Power grid peak regulation and frequency modulation cooperation method and device considering multiple types of power generation energy

Abstract

The invention belongs to the field of electric power, and discloses a power grid peak load and frequency modulation cooperation method and device considering multi-type power generation energy, and the method comprises the steps: inputting wind and light load prediction data and conventional parameters of each unit into an upper-layer flexible resource decision model, taking the minimum net load curve variance as a target, coordinating each flexible resource for peak load shifting, and carrying out peak load shifting; obtaining output plans of multiple types of power generation energy and an optimized net load curve; transmitting the optimized net load curve to a lower-layer thermal power generating unit decision model, and carrying out linearization processing on a fuel cost function and a three-section type depth peak regulation interval of the thermal power generating unit by introducing a square auxiliary variable and a large M method; in the lower-layer thermal power generating unit decision-making model, optimization is carried out with the optimal thermal power operation cost as the target, and the power generation plan and the frequency modulation reserve capacity of the thermal power generating unit are obtained; and carrying out peak regulation and frequency modulation on the power grid based on the output plan of the multi-type power generation energy, the power generation plan of the thermal power generating unit and the frequency modulation reserve capacity.

Description

Technical Field

[0001] This invention belongs to the field of power, and in particular relates to a method and apparatus for coordinated peak shaving and frequency regulation of power grids that takes into account multiple types of power generation energy. Background Technology

[0002] With the large-scale grid connection of renewable energy sources such as wind and solar power, the power system is facing increasingly severe pressure on peak and frequency regulation. Traditional thermal power units undertake the main peak and frequency regulation tasks, but due to technical constraints such as ramp rate and minimum output, they lack flexibility in dealing with fluctuations caused by a high proportion of renewable energy. At the same time, the randomness and volatility of wind and solar power lead to an increase in the peak-to-valley difference of the system's net load curve, resulting in serious wind and solar curtailment.

[0003] In related technologies, peak shaving and frequency regulation are typically achieved using single resources or simple combinations, lacking coordinated optimization of multiple types of flexible resources. For example, some schemes only consider the peak shaving role of energy storage or pumped storage, failing to fully utilize the aggregated regulation capabilities of virtual power plants; or they focus only on peak shaving while neglecting frequency regulation reserve requirements, resulting in insufficient system safety margins. Furthermore, deep peak shaving of thermal power units involves nonlinear cost functions and piecewise constraints, which are difficult to solve efficiently using traditional optimization methods, limiting the practical application of multi-energy coordinated dispatch. Summary of the Invention

[0004] In view of this, the present invention discloses a grid peak shaving and frequency regulation coordination method and device that takes into account multiple types of power generation energy, which can solve the shortcomings of related technologies.

[0005] To achieve the above objectives, the present invention discloses the following technical solution:

[0006] According to a first aspect of the present invention, a grid peak-shaving and frequency regulation coordination method considering multiple types of power generation energy is proposed, comprising: The wind and solar load forecast data and the conventional parameters of each unit are input into the upper-level flexible resource decision model. With the goal of minimizing the variance of the net load curve, the various flexible resources are coordinated to perform peak shaving and valley filling, resulting in the output plan of multiple types of power generation energy and the optimized net load curve. The upper-level flexible resource decision model considers the curtailment rate constraint, energy storage operation constraint, pumped storage operation constraint and virtual power plant operation constraint. The optimized net load curve is passed to the decision model of the lower-level thermal power unit. The fuel cost function and the three-stage deep peak shaving interval of the thermal power unit are linearized by introducing square auxiliary variables and the big M method. In the lower-level thermal power unit decision model, optimization is performed with the goal of minimizing the operating cost of thermal power, resulting in the power generation plan and frequency regulation reserve capacity of the thermal power units; wherein, the lower-level thermal power unit decision model considers system frequency regulation reserve demand constraints, power balance constraints, and thermal power unit operation constraints; Based on the output plans of the various types of power generation energy, the power generation plans of the thermal power units, and the frequency regulation reserve capacity, the power grid is used for peak shaving and frequency regulation.

[0007] According to a second aspect of the present invention, a grid peak-shaving and frequency regulation coordination device considering multiple types of power generation energy is proposed, the device comprising: Peak shaving unit: Input wind and solar load forecast data and conventional parameters of each unit into the upper-level flexible resource decision model. With the goal of minimizing the variance of the net load curve, coordinate various flexible resources to perform peak shaving and valley filling, and obtain the output plan of multiple types of power generation energy and the optimized net load curve. The upper-level flexible resource decision model considers the curtailment rate constraint, energy storage operation constraint, pumped storage operation constraint and virtual power plant operation constraint. Processing unit: The optimized net load curve is transmitted to the decision model of the lower-level thermal power unit, and the fuel cost function and the three-stage deep peak shaving interval of the thermal power unit are linearized by introducing square auxiliary variables and the big M method; Optimization Unit: In the decision-making model of the lower-level thermal power units, optimization is performed with the goal of optimizing the operating cost of thermal power, to obtain the power generation plan and frequency regulation reserve capacity of the thermal power units; wherein, the decision-making model of the lower-level thermal power units considers the system frequency regulation reserve demand constraints, power balance constraints and thermal power unit operation constraints; Frequency regulation unit: Based on the output plans of the various types of power generation energy, the power generation plans of the thermal power units, and the frequency regulation reserve capacity, it performs peak shaving and frequency regulation on the power grid.

[0008] According to a third aspect of the present invention, an electronic device is provided, comprising: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in the first aspect by running the executable instructions.

[0009] According to a fourth aspect of the invention, a computer-readable storage medium is provided having computer instructions stored thereon that, when executed by a processor, implement the steps of the method as described in the first aspect.

[0010] As can be seen from the above technical solutions, the grid peak shaving and frequency regulation coordination method disclosed in this invention, which takes into account multiple types of power generation energy, is as follows: On the one hand, a hierarchical structure is adopted, with upper-level coordination of flexible resources and lower-level optimization of thermal power operation. This fully utilizes the peak-shaving capabilities of resources such as wind power, photovoltaics, energy storage, pumped storage, and virtual power plants. Furthermore, the Big M method is used to linearize the three-stage deep peak-shaving intervals of thermal power units, transforming the complex nonlinear problem into a mixed-integer linear programming problem. This accurately characterizes the cost characteristics under different peak-shaving states, thereby improving the solution efficiency of the thermal power optimization problem and solving the problem that traditional mixed-integer nonlinear programming is difficult to solve. On the other hand, the operational characteristics of various flexible resources are comprehensively considered. Through constraints such as curtailment rate and operation constraints, a balance is achieved between wind and solar power consumption and system peak-shaving, avoiding the limitations of single-resource scheduling. In addition, peak-shaving and frequency regulation reserve requirements are considered simultaneously during the optimization process. By reserving frequency regulation capacity constraints, the safe operation of the system is ensured, achieving coordinated optimization of peak-shaving and frequency regulation. Attached Figure Description

[0011] Figure 1 This is an exemplary embodiment of an architecture diagram of a grid peak-shaving and frequency regulation collaborative system that takes into account multiple types of power generation energy. Figure 2 This is a flowchart of an exemplary embodiment of a grid peak shaving and frequency regulation coordination method that takes into account multiple types of power generation energy. Figure 3 This is a schematic diagram of a two-layer optimization model provided in an exemplary embodiment; Figure 4 This is a schematic diagram of a deep peak shaving method for thermal power plants, provided as an exemplary embodiment. Figure 5 This is a schematic diagram of a wind, solar, and load curve provided in an exemplary embodiment; Figure 6 This is a schematic structural diagram of a device provided in an exemplary embodiment; Figure 7 This is a block diagram of an exemplary embodiment of a grid peak shaving and frequency regulation coordination device that takes into account multiple types of power generation energy. Detailed Implementation

[0012] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of the present invention as detailed in the appended claims.

[0013] It should be noted that in other embodiments, the corresponding methods are not necessarily performed in the order shown and described in this invention. The method comprises steps. In some other embodiments, the method may include more or fewer steps than those described in this invention. Furthermore, a single step described in this invention may be broken down into multiple steps in other embodiments; and multiple steps described in this invention may be combined into a single step in other embodiments.

[0014] With the large-scale grid connection of renewable energy sources such as wind and solar power, the power system is facing increasingly severe pressure on peak and frequency regulation. Traditional thermal power units undertake the main peak and frequency regulation tasks, but due to technical constraints such as ramp rate and minimum output, they lack flexibility in dealing with fluctuations caused by a high proportion of renewable energy. At the same time, the randomness and volatility of wind and solar power lead to an increase in the peak-to-valley difference of the system's net load curve, resulting in serious wind and solar curtailment.

[0015] In related technologies, peak shaving and frequency regulation are typically achieved using single resources or simple combinations, lacking coordinated optimization of multiple types of flexible resources. For example, some schemes only consider the peak shaving role of energy storage or pumped storage, failing to fully utilize the aggregated regulation capabilities of virtual power plants; or they focus only on peak shaving while neglecting frequency regulation reserve requirements, resulting in insufficient system safety margins. Furthermore, deep peak shaving of thermal power units involves nonlinear cost functions and piecewise constraints, which are difficult to solve efficiently using traditional optimization methods, limiting the practical application of multi-energy coordinated dispatch.

[0016] To address the shortcomings in related technologies, this invention proposes a grid peak shaving and frequency regulation coordination method and device that takes into account multiple types of power generation energy.

[0017] Figure 1 This is an exemplary embodiment of an architecture diagram of a grid peak-shaving and frequency regulation collaborative system that considers multiple types of power generation energy. (See diagram for example.) Figure 1As shown, in this architecture, thermal power, pumped storage, virtual power plants, and energy storage systems work collaboratively to undertake the system's peak shaving and frequency regulation tasks. The output of renewable energy sources such as wind and solar power is volatile and intermittent, and the anti-peak shaving characteristics of wind power further exacerbate the grid regulation pressure. Against this backdrop, traditional thermal power units, limited by minimum output constraints, cannot effectively cope with the peak shaving and frequency regulation problems caused by the large-scale integration of new energy sources. The system needs more ample and flexible regulation means. Therefore, the optimal collaborative strategy proposed in this invention introduces electrochemical energy storage and pumped storage: the former has millisecond to minute-level rapid charging and discharging capabilities, enabling it to absorb electricity during off-peak periods and release electricity during peak periods; the latter can smooth out grid net load fluctuations by pumping water during off-peak periods and generating electricity during peak periods. The synergistic effect of multiple flexible resources can significantly alleviate the regulation pressure on the thermal power side. The virtual power plant is responsible for aggregating and coordinating the output response of distributed resources, optimizing the output of various heterogeneous resources to reduce the overall peak shaving and frequency regulation costs of the system. Furthermore, in addition to participating in basic peak shaving, the thermal power units in the optimal coordination strategy proposed in this invention can reduce their output to below the output of conventional technologies, achieving deep peak shaving. Although this operating mode increases the operating loss cost of the units, it provides the system with more flexibility in reducing output, thereby providing space for the consumption of renewable energy.

[0018] Figure 2 This is a flowchart illustrating an exemplary method for coordinated peak shaving and frequency regulation of a power grid, taking into account multiple types of power generation energy. For example... Figure 2 As shown, the method may include the following steps: Step 201: Input the wind and solar load forecast data and the conventional parameters of each unit into the upper-level flexible resource decision model. With the goal of minimizing the variance of the net load curve, coordinate the various flexible resources to perform peak shaving and valley filling, and obtain the output plan of multiple types of power generation energy and the optimized net load curve. The upper-level flexible resource decision model considers the curtailment rate constraint, energy storage operation constraint, pumped storage operation constraint and virtual power plant operation constraint.

[0019] The input photovoltaic forecast curve exhibits Gaussian waveform characteristics. The wind power forecast curve displays anti-peak-shaving characteristics: wind power output is lower during load troughs and higher during load peaks. The load forecast curve exhibits a bimodal characteristic. The wind, solar, and load forecast curves are input into the upper-level flexible resource decision-making model. Since a certain amount of power can be curtailed during peak shaving to reduce the net load peak-valley difference, the curtailment rate constraint is considered. Simultaneously, the operational constraints of various types of power generation energy, such as energy storage, pumped storage, and virtual power plants, are also taken into account. With the minimum net load variance as the objective function, various peak-shaving resources are coordinated to achieve peak shaving and valley filling of the net load curve, resulting in the output plans for multiple types of power generation energy and the optimized net load curve.

[0020] Net load refers to the remaining load actually borne by thermal power units after deducting the combined output of multiple energy sources in the system. To minimize the fluctuation of net load borne by thermal power units and avoid frequent adjustments in their output, the upper-level model uses the minimum net load variance as its objective function. ; in, The net load of the power grid during time period t. This represents the average net load. The original load for time period t, The wind power consumption during period t. Let t be the amount of photovoltaic power consumed during the period. Let t be the output power of the virtual power plant during time period t. The output power of pumped storage during time period t. Let t be the output power of the energy stored during time period t.

[0021] The constraints of the upper-level model include output constraints for energy storage, pumped storage, and virtual power plants, as well as constraints on wind and solar curtailment rates. Energy storage operation constraints include charge / discharge power constraints, state of charge (SOC) constraints, charge / discharge balance constraints within a cycle, and upper capacity constraints. Pumped storage operation constraints include output power constraints and reservoir capacity constraints. Virtual power plant operation constraints include upper and lower output limits, which are proportional to the total wind and solar power output.

[0022] Among them, energy storage constraints include energy storage charging and discharging power constraints: ; ; in, , This refers to the charging and discharging power of energy storage. This is the upper limit of charging and discharging power. This is the ratio of energy storage capacity to power limit. For the rated capacity of energy storage, This is the energy storage charging and discharging state.

[0023] The state of charge (SOC) of energy storage is the ratio of the remaining energy capacity to the rated energy capacity, which can be expressed as: ; in, The time step is 1 hour in this paper. The initial energy storage capacity. , These are the upper and lower limits of the state of charge of energy storage, respectively. This refers to the energy storage charging and discharging efficiency.

[0024] Within a certain period of time, the amount of energy storage charging should be equal to the amount of energy discharging in order to ensure that energy storage can participate in the scheduling of the next cycle.

[0025] ; Energy storage capacity degradation follows electrochemical laws; exceeding the upper limit will accelerate irreversible damage. Therefore, the corresponding lithium battery energy storage capacity should not exceed the upper limit. ; in, This represents the upper limit of the rated capacity of energy storage.

[0026] Pumped storage constraints mainly include pumped storage output power constraints: (14) ; ; in, This represents the maximum pumping power. This represents the maximum power generation capacity. , These are the pumping efficiency of the water pump and the power generation efficiency of the water turbine unit, respectively. , Let be the capacities of the upper and lower reservoirs at time t, respectively. Let t be the power of the pumped storage unit at time t.

[0027] The capacity constraint of a pumped storage reservoir is expressed as: ; ; ; ; in, , These are the minimum and maximum values ​​of the upper reservoir capacity, respectively. , These represent the minimum and maximum values ​​of the reservoir capacity, respectively.

[0028] A virtual power plant can be viewed as a controllable collection of distributed energy sources, including distributed power sources, controllable loads, and energy storage devices. Its output can be expressed as: ; The lower and upper limits of the output of the virtual power plant are as follows: ; ; in, The basic adjustment coefficient for the virtual power plant is 1, which determines the ratio between the maximum adjustable power of the virtual power plant and the total output of wind and solar power. The virtual power plant's base regulation coefficient of 2 determines the ratio between the virtual power plant's base output and the total wind and solar power output. This is the hard power limit for virtual power plants.

[0029] When the power grid is in a period of low load, moderately reducing the output of wind and solar power can help reduce the difference between the peak and valley loads and shorten the deep peak-shaving operation period of the units. Therefore, this invention considers the constraint of the maximum wind and solar curtailment rate.

[0030] ; ; in, Let be the wind power curtailment rate at time t. Let be the photovoltaic curtailment rate at time t; ; ; in, The maximum allowable wind curtailment rate, The maximum allowable waste rate, Contribute to photovoltaic power plants To contribute to wind farms.

[0031] Step 202: The optimized net load curve is transferred to the decision model of the lower-level thermal power unit. The fuel cost function and the three-segment deep peak shaving interval of the thermal power unit are linearized by introducing square auxiliary variables and the big M method.

[0032] Two-level optimization model such as Figure 3 As shown, the upper-level flexible resource decision-making model considers the constraint of renewable energy curtailment rate and aims to minimize the fluctuation of system net load. It optimizes the active power output of energy storage, pumped storage units, and virtual power plants to achieve peak shaving and valley filling of the net load curve, thereby alleviating the peak shaving pressure of thermal power units. The lower-level thermal power unit decision-making model, through the net load curve transmitted from the upper-level model, considers the constraints of deep peak shaving and frequency regulation reserve requirements of thermal power units, and determines the output and frequency regulation reserve capacity of thermal power units with the optimization objective of minimizing the operating cost of thermal power units.

[0033] Since the lower-level model considers the refined three-stage deep peak shaving of thermal power units, the above model is an integer nonlinear programming problem, thus requiring a linearization method for solution. By introducing a squared auxiliary variable, the Big M method is used to linearize the product after introducing the auxiliary variable. This transforms the nonlinear product term in the fuel cost of thermal power units into a linear product term. For the three states of basic peak shaving, deep peak shaving without oil injection, and deep peak shaving with oil injection, the Big M interval activation method is used to characterize the output interval of each state. Simultaneously, a minimum value is introduced to prevent interval overlap, thereby maintaining a mixed integer linear structure.

[0034] Peak shaving for thermal power plants can be divided into basic peak shaving, deep peak shaving without oil injection, and deep peak shaving with oil injection. A schematic diagram of deep peak shaving for thermal power plants is shown below. Figure 4 As shown, This is the maximum output that the unit can achieve. This represents the minimum output during the basic peak-shaving phase. The minimum output for peak shaving without oil injection. This refers to the minimum output for deep oil injection peak shaving. Thermal power units can be categorized into basic peak shaving, non-oil injection deep peak shaving, and oil injection deep peak shaving based on their peak output level. Basic peak shaving refers to adjusting the unit's output between its rated output and minimum technical output; this is the conventional operating mode, but its adjustment depth is limited and it cannot adapt to large fluctuations in renewable energy output. Non-oil injection deep peak shaving refers to the unit operating below its minimum technical output without using fuel oil for combustion assistance. Although fuel efficiency decreases and operating costs are slightly higher, it provides more room for renewable energy consumption. Deep oil injection peak shaving uses fuel oil injection combustion assistance technology under extremely low loads to ensure stable boiler combustion. Although this method has high fuel costs and causes significant equipment wear, it offers the strongest peak shaving capability.

[0035] The basic peak-shaving cost of thermal power units includes fuel costs and start-up and shutdown costs: ; The fuel cost is modeled using a quadratic function: ; in, Let i be the output of unit i during time period t; , , This is the cost coefficient; This refers to the unit price of coal. This indicates the unit's start-up and shutdown status. This indicates that thermal power unit i is in the powered-on state at time t. This indicates that thermal power unit i is in the off state at time t.

[0036] The start-up and shutdown costs of thermal power units are expressed as follows: ; ; in, For unit start-up costs, This refers to the unit downtime cost.

[0037] When the unit is in deep peak-shaving operation without oil injection, the alternating stress caused by cyclic loads will cause additional damage to the rotor metal, thereby shortening the unit's service life. The formula for calculating the resulting service life loss cost is as follows: ; in, This is the lifespan loss coefficient; For the cost of purchasing the generating unit; The number of cycles for rotor cracking.

[0038] In scenarios where deep peak shaving is maintained by injecting oil, the total peak shaving cost of the unit should include the cost of injecting oil in addition to the costs mentioned above. The specific formula is as follows: ; in, For oil prices; This refers to fuel consumption per unit.

[0039] In summary, the peak-shaving cost of thermal power units It can be represented as a piecewise function as follows: .

[0040] For the secondary term in the fuel cost of thermal power units First, we introduce a squared auxiliary variable: ; Then, the Big M method is used to linearize the product after introducing auxiliary variables. ; Subsequently, in the fuel cost of thermal power units, replace This transforms the nonlinear product into a linear constraint while retaining the convex quadratic term in the fuel cost, thus placing the problem within the MIQP framework.

[0041] For generating units that can participate in deep peak shaving, three binary variables are defined to represent the three operating states of the unit: ; in, As a basic peak-shaving binary variable, For the absence of oil injection, the depth of peak shaving is a binary variable. The peak-shaving variable is the oil injection depth.

[0042] The output range of each state is characterized by a large M-range activation method, while a minimum value is introduced. To prevent interval overlap, ; When a binary variable in a certain state is 1, the Big M constraint degenerates into a hard constraint for that interval; when it is 0, the bound is relaxed. Mutual exclusion ensures that only one interval is activated at any given time, thus maintaining the structure of mixed-integer linearity.

[0043] Step 203: In the decision-making model of the lower-level thermal power units, optimization is performed with the goal of optimizing the operating cost of thermal power, to obtain the power generation plan and frequency regulation reserve capacity of the thermal power units; wherein, the decision-making model of the lower-level thermal power units considers the system frequency regulation reserve demand constraint, power balance constraint and thermal power unit operation constraint.

[0044] The lower-level model receives the optimized net load curve from the upper-level model. Based on this, to improve the system's operational economy and ensure its ability to cope with frequency disturbances, the lower-level model considers the deep peak-shaving effect of thermal power units and the constraint of frequency regulation reserve requirements, with the objective function being the minimum operating cost of thermal power units. ; ; in, To take into account the operating costs of thermal power units with deep peak shaving capacity, For frequency regulation backup costs, and The unit capacity cost coefficients for providing up-frequency regulation and down-frequency regulation reserves for thermal power units are respectively provided; and These are the reserved up-frequency regulation and down-frequency regulation backup capacities for the generating units, respectively.

[0045] The total output of thermal power units must be balanced with the net load curve transmitted from the upper level at every moment in order to meet the power balance requirements of the system. ; Upper and lower limits of thermal power unit output constraints: ; in, Let be the minimum output of the i-th thermal power unit. This represents the maximum output of the i-th thermal power unit. Let be the reserve rate of the i-th thermal power unit.

[0046] Thermal power unit ramping constraints: ; in, Let be the downhill gradient rate of the i-th thermal power unit. Let be the ramp rate of the i-th thermal power unit.

[0047] Thermal power unit start-up and shutdown time constraints: ; ; in, Let i be the minimum operating time of the i-th thermal power unit. Let be the minimum downtime of the i-th thermal power unit.

[0048] The reserve capacity for frequency regulation in thermal power units should meet the following formula: ; ; in, Reserved for frequency regulation standby capacity for the i-th thermal power unit during time period t. Reserved down-regulation capacity for the i-th thermal power unit during time period t.

[0049] In the upper-level model, each power generation source, besides participating in system peak shaving, can also provide a certain amount of frequency regulation reserve. The sum of the frequency regulation reserves provided by each energy source should be sufficient to meet the frequency regulation reserve requirements to cope with frequency fluctuations, as shown in the following formula. ; ; ; ; in, The sum of the up-regulation reserve capacity of each energy source during time period t. This is the sum of the down-regulation reserve capacity of each energy source during time period t. To meet the frequency adjustment backup requirements during time period t. For frequency regulation reserve requirements during time period t, based on a fixed proportion of the load. Sure.

[0050] Step 204: Based on the output plans of the various types of power generation energy, the power generation plans of the thermal power units, and the frequency regulation reserve capacity, perform peak shaving and frequency regulation on the power grid.

[0051] The optimized net load curve from the upper-level model is then passed to the lower-level thermal power units. To improve system stability in response to frequency fluctuations, constraints on frequency regulation reserve demand, power balance, and thermal power unit requirements are considered. The operating cost of thermal power is transformed into a linearized deep peak-shaving cost. The thermal power generation plan and frequency regulation reserve capacity are obtained with the goal of optimizing the thermal power operating cost. Finally, the output plans and frequency regulation reserve capacities of each energy source are output, achieving coordinated optimization of peak-shaving and frequency regulation across multiple energy sources.

[0052] In this embodiment, on the one hand, a hierarchical structure is adopted, with the upper layer coordinating flexible resources and the lower layer optimizing thermal power operation. This fully utilizes the peak-shaving capabilities of resources such as wind power, photovoltaics, energy storage, pumped storage, and virtual power plants. Furthermore, the Big M method is used to linearize the three-stage deep peak-shaving intervals of thermal power units, transforming the complex nonlinear problem into a mixed-integer linear programming problem. This accurately characterizes the cost characteristics under different peak-shaving states, thereby improving the solution efficiency of the thermal power optimization problem and solving the problem that traditional mixed-integer nonlinear programming is difficult to solve. On the other hand, the operational characteristics of various flexible resources are comprehensively considered. A balance between wind and solar power consumption and system peak-shaving is achieved through constraints on curtailment rates and operational constraints, avoiding the limitations of single-resource scheduling. In addition, peak-shaving and frequency regulation reserve requirements are considered simultaneously during the optimization process. Reserved frequency regulation capacity constraints ensure safe system operation, achieving coordinated optimization of peak-shaving and frequency regulation.

[0053] In one embodiment, the input data of the present invention is as follows: Figure 5 The typical daily wind, solar, and load forecast diagram shown has installed capacities of 200 MW and 150 MW for wind power and solar power, respectively. Local users are represented by a bimodal curve with a peak power of 1200 MW and a time resolution of 1 hour. Resources participating in peak shaving and frequency regulation reserve include 4 thermal power units, 2 pumped storage units, 2 energy storage units, and 2 virtual power plants. Unit 4 of the thermal power units can participate in deep peak shaving, with a minimum output of 120 MW for deep peak shaving without oil injection and a minimum output of 50 MW for deep peak shaving with oil injection.

[0054] Figure 6 This is a schematic structural diagram of a device provided in an exemplary embodiment. Please refer to... Figure 6 At the hardware level, the device includes a processor 602, an internal bus 604, a network interface 606, memory 608, and non-volatile memory 610, and may also include other hardware required for its functions. One or more embodiments of the present invention can be implemented in software, for example, the processor 602 reads the corresponding computer program from the non-volatile memory 610 into memory 608 and then runs it. Of course, in addition to software implementation, one or more embodiments of the present invention do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0055] Please refer to Figure 7 A grid peak-shaving and frequency regulation coordination device that takes into account multiple types of power generation energy can be applied to, for example... Figure 7 The device shown, in order to implement the technical solution of the present invention, includes: Peak shaving unit 701 is used to input wind and solar load forecast data and conventional parameters of each unit into the upper-level flexible resource decision model. With the goal of minimizing the variance of the net load curve, it coordinates various flexible resources to perform peak shaving and valley filling, and obtains the output plan of multiple types of power generation energy and the optimized net load curve. The upper-level flexible resource decision model considers the curtailment rate constraint, energy storage operation constraint, pumped storage operation constraint and virtual power plant operation constraint. Processing unit 702 is used to transmit the optimized net load curve to the lower-level thermal power unit decision model, and to linearize the fuel cost function and the three-segment deep peak shaving interval of the thermal power unit by introducing square auxiliary variables and the big M method. The optimization unit 703 is used to optimize the lower-level thermal power unit decision model with the goal of minimizing the thermal power operating cost, and obtain the power generation plan and frequency regulation reserve capacity of the thermal power unit; wherein, the lower-level thermal power unit decision model considers system frequency regulation reserve demand constraints, power balance constraints and thermal power unit operation constraints; Frequency regulation unit 704 is used to perform peak shaving and frequency regulation on the power grid based on the output plans of the multiple types of power generation energy, the power generation plans of the thermal power units, and the frequency regulation reserve capacity.

[0056] Optionally, the objective function of the upper-level flexible resource decision-making model is to minimize the net load variance, and its expression is: ; in, The net load of the power grid during time period t. This represents the average net load. The original load for time period t, The wind power consumption during period t. Let t be the amount of photovoltaic power consumed during the period. Let t be the output power of the virtual power plant during time period t. The output power of pumped storage during time period t. Let t be the output power of the energy stored during time period t.

[0057] Optionally, the curtailment rate constraint includes: the wind and solar curtailment rate ranges from [0, 1], and the total wind curtailment and total solar curtailment do not exceed a preset proportion of their predicted output, expressed as: ; ; in, Let be the wind power curtailment rate at time t. The photovoltaic curtailment rate at time t ; ; in, The maximum allowable wind curtailment rate, The maximum allowable waste rate, Contribute to photovoltaic power plants To contribute to wind farms.

[0058] Optionally, the energy storage operation constraints include charge and discharge power constraints, state of charge (SOC) constraints, charge and discharge balance constraints within a cycle, and capacity upper limit constraints; the pumped storage operation constraints include output power constraints and reservoir capacity constraints; the virtual power plant operation constraints include its upper and lower output limits, which are proportional to the total wind and solar power output.

[0059] Optionally, the processing unit 702 is specifically used for: For the secondary term in the fuel cost of thermal power units Introduce a squared auxiliary variable: , The Big M method is used to linearize the product after introducing auxiliary variables. ; In the fuel cost of thermal power units replace Furthermore, the nonlinear product is transformed into a linear constraint, and the convex quadratic term in the fuel cost is retained, so that the problem falls within the MIQP framework.

[0060] Furthermore, the processing unit 702 is specifically used for: Define three mutually exclusive binary variables. , , These respectively indicate that the thermal power unit is in the basic peak-shaving, deep peak-shaving without oil injection, and deep peak-shaving with oil injection state; An activation constraint consisting of the Big M method is set for the output range corresponding to each state, and a minimum value ε is introduced to prevent range overlap, ensuring that only one range is activated at any given time.

[0061] Optionally, the objective function of the lower-level thermal power unit decision model is to minimize the total thermal power operating cost, and its expression is: ; in, The operating cost of unit i during time period t, taking into account the cost of deep peak shaving.

[0062] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0063] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0064] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0065] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0066] For any computer-readable medium (or computer-readable storage medium) as described above or otherwise, computer instructions may be stored thereon, which, when executed by a processor, implement one or more of the above embodiments, thereby realizing the technical solution of the present invention.

[0067] The present invention also proposes a computer program that, when executed by a processor, implements one or more of the embodiments described above, thereby realizing the technical solution of the present invention. This computer program may be specifically recorded on the above-described or other computer-readable media, and the present invention does not impose any limitations on this.

[0068] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0069] The foregoing has described specific embodiments of the invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0070] The terminology used in one or more embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in one or more embodiments of the invention and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0071] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of the present invention, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of the present invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0072] The above description is merely a preferred embodiment of one or more embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of the present invention should be included within the protection scope of one or more embodiments of the present invention.

Claims

1. A grid peak-shaving and frequency regulation coordination method considering multiple types of power generation energy, characterized in that, include: The wind and solar load forecast data and the conventional parameters of each unit are input into the upper-level flexible resource decision model. With the goal of minimizing the variance of the net load curve, the various flexible resources are coordinated to perform peak shaving and valley filling, resulting in the output plan of multiple types of power generation energy and the optimized net load curve. The upper-level flexible resource decision model considers the curtailment rate constraint, energy storage operation constraint, pumped storage operation constraint and virtual power plant operation constraint. The optimized net load curve is passed to the decision model of the lower-level thermal power unit. The fuel cost function and the three-stage deep peak shaving interval of the thermal power unit are linearized by introducing square auxiliary variables and the big M method. In the lower-level thermal power unit decision model, optimization is performed with the goal of minimizing the operating cost of thermal power, resulting in the power generation plan and frequency regulation reserve capacity of the thermal power units; wherein, the lower-level thermal power unit decision model considers system frequency regulation reserve demand constraints, power balance constraints, and thermal power unit operation constraints; Based on the output plans of the various types of power generation energy, the power generation plans of the thermal power units, and the frequency regulation reserve capacity, the power grid is used for peak shaving and frequency regulation.

2. The method as described in claim 1, characterized in that, The objective function of the upper-level flexible resource decision-making model is to minimize the net load variance, and its expression is: ； in, The net load of the power grid during time period t. This represents the average net load. The original load for time period t, The wind power consumption during period t. Let t be the amount of photovoltaic power consumed during the period. Let t be the output power of the virtual power plant during time period t. The output power of pumped storage during time period t. Let t be the output power of the energy stored during time period t.

3. The method as described in claim 1, characterized in that, The curtailment rate constraint includes: the wind and solar curtailment rate ranges from [0, 1], and the total wind curtailment and total solar curtailment do not exceed a preset proportion of their predicted output, expressed as: ； ； in, Let be the wind power curtailment rate at time t. The photovoltaic curtailment rate at time t ； ； in, The maximum allowable wind curtailment rate, The maximum allowable waste rate, Contribute to photovoltaic power plants To contribute to wind farms.

4. The method as described in claim 1, characterized in that, The energy storage operation constraints include charge and discharge power constraints, state of charge (SOC) constraints, charge and discharge balance constraints within a cycle, and capacity upper limit constraints; the pumped storage operation constraints include output power constraints and reservoir capacity constraints; the virtual power plant operation constraints include its upper and lower output limits, which are proportional to the total wind and solar power output.

5. The method as described in claim 1, characterized in that, The linearization of the fuel cost function and the three-segment deep peak-shaving interval of thermal power units by introducing a squared auxiliary variable and the big M method includes: For the secondary term in the fuel cost of thermal power units Introduce a squared auxiliary variable: ， The Big M method is used to linearize the product after introducing auxiliary variables. ； In the fuel cost of thermal power units replace Furthermore, the nonlinear product is transformed into a linear constraint, and the convex quadratic term in the fuel cost is retained, so that the problem falls within the MIQP framework.

6. The method as described in claim 5, characterized in that, The linearization of the fuel cost function and the three-segment deep peak-shaving interval of thermal power units by introducing a squared auxiliary variable and the big M method includes: Define three mutually exclusive binary variables. , , These respectively indicate that the thermal power unit is in the basic peak-shaving, deep peak-shaving without oil injection, and deep peak-shaving with oil injection state; An activation constraint consisting of the Big M method is set for the output range corresponding to each state, and a minimum value ε is introduced to prevent range overlap, ensuring that only one range is activated at any given time.

7. The method as described in claim 1, characterized in that, The objective function of the decision-making model for the lower-level thermal power units is to minimize the total operating cost of thermal power plants, and its expression is as follows: ； in, The operating cost of unit i during time period t, taking into account the cost of deep peak shaving.

8. A grid peak-shaving and frequency-regulating coordinated device that considers multiple types of power generation energy, characterized in that, The device includes: Peak shaving unit: Input wind and solar load forecast data and conventional parameters of each unit into the upper-level flexible resource decision model. With the goal of minimizing the variance of the net load curve, coordinate various flexible resources to perform peak shaving and valley filling, and obtain the output plan of multiple types of power generation energy and the optimized net load curve. The upper-level flexible resource decision model considers the curtailment rate constraint, energy storage operation constraint, pumped storage operation constraint and virtual power plant operation constraint. Processing unit: The optimized net load curve is transmitted to the decision model of the lower-level thermal power unit, and the fuel cost function and the three-stage deep peak shaving interval of the thermal power unit are linearized by introducing square auxiliary variables and the big M method; Optimization Unit: In the decision-making model of the lower-level thermal power units, optimization is performed with the goal of optimizing the operating cost of thermal power, to obtain the power generation plan and frequency regulation reserve capacity of the thermal power units; wherein, the decision-making model of the lower-level thermal power units considers the system frequency regulation reserve demand constraints, power balance constraints and thermal power unit operation constraints; Frequency regulation unit: Based on the output plans of the various types of power generation energy, the power generation plans of the thermal power units, and the frequency regulation reserve capacity, it performs peak shaving and frequency regulation on the power grid.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in any one of claims 1-7 by running the executable instructions.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-7.

Images