Cross-regional power transaction scheduling method, device and equipment based on epsilon-constraint method and adaptive step length

Abstract

The invention relates to a cross-regional power transaction scheduling method, device and equipment based on an epsilon-constraint method and an adaptive step length. The method comprises the steps of obtaining power data of each regional power system in each scene meeting a step length constraint condition, and establishing a cross-regional power transaction scheduling model formed by an economic objective function and an environmental objective function according to the power data; respectively solving the economic objective function and the environmental objective function by adopting k different epsilon values of an epsilon-constraint method to obtain k first Pareto optimal solutions and k second Pareto optimal solutions; according to all the first Pareto optimal solutions and the second Pareto optimal solutions, a fuzzy membership function and an extreme value standardization method are adopted for processing to obtain an optimal compromise solution of all regional power transaction scheduling in each scene, and cross-regional power transaction scheduling of a power system is facilitated; and an epsilon-constraint method is adopted to solve the cross-regional power transaction scheduling model, and an optimal compromise solution is selected from a plurality of Pareto optimal solutions, so that the operation efficiency and reliability of a power system are improved.

Description

Technical Field

[0001] The present application relates to the technical field of inter-regional power transaction scheduling, and in particular to an inter-regional power transaction scheduling method, apparatus and device based on an ε-constraint method and an adaptive step size. Background Art

[0002] Power grids typically consist of multiple regional power systems interconnected by interconnecting lines. Coordinated dispatch between these regions plays a key role in ensuring the stable, economical, and efficient operation of the entire power system. Optimal dispatch of multi-regional power systems aims to determine the optimal operating mode for each generator set and the power transmission along the interconnecting lines over a given period of time, in order to minimize costs and pollutant emissions while meeting various operational constraints.

[0003] With the large-scale integration of renewable energy sources, such as wind power, the inherent intermittency and uncertainty of renewable energy generation present significant challenges to power system scheduling. The volatility of wind power leads to uncertainty in regional power generation, increasing the difficulty of maintaining power balance and stability in the power system. Solar power generation, on the other hand, is significantly affected by weather and diurnal variations, resulting in unstable output. These uncertainties require scheduling methods to be highly flexible and adaptable to address the power fluctuations caused by renewable energy generation. Furthermore, the generation resource mix and load characteristics vary across regions in a multi-regional power system, further complicating scheduling. Different regions may have different types and sizes of generators, each with varying cost characteristics, environmental impacts, and operational constraints. For example, some regions may rely primarily on traditional fossil fuel generation, while others may have a higher concentration of renewable energy generation facilities. Furthermore, load demand varies significantly across time and space, necessitating effective scheduling to achieve complementary power generation and optimal allocation across regions. Summary of the Invention

[0004] The present application provides a cross-regional power trading scheduling method, device and equipment based on the ε-constraint method and adaptive step size, which is used to solve the technical problem that the power system scheduling is difficult due to differences in the power generation resource composition and load characteristics of each region in the existing multi-regional power system.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] On the one hand, a method for inter-regional power trading scheduling based on an ε-constraint method and an adaptive step size is provided, comprising the following steps:

[0007] Obtain the power data of each regional power system in each scenario that meets the step size constraints;

[0008] Establishing an inter-regional power transaction scheduling model based on the power data of all regions in each scenario, wherein the inter-regional power transaction scheduling model includes an economic objective function and an environmental objective function for multi-regional power transaction scheduling that meet constraint conditions;

[0009] The economic objective function and the environmental objective function are respectively solved using k different ε values ​​of the ε-constraint method to obtain k first Pareto optimal solutions corresponding to the economic objective function and k second Pareto optimal solutions corresponding to the environmental objective function;

[0010] According to all the first Pareto optimal solutions and all the second Pareto optimal solutions, fuzzy membership function and extreme value normalization method are used to process them, so as to obtain the optimal compromise solution for all regional power trading scheduling in each scenario.

[0011] Preferably, obtaining the power data of each regional power system in each scenario that satisfies the step size constraint condition includes:

[0012] S11. Obtain the wind power output power forecast value, wind power output power actual value, photovoltaic output power forecast value, and photovoltaic output power actual value of the power system in each region according to the sampling time step;

[0013] S12. Calculate the adaptive index corresponding to the sampling time step based on the wind power output power prediction value, the wind power output power actual value, the photovoltaic output power prediction value, and the photovoltaic output power actual value;

[0014] S13. If the adaptive index is less than the error calibration value, then obtaining the power data of each regional power system in each scenario according to the sampling time step as the power data of each regional power system in each scenario that satisfies the step constraint;

[0015] S14. If the adaptive index is not less than the error calibration value, dynamically adjust the sampling time step to obtain a sampling update step until an adaptive update index less than the error calibration value is obtained according to the sampling update step using steps S11 and S12. The sampling update step corresponding to the adaptive update index is used as the sampling step, and the power data of each regional power system in each scenario is obtained using the sampling step as the power data of each regional power system in each scenario that satisfies the step constraint.

[0016] The step size constraint condition is that the adaptive index is smaller than the error calibration value.

[0017] Preferably, dynamically adjusting the sampling time step to obtain a sampling update step includes: obtaining a reference step correction value, and dynamically adjusting the sampling time step using a dynamic adjustment step formula according to the reference step correction value, the sampling time step, and the adaptive index to obtain a sampling update step, wherein the dynamic adjustment step formula is: Where, is the dynamically adjusted sampling update step size, is the initial sampling time step, To adjust the time step, is the reference step length correction amount.

[0018] Preferably, the economic objective function is:

[0019]

[0020] The environmental objective function is:

[0021]

[0022] The constraints are:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030] Where s is the scenario, t is the time, a is the region, OF1 is the expected total operating cost, OF2 is the expected total emissions, and p s is the probability of scene s, (t) is the electricity cost transmitted by region a at time t, (t, s) is the power generation cost of the compressed air energy storage system in the power system at time t under scenario s, is the power generation cost of the i-th thermoelectric unit in the power system in the a-th region, is the output power of the ith thermoelectric unit of the power system in the ath region, is the emission of the compressed air energy storage system in the power system of region a, is the emission of the ith thermoelectric unit in the power system in the ath region, is the charging power of the compressed air energy storage system in the a-th regional power system under scenario s, is the wind power output power of the power system in the ath region, is the photovoltaic output power of the power system in the ath region, is the power demand load of the power system in the ath region, is the transmission power of the tie line between the ath region and the a'th region of the power system, The discharge power of the compressed air energy storage system in the power system of the ath region, is the power transmission capacity of the tie line between the ath area and the a'th area, is the transmission power of the tie line between the a'th region and the ath region of the power system, is the minimum output power of the thermoelectric unit; is the maximum output power of the thermoelectric unit, is the ramp-down power of the i-th thermoelectric unit, is the ramp-up power of the i-th thermoelectric unit, The power system in the ath region The output power of the i-th thermoelectric unit at the moment, 、 and are the power generation cost coefficients of the i-th thermoelectric unit, 、 and are the power generation emission coefficients of the i-th thermal power unit.

[0031] Preferably, the best compromise solution for all regional power trading scheduling in each scenario is obtained by processing all the first Pareto optimal solutions and all the second Pareto optimal solutions using a fuzzy membership function and an extreme value normalization method, including:

[0032] Processing each of the first Pareto optimal solutions using a fuzzy membership function to obtain k first objective function values; processing each of the second Pareto optimal solutions using a fuzzy membership function to obtain k second objective function values;

[0033] Select the smallest of the first objective function value and the second objective function value with the same ε value as the target value;

[0034] The largest target value is selected from all the target values ​​as the best compromise solution for all regional power trading scheduling in each scenario.

[0035] On the other hand, an inter-regional power trading scheduling device based on the ε-constraint method and adaptive step size is provided, which includes a data acquisition module, a model construction module, a Pareto solution module and an optimal scheduling solution module;

[0036] The data acquisition module is used to acquire power data of each regional power system in each scenario that meets the step size constraint condition;

[0037] The model building module is used to establish an inter-regional power trading scheduling model based on the power data of all regions in each scenario, wherein the inter-regional power trading scheduling model includes an economic objective function and an environmental objective function for multi-regional power trading scheduling that meet constraint conditions;

[0038] The Pareto solution module is used to solve the economic objective function and the environmental objective function respectively using k different ε values ​​of the ε-constraint method to obtain k first Pareto optimal solutions corresponding to the economic objective function and k second Pareto optimal solutions corresponding to the environmental objective function;

[0039] The optimal scheduling solution module is used to process all the first Pareto optimal solutions and all the second Pareto optimal solutions using a fuzzy membership function and an extreme value normalization method to obtain the optimal compromise solution for all regional power trading scheduling in each scenario.

[0040] Preferably, the data acquisition module includes a data acquisition submodule, a calculation submodule, a first judgment submodule and a second judgment submodule;

[0041] The data acquisition submodule is used to obtain the wind power output power forecast value, wind power output power actual value, photovoltaic output power forecast value and photovoltaic output power actual value of the power system in each area according to the sampling time step;

[0042] The calculation submodule is configured to calculate, based on the wind power output power prediction value, the wind power output power actual value, the photovoltaic output power prediction value, and the photovoltaic output power actual value, to obtain an adaptive index corresponding to the sampling time step;

[0043] The first judgment submodule is configured to obtain, according to the adaptive index being less than the error calibration value, the power data of each regional power system in each scenario according to the sampling time step as the power data of each regional power system in each scenario that satisfies the step constraint condition;

[0044] The second judgment submodule is used to ensure that the adaptive index is not less than the error calibration value, dynamically adjust the sampling time step to obtain a sampling update step, until the adaptive update index less than the error calibration value is obtained by using the data acquisition submodule and the calculation submodule according to the sampling update step, and use the sampling update step corresponding to the adaptive update index as the sampling step, and use the sampling step to obtain the power data of each regional power system in each scenario as the power data of each regional power system in each scenario that meets the step constraint condition.

[0045] Preferably, dynamically adjusting the sampling time step to obtain a sampling update step includes: obtaining a reference step correction value, and dynamically adjusting the sampling time step using a dynamic adjustment step formula according to the reference step correction value, the sampling time step, and the adaptive index to obtain a sampling update step, wherein the dynamic adjustment step formula is: Where, is the dynamically adjusted sampling update step size, is the initial sampling time step, To adjust the time step, is the reference step length correction amount.

[0046] Preferably, the optimal scheduling solution module is also used to use a fuzzy membership function to process each of the first Pareto optimal solutions to obtain k first objective function values; use a fuzzy membership function to process each of the second Pareto optimal solutions to obtain k second objective function values; filter out the smallest one from the first objective function values ​​and the second objective function values ​​with the same ε value as the target value; and filter out the largest one from all the target values ​​as the optimal compromise solution for all regional power trading scheduling in each scenario.

[0047] In another aspect, a terminal device is provided, comprising a processor and a memory;

[0048] The memory is used to store program code and transmit the program code to the processor;

[0049] The processor is configured to execute the above-mentioned inter-regional power transaction scheduling method based on the ε-constraint method and adaptive step size according to the instructions in the program code.

[0050] The inter-regional power transaction scheduling method, device and equipment based on the ε-constraint method and adaptive step size include obtaining power data of each regional power system in each scenario that meets the step size constraint condition; establishing an inter-regional power transaction scheduling model based on the power data of all regions in each scenario, the inter-regional power transaction scheduling model including an economic objective function and an environmental objective function for multi-regional power transaction scheduling that meet the constraint condition; using k different ε values ​​of the ε-constraint method to solve the economic objective function and the environmental objective function respectively, and obtaining k first Pareto optimal solutions corresponding to the economic objective function and k second Pareto optimal solutions corresponding to the environmental objective function; using fuzzy membership function and extreme value normalization method to process all the first Pareto optimal solutions and all the second Pareto optimal solutions, and obtaining the best compromise solution for all regional power transaction scheduling in each scenario.

[0051] It can be seen from the above technical solutions that the present application has the following advantages: the inter-regional power trading scheduling method based on the ε-constraint method and adaptive step size obtains power data through step size constraints to establish an inter-regional power trading scheduling model, reduces the inherent intermittency and uncertainty in the power system, copes with power fluctuations caused by renewable energy generation, and facilitates inter-regional power trading scheduling of the power system; the ε-constraint method is used to transform the multi-objective optimization problem of the inter-regional power trading scheduling model into a single-objective optimization problem, and selects the best compromise solution from several Pareto optimal solutions, providing flexible decision support for decision makers, improving the operating efficiency and reliability of the power system, and solving the technical problem that the power generation resource composition and load characteristics of each region in the existing multi-regional power system are different, resulting in difficult scheduling of the power system.

[0052] The inter-regional power transaction scheduling device based on the ε-constraint method and adaptive step size obtains power data using step size constraints through a data acquisition module, a model construction module, a Pareto solution module, and an optimal scheduling solution module to establish an inter-regional power transaction scheduling model, thereby reducing the inherent intermittency and uncertainty in the power system, coping with power fluctuations caused by renewable energy generation, and facilitating inter-regional power transaction scheduling of the power system; the ε-constraint method is used to transform the multi-objective optimization problem of the inter-regional power transaction scheduling model into a single-objective optimization problem, and the best compromise solution is selected from several Pareto optimal solutions, providing flexible decision support for decision makers and improving the operating efficiency and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0054] Figure 1 This is a flowchart of the steps of the inter-regional power transaction scheduling method based on the ε-constraint method and adaptive step size according to an embodiment of the present application;

[0055] Figure 2 This is a flow chart of the steps for data acquisition in the inter-regional power transaction scheduling method based on the ε-constraint method and adaptive step size described in an embodiment of the present application;

[0056] Figure 3 This is a schematic diagram of the framework of the inter-regional power transaction scheduling device based on the ε-constraint method and adaptive step size according to an embodiment of the present application;

[0057] Figure 4 This is a schematic diagram of the terminal device described in an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0059] In the description of the embodiments of the present application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0060] In the embodiments of the present application, unless otherwise expressly specified or limited, the terms "installed," "connected," "connected," "fixed," etc. should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections, electrical connections; direct connections, or indirect connections through an intermediate medium; internal connections between two components, or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0061] The embodiments of the present application provide a cross-regional power trading scheduling method, device and equipment based on the ε-constraint method and adaptive step size, which solves the technical problem that the power system scheduling is difficult due to differences in the power generation resource composition and load characteristics of each region in the existing multi-regional power system.

[0062] Example 1:

[0063] Figure 1 This is a flowchart of the steps of the inter-regional power trading scheduling method based on the ε-constraint method and adaptive step size described in an embodiment of the present application.

[0064] like Figure 1 As shown, the embodiment of the present application provides an inter-regional power transaction scheduling method based on the ε-constraint method and the adaptive step size, comprising the following steps:

[0065] S1. Obtain the power data of each regional power system in each scenario that meets the step size constraint.

[0066] It should be noted that in step S1, the power data of each regional power system in each scenario that satisfies the step-size constraint is obtained. In this embodiment, with the large-scale integration of renewable energy generation into the power system, such as wind power generation, its inherent intermittency and uncertainty pose a huge challenge to the scheduling of the power system. The volatility of wind power makes the power generation of each region uncertain, increasing the difficulty of maintaining system power balance and stability; solar power generation is also significantly affected by weather and diurnal changes, and its output is unstable. These uncertainties require that the scheduling of each regional power system must have higher flexibility and adaptability to cope with the power fluctuations caused by renewable energy generation. Therefore, in order to reduce the scheduling uncertainty of each regional power system, this inter-regional power trading scheduling method based on the ε-constraint method and adaptive step-size first uses the step-size constraint to obtain the power data of each regional power system in each scenario, providing data for the subsequent construction of the inter-regional power trading scheduling model. The constructed inter-regional power trading scheduling model is more in line with scheduling requirements.

[0067] In an embodiment of the present application, the power data includes: expected total operating cost, expected total emissions, electricity cost, power generation cost, output power, emissions, wind power output power, photovoltaic output power, power demand load, interconnection line transmission power, discharge power, capacity, power generation cost coefficient and power generation emission coefficient, etc.

[0068] S2. Establish an inter-regional power trading and scheduling model based on the power data of all regions in each scenario. The inter-regional power trading and scheduling model includes an economic objective function and an environmental objective function for multi-regional power trading scheduling that meet constraint conditions.

[0069] It should be noted that in step S2, an inter-regional power trading and dispatching model is constructed based on the power data obtained in step S1. The inter-regional power trading and dispatching model includes an economic objective function and an environmental objective function. These two objective functions respectively represent the expected total operating cost and expected total emissions of the total system composed of multiple regional power systems, covering the thermal power production cost, energy storage cost, transmission power cost, and emissions of power generation units and compressed air energy storage systems CAES.

[0070] In the embodiment of the present application, the economic objective function is:

[0071]

[0072] The environmental objective function is:

[0073]

[0074] Where s is the scenario, t is the time, a is the region, OF1 is the expected total operating cost, OF2 is the expected total emissions, and p s is the probability of scene s, (t) is the electricity cost transmitted by region a at time t, (t, s) is the power generation cost of the compressed air energy storage system in the power system at time t under scenario s, is the power generation cost of the i-th thermoelectric unit in the power system in the a-th region, is the output power of the ith thermoelectric unit of the power system in the ath region, is the emission of the compressed air energy storage system in the power system of region a, is the emission of the ith thermoelectric unit in the power system in the ath region, is the charging power of the compressed air energy storage system in the a-th regional power system under scenario s.

[0075] In an embodiment of the present application, for each region, the power balance equation needs to be satisfied, that is, the sum of the output power of the thermoelectric unit, wind power generation power, photovoltaic power generation power, CAES output power and the power of the interconnection line between the adjacent regions in the region is equal to the load demand of the region. Taking into account the dynamic line capacity, the power transmission of the interconnection line must meet the dynamic capacity limit. These constraints together ensure that the power generation scheduling plan can fully consider the characteristics of each power generation unit and energy storage system and the transmission capacity of the interconnection line while meeting the system power balance and operation safety requirements. The constraints include power constraints and thermoelectric unit operation constraints. The power constraints include:

[0076]

[0077]

[0078]

[0079] The operating constraints of the thermoelectric unit are:

[0080]

[0081]

[0082]

[0083]

[0084] Where, is the wind power output power of the power system in the ath region, is the photovoltaic output power of the power system in the ath region, is the power demand load of the power system in the ath region, is the transmission power of the tie line between the ath region and the a'th region of the power system, The discharge power of the compressed air energy storage system in the power system of the ath region, is the power transmission capacity of the tie line between the ath area and the a'th area, is the transmission power of the tie line between the a'th region and the ath region of the power system, is the minimum output power of the thermoelectric unit; is the maximum output power of the thermoelectric unit, is the ramp-down power of the i-th thermoelectric unit, is the ramp-up power of the i-th thermoelectric unit, The power system in the ath region The output power of the i-th thermoelectric unit at the moment, 、 and are the power generation cost coefficients of the i-th thermoelectric unit, 、 and are the power generation emission coefficients of the i-th thermal power unit.

[0085] S3. Use k different ε values ​​of the ε-constraint method to solve the economic objective function and the environmental objective function respectively, and obtain k first Pareto optimal solutions corresponding to the economic objective function and k second Pareto optimal solutions corresponding to the environmental objective function.

[0086] It should be noted that in step S3, the economic and environmental objective functions obtained in step S2 are solved using the ε-constraint method to obtain the corresponding Pareto optimal solutions, providing data for the subsequent optimal scheduling solution. In this embodiment, the ε-constraint method is used to transform the multi-objective optimization problem of the economic and environmental objective functions into a single-objective optimization problem. Specifically, for example, the environmental objective function is converted into a constraint. By setting an upper limit for the environmental constraint (determined by the ε vector), the original problem is transformed into a problem of minimizing the economic objective function while satisfying the environmental constraint. In multi-objective optimization, the Pareto set of Pareto optimal solutions reflects the trade-off between the two objectives. Even if one objective is converted into a constraint, this trade-off still exists. Different ε values ​​effectively explore different aspects of this trade-off, resulting in multiple Pareto optimal solutions. This method can generate a series of Pareto optimal solutions under different ε values, forming a Pareto frontier.

[0087] The ε-constraint formula is as follows. Converting the environmental objective function into the ε constraint condition, we get:

[0088]

[0089]

[0090] Where, is the environmental constraint coefficient; and are the minimum and maximum values ​​of a single environmental target, respectively; is the first current iteration number; The first total number of iterations M is generally set directly, which can be understood as setting the number of different ε values. According to the number of iterations, we can calculate the following:

[0091]

[0092]

[0093] Where, is the constraint coefficient; and are the minimum and maximum values ​​of a single economic target, respectively; is the second current iteration number; is the second total number of iterations, where the second total number of iterations Generally, it is set directly, which can be understood as setting the number of different ε values, and the second current iteration number. According to the number of iterations, the ε constraint condition indicates that the economic objective function is optimized under the premise of satisfying the environmental constraints. The environmental constraint upper limit, determined by the ε-vector, is used to control emissions. The original objective function is transformed into new constraints, thereby converting the multi-objective function into a single objective function for solution. The ε-constraint method allows the environmental objective function to vary within different ranges by changing the value of ε. Each ε value represents a different degree of restriction on environmental emissions. When ε takes different values, the feasible domain of the optimization problem changes, potentially leading to different optimal solutions. Although the problem is transformed into a single-objective form, it still contains two conflicting objectives (minimizing economic costs and minimizing environmental emissions). Therefore, under different values ​​of ε, a series of solutions that achieve different balances between economic costs and environmental emissions may be obtained. The Pareto optimal solution set, composed of k first Pareto optimal solutions and k second Pareto optimal solutions, provides data for the subsequent search for the optimal compromise solution.

[0094] S4. According to all the first Pareto optimal solutions and all the second Pareto optimal solutions, the fuzzy membership function and extreme value normalization method are used to process them to obtain the optimal compromise solution for all regional power trading scheduling in each scenario.

[0095] It should be noted that in step S4, the best compromise solution is selected from the Pareto optimal solution set consisting of the k first Pareto optimal solutions and the k second Pareto optimal solutions obtained in step S3 using a fuzzy satisfaction method. In this embodiment, a fuzzy membership function is assigned to each solution on the Pareto frontier, and linear fuzzy membership functions are used for total cost and total emissions, respectively. The best compromise solution is then selected based on an extreme value normalization method (such as the min-max method). That is, for each ε value, the minimum value of the economic objective function and the environmental objective function is selected, and the solution with the largest of all minimum values ​​is selected as the best compromise solution.

[0096] In an embodiment of the present application, the inter-regional power trading scheduling method based on the ε-constraint method and adaptive step size obtains power data through step size constraints to establish an inter-regional power trading scheduling model, reduces the intermittency and uncertainty inherent in the power system, copes with power fluctuations caused by renewable energy generation, and facilitates inter-regional power trading scheduling of the power system; the ε-constraint method is used to transform the multi-objective optimization problem of the inter-regional power trading scheduling model into a single-objective optimization problem, and selects the best compromise solution from several Pareto optimal solutions, providing decision makers with flexible decision support and improving the operating efficiency and reliability of the power system.

[0097] It should be noted that this inter-regional power trading scheduling method based on the ε-constraint method and adaptive step size adopts an inter-regional power trading scheduling model that takes into account the uncertainty of renewable energy sources, such as the volatility of wind power generation. Through the application of reasonable scheduling strategies and energy storage technologies, it can reduce the impact of renewable energy uncertainty on the power system and improve the operating efficiency and reliability of the power system. Therefore, this inter-regional power trading scheduling method based on the ε-constraint method and adaptive step size can be widely applied in the field of inter-regional power trading scheduling optimization.

[0098] The present application provides an inter-regional power trading scheduling method based on an ε-constraint method and an adaptive step size, comprising obtaining power data of each regional power system in each scenario that satisfies a step size constraint; establishing an inter-regional power trading scheduling model based on the power data of all regions in each scenario, the inter-regional power trading scheduling model including an economic objective function and an environmental objective function for multi-regional power trading scheduling that satisfy the constraint conditions; using k different ε values ​​of the ε-constraint method to solve the economic objective function and the environmental objective function respectively, obtaining k first Pareto optimal solutions corresponding to the economic objective function and k second Pareto optimal solutions corresponding to the environmental objective function; using a fuzzy membership function and an extreme value normalization method to process all the first Pareto optimal solutions and all the second Pareto optimal solutions, obtaining the best compromise solution for power trading scheduling in all regions in each scenario. This inter-regional power trading scheduling method based on the ε-constraint method and adaptive step size obtains power data through step size constraints to establish an inter-regional power trading scheduling model, reduces the inherent intermittency and uncertainty in the power system, copes with power fluctuations caused by renewable energy generation, and facilitates inter-regional power trading scheduling of the power system; the ε-constraint method is used to transform the multi-objective optimization problem of the inter-regional power trading scheduling model into a single-objective optimization problem, and the best compromise solution is selected from several Pareto optimal solutions, providing flexible decision support for decision makers, improving the operating efficiency and reliability of the power system, and solving the technical problem that the power generation resource composition and load characteristics of each region in the existing multi-regional power system are different, resulting in difficult scheduling of the power system.

[0099] Figure 2This is a flow chart of the steps for data acquisition in the inter-regional power trading scheduling method based on the ε-constraint method and adaptive step size described in an embodiment of the present application.

[0100] like Figure 2 As shown, in one embodiment of the present application, obtaining power data of each regional power system in each scenario that satisfies the step size constraint condition includes:

[0101] S11. Obtain the wind power output power forecast value, wind power output power actual value, photovoltaic output power forecast value, and photovoltaic output power actual value of the power system in each region according to the sampling time step;

[0102] S12. Calculate the adaptive index corresponding to the sampling time step based on the predicted wind power output power, the actual wind power output power, the predicted photovoltaic output power, and the actual photovoltaic output power;

[0103] S13. If the adaptive index is less than the error calibration value, the power data of each regional power system in each scenario is obtained according to the sampling time step as the power data of each regional power system in each scenario that meets the step constraint;

[0104] S14. If the adaptive index is not less than the error calibration value, dynamically adjust the sampling time step to obtain a sampling update step until an adaptive update index less than the error calibration value is obtained according to the sampling update step using steps S11 and S12. The sampling update step corresponding to the adaptive update index is used as the sampling step. The power data of each regional power system in each scenario is obtained using the sampling step as the power data of each regional power system in each scenario that satisfies the step constraint.

[0105] Among them, the step size constraint condition is that the adaptive index is less than the error calibration value.

[0106] It should be noted that the cross-regional power trading scheduling method based on the ε-constraint method and adaptive step size first collects the wind power output power forecast value, wind power output power actual value, photovoltaic output power forecast value and photovoltaic output power actual value according to the preset sampling time step ΔT, and then calculates the adaptive index based on the collected data using the adaptive index formula; finally, it is judged whether the sampling time step needs to be adjusted based on the adaptive index and the error calibration value. If adjustment is required, the rolling step size needs to be shortened to improve the scheduling accuracy. Specifically, the time step size dynamic adjustment rule is used for adjustment to improve the accuracy of the collected data. In this embodiment, the adaptive index formula is:

[0107]

[0108] Where, is the wind power prediction error index, is the photovoltaic prediction error index, is an adaptive indicator, 、 are the wind power output power forecast value and the wind power output power actual value, 、 are the predicted value of photovoltaic output power and the actual value of photovoltaic output power, and are weight coefficients of different values. < When , the sampling time step needs to be adjusted. is the error calibration value.

[0109] In one embodiment of the present application, dynamically adjusting the sampling time step to obtain the sampling update step includes: obtaining a reference step correction value, and dynamically adjusting the sampling time step using a dynamic adjustment step formula based on the reference step correction value, the sampling time step, and the adaptive index to obtain the sampling update step. The dynamic adjustment step formula is: Where, is the dynamically adjusted sampling update step size, is the initial sampling time step, To adjust the time step, is the reference step length correction amount.

[0110] It should be noted that the time step dynamic adjustment rule refers to the use of the dynamic adjustment step formula to adjust the sampling time step under the prediction time scale to obtain an adaptive update index that satisfies the error calibration value. In this embodiment, the inter-regional power transaction scheduling method based on the ε-constraint method and the adaptive step size also uses the sampling update step size to adjust the sampling time step size. and the minimum sampling interval Perform product calculation to obtain the control time domain , but also by controlling the time domain and the number of prediction time domains set Perform product calculation to get the predicted time domain .

[0111] In one embodiment of the present application, all first Pareto optimal solutions and all second Pareto optimal solutions are processed using a fuzzy membership function and an extreme value normalization method to obtain the optimal compromise solution for all regional power trading scheduling in each scenario, including:

[0112] Each first Pareto optimal solution is processed using a fuzzy membership function to obtain k first objective function values; each second Pareto optimal solution is processed using a fuzzy membership function to obtain k second objective function values;

[0113] Select the smallest one from the first objective function value and the second objective function value with the same ε value as the target value;

[0114] The largest target value is selected from all target values ​​as the optimal compromise solution for all regional power trading dispatch in each scenario.

[0115] It should be noted that the fuzzy membership function is:

[0116]

[0117] Where, is the first Pareto optimal solution or the second Pareto optimal solution, is the minimum of all first Pareto optimal solutions or the minimum of all second Pareto optimal solutions, is the maximum of all first Pareto optimal solutions or the maximum of all second Pareto optimal solutions, is the first objective function value or the second objective function value. In this embodiment, the smallest of the first and second objective function values ​​for the same ε value is selected as a target value; the largest of the k target values ​​is then selected as the optimal compromise solution for all regional power trading scheduling in each scenario. Power trading scheduling in each regional power system is performed using the optimal compromise solution, thereby improving the economic and environmental benefits of power system scheduling.

[0118] Example 2:

[0119] Figure 3 This is a schematic diagram of the framework of the inter-regional power trading scheduling device based on the ε-constraint method and adaptive step size described in an embodiment of the present application.

[0120] like Figure 3 As shown, the embodiment of the present application provides an inter-regional power transaction scheduling device based on the ε-constraint method and adaptive step size, including a data acquisition module 10, a model construction module 20, a Pareto solution module 30 and an optimal scheduling solution module 40;

[0121] The data acquisition module 10 is used to obtain the power data of each regional power system in each scenario that meets the step size constraint condition;

[0122] a model building module 20 for establishing an inter-regional power trading scheduling model based on the power data of all regions in each scenario, wherein the inter-regional power trading scheduling model includes an economic objective function and an environmental objective function for multi-regional power trading scheduling that satisfy constraint conditions;

[0123] A Pareto solution module 30 is configured to solve the economic objective function and the environmental objective function respectively using k different ε values ​​of the ε-constraint method to obtain k first Pareto optimal solutions corresponding to the economic objective function and k second Pareto optimal solutions corresponding to the environmental objective function;

[0124] The optimal scheduling solution module 40 is used to process all the first Pareto optimal solutions and all the second Pareto optimal solutions using a fuzzy membership function and an extreme value normalization method to obtain the best compromise solution for all regional power trading scheduling in each scenario.

[0125] It should be noted that the contents of the modules in the device of Example 2 have been described in the contents of the steps in the method of Example 1. In this embodiment, the contents of the modules of the inter-regional power trading and scheduling device based on the ε-constraint method and adaptive step size are no longer repeated. In this embodiment, the inter-regional power trading and scheduling device based on the ε-constraint method and adaptive step size uses step size constraints to obtain power data through a data acquisition module, a model construction module, a Pareto solution module, and an optimal scheduling solution module to establish an inter-regional power trading and scheduling model, thereby reducing the inherent intermittency and uncertainty in the power system, coping with power fluctuations caused by renewable energy generation, and facilitating inter-regional power trading and scheduling of the power system; the ε-constraint method is used to transform the multi-objective optimization problem of the inter-regional power trading and scheduling model into a single-objective optimization problem, and the best compromise solution is selected from several Pareto optimal solutions, providing flexible decision support for decision makers and improving the operating efficiency and reliability of the power system.

[0126] In the embodiment of the present application, the data acquisition module 10 includes a data acquisition submodule, a calculation submodule, a first judgment submodule and a second judgment submodule;

[0127] The data acquisition submodule is used to obtain the wind power output power forecast value, wind power output power actual value, photovoltaic output power forecast value and photovoltaic output power actual value of the power system in each area according to the sampling time step;

[0128] A calculation submodule is used to calculate the adaptive index corresponding to the sampling time step based on the wind power output power prediction value, the wind power output power actual value, the photovoltaic output power prediction value and the photovoltaic output power actual value;

[0129] The first judgment submodule is configured to obtain the power data of each regional power system in each scenario according to the sampling time step according to the adaptive index being less than the error calibration value as the power data of each regional power system in each scenario that satisfies the step constraint condition;

[0130] The second judgment submodule is used to ensure that the adaptive index is not less than the error calibration value, and dynamically adjust the sampling time step to obtain the sampling update step until the adaptive update index less than the error calibration value is obtained by adopting the data acquisition submodule and the calculation submodule according to the sampling update step, and the sampling update step corresponding to the adaptive update index is used as the sampling step, and the sampling step is used to obtain the power data of each regional power system in each scenario as the power data of each regional power system in each scenario that meets the step constraint condition.

[0131] In an embodiment of the present application, dynamically adjusting the sampling time step to obtain the sampling update step includes: obtaining a reference step correction value, and dynamically adjusting the sampling time step using a dynamic adjustment step formula according to the reference step correction value, the sampling time step, and the adaptive index to obtain the sampling update step. The dynamic adjustment step formula is: Where, is the dynamically adjusted sampling update step size, is the initial sampling time step, To adjust the time step, is the reference step length correction amount.

[0132] In an embodiment of the present application, the optimal scheduling solution module 40 is also used to use a fuzzy membership function to process each first Pareto optimal solution to obtain k first objective function values; use a fuzzy membership function to process each second Pareto optimal solution to obtain k second objective function values; filter out the smallest one from the first objective function values ​​and the second objective function values ​​with the same ε value as the target value; and filter out the largest one from all target values ​​as the optimal compromise solution for all regional power trading scheduling in each scenario.

[0133] Example 3:

[0134] Figure 4 This is a schematic diagram of the terminal device described in an embodiment of the present application.

[0135] like Figure 4 As shown, an embodiment of the present application provides a terminal device, including a processor and a memory;

[0136] A memory, configured to store program codes and transmit the program codes to a processor;

[0137] The processor is configured to execute the above-mentioned inter-regional power transaction scheduling method based on the ε-constraint method and the adaptive step size according to the instructions in the program code.

[0138] It should be noted that the processor is configured to execute the steps in the aforementioned embodiment of the inter-regional power trading scheduling method based on the ε-constraint method and adaptive step size according to the instructions in the program code. Alternatively, the processor implements the functions of the modules / units in the aforementioned system / device embodiments when executing the computer program.

[0139] For example, a computer program may be divided into one or more modules / units, one or more of which are stored in a memory and executed by a processor to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in a terminal device.

[0140] Terminal devices can be computing devices such as desktop computers, laptops, PDAs, and cloud servers. Terminal devices may include, but are not limited to, processors and memory. Those skilled in the art will appreciate that this does not constitute a limitation on terminal devices and may include more or fewer components than shown, or a combination of certain components, or different components. For example, terminal devices may also include input / output devices, network access devices, buses, and the like.

[0141] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0142] Memory can be an internal storage unit of a terminal device, such as a hard drive or memory. It can also be an external storage device, such as a plug-in hard drive, a SmartMedia Card (SMC), a Secure Digital (SD) card, or a flash memory card. Furthermore, memory can include both internal and external storage units. Memory is used to store computer programs and other programs and data required by the terminal device. Memory can also be used to temporarily store data that has been output or is about to be output.

[0143] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0144] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0145] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0146] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0147] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0148] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A cross-regional power trading scheduling method based on ε-constraint method and adaptive step size, characterized by: The following steps are involved: Obtain the power data of each regional power system in each scenario that meets the step size constraints; Establishing an inter-regional power transaction scheduling model based on the power data of all regions in each scenario, wherein the inter-regional power transaction scheduling model includes an economic objective function and an environmental objective function for multi-regional power transaction scheduling that meet constraint conditions; The economic objective function and the environmental objective function are respectively solved using k different ε values ​​of the ε-constraint method to obtain k first Pareto optimal solutions corresponding to the economic objective function and k second Pareto optimal solutions corresponding to the environmental objective function; According to all the first Pareto optimal solutions and all the second Pareto optimal solutions, fuzzy membership function and extreme value normalization method are used to process them, so as to obtain the optimal compromise solution for all regional power trading scheduling in each scenario.

2. The inter-regional power transaction scheduling method based on the ε-constraint method and adaptive step size according to claim 1 is characterized in that: Obtaining the power data of each regional power system in each scenario that meets the step size constraints includes: S11. Obtain the wind power output power forecast value, wind power output power actual value, photovoltaic output power forecast value, and photovoltaic output power actual value of the power system in each region according to the sampling time step; S12. Calculate the adaptive index corresponding to the sampling time step based on the wind power output power prediction value, the wind power output power actual value, the photovoltaic output power prediction value, and the photovoltaic output power actual value; S13. If the adaptive index is less than the error calibration value, then obtaining the power data of each regional power system in each scenario according to the sampling time step as the power data of each regional power system in each scenario that satisfies the step constraint; S14. If the adaptive index is not less than the error calibration value, dynamically adjust the sampling time step to obtain a sampling update step until an adaptive update index less than the error calibration value is obtained according to the sampling update step using steps S11 and S12. The sampling update step corresponding to the adaptive update index is used as the sampling step, and the power data of each regional power system in each scenario is obtained using the sampling step as the power data of each regional power system in each scenario that satisfies the step constraint. The step size constraint condition is that the adaptive index is smaller than the error calibration value.

3. The inter-regional power transaction scheduling method based on the ε-constraint method and adaptive step size according to claim 2 is characterized in that: Dynamically adjusting the sampling time step to obtain a sampling update step includes: obtaining a reference step correction value, and dynamically adjusting the sampling time step using a dynamic adjustment step formula according to the reference step correction value, the sampling time step, and the adaptive index to obtain a sampling update step, wherein the dynamic adjustment step formula is: Where, is the dynamically adjusted sampling update step size, is the initial sampling time step, To adjust the time step, is the reference step length correction amount.

4. The inter-regional power transaction scheduling method based on the ε-constraint method and adaptive step size according to any one of claims 1 to 3, characterized in that: The economic objective function is: The environmental objective function is: The constraints are: Where s is the scenario, t is the time, a is the region, OF1 is the expected total operating cost, OF2 is the expected total emissions, and p s is the probability of scene s, (t) is the electricity cost transmitted by region a at time t, (t, s) is the power generation cost of the compressed air energy storage system in the power system at time t under scenario s, is the power generation cost of the i-th thermoelectric unit in the power system in the a-th region, is the output power of the ith thermoelectric unit of the power system in the ath region, is the emission of the compressed air energy storage system in the power system of region a, is the emission of the ith thermoelectric unit in the power system in the ath region, is the charging power of the compressed air energy storage system in the a-th regional power system under scenario s, is the wind power output power of the power system in the ath region, is the photovoltaic output power of the power system in the ath region, is the power demand load of the power system in the ath region, is the transmission power of the tie line between the ath region and the a'th region of the power system, The discharge power of the compressed air energy storage system in the power system of the ath region, is the power transmission capacity of the tie line between the ath area and the a'th area, is the transmission power of the tie line between the a'th region and the ath region of the power system, is the minimum output power of the thermoelectric unit; is the maximum output power of the thermoelectric unit, is the ramp-down power of the i-th thermoelectric unit, is the ramp-up power of the i-th thermoelectric unit, The power system in the ath region The output power of the i-th thermoelectric unit at the moment, 、 and are the power generation cost coefficients of the i-th thermoelectric unit, 、 and are the power generation emission coefficients of the i-th thermal power unit.

5. The inter-regional power transaction scheduling method based on the ε-constraint method and adaptive step size according to any one of claims 1 to 3, characterized in that: Based on all the first Pareto optimal solutions and all the second Pareto optimal solutions, the fuzzy membership function and extreme value normalization method are used to process and obtain the optimal compromise solutions for all regional power trading scheduling in each scenario, including: Processing each of the first Pareto optimal solutions using a fuzzy membership function to obtain k first objective function values; processing each of the second Pareto optimal solutions using a fuzzy membership function to obtain k second objective function values; Select the smallest of the first objective function value and the second objective function value with the same ε value as the target value; The largest target value is selected from all the target values ​​as the best compromise solution for all regional power trading scheduling in each scenario.

6. A cross-regional power trading scheduling device based on ε-constraint method and adaptive step size, characterized in that: include: Data acquisition module, model building module, Pareto solution module and optimal scheduling solution module; The data acquisition module is used to obtain power data of each regional power system in each scenario that meets the step size constraint condition; The model building module is used to establish an inter-regional power trading scheduling model based on the power data of all regions in each scenario, wherein the inter-regional power trading scheduling model includes an economic objective function and an environmental objective function for multi-regional power trading scheduling that meet constraint conditions; The Pareto solution module is used to solve the economic objective function and the environmental objective function respectively using k different ε values ​​of the ε-constraint method to obtain k first Pareto optimal solutions corresponding to the economic objective function and k second Pareto optimal solutions corresponding to the environmental objective function; The optimal scheduling solution module is used to process all the first Pareto optimal solutions and all the second Pareto optimal solutions using a fuzzy membership function and an extreme value normalization method to obtain the optimal compromise solution for all regional power trading scheduling in each scenario.

7. The inter-regional power transaction scheduling device based on the ε-constraint method and adaptive step size according to claim 6 is characterized in that: The data acquisition module includes a data acquisition submodule, a calculation submodule, a first judgment submodule and a second judgment submodule; The data acquisition submodule is used to obtain the wind power output power forecast value, wind power output power actual value, photovoltaic output power forecast value and photovoltaic output power actual value of the power system in each area according to the sampling time step; The calculation submodule is configured to calculate, based on the wind power output power prediction value, the wind power output power actual value, the photovoltaic output power prediction value, and the photovoltaic output power actual value, to obtain an adaptive index corresponding to the sampling time step; The first judgment submodule is configured to obtain, according to the adaptive index being less than the error calibration value, the power data of each regional power system in each scenario according to the sampling time step as the power data of each regional power system in each scenario that satisfies the step constraint condition; The second judgment submodule is used to ensure that the adaptive index is not less than the error calibration value, dynamically adjust the sampling time step to obtain a sampling update step, until the adaptive update index less than the error calibration value is obtained by using the data acquisition submodule and the calculation submodule according to the sampling update step, and use the sampling update step corresponding to the adaptive update index as the sampling step, and use the sampling step to obtain the power data of each regional power system in each scenario as the power data of each regional power system in each scenario that meets the step constraint condition.

8. The inter-regional power transaction scheduling device based on the ε-constraint method and adaptive step size according to claim 7 is characterized in that: Dynamically adjusting the sampling time step to obtain a sampling update step includes: obtaining a reference step correction value, and dynamically adjusting the sampling time step using a dynamic adjustment step formula according to the reference step correction value, the sampling time step, and the adaptive index to obtain a sampling update step, wherein the dynamic adjustment step formula is: Where, is the dynamically adjusted sampling update step size, is the initial sampling time step, To adjust the time step, is the reference step length correction amount.

9. The inter-regional power transaction scheduling device based on the ε-constraint method and adaptive step size according to claim 6, characterized in that: The optimal scheduling solution module is also used to use a fuzzy membership function to process each of the first Pareto optimal solutions to obtain k first objective function values; use a fuzzy membership function to process each of the second Pareto optimal solutions to obtain k second objective function values; filter out the smallest one from the first objective function values ​​and the second objective function values ​​with the same ε value as the target value; and filter out the largest one from all the target values ​​as the optimal compromise solution for all regional power trading scheduling in each scenario.

10. A terminal device, characterized in that: including a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the inter-regional power trading scheduling method based on the ε-constraint method and adaptive step size as described in any one of claims 1 to 5 according to the instructions in the program code.

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