous start-stop time constraint is taken as a ninth constraint condition, the unit maximum start-stop times constraint is taken as a tenth constraint condition, the unit output constraint is taken as an eleventh constraint condition, the power plant power constraint is taken as a twelfth constraint condition, and the energy storage battery constraint is taken as a thirteenth constraint condition.

[0008] The output solved in the last output period in the above preset balance period is taken as the initial output of the initial output period in the next preset balance period, the output of each unit in each output period in other preset balance periods is sequentially solved, and each unit is adjusted based on the solved output of each unit in each output period in each preset balance period in the target balance year, until the power system reaches power balance.

[0009] According to another aspect of the present application, a computer device is provided, which comprises a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, and the processor implements the above-mentioned power balance method based on fuzzy opportunity constraint conversion and the computer device when executing the program.

[0010] Through the above technical solution, the power balance method based on fuzzy opportunity constraint conversion and the computer device provided by the present application can flexibly balance economy and reliability through the establishment of long-period (i.e., annual) power balance uncertainty modeling, fuzzy opportunity constraint, and gradient penalty coefficient, directly depict the fuzziness of expert experience or sample data through membership functions, avoid the deviation of single probability distribution assumption, convert fuzzy boundaries into deterministic boundaries to reduce the reserve capacity redundancy cost, and support decision transparency and ensure interpretability through risk quantification.

[0011] The above description is only a summary of the technical solutions of the present application, in order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0012] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0013] Figure 1 A flowchart of a power balance method based on fuzzy chance constraint conversion is shown.

[0014] Figure 2 A flowchart of a constraint condition using method is shown. DETAILED DESCRIPTION

[0015] The application will be described in detail below with reference to the drawings and embodiments. It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict.

[0016] In this embodiment, a power balance method based on fuzzy chance constraint conversion and a computer device are provided, as shown in Figure 1 The power system includes units, power plants, power grids and energy storage batteries. The units are used to convert various energies into electric energy. The power plants are used to deliver the electric energy generated by the units to the power grid. The energy storage batteries are used to provide flexible adjustment resources for the power grid. When the output of the units is balanced with the load of the power grid, the power system reaches power balance. The method includes the following steps.

[0017] In step 101, for a target balance year including a plurality of continuous preset balance periods, the conventional power generation cost, standby capacity cost and new energy curtailment cost of the power system in the first preset balance period in the target balance year are obtained. Each preset balance period includes a plurality of continuous output time instants, and the adjacent output time instants are output time periods.

[0018] Currently, in the long-period power balance scenario, the uncertainty of new energy output is mainly modeled and analyzed by stochastic programming, robust optimization, fuzzy chance constraint and data-driven methods. In recent years, optimization methods based on confidence theory have gradually emerged. By establishing an uncertainty envelope model, it provides an effective tool for decision-making in uncertain environments, reflecting the available capacity of new energy that can be "trusted" by the system under a certain reliability target. The method of confidence theory not only can flexibly handle system uncertainty, but also can show strong adaptability in modeling, analyzing and optimizing complex systems. In power system planning, the "new energy confidence capacity" is defined as the minimum guaranteed output of new energy under a certain confidence level (such as 95%), and in operation, the confidence threshold is set based on extreme scenarios (such as consecutive windless and sunny days) for reserve capacity configuration. The uncertainty is converted into a deterministic reference value (such as the confidence capacity is 30%-50% of the installed capacity), which directly supports system safety constraints.

[0019] The current trend is towards hybrid methods (such as stochastic-robust joint optimization) and emerging theories (information gap decision-making, quantum optimization) to balance economic, reliability and low-carbon targets, but still needs to break through the bottlenecks of multi-time scale coupling, high-dimensional decision-making and physical-data fusion.

[0020] Therefore, traditional stochastic programming relies on probability distribution to describe wind and light fluctuations, but it is difficult to model long-period correlations; robust optimization takes the worst scenario as a benchmark, which is highly reliable but too conservative; fuzzy mathematics combines with chance constraints to quantify subjective cognitive uncertainty through membership functions, which is suitable for small sample scenarios; and data-driven techniques (such as GAN generative adversarial networks, reinforcement learning) train through massive time series data to dynamically optimize reserve strategies, but face the challenges of interpretability and computational complexity.

[0021] In the above embodiments of the application, the "periodic power balance model" is constructed based on fuzzy chance constraint conversion and gradient penalty coefficient, which is suitable for new power system planning and dispatching operation scenarios with significant long-period climate fluctuations, and can achieve the balance goal of "uncertainty quantifiable, risk controllable and cost affordable".

[0022] Specifically, the conventional power generation cost includes fuel cost, purchased power cost, water cost, material cost, salary and welfare cost, depreciation cost, repair cost, and other costs. For the fuel cost, it can be calculated according to the fuel consumption of the power plant and the fuel unit price. For example, a thermal power plant consumes 300-350 grams of coal per kilowatt-hour, and if the price of coal is 1 yuan per 1000 grams, the fuel cost for power generation is between 0.3 and 0.35 yuan. For the purchased power cost, it can be calculated according to the purchased power and the purchased power price. For other costs, each cost can be calculated according to the actual situation and cost standard of the power plant, and the total cost in the first preset balancing period is obtained by adding each cost. In particular, the conventional power generation unit cost can also be calculated according to the power generation capacity.

[0023] The reserve capacity cost includes capacity cost and power cost, which is used to ensure the normal power supply of the power grid in the event of an accident. According to the reliability requirement and load forecast of the power grid, the reserve capacity demand in the first preset balancing period can be determined. Then, the capacity cost and the power cost are calculated, and the total reserve capacity cost in the first preset balancing period is obtained by adding the capacity cost and the power cost.

[0024] The new energy curtailment cost is the cost generated by the new energy power generation capacity that cannot be absorbed by the power grid due to low load valley and large new energy generation.

[0025] In step 102, the new energy output with uncertainty is taken as a fuzzy variable, a gradient penalty coefficient for punishing the expected value of power shortage of the power system is introduced to the fuzzy variable to obtain an uncertainty penalty term, and a power system power balance objective function containing the uncertainty penalty term is constructed by combining the conventional power generation cost, the reserve capacity cost, the new energy curtailment cost, and the uncertainty penalty term.

[0026] Next, since the new energy output has uncertainty, regarding it as a fuzzy variable can more truly reflect the actual situation. In traditional methods, the new energy output is often regarded as a deterministic value or is simply processed by probability, which will lead to overly conservative or optimistic planning results. Fuzzy variable processing can cover a wider range of possibilities, making power system planning more flexible and better adapting to changes in new energy output. By introducing a gradient penalty coefficient to punish the expected value of power shortage, the power system can be encouraged to fully consider the risk of power shortage during planning. This penalty mechanism can encourage the power system to maintain sufficient reserve capacity during operation to cope with uncertain situations such as insufficient new energy output or sudden increases in load, thereby improving the reliability of the power system. By considering the conventional power generation cost, reserve capacity cost, new energy curtailment cost, and uncertainty penalty term, a more comprehensive and accurate power system power balance objective function can be constructed. This objective function can guide the power system to meet power demand while minimizing operating costs. By optimizing the configuration of new energy output, conventional power generation output, and reserve capacity, the power system can achieve economic and efficient operation.

[0027] Step 103, taking the minimization of the power system power balance objective function as the target, taking the converted fuzzy opportunity constraint corresponding to the fuzzy variable as the first constraint condition, taking the load balance constraint as the second constraint condition, taking the positive reserve capacity constraint of the power system as the third constraint condition, taking the negative reserve capacity constraint of the power system as the fourth constraint condition, taking the special unit state constraint as the fifth constraint condition, taking the upper and lower limits of unit output constraint as the sixth constraint condition, taking the upper and lower limits of unit group output constraint as the seventh constraint condition, taking the unit ramping constraint as the eighth constraint condition, taking the minimum continuous start-stop time constraint of the unit as the ninth constraint condition, taking the maximum start-stop times constraint of the unit as the tenth constraint condition, taking the unit output constraint as the eleventh constraint condition, taking the power plant power constraint as the twelfth constraint condition, and taking the energy storage battery constraint as the thirteenth constraint condition, the Lagrange function is solved to make the power system achieve power balance in the first preset balance period, and each unit needs to meet its own output at each output period.

[0028] Next, by minimizing the power balance objective function, optimal allocation of power resources can be achieved. Considering load balance constraints, positive and negative reserve capacity constraints, ensures that the power system can meet load demand during operation and has sufficient reserve capacity to cope with uncertainties. By considering special unit state constraints and upper and lower limits of unit output constraints, ensures that the power system can flexibly respond to various changes during operation. For example, when some units fail or require maintenance, other units can quickly adjust their output to make up for the power shortage. Treating renewable energy output as a fuzzy variable and considering its uncertainty on the power system helps promote the consumption of renewable energy. Considering unit ramp-up constraints and minimum continuous start-up and shutdown time constraints ensures that units will not be damaged by excessively fast or slow output changes during operation, while guaranteeing stable unit operation. By considering upper and lower limits of unit group output constraints and power plant power constraints, ensures that the power system can meet various needs during operation. For example, when a region needs to increase power supply, this can be achieved by adjusting the output of the unit group. Considering energy storage battery constraints allows for the optimal use of energy storage batteries. Energy storage batteries can provide power support when renewable energy output is insufficient or when load suddenly increases, thereby enhancing the reliability and stability of the power system. By optimizing the charging and discharging strategies of energy storage batteries, their lifespan can be extended and operating costs reduced.

[0029] Optionally, the objective function for power system balance with uncertainty penalty term constructed in step 101 is:

[0030] , ,

[0031] , ,

[0032] For the first During the power output period of each unit The output operating cost, For the first During the power output period of each unit of effort, For the first During the power output period of each unit The cost of conventional power generation, As a measure of the reliability of power systems, For the power system during its output period The spare capacity, For the power system during its output period The cost of spare capacity, As a penalty factor for power curtailment, For the first Each new energy unit during its power output period The amount of electricity wasted For the cost of curtailing renewable energy, To be used for power shortages in the power system Expected value The gradient penalty coefficient for applying the penalty. For the power system during its output period The load, For the unit during its output period of effort, For new energy units during their power output period The new energy output is T, which is the total number of output periods within the preset balance cycle, N is the total number of generating units in the power system, D is the total number of new energy generating units in the power system, and x is used to represent differentiation.

[0033] Optionally, refer to Figure 2 As shown, for step 103, "taking the transformed fuzzy chance constraint corresponding to the fuzzy variable as the first constraint condition" specifically includes:

[0034] Step 1031: For renewable energy output with uncertainty, construct a probability measure representing various possible values ​​of renewable energy output. Based on the probability measure, construct an inequality for the demand power system to achieve power balance under a pre-set confidence level, considering the uncertainty of renewable energy output. Use the constructed inequality as a fuzzy opportunity constraint considering uncertainty, where the fuzzy opportunity constraint considering uncertainty is:

[0035] , ,

[0036] Step 1032: Construct membership functions for fuzzy variables. Use these membership functions to perform a deterministic transformation on the fuzzy opportunity constraints considering uncertainty, obtaining the first constraint condition for constraining new energy output. The membership function is:

[0037] ,

[0038] The first constraint is:

[0039] , ;

[0040] As a measure of probability, For the first During the power output period of each unit of effort, For new energy units during their power output period New energy power output, for the reserve capacity of the power system in the output period , for the load of the power system in the output period , for the preset confidence level , , , for the new energy output of the new energy unit in the output period , is a triangular fuzzy number, and x is used to represent derivation.

[0041] In the above embodiments of the application, before constructing the power balance objective function of the power system, the total cost (the total cost includes the conventional power generation cost, the reserve capacity cost, the new energy curtailment cost and the uncertainty penalty term) is minimized to construct an initial objective function, and the initial objective function is specifically as follows:

[0042] , ,

[0043] wherein the unit operation cost is a multi-segment linear function related to the unit declared output period of each segment and the corresponding energy price.

[0044] Then, the new energy output is modeled as a fuzzy variable, and the membership function thereof is . It is required to satisfy the power balance under the confidence level , that is .

[0045] The constraint can be converted to when .

[0046] ,

[0047] that is, it is required to satisfy the demand under the most pessimistic scenario (the lower limit of the new energy output is raised).

[0048] The power shortage amount is defined, and the expected value thereof is

[0049] ,

[0050] The shortage risk is included in the initial objective function, and after the tolerance to the risk is introduced, the power balance objective function of the power system containing the uncertainty penalty term is constructed:

[0051] .

[0052] Optionally, for step 103, the constraints of load balancing as the second constraint, positive reserve capacity of the power system as the third constraint, and negative reserve capacity of the power system as the fourth constraint specifically include:

[0053] Step 1033: Constrain the load balance of the power system through the second constraint, constrain the positive reserve capacity of the power system through the third constraint, and constrain the negative reserve capacity of the power system through the fourth constraint, wherein the second constraint is:

[0054] ,

[0055] The third constraint is:

[0056] ,

[0057] The fourth constraint is:

[0058] ,

[0059] N is the total number of generating units in the power system. For the first During the power output period of each unit The output power, where NT is the total number of tie lines in the power system. For the first Each connection line during its power output period The planned power is positive when the planned power is input and negative when the planned power is output. For the power system during its output period The load, For the first During the power output period of each unit Start-stop status, =0 indicates that the unit is stopped, corresponding to the stopped state. =1 indicates that the unit is powered on, corresponding to the startup status. and The first During the power output period of each unit Maximum and minimum output, and These represent the power system's output periods. Demand for positive reserve capacity and demand for negative reserve capacity. and These represent the power system's output periods. The confidence factors for wind power and photovoltaic power, and These represent the constraints imposed by the wind power confidence factor and the photovoltaic confidence factor, respectively.

[0060] In the above embodiments of the present application, the units can be divided into A and B types, and in the second constraint condition, the output of the A type units is included in the left side of the equation.

[0061] For the third and fourth constraint conditions, in order to prevent system supply-demand imbalance fluctuations caused by system load prediction deviation and various actual operation accidents, a certain capacity reserve is generally required for the entire system under the premise of ensuring system power balance, that is, the total on-line capacity per day needs to meet the minimum reserve capacity of the system, in particular, the types of units that do not provide reserve are new energy, nuclear power, hydropower, commissioning and fixed output, and other types.

[0062] Optionally, for the step 103, the "special unit state constraint is the fifth constraint condition, the unit output upper and lower limit constraint is the sixth constraint condition, the unit group output upper and lower limit constraint is the seventh constraint condition, and the unit climbing constraint is the eighth constraint condition", specifically includes:

[0063] In step 1034, the fifth constraint condition is used to constrain the on-line units, combined heat and power units and commissioning units to be in the on-line state, the sixth constraint condition is used to constrain the output of the units to be within the unit output range, the seventh constraint condition is used to constrain the output of the unit group to be within the unit group output range, and the eighth constraint condition is used to constrain the units to meet the climbing rate requirement when climbing up and down, respectively, wherein the fifth constraint condition is:

[0064] ,

[0065] The sixth constraint condition is:

[0066] ,

[0067] The seventh constraint condition is:

[0068] ,

[0069] The eighth constraint condition is:

[0070] ,

[0071] ,

[0072] For the first unit in the set of on-line units, combined heat and power units and commissioning units , the start-stop state of the unit in the output period , the start-stop state of the unit in the output period , the start-stop state of the unit in the output period , the output of the i-th unit in the output period, and are the maximum output and the minimum output of the i-th unit in the output period, respectively, F is the total number of units, the output of the i-th unit group in the output period, and are the maximum output and the minimum output of the i-th unit group in the output period, respectively, F is the total number of units, the output of the i-th unit in the output period, and are the maximum output and the minimum output of the i-th unit in the output period, respectively, F is the total number of units, the output of the i-th unit in the output period, and are the maximum ramp-up rate and the maximum ramp-down rate of the i-th unit, respectively. In the above embodiments of the present application, for the fifth constraint condition, the must-run units, the cogeneration units and the commissioning units should be in the on state.

[0073] For the sixth constraint condition, the output of the unit should be within its maximum and minimum output range, specifically:

[0074] For the A-type units, the planned output is arranged by the power dispatching agency, and in the on period of the units, it is required that

[0075] , and in the formula of the sixth constraint condition, , are taken as the planned output of the A-type units in the corresponding period; in the off period of the units, it is required that .

[0076] For the must-run units, in the must-run period of the units, it is required that , and if there is a minimum output requirement, then in the above formula, is taken as the minimum output of the must-run units in the corresponding period.

[0077] For the cogeneration units, in the cogeneration operation period of the units, it is required that , and in the formula of the sixth constraint condition, is taken as the lower limit of the unit output converted from the planned heat supply flow in the corresponding period, is taken as the upper limit of the unit output converted from the planned heat supply flow in the corresponding period.

[0078] For the commissioning units, in the commissioning period of the units, it is required that , and in the formula of the sixth constraint condition, , are taken as the planned output of the units in the corresponding period.

[0079] ​Regarding the seventh constraint, the output of the unit group should be within its maximum and minimum output range.

[0080] Regarding the eighth constraint, when constraining the unit to climb uphill or downhill, the climbing rate requirement must be met.

[0081] Optionally, for step 103, "taking the minimum continuous start-up and shutdown time constraint of the unit as the ninth constraint condition and the maximum number of start-ups and shutdowns of the unit as the tenth constraint condition" specifically includes:

[0082] Step 1035: The thermal power unit is constrained to meet the minimum continuous start-up and shutdown times by the ninth constraint condition, and the number of start-ups and shutdowns of the unit is limited by the tenth constraint condition. The ninth constraint condition is as follows:

[0083] , ,

[0084] The tenth constraint is:

[0085] , ,

[0086] ,

[0087] ,

[0088] For the first During the power output period of each unit Start-stop status, and These are the minimum continuous start-up time and the minimum continuous shutdown time of the unit, respectively. and The first During the power output period of each unit The duration of continuous power-on and continuous power-off. and The first During the power output period of each unit Whether to switch to startup mode and whether to switch to shutdown mode. and These are the threshold values ​​for the number of devices in the startup state and the threshold values ​​for the number of devices in the shutdown state, respectively.

[0089] In the above embodiments of this application, regarding the ninth constraint, due to the physical properties and actual operational needs of the thermal power unit, the thermal power unit is required to meet the minimum continuous start-up and shutdown time. Specifically, and The first During the power output period of each unit The time of continuous start-up and the time of continuous stop can be represented by state variables .

[0090] For the tenth constraint condition, the start-stop times of the unit are limited.

[0091] Optionally, for step 103, “taking the unit output constraint as the eleventh constraint condition”, specifically includes:

[0092] Step 1036, the unit output is constrained between the upper and lower bounds by the eleventh constraint condition, wherein the eleventh constraint condition is:

[0093] , , ,

[0094] is the total number of each output period in the preset balance period under the eleventh constraint condition, is the th unit in the output period belonging to the th output interval the middle mark power, and are the upper and lower bounds of the th output interval declared by the th unit, is the energy price declared by the th unit corresponding to the th output interval.

[0095] In the above embodiments of the present application, for the eleventh constraint condition, the unit output is constrained between the upper and lower bounds.

[0096] Optionally, for step 103, “taking the power plant power constraint as the twelfth constraint condition and taking the energy storage battery constraint as the thirteenth constraint condition”, specifically includes:

[0097] Step 1037, the winning power of the primary energy supply power plant on the day-ahead electricity market is constrained below the power upper limit by the twelfth constraint condition, and the charging and discharging power and the load state of the energy storage battery are respectively constrained by the thirteenth constraint condition, wherein the twelfth constraint condition is:

[0098] ,

[0099] The thirteenth constraint condition is:

[0100] ,

[0101] ,

[0102] ,

[0103] is the total number of output periods in the preset balance period under the twelfth constraint condition, is the power upper limit of the nth power plant, is the output of the nth unit in the output period, is the load state, is the state of charge of the energy storage battery in the output period, and are the upper and lower limits of the state of charge of the energy storage battery, respectively, and E is the rated capacity of the energy storage battery, and are the charging and discharging efficiencies of the energy storage battery, respectively, and are the upper and lower limits of the charging and discharging power, respectively. In the above embodiments of the present application, SOC represents State of Charge, i.e. the state of charge. SOC refers to the percentage of the current charge capacity of the battery relative to its capacity. It can be used to represent the remaining capacity and charging state of the battery. In the management and optimization of energy storage batteries, SOC is an important parameter for ensuring that the battery is charged and discharged within a safe range and avoiding damage to the battery caused by overcharging or overdischarging. For the twelfth constraint condition, the bidding power of the primary energy supply power plant in the day-ahead electricity market is constrained to be lower than the power upper limit.

[0104] For the thirteenth constraint condition, the charging and discharging power and the state of charge of the energy storage battery are respectively constrained.

[0105] Optionally, for step 103, "solving by Lagrange function to make the power system achieve power balance in the first preset balance period, and requiring each unit to output in each output period", specifically includes:

[0106] Step 1036, introducing a Lagrange multiplier to construct a Lagrange function.

[0107] Step 1037, taking the partial derivative of the output of each unit in each output period in the first preset balance period by the Lagrange function, so that the power system adjusts each unit based on the optimal solution obtained by the partial derivative until the power balance is achieved.

[0108] Step 1037, taking the partial derivative of the output of each unit in each output period in the first preset balance period by the Lagrange function, so that the power system adjusts each unit based on the optimal solution obtained by the partial derivative until the power balance is achieved.

[0109] Step 1037, taking the partial derivative of the output of each unit in each output period in the first preset balance period by the Lagrange function, so that the power system adjusts each unit based on the optimal solution obtained by the partial derivative until the power balance is achieved.

[0110] ​​In the above embodiments of this application, introducing Lagrange multipliers to construct the Lagrange function and taking partial derivatives with respect to relevant variables to obtain the optimality conditions is a common method for handling constrained optimization problems. In power systems, this can be applied to the first... During the power output period of each unit contribution , No. A new energy unit in the power system abandoned electricity and the power system during power output periods Optimize the spare capacity Specifically:

[0111] for Introducing multipliers and ,for Introducing multipliers and ,for Introducing multipliers The Lagrange function can be expressed as:

[0112] ,

[0113] For the Lagrange function with respect to Taking the partial derivative and setting it equal to zero, we get:

[0114] ,

[0115] For the Lagrange function with respect to Taking the partial derivative and setting it equal to zero, we get:

[0116] ,

[0117] For the Lagrange function with respect to Taking the partial derivative and setting it equal to zero, we get:

[0118] ,

[0119] Combining the equations where the partial derivatives are equal to zero with the constraints forms a set of equations. This set of equations constitutes the optimality conditions; solving these equations yields the equation that optimizes the objective function. , and The value of , and the corresponding Lagrange multiplier.

[0120] Step 104, the output solved in the last output period in the previous preset balance period is taken as the initial output of the initial output period in the next preset balance period, and the output of each output period in each preset balance period of the target balance year is sequentially solved, and each unit is adjusted based on the solved output of each unit in each output period in each preset balance period of the target balance year until the power system reaches power balance.

[0121] In the above embodiments of the present application, the output state of the last period of the previous period can be taken as the initial condition of the next period to establish the time continuity of the unit state. For example, if the output of unit A at T period of period 1 is 50 MW, the initial output of period 2 directly inherits this value instead of being reinitialized to 0 to reflect continuity. In particular, key parameters that affect the operation of the next period, such as energy storage battery SOC (state of charge), unit start-stop state, and ramping margin, can also be passed on.

[0122] By applying the technical solutions of the embodiments, the uncertainty is quantified by fuzzy chance constraints, the economic risk is balanced by gradient penalty coefficients, and the solvability is improved by clear conversion, which reduces the standby redundancy cost while ensuring reliability, especially for new power system planning and dispatching operation scenarios with significant long-period climate fluctuations, achieving the balance goal of "quantifiable uncertainty, controllable risk, and affordable cost", representing the forefront of the high-proportion new energy power system optimization field. By adjusting the tolerance of uncertainty through the confidence parameter, the robust optimization "one-size-fits-all" conservatism is avoided. The expected penalty term of power shortage is introduced into the objective function to dynamically balance the standby capacity cost and power shortage risk cost, achieving Pareto optimality (economic and reliability balance). Reducing standby capacity redundancy cost, the clear conversion of opportunity constraints converts fuzzy constraints into deterministic boundaries, which can reduce standby redundancy compared to robust optimization that designs standby capacity based on "worst-case scenario". Traditional stochastic programming relies on a large amount of historical data to construct wind and light output probability models, while fuzzy mathematics directly describes the fuzziness of expert experience or sample data through membership functions (such as triangular fuzzy numbers), which is more suitable for scenarios with long-period prediction data deviation and deficiency of new energy output. Combined with the possibility (Pos) and necessity (Nec) measures, the deviation of single probability distribution assumption is avoided, which is closer to human intuition of "possibility". Risk can be quantified directly, and the expected value of power shortage directly reflects the system risk, which is easy for decision-makers to understand; the confidence level β and the penalty coefficient λ can be calibrated through sensitivity analysis to avoid the decision-making blind area of black box models (such as deep learning).

[0123] Based on the above as Figures 1 to 2The method shown, in order to achieve the above object, the embodiment of the application further provides a computer device, which can be a personal computer, a server, a network device and the like, the computer device comprises a storage medium and a processor; the storage medium is used for storing a computer program; the processor is used for executing the computer program to realize the above method. Figures 1 to 2 The power balance method based on the fuzzy opportunity constraint conversion.

[0124] Optionally, the computer device can further comprise a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module and the like. The user interface can comprise a display screen (Display), an input unit such as a keyboard (Keyboard) and the like, and the optional user interface can further comprise a USB interface, a card reader interface and the like. The network interface can optionally comprise a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface) and the like.

[0125] Those skilled in the art can understand that the structure of the computer device provided by the embodiment does not constitute a limitation on the computer device, and can comprise more or fewer components, or combine certain components, or different component arrangements.

[0126] The storage medium can further comprise an operating system and a network communication module. The operating system is a program for managing and saving computer device hardware and software resources, supporting information processing programs and the running of other software and / or programs. The network communication module is used for realizing communication between the components in the storage medium and communication with other hardware and software in the entity device.

[0127] Through the above description of the embodiments, those skilled in the art can clearly understand that the application can be realized by means of software and necessary general hardware platforms, or by hardware implementation to build a tolerance level based on power shortage risk, with a comprehensive objective function of conventional power generation cost, standby capacity cost, new energy curtailment cost and uncertainty penalty term, and with system constraints, unit constraints, unit power constraints, cross-section safety constraints and energy storage constraints as constraint conditions, the accuracy of the medium and long-term power balance model under the consideration of new energy uncertainty single modeling is improved, and the economy and safety of the power system are improved. By establishing a long-period power balance uncertainty modeling method, a fuzzy opportunity constraint and a gradient penalty coefficient, the economy and reliability can be flexibly balanced, and by converting the fuzzy boundary into a deterministic boundary, the redundancy cost of standby capacity is reduced; through risk quantification, decision transparency can be supported and interpretability can be ensured.

[0128] Those skilled in the art can understand that the modules or flows in the drawings are not necessarily required for implementing the present application. Those skilled in the art can understand that the modules in the devices in the implementation scenarios can be distributed in the devices in the implementation scenarios according to the description of the implementation scenarios, or can be changed to be located in one or more devices different from the implementation scenarios. The modules in the above implementation scenarios can be combined into one module, or can be further split into multiple sub-modules.

[0129] The above application numbers are only for description, and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only some specific implementation scenarios of the present application, but the present application is not limited thereto, and any variations made by those skilled in the art shall fall within the protection scope of the present application.

Claims

1. A power power balance method based on fuzzy chance constraint transformation, characterized in that, The method is applied to a power system connected with new energy, and the power system comprises generating units, power plants, a power grid and energy storage batteries. The generating units are used to convert various energy into electric energy. The power plants are used to deliver the electric energy generated by the generating units to the power grid. The energy storage batteries are used to provide flexible adjustment resources for the power grid. When the output of the generating units is balanced with the load of the power grid, the power system reaches electric quantity balance. The method comprises the following steps: For a target balance year comprising a plurality of continuous preset balance periods, the conventional power generation cost, the standby capacity cost and the new energy curtailment cost of the power system in the first preset balance period in the target balance year are obtained. Each preset balance period comprises a plurality of continuous output time instants, and the adjacent output time instants are output time periods. The uncertain new energy output is taken as a fuzzy variable, a gradient penalty coefficient for punishing the expected value of the power shortage of the power system is introduced to the fuzzy variable to obtain an uncertainty penalty term. The conventional power generation cost, the standby capacity cost, the new energy curtailment cost and the uncertainty penalty term are combined to construct a power system electric quantity balance target function comprising the uncertainty penalty term. The minimization of the power system electric quantity balance target function is taken as an objective, the converted fuzzy chance constraint corresponding to the fuzzy variable is taken as a first constraint condition, the load balance constraint is taken as a second constraint condition, the positive standby capacity constraint of the power system is taken as a third constraint condition, the negative standby capacity constraint of the power system is taken as a fourth constraint condition, the special generating unit state constraint is taken as a fifth constraint condition, the upper and lower limits of the output of the generating unit are taken as a sixth constraint condition, the upper and lower limits of the output of the generating unit group are taken as a seventh constraint condition, the output ramping constraint of the generating unit is taken as an eighth constraint condition, the minimum continuous start-stop time constraint of the generating unit is taken as a ninth constraint condition, the maximum start-stop times constraint of the generating unit is taken as a tenth constraint condition, the output constraint of the generating unit is taken as an eleventh constraint condition, the electric quantity constraint of the power plant is taken as a twelfth constraint condition, and the energy storage battery constraint is taken as a thirteenth constraint condition. The Lagrange function is solved to make the power system reach electric quantity balance in the first preset balance period, and the output of each generating unit in each output time period is required. The output solved in the last output time period in the previous preset balance period is taken as the initial output of the initial output time period in the next preset balance period. The output of each generating unit in each output time period in other preset balance periods is sequentially solved. The generating units are adjusted based on the output of each generating unit in each output time period in each preset balance period in the target balance year until the power system reaches electric quantity balance. The power system electric quantity balance target function comprising the uncertainty penalty term is constructed as follows: ， ， ， ， For the first During the power output period of each unit The output operating cost, For the first During the power output period of each unit of efforts, For the first During the power output period of each unit The cost of conventional power generation, As a measure of the reliability of power systems, For the power system during its output period Backup capacity, For the power system during its output period The cost of spare capacity, As a penalty factor for power curtailment, For the first Each new energy unit during its power output period The amount of electricity wasted For the cost of curtailing renewable energy, To be used for power shortages in the power system Expected value The gradient penalty coefficient for applying the penalty. For the power system during its output period The load, For the unit during its output period of efforts, For new energy units during their power output period The new energy output is T, which is the total number of output periods within the preset balance cycle, N is the total number of generating units in the power system, D is the total number of new energy generating units in the power system, and x is used to represent differentiation.

2. The method of claim 1, wherein, The converted fuzzy chance constraint corresponding to the fuzzy variable is taken as a first constraint condition, which comprises the following steps: For the uncertain new energy output, a possibility measure representing various possible values of the new energy output is constructed. Based on the possibility measure, an inequality is constructed for the power system to reach electric quantity balance under a preset confidence level considering the uncertainty of the new energy output. The constructed inequality is taken as the fuzzy chance constraint considering the uncertainty, which is as follows: ， ， The membership function of the fuzzy variable is constructed, the fuzzy chance constraint considering uncertainty is converted into a deterministic constraint through the membership function, and a first constraint condition is obtained to constrain the new energy output, wherein the membership function is: ， The first constraint condition is: ， ； As a measure of probability, For the first During the power output period of each unit of efforts, For new energy units during their power output period New energy power output, For the power system during its output period Backup capacity, To preset the credit level, Membership function; ( , , ) is a triangular fuzzy number of the new energy output of the new energy unit in the output period of the new energy unit , x is used to represent derivation.

3. The method of claim 1, wherein, A load balance constraint is taken as a second constraint condition, a power system positive reserve capacity constraint is taken as a third constraint condition, and a power system negative reserve capacity constraint is taken as a fourth constraint condition, and the method comprises the following steps: The load balance of the power system is constrained through the second constraint condition, the positive reserve capacity of the power system is constrained through the third constraint condition, and the negative reserve capacity of the power system is constrained through the fourth constraint condition, wherein the second constraint condition is: ， The third constraint condition is: ， The fourth constraint condition is: ， N is the total number of generating units in the power system. For the first During the power output period of each unit The output power, where NT is the total number of tie lines in the power system. For the first Each connection line during its power output period The planned power is positive when the planned power is input and negative when the planned power is output. For the first During the power output period of each unit Start-stop status, =0 indicates that the unit is stopped, corresponding to the stopped state. =1 indicates that the unit is powered on, corresponding to the startup status. and The first During the power output period of each unit Maximum and minimum output, and These represent the power system's output periods. Demand for positive reserve capacity and demand for negative reserve capacity. and These represent the power system's output periods. The confidence factors for wind power and photovoltaic power, and These represent the constraints imposed by the wind power confidence factor and the photovoltaic confidence factor, respectively.

4. The method of claim 1, wherein, A special unit state constraint is taken as a fifth constraint condition, a unit output upper and lower limit constraint is taken as a sixth constraint condition, a unit group output upper and lower limit constraint is taken as a seventh constraint condition, and a unit ramping constraint is taken as an eighth constraint condition, and the method comprises the following steps: The fifth constraint condition is used to constrain the units that must be started, the cogeneration units and the units under commissioning to be in the started state, wherein the fifth constraint condition is: ， The sixth constraint condition is used to constrain the output of the units to be within the unit output range, wherein the sixth constraint condition is: ， The seventh constraint condition is used to constrain the output of the unit group to be within the unit group output range, wherein the seventh constraint condition is: ， The eighth constraint condition is used to constrain the units to meet the ramping rate requirement when the units are ramping up and ramping down, respectively, wherein the eighth constraint condition is: ， ， For in the full set of on-off units, cogeneration units and debugging units The first unit is in the start-stop state of the output period The first unit is in the start-stop state of the output period​ For the first During the power output period of each unit Start-stop status, For the first During the power output period of each unit of efforts, and The first During the power output period of each unit The maximum and minimum output, where F is the total number of generator sets. For the first During the power output period of individual generator groups of efforts, and The first During the power output period of individual generator groups Maximum and minimum output, and The first The maximum uphill and downhill rates of each unit.

5. The method of claim 1, wherein, A unit minimum continuous start-stop time constraint is taken as a ninth constraint condition, and a unit maximum start-stop times constraint is taken as a tenth constraint condition, and the method comprises the following steps: The ninth constraint condition is used to constrain the thermal power units to meet the minimum continuous start time and stop time, wherein the ninth constraint condition is: ， ， The tenth constraint condition is used to limit the start-stop times of the units, wherein the tenth constraint condition is: ， ， ， ， For the first During the power output period of each unit Start-stop status, and These are the minimum continuous start-up time and the minimum continuous shutdown time of the unit, respectively. and The first During the power output period of each unit The duration of continuous power-on and continuous power-off. and The first During the power output period of each unit Whether to switch to startup mode and whether to switch to shutdown mode. and These are the threshold values ​​for the number of devices in the startup state and the threshold values ​​for the number of devices in the shutdown state, respectively.

6. The method of claim 1, wherein, A unit output constraint is taken as an eleventh constraint condition, and the method comprises the following steps: The eleventh constraint condition is used to constrain the unit output to be between the upper and lower bounds, wherein the eleventh constraint condition is: ， ， ， This represents the total number of output periods within the preset equilibrium cycle under the eleventh constraint. For the first The unit belongs to the first The power output period in each power output range The winning bid for electricity, and The first The first unit to apply for The upper and lower bounds of each output range For the first The application submitted by each unit belongs to the category of the first The energy price corresponding to each output range.

7. The method according to any one of claims 1 to 6, characterized in that, A power plant power constraint is taken as a twelfth constraint condition, and a storage battery constraint is taken as a thirteenth constraint condition, and the method comprises the following steps: The twelfth constraint condition is used to constrain the winning power of the primary energy supply power plant in the day-ahead electricity market to be lower than the upper limit of the power, wherein the twelfth constraint condition is: ， The thirteenth constraint condition is used to constrain the charging and discharging power and the load state of the storage battery, wherein the thirteenth constraint condition is: , ， ， a total number of output periods in a preset balance period under the twelfth constraint condition, an upper limit of power of the nth power plant, an upper limit of power of the nth power plant, an output of the nth unit in an output period, an output of the nth unit in an output period, an output of the nth unit in an output period, a load state, a load state of the energy storage battery in an output period, a load state of the energy storage battery in an output period, a lower limit of the load state of the energy storage battery, and an upper limit of the load state of the energy storage battery, E being a rated capacity of the energy storage battery, a charging efficiency of the energy storage battery, and a discharging efficiency of the energy storage battery, a lower limit of the charging and discharging power, and an upper limit of the charging and discharging power.

8. The method of claim 7, wherein, The solving through the Lagrange function is to make the power system reach power balance in the first preset balance period, and the method comprises the following steps: A Lagrange multiplier is introduced to construct a Lagrange function; The Lagrange function is used to take the partial derivative of the output of the units in the first preset balance period to make the power system adjust the units based on the optimal solution obtained by the partial derivative until the power balance is reached.

9. A computer device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, The processor implements the power balance method based on the fuzzy chance constraint conversion in any one of claims 1 to 8 when executing the computer program.

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