Multi-flexible resource aggregation matching method based on reducing wind energy waste rate

Abstract

The present application proposes a multi-flexible resource aggregation matching method based on reducing wind energy waste rate in the field of energy. The method supports the consumption of wind power by electric vehicles, and if there is still surplus wind power, the producer and consumer are responsible for the consumption of wind power to ensure the tracking of the wind power abandoned curve. By analyzing the feasibility of electric vehicles consuming wind power, and based on the wind power output law, the charging law of electric vehicles is analyzed, after the preliminary aggregation optimization of the electric vehicle load and the abandoned wind curve, the deviation between the aggregated load and the tracking abandoned wind curve is further reduced, and the producer and consumer are taken as the coordinated supplementary resources. While reducing the continuous tracking deviation of the abandoned wind curve, the total cost is considered, finally, the minimum deviation of the tracking abandoned wind curve and the minimum cost are taken as the target, the weight of each index is determined by the entropy value of the index through the entropy weight method, and the flexible resource system considering the electric vehicles is optimized and evaluated. It is suitable to be used as a resource aggregation matching method.

Description

Technical Field

[0001] This invention relates to the comprehensive energy allocation in the field of electricity, and is particularly applicable to the energy allocation of wind power generation. Specifically, it is a flexible resource aggregation and matching method based on reducing wind energy waste. Background Technology

[0002] As the penetration rate of new energy power generation in the power system increases, the output of new energy sources has strong randomness and intermittency. For example, solar power generation has obvious periodicity, while wind power generation has obvious randomness. Moreover, wind turbines need to absorb reactive power during operation, which is not conducive to the safe and economical operation of the power grid.

[0003] Because new energy sources have lower costs, higher flexibility, and less pollution, they can replace traditional non-renewable energy sources and are gradually gaining prominence in new power systems. As large-scale new energy sources are integrated into the power grid, the demand for flexible resources is also increasing significantly.

[0004] Wind energy is formed by the flow of solar radiation. Compared with other energy sources, wind energy has significant advantages: it has vast reserves, ten times that of hydropower, is widely distributed, and is inexhaustible, making it particularly important for islands and remote areas with poor transportation and far from the main power grid. The most common form of wind energy utilization is wind power generation. Common problems with wind power generation include poor stability, high uncontrollability, and strong random fluctuations, resulting in a large amount of wasted wind energy and posing challenges to the economic operation of the power system.

[0005] The strong random fluctuations of wind power, leading to significant wind curtailment, pose a challenge to the economic operation of the power system. Utilizing demand-side flexibility resources in renewable energy consumption offers a solution to this problem. However, most current methods consider only a single aspect of demand-side flexibility measures, failing to meet the flexibility requirements of high-proportion renewable energy grid integration. Existing research largely focuses on power modeling, optimization simulation, and evaluation of dual-segment flexibility resources such as wind-solar, wind-hydro, or wind-solar-storage. For electric vehicle integration, it only considers matching with a single distributed power source, rarely considering scenarios involving integrated flexibility resources such as wind farms, producers / consumers, and electric vehicles. The rapid development of distributed generation technology, microgrids, and V2G (Vehicle-to-grid) technology has led to the integration of more and more flexibility resources into the grid, injecting flexibility into the power system. However, how to optimize the allocation of diverse flexibility resources and ensure their effective utilization remains a pressing issue. Summary of the Invention

[0006] To optimize the allocation of flexible resources and ensure their rational and effective distribution, this invention proposes a multi-flexible resource aggregation and matching method based on reducing wind energy waste. This method analyzes the feasibility of electric vehicles absorbing wind power and the charging patterns of electric vehicles based on wind power output patterns. After initially aggregating and optimizing the wind curtailment curve for electric vehicle loads, it further reduces the deviation between the aggregated load and the tracking wind curtailment curve. Producers and consumers are used as a coordinated supplementary resource to improve the tracking accuracy of the renewable energy wind curtailment curve, reduce wind curtailment, and lower the aggregation cost of electric vehicles and producers / consumers. This addresses the technical problems of the intermittency and volatility of wind power output.

[0007] The solution adopted by this invention to solve the technical problem is: The multi-flexible resource aggregation and matching method based on the lowest wind energy waste rate includes the following steps: Step 1: Analyze the feasibility of electric vehicles absorbing wind power, and track the amount of wind power curtailment based on the charging pattern of electric vehicles and the coordination relationship between electric vehicle load and wind power output. Step 2: Based on the flexibility and controllability of electric vehicle load, adjust the electric vehicle load aggregation scheme and establish an electric vehicle load aggregation model; Step 3: After the initial aggregation of electric vehicle load, if there are still times when wind curtailment occurs, it is necessary to further calculate the deviation between the aggregated load and the tracking wind curtailment curve, and introduce electric vehicles as a priority adjustment resource, and introduce producers and consumers as a coordinated supplementary resource. Step 4: The larger the difference between the aggregated load of electric vehicles and the wind curtailment power consumption stipulated in the contract, the greater the cost of the load aggregator. The total cost of wind power consumption is calculated, including the aggregation cost and the coordination cost between producers and consumers. Step 5: Based on the objectives of minimizing tracking deviation and minimizing total cost, the Grey Wolf algorithm is applied for optimization and solution. The entropy weight method is used to assign weights to each index, which yields the comprehensive evaluation index (CEI) for this multi-objective programming problem. The optimization result is determined based on the CEI of the pre-selected scheme.

[0008] Positive effects: Compared with the prior art, the present invention has the following beneficial effects: (1) The proportion of electric vehicle charging load participating in the interactive response of power grids at all levels in my country is less than 1%. Electric vehicle load is currently recognized as a potential resource yet to be developed. This invention considers aggregating various flexible resources including electric vehicles in the power system to fully explore and coordinate the regulation potential of flexible resources.

[0009] (2) Eliminate the constraints that limit the absorption of new energy in the distribution network and reduce the waste rate of clean energy. Since the mobile energy storage capacity of electric vehicles is limited, this invention introduces a producer-consumer, a backup resource with adjustment capabilities, in order to more accurately absorb wind power.

[0010] (3) This invention determines the objective function, constraints and optimization algorithm of the optimization problem, and performs a comprehensive optimization strategy based on the optimized solution set.

[0011] In summary, this invention is suitable for application as a flexible resource aggregation and matching method based on reducing wind energy waste. Attached Figure Description

[0012] Figure 1 This is a framework diagram of the present invention based on the wind curtailment curve after electric vehicle load aggregation; Figure 2 This is a flowchart illustrating the process of electric vehicles and producer-consumer joint tracking of wind power according to the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0014] In this invention, all embodiments, implementation methods, and features can be combined with each other without contradiction or conflict. In this invention, conventional equipment, devices, and components can be commercially available or self-made according to the disclosure of this invention. In this invention, to highlight the key points, some conventional operations and equipment, devices, and components are omitted or only briefly described.

[0015] Figure 1 This is a framework diagram of the wind energy absorption curve after the electric vehicle load aggregation according to the present invention. (See also...) Figure 1 A flexible resource aggregation and matching method based on reducing wind energy waste rate is implemented through the following steps: Step 1: Analyze the feasibility of electric vehicles absorbing wind power, and track the amount of wind power curtailed based on the charging pattern of electric vehicles and the coordination relationship between electric vehicle load and wind power output.

[0016] Peak wind power output is concentrated between 0:00 and 6:15, while peak load is concentrated between 07:00 and 22:00. The overlap between the two peak periods results in the incomplete utilization of energy generated by wind power. Electric vehicles, as mobile energy storage units, can store electricity during peak wind power output periods, serving as a unit for wind power consumption. Peak charging times for electric vehicles are concentrated in the periods of 12:00-14:00, 15:00-17:00, and 23:00 to 01:00 the next day, which improves the feasibility of effectively aggregating electric vehicle load and tracking the consumption of wind power.

[0017] Step 2: Based on the flexibility and controllability of electric vehicle load, adjust the electric vehicle load aggregation scheme and establish an electric vehicle load aggregation model.

[0018] The difference between load power and curtailed power on the generation side in each time period is used as an indicator to measure the tracking deviation of the wind curtailment curve. The smaller the deviation, the smaller the sum of the absolute values ​​of the deviations between load and wind curtailment power in each time period is taken as the optimization objective. (1) In the formula, F is the deviation of wind power consumption after the electric vehicle load is aggregated and dispatched; T is the total number of time periods; For time period t, the predicted wind curtailment power of wind power generators; The electrical load power excluding electric vehicles during time period t; For Boolean variables, =1 indicates that the electric vehicle cluster i is invoked. =0 indicates that the electric vehicle cluster i is not mobilized; Let t be the load power of electric vehicle cluster i during time period t; Let J be the power generation capacity of conventional power source j during time period t; J is the total number of conventional power sources. For time intervals.

[0019] The state of charge (SOC) of the electric vehicle cluster is based on a single electric vehicle, and the SOC constraints are as follows: (2) (3) (4) (5) In the formula, , These represent the charging and discharging power of electric vehicle cluster i during time period t; , These are the upper limits for charging and discharging power of the electric vehicle cluster, respectively. , Time periods t , t +1 Electric Vehicle Cluster i The state of charge; The charging and discharging efficiency of electric vehicle clusters; Let i be the rated capacity of the electric vehicle cluster.

[0020] Electric vehicle loads are characterized by small capacity, dispersed distribution, and large numbers. Aggregating and absorbing wind power from these loads requires a certain scale. Therefore, the aggregated electric vehicle charging capacity should be sufficient in scale and considerable in capacity, and possess a certain response potential to enable unified dispatch of demand-side resources by the electricity market. In other words, it needs to meet the following requirements: (6) In the formula, This represents the minimum proportion of resources aggregated for electric vehicles that constitute the demand-side resources. This refers to the electricity consumption of demand-side resources.

[0021] Step 3: After the initial aggregation of electric vehicle load, if there are still times when wind curtailment occurs, it is necessary to further calculate the deviation between the aggregated load and the tracking wind curtailment curve, and introduce electric vehicles as a priority adjustment resource, and introduce producers and consumers as a coordinated supplementary resource.

[0022] Based on the effect of load aggregators in tracking the curtailment curve, prosumers can choose between charging and discharging states to achieve bidirectional energy regulation, thereby truly realizing continuous tracking of the curtailment curve. The overall flowchart of the aggregation optimization model is shown in Figure 2. When the electric vehicle load is less than the curtailment power, the prosumer charges; when the electric vehicle load is greater than the curtailment power, the prosumer discharges. The objective function of the prosumer joint optimization model is shown in Equation (7), and the constraints are shown in Equations (8) to (14).

[0023] (7) (8) (9) (10) + =1 (11) (12) (13) (14) In the formula, The net charging and discharging power of the energy storage system during time period t; For the charging and discharging efficiency of the energy storage system; , These are 0-1 variables representing the charging and discharging states of consumers during time period t, respectively. During charging... =1、 =0, during discharge there is =0、 =1; When neither charging nor discharging, there is... =0、 =0; , These represent the charging and discharging power of producers and consumers during time period t; , These represent the upper limits of charging and discharging power for consumers during time period t; , These are the charging / discharging power configuration and capacity configuration for producers and consumers, respectively. , The states of charge of producers and consumers at time periods t and t+1 are respectively; , These are the initial electric vehicle aggregation cost and the electric vehicle aggregation cost after incorporating joint optimization by prosumers and consumers, respectively. For time intervals.

[0024] Step 4: The greater the difference between the aggregated load of electric vehicles and the wind curtailment power consumption stipulated in the contract, the greater the cost of the aggregated load provider. The total cost of wind power consumption is calculated, including the aggregation cost and the coordination cost between producers and consumers.

[0025] (15) (16) The sum of the order price and incentive price S for the user load.

[0026] (17) Compensate users for the price; The charging power is changed for electric vehicle cluster i.

[0027] (18) The unit price for breach of contract compensation; The amount of wind power curtailed and consumed as stipulated in the contract; The actual charging amount of the defaulted electric vehicle cluster i.

[0028] (19) For load aggregators, the unit price for wind curtailment revenue is stipulated in the contract signed with the exchange to track the wind curtailment curve. The electric vehicle cluster i exceeds the charging capacity specified in the contract.

[0029] (20) , These are the price coefficients for power weight and capacity weight, respectively.

[0030] Step 5: Based on the objectives of minimizing wind power tracking deviation and minimizing total cost, the Grey Wolf algorithm is applied for optimization, and the entropy weight method is used to assign weights to each index to obtain the comprehensive evaluation index (CEI) for this multi-objective programming problem. The optimization result is determined based on the CEI of the pre-selected scheme.

[0031] Step 5.1: Standardize the data samples for each indicator. Assume there are K indicators given. , … ,in, That is, each data point contains n samples, and the formula for standardizing the data is: (twenty one) In the formula, The value of j is the standardized value of the data for index i.

[0032] Step 5.2: Calculate the information entropy of each indicator. According to the definition of information entropy, the formula for calculating the information entropy of a set of data j is: (twenty two) in, .if =0, then define .

[0033] Step 5.3: Based on the formula for calculating information entropy, the information entropy of each indicator can be obtained as follows: The formula for calculating the weights of each indicator using information entropy is as follows: (twenty three) In the formula, is the weight of indicator i; k indicates that there are a total of k indicators.

[0034] (twenty four) , , The weighting coefficients calculated using the entropy weighting method satisfy... , , These are the normalized values ​​of each objective function. The comprehensive evaluation index (CEI) is calculated, and finally, the pre-selected schemes are sorted from smallest to largest according to their CEI values, with the schemes ranked first being the power supply configuration schemes.

[0035] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A flexible resource aggregation and matching method based on minimizing wind energy waste, characterized by: Includes the following steps: Step 1: Analyze the feasibility of electric vehicles absorbing wind power, and track the amount of wind power curtailment based on the charging pattern of electric vehicles and the coordination relationship between electric vehicle load and wind power output. Step 2: Based on the flexibility and controllability of electric vehicle load, adjust the electric vehicle load aggregation scheme and establish an electric vehicle load aggregation model; Step 3: After the initial aggregation of electric vehicle load, if there are still times when wind curtailment occurs, it is necessary to further calculate the deviation between the aggregated load and the tracking wind curtailment curve, and introduce electric vehicles as a priority adjustment resource, and introduce producers and consumers as a coordinated supplementary resource. In step 3, when the electric vehicle load is less than the curtailed wind power, the producer-consumer charges; when the electric vehicle load is greater than the curtailed wind power, the producer-consumer discharges. The objective function of the producer-consumer joint optimization model is shown in equation (7), and the constraints are shown in equations (8)-(14). （7）； （8）； （9）； （10）； + =1 （11）； （12）； （13）； （14）； In the formula, F is the deviation in the amount of wind power consumed after the electric vehicle load is aggregated and dispatched; For time period t, the predicted wind curtailment power of wind power generators; The electrical load power excluding electric vehicles during time period t; Let t be the load power of electric vehicle cluster i during time period t; It is a Boolean variable. =1 indicates that the electric vehicle cluster i is invoked. =0 indicates that the electric vehicle cluster i is not mobilized; The net charging and discharging power of the energy storage system during time period t; For the charging and discharging efficiency of the energy storage system; , These are 0-1 variables representing the charging and discharging states of consumers during time period t, respectively. During charging... =1、 =0, during discharge there is =0、 =1; When neither charging nor discharging, there is... =0、 =0; , These represent the charging and discharging power of producers and consumers during time period t; , These represent the upper limits of charging and discharging power for consumers during time period t; , These are the charging / discharging power configuration and capacity configuration for producers and consumers, respectively. , The states of charge of producers and consumers at time periods t and t+1 are respectively; , These are the initial electric vehicle aggregation cost and the electric vehicle aggregation cost after incorporating joint optimization by prosumers and consumers, respectively. For time intervals; Step 4: The larger the difference between the aggregated load of electric vehicles and the wind curtailment power consumption stipulated in the contract, the greater the cost of the load aggregator. The total cost of wind power consumption is calculated, including the aggregation cost and the coordination cost between producers and consumers. The aggregation cost and the coordination cost of prosumers in step 4: （15）； （16）； The sum of the order price and incentive price S for the user load; （17）； Compensate users for the price; The charging power level is changed for electric vehicle cluster i participating in the call; （18）； The unit price for breach of contract compensation; The amount of wind power curtailed as stipulated in the contract; The actual charging capacity of the defaulting electric vehicle cluster i; （19）； For load aggregators, the unit price for wind curtailment revenue is stipulated in the contract signed with the exchange to track the wind curtailment curve. For electric vehicle cluster i, the charging amount exceeds the contractually stipulated charging amount; （20）； , These are the power-weighted and capacity-weighted price coefficients, respectively. Step 5: Based on the objectives of minimizing tracking deviation and minimizing total cost, the Grey Wolf algorithm is applied for optimization and solution. The entropy weight method is used to assign weights to each index, which yields the comprehensive evaluation index (CEI) for this multi-objective programming problem. The optimization result is determined based on the CEI of the pre-selected scheme.

2. The multi-flexible resource aggregation and matching method based on minimizing wind energy waste rate according to claim 1, characterized in that: Step 2 uses the interpolation of load power and power curtailment on the generation side for each time period as an indicator to measure the tracking deviation of the wind curtailment curve. Equation (1) takes minimizing the sum of the absolute values ​​of the load and power curtailment deviations for each time period as the optimization objective, and Equations (2)-(6) are the constraints: （1）； （2）； （3）； （4）； （5）； （6）； In the formula, F is the deviation of wind power consumption after the electric vehicle load is aggregated and dispatched; T is the total number of time periods; For time period t, the predicted wind curtailment power of wind power generators; The electrical load power excluding electric vehicles during time period t; Let t be the load power of electric vehicle cluster i during time period t; Let J be the power generation capacity of conventional power source j during time period t; J is the total number of conventional power sources. Time interval , These represent the charging and discharging power of electric vehicle cluster i during time period t; , These are the upper limits for charging and discharging power of the electric vehicle cluster, respectively. , Time periods t , t +1 Electric Vehicle Cluster i The state of charge; The charging and discharging efficiency of electric vehicle clusters; The rated capacity of electric vehicle cluster i; This represents the minimum proportion of resources aggregated for electric vehicles that constitute the demand-side resources. This refers to the electricity consumption of demand-side resources.

3. The multi-flexible resource aggregation and matching method based on minimizing wind energy waste rate according to claim 1, characterized in that: Step 5 includes: Step 5.1: Standardize the data samples for each indicator; Step 5.2: Calculate the information entropy of each indicator; Step 5.3: Based on the formula for calculating information entropy, obtain the information entropy of each indicator, calculate the comprehensive evaluation index value CEI, and finally sort the pre-selected schemes according to the comprehensive evaluation index value CEI from smallest to largest. The scheme with the highest ranking is the power configuration scheme.

4. The multi-flexible resource aggregation and matching method based on minimizing wind energy waste rate according to claim 3, characterized in that: The data standardization calculation in step 5.1 is as follows: （21）； In the formula, The value of j is the standardized value of the data for index i.

5. The multi-flexible resource aggregation and matching method based on minimizing wind energy waste rate according to claim 3, characterized in that: Step 5.2 Information Entropy Calculation: （22）； in, ;if =0, then define .

6. The multi-flexible resource aggregation and matching method based on minimizing wind energy waste rate according to claim 3, characterized in that: In step 5.3, the information entropy of each indicator is calculated based on step 5.

2. The weights of each indicator are calculated using information entropy as follows: （23）； In the formula, The weight of indicator i; k indicates that there are a total of k indicators; （24）； , , The weighting coefficients calculated using the entropy weighting method satisfy... ; , , These are the normalized values ​​for each objective function.

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