Distributed energy resource regulation and control method and system considering market demands

Abstract

The invention relates to a distributed energy resource regulation and control method and system considering market demands, and the method comprises the following steps: carrying out the clustering analysis of the electrical characteristic data, geographic position data and available time data of each distributed energy resource, and obtaining a distributed energy resource characteristic classification result; based on a distributed energy resource characteristic classification result, constructing a distributed energy resource aggregator clearing model; solving the distributed energy resource aggregator clearing model through an optimization algorithm to obtain a distributed energy resource combination participating in the market transaction; and calculating the final payment price of the user and the actual contribution degree of the distributed energy resources in the electricity market based on the optimal distributed energy resource combination participating in the market transaction. A market transaction combination is determined through an optimization algorithm, the capability of dealing with market changes is enhanced, all parties are promoted to actively participate in market activities through a pricing mechanism integrating three aspects of marginal cost, time contribution and flexible contribution, and a fair competition environment is ensured.

Description

Technical Field

[0001] The present invention relates to the field of distributed energy resource regulation, and particularly to a distributed energy resource regulation method and system considering market demand. Background Art

[0002] With the rapid development of renewable energy and the popularization of distributed energy resources (DERs), the role of distributed energy aggregators in the power market has become increasingly important. They integrate and optimize distributed energy from different sources, such as solar energy, wind energy, energy storage systems, etc., to improve energy utilization efficiency and enhance grid stability. However, the current management systems face a series of challenges in dealing with diverse distributed energy, including increased resource management complexity, inflexible market participation strategies, and insufficient incentive mechanisms for resource holders, etc. Specifically, it is reflected in the following aspects:

[0003] Insufficient analysis and classification of resource characteristics: Existing management systems often fail to deeply analyze and effectively classify various distributed energy resources, resulting in the inability to fully identify and utilize the potential value of different types of resources;

[0004] The clearing model lacks flexibility: Traditional clearing models are usually too simplified or static, unable to quickly adapt to market fluctuations, nor can they effectively make dynamic adjustments according to real-time supply and demand situations;

[0005] Unreasonable pricing mechanism: Existing pricing mechanisms may not accurately reflect the actual contributions of various energy resources, thus affecting the enthusiasm of resource holders to participate in the market and may lead to unfair resource allocation;

[0006] Poor control strategy: There are deficiencies in designing fair and reasonable control strategies based on the actual contribution degree, which limits the cooperation among market participants and the efficiency improvement of the entire system. Summary of the Invention

[0007] In view of the above problems, the present invention provides a distributed energy resource regulation method and system considering market demand. First, through detailed clustering analysis of distributed energy resources, they are classified according to factors such as electrical characteristics and geographical location to better understand and utilize the characteristics of various resources. A clearing model considering multiple factors is established, and the optimal market trading combination is determined through an optimization algorithm, enhancing the ability to respond to market changes. A pricing mechanism integrating three aspects of comprehensive marginal cost, time contribution, and flexibility contribution is designed to ensure more reasonable price setting. Finally, a control strategy based on the actual contribution degree is implemented, which promotes all parties to actively participate in market activities while ensuring a fair competition environment. Through these innovative means, it aims to significantly improve the deficiencies of the existing technology and promote the healthy development of the distributed energy market.

[0008] The solution of the present invention to the above technical problems is as follows: A distributed energy resource regulation method considering market demand, comprising the following steps:

[0009] Perform clustering analysis on the electrical characteristic data, geographical location data, and available time data of each distributed energy resource to obtain the classification result of distributed energy resource characteristics;

[0010] Based on the classification result of distributed energy resource characteristics, construct a clearing model for distributed energy resource aggregators; solve the clearing model for distributed energy resource aggregators through an optimization algorithm to obtain the optimal combination of distributed energy resources participating in market transactions;

[0011] Based on the optimal combination of distributed energy resources participating in market transactions, calculate the final payment price of users and the actual contribution degree of distributed energy resources in the electricity market;

[0012] If the final payment price of users and the actual contribution degree of distributed energy resources in the electricity market do not meet the requirements of users and the electricity market, then perform clustering analysis on the electrical characteristics, geographical locations, and available times of each distributed energy resource through a dynamic optimization method until the final payment price of users and the actual contribution degree of distributed energy resources in the electricity market meet the requirements of users and the electricity market.

[0013] Preferably, the clustering analysis (such as the schedulable time period) is performed on the electrical characteristic data (such as output power range, response speed), geographical location data (such as the distance from the power grid), and available time data of each distributed energy resource to obtain the classification result of distributed energy resource characteristics, including:

[0014] Perform standardization processing on the electrical characteristic data, geographical location data, and available time data of each distributed energy resource, including data cleaning and standardization;

[0015] If there are n distributed energy resources, each resource has m features, and the jth feature of the ith resource is represented as x ij ; perform standardization processing on each feature to eliminate the influence of dimensions and enable comparison of each feature on the same scale:

[0016]

[0017] In the formula, μ j represents the average value of the jth feature, and σ j represents the standard deviation of the jth feature.

[0018] Then perform calculations on the standardized data through the K-Means clustering algorithm to obtain the classification result of distributed energy resource characteristics;

[0019] The data is divided into k clusters, each cluster consists of similar objects, and k data points are randomly selected as the initial clustering centers. Let c i be the i-th clustering center.

[0020] Repeat the following steps until convergence:

[0021] For each data point x i , calculate its distances to each clustering center, and then assign it to the cluster corresponding to the nearest clustering center. The distance can be calculated using the Euclidean distance:

[0022]

[0023] Assign x i to the clustering center c j with the minimum distance. For all data points in each cluster C j , calculate the new clustering center c j :

[0024]

[0025] where |C j | represents the number of data points in cluster C j .

[0026] When the clustering centers no longer change or the change is less than a certain threshold, the algorithm converges.

[0027] Once the clustering is completed, we can analyze the results to determine the characteristics of each cluster.

[0028] For example, if we obtain k = 3 clusters, then each cluster may represent a group of resources with similar characteristics. We can further calculate the mean of the characteristics of each cluster to understand the typical attributes of each cluster. For each cluster C j , we can calculate its mean of characteristics

[0029]

[0030] Evaluate the classification results of the characteristics of distributed energy resources by the silhouette coefficient. According to the evaluation results, the K-Means clustering algorithm can be iterated until the classification results of the characteristics of distributed energy resources that meet the evaluation requirements are obtained. The formula for the silhouette coefficient s i is as follows:

[0031]

[0032] where, a i represents the average distance between the data point x i and other data points within its cluster, and b iRepresents the data point x i The average distance to the nearest non - belonging cluster. The overall silhouette coefficient is the average of the silhouette coefficients of all data points, with a value range of [-1, 1]. The closer the value is to 1, the better the clustering effect.

[0033] Preferably, the equivalent aggregation model that converts multiple distributed energy systems into an equivalent system helps the aggregator better manage resources, improve the overall performance of the system, and make more optimal decisions in market transactions. This process not only helps identify different types of resources and their potential values, but also provides basic information for subsequent establishment of the clearing model, as follows:

[0034]

[0035] Among them, P eq is the total output power of the equivalent system, P i is the total output power of the i - th clustering group, and N is the total number of clustering groups.

[0036]

[0037] Among them, τ eq is the response speed of the equivalent system, τ i is the response speed of the i - th clustering group, and w i is the weight of the i - th clustering group, which can be the proportion of output power, etc.

[0038]

[0039] Among them, Location eq is the equivalent geographical location, Location i is the geographical location of the i - th clustering group, and w i is the weight of the i - th clustering group.

[0040]

[0041] Among them, T eq is the available time of the equivalent system, T i is the available time of the i - th clustering group, and w i is the weight of the i - th clustering group.

[0042] This step classifies and optimizes each distributed energy resource into different categories. This process helps the aggregator identify different types of resources and their potential values, thus providing a basis for subsequent market strategies

[0043] Preferably, based on the classification results of distributed energy resource characteristics, a clearing model for distributed energy resource aggregators is constructed; the clearing model for distributed energy resource aggregators is solved through an optimization algorithm to obtain an optimal distributed energy resource portfolio participating in market transactions, including:

[0044] Construct a clearing model for distributed energy resource aggregators with an objective function of minimizing the aggregator's cost under market demand conditions and constraint conditions established based on the classification results of distributed energy resource characteristics;

[0045] Objective function:

[0046] In the formula, N is the number of distributed energy resources, M is the number of markets participated by the aggregator, c i is the cost coefficient of the i-th resource, p i is the power provided by the i-th resource, r j is the price of the j-th market, s j is the total power sold by the aggregator in the j-th market;

[0047] The establishment of constraint conditions needs to ensure that the total power provided by the aggregator is equal to the market demand, the output power of each resource cannot exceed its maximum power and cannot be lower than its minimum power, and the selling power of the aggregator in each market cannot exceed the maximum allowable power of that market. Specifically as follows:

[0048]

[0049] In the formula, D is the total market demand, and are the minimum and maximum power outputs of the i-th resource respectively, and are the minimum and maximum allowable powers of the j-th market respectively;

[0050] An optimization algorithm is used to solve the objective function based on the constraint conditions to obtain an optimal distributed energy resource portfolio participating in market transactions;

[0051] The optimization algorithm can be linear programming, mixed integer programming, genetic algorithm, particle swarm optimization. For example, if a linear programming model is adopted, it can be solved through tools such as solvers Gurobi, CPLEX or open-source GLPK. The model can be expressed as:

[0052]

[0053] Over time, factors such as market demand, resource cost, and availability will change. Therefore, the model needs to be re-run regularly to adapt to new situations. In addition, dynamic optimization methods (such as rolling optimization) can be introduced to adjust the optimization plan in real time.

[0054] Specifically, it can be implemented through the following steps:

[0055] 1. Define the time window for rolling optimization

[0056] Time window: Select a fixed time window (e.g., 1 hour, 24 hours, etc.), and perform optimization calculations within this window.

[0057] Rolling step: Define the step size by which the time window moves forward after each optimization (e.g., every 15 minutes, every hour, etc.).

[0058] 2. Initial optimization

[0059] Initial conditions: At the starting point of the time window, collect data such as current market demand, resource status, cost, and price.

[0060] Optimization calculation: Use linear programming or other optimization algorithms to calculate the optimal resource scheduling plan based on the current data.

[0061] Generate plan: Generate the optimal scheduling plan within the current time window, including the output power of each resource and the selling power of each market.

[0062] 3. Real-time data update

[0063] Data collection: Regularly collect real-time data within the time window, including changes in market demand, updates to resource status, and changes in cost and price.

[0064] Data processing: Preprocess the collected real-time data to ensure data accuracy and consistency.

[0065] 4. Rolling optimization

[0066] Time window movement: Whenever the time window moves forward by one step, re-perform the optimization calculation.

[0067] Optimization calculation: Use the latest real-time data to recalculate the optimal resource scheduling plan.

[0068] Plan adjustment: Adjust the current resource scheduling plan according to the new optimization results to ensure the real-time performance and adaptability of the plan.

[0069] 5. Repeat steps

[0070] Loop execution: Repeat the above steps, continuously update and adjust the optimization plan to ensure that the system always operates in an optimal state.

[0071] Through the above steps, aggregators can establish and optimize their internal clearing models to ensure the efficient use of resources and remain competitive in changing market conditions. Based on market demand and energy supply, an clearing model is established by combining the resource classification results obtained in the first step. This model determines the optimal combination of energy resources participating in market transactions through optimization algorithms to ensure the most efficient use of resources. At the same time, considering the impact of market fluctuations and how to quickly adjust strategies to respond to emergencies, aggregators can flexibly respond to market changes.

[0072] Preferably, based on the optimal combination of distributed energy resources participating in market transactions, calculate the final payment price of users and the actual contribution of distributed energy resources in the electricity market, including:

[0073] Based on the optimal combination of distributed energy resources participating in market transactions, through marginal cost pricing method, time-weighted average price method and willingness-to-pay pricing method, calculate the final payment price of users and the actual contribution of distributed energy resources in the electricity market, as follows

[0074] The marginal cost pricing method is used to determine the cost of supplying each unit of electric energy. Assume MC i represents the marginal cost of the i-th distributed energy resource (DERs), which can be expressed as:

[0075] P MC，i =MC i

[0076] where P MC，i is the marginal cost price of the i-th DERs.

[0077] The time-weighted average price method is used to measure the contribution of DERs in different time periods. If TWAP t represents the time-weighted average price at time t, it can be expressed as:

[0078] P TWAP，i =∑ t w t ·P t ·ΔT t

[0079] where w t is the weight at time t, P t is the market price at time t, and ΔT t is the time interval.

[0080] The willingness-to-pay pricing method reflects the amount that consumers or system operators are willing to pay for the flexibility provided by DERs. Assume WTP j represents the willingness to pay for the j-th flexibility service, which can be expressed as:

[0081] P WTP，j = WTP j

[0082] Combining the above three aspects, the final price P paid by the user is formed final，i , which can be expressed as:

[0083] P final，i = α·P MC，i + β·P TWAP，i + γ·P WTP，j

[0084] Among them, α, β, and γ are weight factors assigned to marginal cost, time-weighted average price, and willingness to pay, and α + β + γ = 1.

[0085] The design of these pricing mechanisms aims to accurately reflect the actual contributions of various types of resources, thereby motivating resource holders to actively participate in market activities. Through reasonable pricing, not only can fair competition be promoted, but also the resource scheduling plan can be further optimized.

[0086] Based on the marginal cost pricing method to measure the electricity contribution, that is, the value of the electricity provided by each DERs relative to its marginal cost. The marginal cost pricing method assumes that the market price is determined by the cost of the last DERs entering the market. If C i represents the cost of the electricity provided by the i-th DERs, and E i represents the electricity it provides, then the electricity contribution P E，i can be expressed as:

[0087] P E，i = C i / E i

[0088] Based on the time-weighted average price method to measure the time contribution, which depends on the availability and demand matching degree of DERs in different time periods. If P t represents the market price within a certain period of time t, and D t represents the demand during this period, then the time contribution P T，i can be expressed as:

[0089] P T，i = ∑ t (P t × D t ) / ∑ t D t

[0090] This measures the flexibility contribution through the willingness-to-pay pricing method, and the flexibility contribution reflects the fast response ability and dispatchability that DERs can provide. That is, the amount that the system operator or consumer is willing to pay for the flexibility provided by DERs. Assume F i represents the flexibility contribution of the i-th DER, and WTP i represents the corresponding willingness to pay, then the flexibility contribution P F，i can be expressed as:

[0091] P F，i = WTP i / F i

[0092] Based on the contribution degrees in the above three aspects, we can calculate a comprehensive contribution degree P total，i , which is used to reflect the overall contribution level of each DER. The comprehensive contribution degree can be a weighted average, where the weights w E , w T , w F reflect the importance of different contribution degrees:

[0093] P total，i = w E · P E ，i + w T · P T，i + w F · P F，i

[0094] By applying this control strategy based on the actual contribution degree, the aggregator can not only encourage DERs to participate more actively in market activities, but also promote the healthy and sustainable development of the distributed energy market. Such a strategy ensures market transparency and also guarantees a fair competition environment among participants. As the market matures and technology advances, these control strategies should be evaluated and adjusted regularly to adapt to the changing market conditions.

[0095] The present invention also provides a distributed energy resource regulation system considering market demand, including:

[0096] A distributed energy resource characteristic classification module, which is used to perform cluster analysis on the electrical characteristic data, geographical location data, and available time data of each distributed energy resource to obtain the distributed energy resource characteristic classification result;

[0097] A distributed energy resource portfolio solving module for participating in market transactions, which is used to construct a distributed energy resource aggregator clearing model based on the distributed energy resource characteristic classification result; and solve the distributed energy resource aggregator clearing model through an optimization algorithm to obtain the optimal distributed energy resource portfolio for participating in market transactions;

[0098] A user and market evaluation module, which is used to calculate the final payment price of the user and the actual contribution degree of distributed energy resources in the electricity market based on the optimal portfolio of distributed energy resources participating in market transactions;

[0099] A user and market incentive module, which is used to perform cluster analysis on the electrical characteristics, geographical locations, and available times of each distributed energy resource through a dynamic optimization method if the final payment price of the user and the actual contribution degree of distributed energy resources in the electricity market do not meet the requirements of the user and the electricity market until the final payment price of the user and the actual contribution degree of distributed energy resources in the electricity market meet the requirements of the user and the electricity market.

[0100] Preferably, the cluster analysis of the electrical characteristic data, geographical location data, and available time data of each distributed energy resource is performed to obtain the classification results of distributed energy resource characteristics, including:

[0101] The electrical characteristic data, geographical location data, and available time data of each distributed energy resource are standardized, and then the standardized data is calculated through the K-Means clustering algorithm to obtain the classification results of distributed energy resource characteristics;

[0102] The classification results of the clustered distributed energy resource characteristics are evaluated through the silhouette coefficient, and the K-Means clustering algorithm can be iterated according to the evaluation results until the classification results of distributed energy resource characteristics that meet the evaluation requirements are obtained.

[0103] Preferably, based on the classification results of distributed energy resource characteristics, a clearing model for distributed energy resource aggregators is constructed; the clearing model for distributed energy resource aggregators is solved through an optimization algorithm to obtain the optimal portfolio of distributed energy resources participating in market transactions, including:

[0104] Construct a target function for minimizing the aggregator's cost under the condition of meeting market demand and a clearing model for distributed energy resource aggregators with constraint conditions established based on the classification results of distributed energy resource characteristics;

[0105] Objective function:

[0106] In the formula, N is the number of distributed energy resources, M is the number of markets participated by the aggregator, c i is the cost coefficient of the i-th resource, p i is the power provided by the i-th resource, r j is the price of the j-th market, s j is the total power sold by the aggregator in the j-th market;

[0107] Constraint conditions:

[0108]

[0109] where D is the total market demand, and are the minimum and maximum power outputs of the i-th resource respectively, and are the minimum and maximum allowable powers of the j-th market respectively;

[0110] An optimization algorithm is used to solve the objective function based on the constraint conditions to obtain an optimal distributed energy resource portfolio participating in the market transaction.

[0111] Preferably, based on the optimal distributed energy resource portfolio participating in the market transaction, the final payment price of the user and the actual contribution degree of the distributed energy resource in the power market are calculated, including:

[0112] Based on the optimal distributed energy resource portfolio participating in the market transaction, the final payment price of the user and the actual contribution degree of the distributed energy resource in the power market are calculated by the marginal cost pricing method, the time-weighted average price method and the willingness-to-pay pricing method.

[0113] The present invention also provides a computer storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the distributed energy resource regulation method considering market demand as described above are implemented.

[0114] The present invention also provides an electronic device, including a memory and a processor: the memory is used to store computer-executable instructions, the processor is used to execute the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the distributed energy resource regulation method considering market demand as described above are implemented.

[0115] The beneficial effects of the present invention are as follows: By performing cluster analysis on data such as the electrical characteristics, geographical location, and available time of distributed energy resources, the present invention classifies them into several groups to facilitate subsequent management and optimization. Based on market demand and energy supply conditions, a clearing model is established, and an optimal combination of energy resources participating in market transactions is determined through an optimization algorithm, ensuring that resources can be efficiently utilized while also considering the variability and uncertainty of the market. The scheduling plan of resources can be adjusted to adapt to changing market conditions. A control mechanism for the actual contribution and supply-demand situation of energy is adopted. The marginal cost pricing method is used for electricity contribution pricing, the time-weighted average price method is used for time contribution pricing, and the willingness-to-pay pricing method is used for flexibility contribution pricing. The final price is formed by integrating the above three aspects, and control technology is used to accurately measure and evaluate the actual contribution of DERs, thereby motivating resource holders to actively participate in market activities. In summary, through the above steps, the aggregator improves resource utilization rate and enhances market fairness and transparency by finely managing DERs, which is of great significance for promoting the prosperity of the distributed energy market.

[0116] The above description is only an overview of the technical solution of the present invention. In order to understand the technical means of the present invention more clearly and be able to implement it according to the content of the specification, the following provides a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation manners of the present invention are given in detail by the following embodiments and their accompanying drawings. Brief Description of the Drawings

[0117] The drawings described herein are used to provide a further understanding of the present invention and form a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0118] Figure 1 It is a flowchart of a distributed energy resource regulation method considering market demand provided for Embodiment 1;

[0119] Figure 2 It is a module diagram of a distributed energy resource regulation system considering market demand provided for Embodiment 2. Detailed Description of the Preferred Embodiments

[0120] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0121] Embodiment 1

[0122] As Figure 1 shown, a distributed energy resource regulation method considering market demand includes the following steps:

[0123] S1. Perform cluster analysis on the electrical characteristic data (such as output power range, response speed), geographical location data (such as distance from the power grid), and available time data (such as schedulable time periods) of each distributed energy resource to obtain the classification results of distributed energy resource characteristics. The specific steps are as follows:

[0124] 1.1 Standardize the electrical characteristic data, geographical location data, and available time data of each distributed energy resource, including data cleaning and standardization;

[0125] If there are n distributed energy resources, and each resource has m characteristics, where the j - th characteristic of the i - th resource is denoted as x ij ; Standardize each characteristic to eliminate the influence of dimension and enable comparison of each characteristic on the same scale:

[0126]

[0127] In the formula, μ j represents the average value of the j - th characteristic, and σ j represents the standard deviation of the j - th characteristic.

[0128] 1.2 Then, use the K - Means clustering algorithm to calculate the standardized data to obtain the classification results of distributed energy resource characteristics;

[0129] Divide the data into k clusters. Each cluster consists of similar objects. Randomly select k data points as the initial clustering centers, and let c i be the i - th clustering center.

[0130] Repeat the following steps until convergence:

[0131] For each data point x i , calculate its distance to each clustering center, and then assign it to the cluster corresponding to the nearest clustering center. The distance can be calculated using the Euclidean distance:

[0132]

[0133] Assign x i to the clustering center c j with the minimum distance. For all data points in each cluster C j , calculate the new clustering center c j :

[0134]

[0135] where |C j | represents the number of data points in cluster C j .

[0136] The algorithm converges when the cluster centers no longer change or change by less than a certain threshold.

[0137] Once the clustering is completed, the results can be analyzed to determine the characteristics of each cluster. Then each cluster may represent a group of resources with similar characteristics. Further calculate the mean of the characteristics of each cluster to understand the typical attributes of each cluster. For each cluster C j , its characteristic mean can be calculated

[0138]

[0139] In this embodiment, k = 3 clusters are set, and the three clusters respectively correspond to three types of distributed energy resources:

[0140] The first cluster may contain resources that are geographically close and have a high output power;

[0141] The second cluster may contain resources that are geographically far away but can provide energy during peak hours;

[0142] The third cluster may be resources that are geographically moderate but can provide energy during off-peak hours.

[0143] In this way, the aggregator can manage resources more effectively according to these clustering results. For example, the resources in the first cluster are preferentially scheduled to meet emergency needs, the resources in the second cluster are used to provide additional support during peak hours, and the third cluster can be used to balance the power grid during off-peak hours.

[0144] 1.3 Evaluate the classification results of the characteristics of the clustered distributed energy resources through the silhouette coefficient. According to the evaluation results, the K-Means clustering algorithm can be iterated until the classification results of the characteristics of the distributed energy resources that meet the evaluation requirements are obtained. The silhouette coefficient s i is calculated as follows:

[0145]

[0146] where a i represents the average distance between the data point x i and other data points within its cluster, and b i represents the average distance between the data point x i and its nearest non-member cluster. The overall silhouette coefficient is the average of the silhouette coefficients of all data points, and the value range is [-1, 1]. The closer the value is to 1, the better the clustering effect.

[0147] 1.4 The equivalent aggregation model that converts multiple distributed energy systems into an equivalent system helps aggregators better manage resources, improve the overall performance of the system, and make more optimal decisions in market transactions. This process not only helps identify different types of resources and their potential values but also provides basic information for subsequent establishment of the clearing model, as follows:

[0148]

[0149] Among them, P eq is the total output power of the equivalent system, P i is the total output power of the i-th clustering group, and N is the total number of clustering groups.

[0150]

[0151] Among them, τ eq is the response speed of the equivalent system, τ i is the response speed of the i-th clustering group, and w i is the weight of the i-th clustering group, which can be the proportion of output power, etc.

[0152]

[0153] Among them, Location eq is the equivalent geographical location, Location i is the geographical location of the i-th clustering group, and w i is the weight of the i-th clustering group.

[0154]

[0155] Among them, T eq is the available time of the equivalent system, T i is the available time of the i-th clustering group, and w i is the weight of the i-th clustering group.

[0156] S2. Based on the classification results of distributed energy resource characteristics, construct a clearing model for distributed energy resource aggregators; solve the clearing model for distributed energy resource aggregators through an optimization algorithm to obtain the optimal distributed energy resource portfolio participating in market transactions, specifically including:

[0157] 2.1 Construct an objective function for minimizing the aggregator's cost under the condition of meeting market demand and a clearing model for distributed energy resource aggregators with constraint conditions established based on the classification results of distributed energy resource characteristics;

[0158] a. Objective function:

[0159] Where N is the number of distributed energy resources, M is the number of markets in which the aggregator participates, c i is the cost coefficient of the i-th resource, p i is the power provided by the i-th resource, r j is the price of the j-th market, s j is the total power sold by the aggregator in the j-th market;

[0160] b. The establishment of constraints needs to ensure that the total power provided by the aggregator is equal to the market demand. The output power of each resource cannot exceed its maximum power and cannot be lower than its minimum power. The selling power of the aggregator in each market cannot exceed the maximum allowable power of that market, as follows:

[0161]

[0162] Where D is the total market demand, and are the minimum and maximum power outputs of the i-th resource respectively, and are the minimum and maximum allowable powers of the j-th market respectively;

[0163] 2.2 Use an optimization algorithm to solve the objective function based on the constraints to obtain the optimal combination of distributed energy resources participating in market transactions;

[0164] The optimization algorithm can be linear programming, mixed integer programming, genetic algorithm, particle swarm optimization. For example, if a linear programming model is adopted, it can be solved by tools such as solvers like Gurobi, CPLEX or the open-source GLPK. The model can be expressed as:

[0165]

[0166] As time changes, factors such as market demand, resource cost and availability will all change. Therefore, it is necessary to re-run the model regularly to adapt to the new situation. In addition, dynamic optimization methods (such as rolling optimization) can be introduced to adjust the optimization plan in real time.

[0167] S3. Based on the optimal combination of distributed energy resources participating in market transactions, calculate the final payment price of the user and the actual contribution degree of the distributed energy resources in the electricity market. The specific steps are as follows:

[0168] 3.1 Based on the optimal combination of distributed energy resources participating in market transactions, calculate the final payment price of the user through marginal cost pricing method, time-weighted average price method and willingness-to-pay pricing method;

[0169] a. Marginal cost pricing

[0170] The marginal cost pricing method is used to determine the cost of per unit of electricity supply. Assume that MC i represents the marginal cost of the i-th distributed energy resource (DERs), and can be expressed as:

[0171] P MC，i = MC i

[0172] where P MC，i is the marginal cost price of the i-th DERs.

[0173] b. Time-weighted average price

[0174] The time-weighted average price method is used to measure the contribution of DERs over different time periods. If TWAP t represents the time-weighted average price at time t, it can be expressed as:

[0175] P TWAP，i = ∑ t w t ·P t ·ΔT t

[0176] where w t is the weight at time t, P t is the market price at time t, and ΔT t is the time interval.

[0177] c. Willingness-to-pay pricing

[0178] The willingness-to-pay pricing method reflects the amount that consumers or system operators are willing to pay for the flexibility provided by DERs. Assume that WTP j represents the willingness-to-pay for the j-th type of flexibility service, and can be expressed as:

[0179] P WTP，j = WTP j

[0180] Combining the above three aspects, the final payment price P final，i of the user is formed and can be expressed as:

[0181] P final，i = α·P MC，i + β·P TWAP，i + γ·P WTP，j

[0182] where α, β, and γ are the weight factors assigned to the marginal cost, time-weighted average price, and willingness-to-pay, and α + β + γ = 1.

[0183] 3.2 Based on the optimal distributed energy resource portfolio participating in the market transaction, through the marginal cost pricing method, the time-weighted average price method, and the willingness-to-pay pricing method, the actual contribution degree of distributed energy resources in the electricity market is as follows

[0184] a. Electricity contribution degree

[0185] The electricity contribution degree is measured based on the marginal cost pricing method, that is, the value of the electricity provided by each DERs relative to its marginal cost. The marginal cost pricing method assumes that the market price is determined by the cost of the last DERs entering the market. If C i represents the cost of the electricity provided by the i-th DERs, and E i represents the electricity it provides, then the electricity contribution degree P E，i can be expressed as:

[0186] P E，i = C i / E i

[0187] b. Time contribution degree

[0188] The time contribution degree is measured based on the time-weighted average price method, which depends on the availability and demand matching degree of DERs in different time periods. If P t represents the market price within a certain period t, and D t represents the demand during this period, then the time contribution degree P T，i can be expressed as:

[0189] P T，i = ∑ t (P t × D t ) / ∑ t D t

[0190] c. Flexibility contribution degree

[0191] The flexibility contribution degree is measured through the willingness-to-pay pricing method. The flexibility contribution degree reflects the fast response ability and dispatchability that DERs can provide. That is, the amount that the system operator or consumer is willing to pay for the flexibility provided by DERs. Assume that F i represents the flexibility contribution of the i-th DERs, and WTP i represents the corresponding willingness to pay, then the flexibility contribution degree P F，i can be expressed as:

[0192] P F，i = WTP i / F i

[0193] Based on the contribution degrees of the above three aspects, we can calculate a comprehensive contribution degree P total，i , which is used to reflect the overall contribution level of each DERs. The comprehensive contribution degree can be a weighted average, where the weights w E , w T , w F reflect the importance of different contribution degrees:

[0194] P total，i = w E ·P E，i + w T ·P T，i + w F ·P F，i .

[0195] S4. If the final payment price of the user and the actual contribution degree of the distributed energy resources in the power market do not meet the requirements of the user and the power market, then through the dynamic optimization method, cluster analysis is carried out on the electrical characteristics, geographical location, and available time of each distributed energy resource until the final payment price of the user and the actual contribution degree of the distributed energy resources in the power market meet the requirements of the user and the power market.

[0196] Embodiment 2

[0197] As Figure 2 shown, this embodiment provides a distributed energy resource regulation system considering market demand, including:

[0198] A distributed energy resource characteristic classification module, which is used to perform cluster analysis on the electrical characteristic data, geographical location data, and available time data of each distributed energy resource to obtain the distributed energy resource characteristic classification result;

[0199] A distributed energy resource combination solving module participating in market transactions, which is used to construct a distributed energy resource aggregator clearing model based on the distributed energy resource characteristic classification result; solve the distributed energy resource aggregator clearing model through an optimization algorithm to obtain the optimal distributed energy resource combination participating in market transactions;

[0200] A user and market evaluation module, which is used to calculate the final payment price of the user and the actual contribution degree of the distributed energy resources in the power market based on the optimal distributed energy resource combination participating in market transactions;

[0201] A user and market incentive module, which is used to perform clustering analysis on the electrical characteristics, geographical locations, and available times of each distributed energy resource through a dynamic optimization method if the final payment price of the user and the actual contribution degree of the distributed energy resource in the power market do not meet the requirements of the user and the power market, until the final payment price of the user and the actual contribution degree of the distributed energy resource in the power market meet the requirements of the user and the power market.

[0202] Performing clustering analysis on the electrical characteristic data, geographical location data, and available time data of each distributed energy resource to obtain a distributed energy resource characteristic classification result, including:

[0203] By performing standardization processing on the electrical characteristic data, geographical location data, and available time data of each distributed energy resource, and then calculating the standardized data through the K-Means clustering algorithm, a distributed energy resource characteristic classification result is obtained;

[0204] Evaluating the clustering distributed energy resource characteristic classification result through the silhouette coefficient, and according to the evaluation result, the K-Means clustering algorithm can be iterated until a distributed energy resource characteristic classification result that meets the evaluation requirements is obtained.

[0205] Based on the distributed energy resource characteristic classification result, a clearing model for distributed energy resource aggregators is constructed; the clearing model for distributed energy resource aggregators is solved through an optimization algorithm to obtain an optimal distributed energy resource combination participating in market transactions, including:

[0206] Constructing an objective function for minimizing the aggregator's cost under the condition of meeting market demand, and a clearing model for distributed energy resource aggregators with constraint conditions established based on the distributed energy resource characteristic classification result;

[0207] Objective function:

[0208] In the formula, N is the number of distributed energy resources, M is the number of markets participated by the aggregator, c i is the cost coefficient of the i-th resource, p i is the power provided by the i-th resource, r j is the price of the j-th market, s j is the total power sold by the aggregator in the j-th market;

[0209] Constraint conditions:

[0210]

[0211] In the formula, D is the total market demand, and are the minimum and maximum power outputs of the i-th resource respectively, and are respectively the minimum and maximum allowable power of the j-th market;

[0212] An optimization algorithm is used to solve the objective function based on the constraints to obtain an optimal distributed energy resource portfolio participating in the market transaction.

[0213] Based on the optimal distributed energy resource portfolio participating in the market transaction, calculate the user's final payment price and the actual contribution degree of the distributed energy resource in the electricity market, including:

[0214] Based on the optimal distributed energy resource portfolio participating in the market transaction, the user's final payment price and the actual contribution degree of the distributed energy resource in the electricity market are calculated by the marginal cost pricing method, the time-weighted average price method and the willingness-to-pay pricing method.

[0215] Embodiment 3

[0216] This embodiment provides a computer storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the distributed energy resource regulation method considering market demand as described in Embodiment 1 are implemented.

[0217] Embodiment 4

[0218] This embodiment provides an electronic device, including a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the distributed energy resource regulation method considering market demand as described in Embodiment 1 are implemented.

[0219] Those skilled in the art should understand that the embodiments of the present disclosure can be provided as a method, a system or a computer program product. Therefore, the present disclosure can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present disclosure can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0220] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce a means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or blocks.

[0221] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or blocks.

[0222] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or blocks.

Claims

1. A distributed energy resource control method considering market demand, characterized in that: The following steps are involved: Perform cluster analysis on the electrical characteristic data, geographic location data, and available time data of each distributed energy resource to obtain the classification results of distributed energy resource characteristics; Based on the classification results of distributed energy resource characteristics, a distributed energy resource aggregator clearing model is constructed; the distributed energy resource aggregator clearing model is solved through the optimization algorithm to obtain the optimal distributed energy resource combination participating in market transactions; Based on the optimal combination of distributed energy resources participating in market transactions, calculate the final payment price of users and the actual contribution of distributed energy resources in the electricity market; If the price that users ultimately pay and the actual contribution of distributed energy resources in the electricity market do not meet the requirements of users and the electricity market, a cluster analysis of the electrical characteristics, geographical location, and available time of each distributed energy resource is performed through a dynamic optimization method until the price that users ultimately pay and the actual contribution of distributed energy resources in the electricity market meet the requirements of users and the electricity market.

2. A distributed energy resource control method considering market demand according to claim 1, characterized in that: The electrical characteristic data, geographic location data, and available time data of each distributed energy resource are clustered and analyzed to obtain the distributed energy resource characteristic classification results, including: By standardizing the electrical characteristic data, geographic location data, and available time data of each distributed energy resource, and then calculating the standardized data using the K-Means clustering algorithm, the classification results of distributed energy resource characteristics are obtained; The distributed energy resource characteristics classification results are clustered by the silhouette coefficient evaluation. According to the evaluation results, the K-Means clustering algorithm can be iterated until the distributed energy resource characteristics classification results that meet the evaluation requirements are obtained.

3. A distributed energy resource control method considering market demand according to claim 1, characterized in that: Based on the classification results of distributed energy resource characteristics, a distributed energy resource aggregator clearing model is constructed; the distributed energy resource aggregator clearing model is solved by an optimization algorithm to obtain the optimal distributed energy resource combination participating in market transactions, including: Construct an objective function to minimize the aggregator's cost under the condition of meeting market demand, and a distributed energy resource aggregator clearing model with constraints established based on the results of distributed energy resource characteristic classification; Objective function: Where N is the number of distributed energy resources, M is the number of markets in which aggregators participate, and c i is the cost coefficient of the ith resource, p i is the power provided by the ith resource, r j is the price of the jth market, s j is the total power sold by the aggregator in the jth market; Constraints: Where D is the total market demand, p i and are the minimum and maximum power outputs of the ith resource, s j and are the minimum and maximum allowed powers of the jth market, respectively; The optimization algorithm is used to solve the objective function based on the constraints to obtain the optimal combination of distributed energy resources participating in market transactions.

4. A distributed energy resource control method considering market demand according to claim 1, characterized in that: Based on the optimal combination of distributed energy resources participating in market transactions, calculate the final payment price of users and the actual contribution of distributed energy resources in the electricity market, including: Based on the optimal combination of distributed energy resources participating in market transactions, the final payment price of users and the actual contribution of distributed energy resources in the electricity market are calculated through marginal cost pricing method, time-weighted average price method and willingness to pay pricing method.

5. A distributed energy resource control system considering market demand, characterized in that: include: The distributed energy resource characteristic classification module is used to perform cluster analysis on the electrical characteristic data, geographic location data, and available time data of each distributed energy resource to obtain the distributed energy resource characteristic classification results; The distributed energy resource combination solution module participating in market transactions is used to build a distributed energy resource aggregator clearing model based on the distributed energy resource characteristic classification results; the distributed energy resource aggregator clearing model is solved by the optimization algorithm to obtain the optimal distributed energy resource combination participating in market transactions; The user and market evaluation module is used to calculate the final payment price of users and the actual contribution of distributed energy resources in the power market based on the optimal combination of distributed energy resources participating in market transactions; The user and market incentive module is used to perform cluster analysis on the electrical characteristics, geographical location, and available time of each distributed energy resource through a dynamic optimization method if the user's final payment price and the actual contribution of distributed energy resources in the power market do not meet the user and power market requirements, until the user's final payment price and the actual contribution of distributed energy resources in the power market meet the user and power market requirements.

6. A distributed energy resource control system considering market demand according to claim 5, characterized in that: The electrical characteristic data, geographic location data, and available time data of each distributed energy resource are clustered and analyzed to obtain the distributed energy resource characteristic classification results, including: By standardizing the electrical characteristic data, geographic location data, and available time data of each distributed energy resource, and then calculating the standardized data using the K-Means clustering algorithm, the classification results of distributed energy resource characteristics are obtained; The distributed energy resource characteristics classification results are clustered by the silhouette coefficient evaluation. According to the evaluation results, the K-Means clustering algorithm can be iterated until the distributed energy resource characteristics classification results that meet the evaluation requirements are obtained.

7. A distributed energy resource control system considering market demand according to claim 5, characterized in that: Based on the classification results of distributed energy resource characteristics, a distributed energy resource aggregator clearing model is constructed; the distributed energy resource aggregator clearing model is solved by an optimization algorithm to obtain the optimal distributed energy resource combination participating in market transactions, including: Construct an objective function to minimize the aggregator's cost under the condition of meeting market demand, and a distributed energy resource aggregator clearing model with constraints established based on the results of distributed energy resource characteristic classification; Objective function: Where N is the number of distributed energy resources, M is the number of markets in which aggregators participate, and c i is the cost coefficient of the ith resource, p i is the power provided by the ith resource, r j is the price of the jth market, s j is the total power sold by the aggregator in the jth market; Constraints: Where D is the total market demand, p i and are the minimum and maximum power outputs of the ith resource, s j and are the minimum and maximum allowed powers of the jth market, respectively; The optimization algorithm is used to solve the objective function based on the constraints to obtain the optimal combination of distributed energy resources participating in market transactions.

8. A distributed energy resource control system considering market demand according to claim 7, characterized in that: Based on the optimal combination of distributed energy resources participating in market transactions, calculate the final payment price of users and the actual contribution of distributed energy resources in the electricity market, including: Based on the optimal combination of distributed energy resources participating in market transactions, the final payment price of users and the actual contribution of distributed energy resources in the electricity market are calculated through marginal cost pricing method, time-weighted average price method and willingness to pay pricing method.

9. A computer storage medium, wherein the computer readable storage medium stores a computer program, characterized in that: When the computer program is executed by a processor, the steps of the distributed energy resource control method considering market demand as described in any one of claims 1 to 4 are implemented.

10. An electronic device, characterized in that: It includes a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the distributed energy resource regulation method considering market demand as described in any one of claims 1 to 4 are implemented.

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