Energy transaction method, device and system of distributed energy system, and storage medium

Abstract

The embodiment of the invention provides an energy transaction method, device and system for a wide-area distributed energy system, and a storage medium, and relates to the technical field of energy internet and intelligent power grids. The method comprises the following steps: obtaining the availability state of a power grid, the energy demand and comfort index of an energy buyer, and the surplus energy and risk type of an energy seller, and directly introducing the personalized comfort preference of the energy buyer and the risk type difference of the energy seller in a transaction process; therefore, the limitation that the energy buyer is regarded as a homogenization main body and the risk preference of the energy seller is ignored is overcome. The coordination of the benefits of the energy buying party, the energy selling party and the power grid is realized, the energy consumption satisfaction degree of the user, the reasonable income of the energy selling party and the economic operation of the power grid are effectively considered in the wide-area distributed energy system, and the problems of unbalanced supply and demand, local congestion and insufficient excitation of participants are improved.

Description

Technical Field

[0001] This application relates to the field of energy internet and smart grid technology, specifically to an energy trading method, device, system, and storage medium for wide-area distributed energy systems. Background Technology

[0002] Currently, with the widespread integration of renewable energy and the diversification of user-side energy demand, wide-area distributed energy systems have become an important form of power system development. In these systems, a large number of distributed power sources and diverse users coexist. How to achieve efficient and fair energy trading and allocation is a key issue in ensuring stable system operation and improving economic benefits.

[0003] To address these issues, relevant technologies employ unified electricity pricing mechanisms, regional electricity pricing mechanisms, or trading methods based on simple optimization models. These methods, through centralized or hierarchical scheduling, perform energy matching and pricing under given supply and demand conditions, enabling basic energy allocation and settlement in specific scenarios.

[0004] In the process of implementing the embodiments of this application, at least the following problems were found in the related technology:

[0005] The relevant technologies treat energy purchasers as homogeneous price responders, failing to consider the comprehensive impact of individual factors on energy comfort, as well as the differences in risk preferences among energy sellers. This results in a situation in wide-area distributed energy systems where transaction outcomes cannot simultaneously satisfy user energy satisfaction, provide reasonable returns for energy sellers, and ensure the overall economic operation of the power grid, leading to supply-demand imbalances, localized congestion, and insufficient incentives for participants. Summary of the Invention

[0006] This application provides an energy trading method, apparatus, system, and storage medium for a wide-area distributed energy system.

[0007] The first aspect of this application provides an energy trading method for a wide-area distributed energy system, comprising:

[0008] Obtain the grid availability status, energy demand and comfort index of multiple energy purchasers, and surplus energy and risk type of multiple energy sellers;

[0009] The transaction price for each energy seller is determined based on the grid availability status and the risk type of the energy seller.

[0010] When the grid availability status is unavailable and there is insufficient energy supply, the energy allocation among energy purchasers is determined based on the energy purchaser's comfort index, energy purchaser's energy demand, and energy seller's surplus energy.

[0011] Based on the determined energy allocation and electricity trading price, power routing optimization is performed with the goal of minimizing wide-area transmission loss to determine the actual transmitted energy of each energy purchaser in each energy sales direction.

[0012] Based on the actual transmitted energy and the corresponding transaction price, calculate the bill for each energy purchaser and the revenue for each energy seller.

[0013] In an optional embodiment of this application, the transaction price for each energy seller is determined based on the grid availability status and the risk type of the energy seller, including:

[0014] Establish a Bayesian game model between energy sellers and power grid companies;

[0015] The grid availability status and the risk type of the energy seller are used as inputs to the game model;

[0016] Solve the Bayesian game model and output differentiated trading prices based on different combinations of the energy seller's risk type and the grid availability status.

[0017] In an optional embodiment of this application, the rules for the Bayesian game model to output differentiated electricity trading prices include:

[0018] For risk-averse energy sellers, the export price of the power grid is used as the transaction price.

[0019] For risk-seeking energy sellers, when the grid availability status is unavailable, the energy seller's own quotation is output as the transaction price;

[0020] For risk-seeking energy sellers, when the grid availability status is available, a penalty price lower than the grid export price is output as the transaction price.

[0021] In an optional embodiment of this application, determining the energy allocation among energy purchasers based on the energy purchaser's comfort index, energy demand, and surplus energy of the energy seller includes:

[0022] Establish a non-cooperative game model between energy purchasers and energy sellers;

[0023] The comfort index of the energy purchaser, the energy demand of the energy purchaser, and the surplus energy of the energy seller are used as inputs to the game model;

[0024] Solve the non-cooperative game model and output an energy allocation scheme based on the comfort index ranking of energy purchasers.

[0025] In an optional embodiment of this application, the output of an energy allocation scheme based on the energy purchaser's comfort index includes:

[0026] The energy needs of energy purchasers will be met in descending order of their comfort index.

[0027] When the total surplus energy of energy sellers is less than the total energy demand of energy buyers, the energy allocated to energy buyers with lower comfort indices will be reduced proportionally.

[0028] In an optional embodiment of this application, obtaining the energy demand and comfort index of each energy purchaser includes:

[0029] Based on the family structure information and economic conditions declared by the energy purchaser, the comfort index of the energy purchaser is calculated.

[0030] Collect information on the energy-consuming equipment of the energy purchaser and classify its load into movable load and non-movable load;

[0031] With the goal of minimizing energy costs and the calculated comfort index as a constraint, the energy demand of the energy purchaser is obtained.

[0032] In an optional embodiment of this application, the comfort index is calculated based on the family structure information and economic conditions declared by the energy purchaser, including:

[0033] Based on data on the number of family members, number of children, age structure, and family economic conditions, calculations are performed using linear functions, piecewise linear functions, or machine learning models.

[0034] In one optional embodiment of this application, movable loads include energy-consuming devices such as washing machines, dryers, dishwashers, and energy storage devices that can adjust their operating time in time; and / or, non-movable loads include devices such as lighting, refrigerators, and medical equipment that must be powered at specific times.

[0035] In an optional embodiment of this application, obtaining the risk type for each energy seller includes:

[0036] Based on prospect theory, a risk perception behavior model for energy sellers is constructed, which includes a value function and a probability weighting function.

[0037] Based on the seller's historical transaction decision data or pre-set questionnaire feedback, the risk type is determined through a risk perception behavior model.

[0038] In an optional embodiment of this application, power routing optimization with the goal of minimizing wide-area transmission loss includes:

[0039] Construct a model for calculating total transmission loss; where total transmission loss includes both ohmic loss and corona loss of the line.

[0040] Based on the total transmission loss calculation model and the determined energy allocation and trading price, power routing optimization is performed.

[0041] In one optional embodiment of this application, the ohmic loss of the line is calculated based on line current, resistance, length, conductor cross-sectional area and resistivity parameters; and / or, the corona loss of the line is calculated based on system frequency, air density factor, conductor radius, conductor spacing, operating voltage and corona initiation critical voltage parameters.

[0042] In an optional embodiment of this application, power routing optimization includes:

[0043] The transportation problem model is established with the total loss obtained from the total transmission loss calculation model as the objective, the surplus energy of all energy sellers as the supply constraint, the energy demand of all energy buyers as the demand constraint, and the tradable relationship or priority between energy sellers and energy buyers defined by the determined energy allocation scheme as the matching constraint.

[0044] The Vogel approximation method is used to solve the transportation problem model, and a power routing scheme is obtained.

[0045] In an optional embodiment of this application, after calculating the bill for each energy purchaser, the method further includes:

[0046] The actual energy consumption patterns of the energy purchasers are compared with the predicted energy consumption patterns based on their declared comfort index.

[0047] If the deviation exceeds the preset threshold, it will be judged as an inflated declaration, and a penalty fee will be added to the energy purchaser's bill.

[0048] A second aspect of this application provides an energy trading device for a wide-area distributed energy system, including a processor and a memory storing program instructions. The processor is configured to execute the energy trading method for a wide-area distributed energy system as described in the first aspect of this application when running the program instructions.

[0049] A third aspect of this application provides a system comprising:

[0050] The system itself; and,

[0051] The energy trading device for a wide-area distributed energy system, as described in the second aspect of this application, is installed on the system body.

[0052] A fourth aspect of the embodiments of this application provides a computer-readable storage medium storing program instructions, which, when executed, cause a computer to perform an energy trading method for a wide-area distributed energy system as described in the first aspect of the embodiments of this application.

[0053] The energy trading method, apparatus, system, and storage medium for wide-area distributed energy systems provided in the embodiments of this application have the following beneficial effects:

[0054] This application's embodiments determine differentiated electricity prices based on grid availability and the risk type of energy sellers. This adapts to the incentive needs of energy sellers with different risk appetites and adjusts prices according to risk type when the grid is available to maintain grid economic benefits. When the grid is unavailable and energy supply is insufficient, energy allocation is based on the comfort index of energy purchasers, prioritizing the needs of users with high comfort levels, which helps improve overall user energy satisfaction. Furthermore, power routing optimization aims to minimize wide-area transmission losses, improving system operating efficiency and alleviating local network congestion by reducing losses during energy transmission. Finally, billing and revenue are calculated based on actual transmitted energy and corresponding electricity prices, achieving coordination of interests among energy purchasers, energy sellers, and the grid. This effectively balances user energy satisfaction, reasonable revenue for energy sellers, and economic grid operation in a wide-area distributed energy system, improving issues such as supply-demand imbalance, local congestion, and insufficient incentives for participants. Attached Figure Description

[0055] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0056] Figure 1 This is a schematic diagram of an energy trading method for a wide-area distributed energy system provided in an embodiment of this application;

[0057] Figure 2 This is a schematic diagram of another energy trading method for a wide-area distributed energy system provided in an embodiment of this application;

[0058] Figure 3 This is a schematic diagram of an energy trading device for a wide-area distributed energy system provided in an embodiment of this application.

[0059] Figure label:

[0060] 800: Energy trading device for wide-area distributed energy systems; 801: Processor; 802: Memory; 803: Communication interface; 804: Bus. Detailed Implementation

[0061] To make the technical solutions and advantages of the embodiments of this application clearer, the exemplary embodiments of this application will be described in further detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0062] This application provides an energy trading system for a wide-area distributed energy system. The system includes a system body and an energy trading device installed on the system body. Specifically, the system includes the following collaborative functional modules:

[0063] The market coordination module, as the core data processing and decision-making unit in the system, is configured to receive and process key data from various participants and perform core calculations for energy trading. Specifically, the market coordination module receives the energy purchaser's comfort index and comprehensive energy demand reported by the energy management module on the energy purchaser's side, and the energy seller's surplus energy, risk type, and pricing information reported by the energy seller's energy management module. Based on this input data, the market coordination module is further configured to perform the following core calculation tasks: determining the differentiated trading price for each energy seller based on a pre-established Bayesian game model, according to the energy seller's risk type and grid availability status; determining the energy allocation scheme among energy purchasers based on a pre-established game model when the grid is unavailable and supply is insufficient, according to the energy purchaser's comfort index, energy demand, and energy seller's surplus energy; performing power routing optimization calculations based on the determined energy allocation scheme and trading price, with the goal of minimizing wide-area transmission losses, to determine the actual energy transmitted from each energy seller to each energy purchaser; and finally, calculating the bill for each energy purchaser and the revenue for each energy seller based on the actual transmitted energy and the corresponding trading price.

[0064] The energy management module on the energy purchaser's side, as an energy management and information collection unit deployed on the user side, is configured to collect and process energy-related data from the energy purchaser and generate standardized demand declarations. This module is specifically deployed in user-side energy management equipment, such as home energy management systems, building energy management systems, or integrated energy stations for industrial users. The data collected by the energy purchaser's side energy management module includes the energy purchaser's household structure information, economic conditions data, and operational information of all energy-consuming equipment. Based on household structure information and economic conditions, this module calculates the energy purchaser's comfort index. By analyzing the operational characteristics of energy-consuming equipment, this module classifies the total load into movable loads and non-movable loads. Movable loads refer to loads whose operation can be flexibly adjusted over time, such as the charging and discharging process of washing machines, dryers, dishwashers, or energy storage devices. Non-movable loads refer to loads that must have guaranteed energy supply within a specific time period, such as basic lighting, refrigerators, or the necessary loads to maintain the operation of medical equipment. Ultimately, this module uses minimizing energy costs as the optimization objective and the comfort index as a constraint to solve for the energy purchaser's energy demand, and then reports this energy demand along with the calculated comfort index to the market coordination module.

[0065] The energy management module on the seller's side, as an energy management and information collection unit deployed at distributed energy sites, is configured to statistically analyze local energy data and determine tradable surplus and risk attributes. This module is specifically deployed at sites with distributed generation resources, such as residential photovoltaic systems, photovoltaic or energy storage power stations in industrial and commercial parks, and regional integrated energy supply centers. The data collected by the energy management module on the seller's side includes the total power generation of all distributed energy sources within the site during a scheduling cycle, as well as the site's own comprehensive energy demand. The module calculates the difference between power generation and self-consumption demand to obtain surplus energy available for external trading. Simultaneously, the module records or analyzes historical trading behavior to determine the seller's risk type and can generate pricing information based on market strategies. The aforementioned surplus energy, risk type, and pricing information are reported to the market coordination module.

[0066] The grid interface module, serving as the interface unit for information exchange and energy exchange between the system and the regional backbone grid, is configured to enable bidirectional communication and energy flow with the grid. This module obtains key grid status information from the backbone grid in real time, including grid availability and grid export price. Grid availability is a binary signal indicating whether the backbone grid is in a normal operating state for supplying or receiving power during the current dispatch cycle. Furthermore, when there is a surplus or deficit of energy within the system, the grid interface module is also responsible for coordinating the bidirectional transmission of energy between the system and the backbone grid.

[0067] The wide-area communication and metering module, serving as the infrastructure unit supporting data flow and transaction metering within the system, is configured to connect all distributed nodes via a communication network and achieve accurate energy metering. This module constructs a communication network covering the wide-area distributed energy system; this network can be wired or wireless. Through smart metering devices deployed at each energy purchaser and seller node, the module collects real-time electricity consumption data, power generation data, and energy transmission data during transactions. This real-time, accurate metering data provides a reliable data foundation for the market coordination module's optimized calculations and the final billing and revenue settlement.

[0068] The above-mentioned market coordination module, energy management module on the energy purchaser side, energy management module on the energy seller side, grid interface module, and wide-area communication and metering module work together to execute a complete energy trading method, realizing point-to-point energy trading, fair allocation, and efficient routing in a wide-area distributed energy system.

[0069] Figure 1 and Figure 2 This is a schematic diagram of an energy trading method for a wide-area distributed energy system provided in an embodiment of this application. Any of the following methods can be executed in the trading system or in a server or terminal device that is connected to the trading system.

[0070] Based on the above structure, such as Figure 1 As shown in the figure, this application provides an energy trading method for a wide-area distributed energy system, including:

[0071] S01, obtain the grid availability status, energy demand and comfort index of multiple energy purchasers, and surplus energy and risk type of multiple energy sellers.

[0072] S02, determine the transaction price for each energy seller based on the grid availability status and the risk type of the energy seller.

[0073] S03 When the grid availability status is unavailable and there is insufficient energy supply, the energy allocation among the energy purchasers is determined based on the energy purchaser's comfort index, the energy purchaser's energy demand, and the energy seller's surplus energy.

[0074] S04. Based on the determined energy allocation and trading price, power routing optimization is performed with the goal of minimizing wide-area transmission losses to determine the actual transmitted energy of each energy purchaser in each energy sales direction.

[0075] S05, calculate the bill for each energy purchaser and the revenue for each energy seller based on the actual transmitted energy and the corresponding transaction price.

[0076] The energy trading method for wide-area distributed energy systems provided in this application overcomes the limitations of related technologies that treat energy buyers as homogeneous entities and ignore the differences in risk preferences of energy sellers by acquiring grid availability status, energy demand and comfort index of energy buyers, and surplus energy and risk type of energy sellers. The comfort index reflects the personalized energy needs of energy buyers, and the risk type reflects the decision-making preferences of energy sellers, laying the foundation for subsequent precise trading. Then, differentiated trading prices are determined based on grid availability status and energy seller risk types, adapting to the grid's operating status while ensuring reasonable returns for energy sellers with different risk preferences, effectively enhancing their participation. When the grid is unavailable and energy supply is insufficient, based on... Energy allocation is based on the comfort index of energy purchasers, energy demand, and surplus energy of energy sellers. Priority is given to the needs of energy purchasers with high comfort levels, while the allocation to energy purchasers with low comfort levels is reasonably reduced, thus balancing the energy satisfaction of energy purchasers with the balance between energy supply and demand. Then, based on the determined energy allocation and trading price, power routing optimization is performed with the goal of minimizing wide-area transmission losses. This reduces local congestion in the wide-area distributed energy system and improves the overall economic efficiency of the power grid. Finally, the energy purchaser's bill and the energy seller's revenue are calculated based on the actual transmitted energy and the corresponding trading price, achieving fair settlement of transactions and further strengthening incentives for both energy purchasers and sellers. This gradually improves the problems of supply and demand imbalance, local congestion, and insufficient incentives for participants in related technologies.

[0077] In an optional embodiment of this application, the transaction price for each energy seller is determined based on the grid availability status and the risk type of the energy seller, including: establishing a Bayesian game model between the energy seller and the grid company; using the grid availability status and the risk type of the energy seller as inputs to the game model; solving the Bayesian game model and outputting differentiated transaction prices based on different combinations of the energy seller's risk type and the grid availability status.

[0078] Thus, by establishing a Bayesian game model between energy sellers and grid companies, and taking grid availability and energy seller risk type as inputs, the model outputs differentiated trading prices. This effectively adapts to the incomplete information trading scenarios and heterogeneous participant behavior in wide-area distributed energy systems. The Bayesian game model itself has the characteristic of handling incomplete information, accurately integrating the grid availability (available or unavailable), a key variable affecting the trading environment, and the energy seller risk type (risk-averse or risk-seeking), a core factor reflecting the energy seller's decision-making preferences. This avoids the limitations of traditional uniform pricing or simple optimization models that ignore participant differences and environmental uncertainties. By solving this model, we can formulate suitable trading prices for combinations of energy sellers with different risk types and different grid availability states. This provides stable grid export prices as a guarantee for risk-averse energy sellers, meeting their need for certain returns, while allowing risk-seeking energy sellers to trade based on their own bids when the grid is unavailable and accept punitive prices when the grid is available. This caters to their preference for higher returns while preserving a reasonable profit margin for the grid, thus protecting the economic interests of the grid while ensuring the participation of energy sellers.

[0079] In an optional embodiment of this application, the rules for the Bayesian game model to output differentiated trading prices include: for risk-averse energy sellers, the grid export price is output as the trading price; for risk-seeking energy sellers, when the grid availability is unavailable, the seller's own quoted price is output as the trading price; for risk-seeking energy sellers, when the grid availability is available, a punitive price lower than the grid export price is output as the trading price.

[0080] Thus, for risk-averse energy sellers, their core need is to obtain certain returns to avoid the risks brought about by price fluctuations. Using the grid export price as the transaction price is appropriate because the grid export price is determined by the grid company based on the real-time electricity market, grid operating costs, and policy constraints, and has relative stability. This matches the risk-averse energy sellers' need for certain returns, thereby increasing their willingness to participate in transactions. For risk-seeking energy sellers, they tend to seek higher returns in uncertain environments. When the grid availability is unavailable, the grid cannot participate in energy supply normally. In this case, using their own quoted price as the transaction price allows them to formulate their bids based on their own judgment of market supply and demand, which aligns with their preference for pursuing higher returns and helps to stimulate their trading enthusiasm. When the grid is available, it remains an important energy supplier. Allowing risk-seeking energy sellers to freely quote prices could affect the grid's economic benefits. However, by imposing a punitive electricity price lower than the grid's export price, risk-seeking energy sellers can participate in transactions while still having room for profit. This also allows the grid company to retain a source of profit through the price difference, mitigating the impact of high distributed energy penetration on the grid's economics. Ultimately, this approach satisfies the reasonable needs of energy sellers with different risk profiles while protecting the interests of the grid company.

[0081] In an optional embodiment of this application, the energy allocation among energy purchasers is determined based on the energy purchaser's comfort index, energy purchaser's energy demand, and energy seller's surplus energy. This includes: establishing a non-cooperative game model between the energy purchaser and the energy seller; using the energy purchaser's comfort index, energy purchaser's energy demand, and energy seller's surplus energy as inputs to the game model; solving the non-cooperative game model and outputting an energy allocation scheme based on the energy purchaser's comfort index ranking.

[0082] This non-cooperative game theory model between energy purchasers and sellers allows both parties to participate in energy allocation decisions based on their respective interests, avoiding the limitations of traditional centralized allocation that ignores the individual needs of participants. The model uses the energy purchaser's comfort index, energy demand, and surplus energy as inputs. The energy purchaser's comfort index reflects their personalized energy preferences based on family structure and economic conditions; the energy purchaser's energy demand clarifies their actual total energy consumption; and the seller's surplus energy defines the upper limit of energy available for allocation. The combination of these three factors provides the model with fundamental data that aligns with real-world scenarios, ensuring that model decisions do not deviate from actual supply and demand and user needs. Solving this non-cooperative game model and outputting an energy allocation scheme based on the comfort index ranking of energy purchasers allows energy allocation to prioritize energy purchasers with higher comfort requirements, aligning with their core demands for energy usage experience. This avoids the problem of poor experience for energy purchasers with high comfort requirements due to homogeneous treatment in traditional allocation. At the same time, combined with the constraint of the surplus energy of the energy seller, it can match the personalized needs of energy purchasers as much as possible under limited energy supply, improve the satisfaction of energy purchasers with the allocation results, and also allow the surplus energy of the energy seller to be used in a more user-friendly way.

[0083] In an optional embodiment of this application, an energy allocation scheme based on the comfort index of the energy purchasers is output, including: meeting the energy needs of the energy purchasers in descending order of their comfort index; and when the total surplus energy of the energy sellers is less than the total energy needs of the energy purchasers, reducing the energy allocated to the energy purchasers with lower comfort indices proportionally.

[0084] In this way, the energy purchaser's comfort index is determined based on their family structure (such as the number of family members, number of children, and age structure) and economic conditions, accurately reflecting the individualized needs of energy purchasers for energy comfort. Energy purchasers' energy needs are met sequentially from highest to lowest comfort index, prioritizing those with high comfort indices. This directly addresses the core demands of these purchasers for their energy experience, avoiding the problem of neglecting high-comfort-demand purchasers due to homogenization in traditional allocation methods. When the total surplus energy of energy sellers is less than the total energy demand of energy purchasers, i.e., when there is an energy supply gap, the energy allocated to purchasers with lower comfort indices is reduced proportionally, rather than being indiscriminately reduced or only targeted at specific individuals. This approach ensures that the needs of high-comfort-index purchasers are not excessively affected under limited energy supply, while still allowing purchasers with lower comfort indices to receive a certain proportion of energy to meet their basic energy needs. This balances priority and fairness in energy allocation, thereby increasing overall acceptance of the allocation results among energy purchasers in scenarios of insufficient energy supply.

[0085] In an optional embodiment of this application, obtaining the energy demand and comfort index of each energy purchaser includes: calculating the comfort index of the energy purchaser based on the family structure information and economic conditions declared by the energy purchaser; collecting the energy-consuming equipment information of the energy purchaser and dividing its load into movable load and non-movable load; and solving for the energy demand of the energy purchaser with the goal of minimizing energy costs and the calculated comfort index as a constraint.

[0086] In this way, family structure information (such as the number of family members, number of children, and age structure) is directly related to the basic energy demand scale and comfort sensitivity of the energy purchaser. For example, families with children or elderly people have higher comfort requirements for environmental stability, while economic conditions affect the energy purchaser's willingness and ability to pay for comfort. The comfort index calculated based on this information can accurately reflect the personalized comfort preferences of the energy purchaser, avoiding the problem of traditional models treating the energy purchaser as a homogeneous subject and ignoring their true comfort needs. Collecting the energy-consuming equipment information of the energy purchaser and dividing its load into movable load and non-movable load, movable loads (such as washing machines and dryers) can adjust their operating time, while non-movable loads (such as lighting and medical equipment) must be supplied with energy at specific times. This division can clearly distinguish the rigidity and flexibility of the energy consumption of different loads of the energy purchaser, providing a load characteristic basis for subsequent accurate calculation of energy demand, and avoiding the disconnect between demand calculation and actual energy consumption scenarios caused by viewing the total load in a general way. By minimizing energy costs as the optimization objective and using the calculated comfort index as a constraint, the energy demand of the energy purchaser can be solved. This approach not only meets the general demand of energy purchasers to control energy costs, but also ensures that the personalized comfort experience of the energy purchaser is not sacrificed during the demand optimization process through the comfort index constraint. For example, it will not excessively compress the energy required to meet the comfort needs of the elderly or children in order to reduce costs. As a result, the energy demand obtained is both economical and in line with the actual energy consumption preferences of the energy purchaser.

[0087] In an optional embodiment of this application, the comfort index is calculated based on the family structure information and economic conditions declared by the energy purchaser, including: calculating using a linear function, a piecewise linear function, or a machine learning model based on data on the number of family members, the number of children, the age structure, and the family's economic conditions.

[0088] Thus, the comfort index is calculated based on data such as the number of family members, the number of children, age structure, and family economic conditions. This data is directly related to the comfort needs of the energy purchaser. The more family members there are, the larger the basic energy consumption, and the greater the overall demand for comfort. Families with more children are more sensitive to comfort dimensions such as ambient temperature and stability. Families with a high proportion of elderly people in their age structure have a more urgent need for energy to ensure comfort, such as heating and cooling. Families with better economic conditions are generally more able to pursue higher levels of comfort. This data comprehensively and accurately reflects the personalized comfort demands of the energy purchaser, providing a realistic input basis for subsequent calculations and avoiding the problem of traditional methods that neglect these individual differences, resulting in a vague portrayal of comfort. Meanwhile, calculations can be performed using linear functions, piecewise linear functions, or machine learning models. Linear functions and piecewise linear functions are characterized by their ease of calculation and clear logic, making them suitable for scenarios with relatively clear data relationships and enabling the rapid output of basic comfort indices. Machine learning models, on the other hand, can capture complex potential correlations between different data points, improving calculation accuracy when the data volume is large or the influencing relationship is non-linear. The flexible selection of multiple calculation methods can adapt to different application scenarios and data conditions, avoiding the limitations of a single calculation method. This allows the calculated comfort index to more accurately match the actual comfort preferences of the energy purchaser, laying a reliable foundation for subsequent steps such as solving energy demand and allocating energy based on comfort constraints.

[0089] In one optional embodiment of this application, movable loads include energy-consuming devices such as washing machines, dryers, dishwashers, and energy storage devices that can adjust their operating time in time; and / or, non-movable loads include devices such as lighting, refrigerators, and medical equipment that must be powered at specific times.

[0090] In this way, by distinguishing the time elasticity of the load, the system can identify and utilize the scheduling potential of movable loads. Under the premise of meeting the comfort index constraints of the energy purchaser, it provides a flexible adjustment means for optimization with the goal of minimizing energy costs. At the same time, it ensures that the rigid energy supply demand of non-movable loads is given priority. As a result, the energy demand of the energy purchaser obtained in the final solution not only meets the actual energy consumption habits and comfort requirements of users, but also has the feasibility of economic optimization, thus improving the precision of demand-side response and overall energy efficiency.

[0091] In an optional embodiment of this application, obtaining the risk type of each energy seller includes: constructing a risk perception behavior model of the energy seller that includes a value function and a probability weighting function based on prospect theory; and determining its risk type through the risk perception behavior model based on the energy seller's historical transaction decision data or pre-set questionnaire feedback.

[0092] Thus, a risk perception behavior model for energy sellers, incorporating a value function and a probability-weighted function, is constructed based on prospect theory. Prospect theory can characterize the true risk attitude of energy sellers in scenarios with uncertain returns. The value function is more sensitive to losses than to gains, while the probability-weighted function amplifies low-probability events and compresses high-probability events. Compared with the traditional model that assumes energy sellers are risk-neutral, this model is more in line with the irrational risk reactions that energy sellers may have in actual transactions (e.g., risk-averse energy sellers are more concerned about potential losses, while risk-seeking energy sellers are more concerned about low-probability high-returns). The combination of the value function and the probability-weighted function can fully capture this risk perception process and avoid the bias in characterizing risk attitudes caused by a single rational assumption. The risk type of energy sellers is determined based on their historical transaction decision data or pre-set questionnaire feedback. Historical transaction decision data reflects the actual behavior of energy sellers in the past when facing risks, while pre-set questionnaire feedback directly collects their subjective risk preferences. Both data sources provide real and specific references, avoiding the subjectivity of classifying risk types solely based on theoretical assumptions. This ensures that the determined risk type (risk-averse or risk-seeking) better reflects the actual situation of the energy sellers. By constructing an accurate risk perception behavior model using prospect theory and combining it with real data to determine the risk type, the differences in risk preferences among different energy sellers can be reliably distinguished. This provides an accurate basis for subsequently formulating differentiated electricity prices based on risk type (e.g., risk-averse sellers use grid export prices, while risk-seeking sellers adjust prices according to grid conditions).

[0093] In an optional embodiment of this application, power routing optimization is performed with the goal of minimizing wide-area transmission loss, including: constructing a total transmission loss calculation model; wherein the total transmission loss includes both ohmic loss and corona loss of the line; and performing power routing optimization based on the total transmission loss calculation model and the determined energy allocation and trading price.

[0094] In this way, by constructing a total transmission loss calculation model that includes both ohmic loss and corona loss of the line, we can better understand the challenges of wide-area distributed energy systems. Ohmic loss, as the basic energy loss caused by the interaction of line current and resistance, and corona loss, as the actual loss affected by factors such as system frequency, conductor parameters, and operating voltage, both directly consume energy during transmission. Considering only one type of loss would lead to an incomplete calculation of the total transmission loss, thus affecting the accuracy of subsequent route planning. Including both types of loss can more realistically and completely reflect the energy loss in wide-area transmission, avoiding routing schemes that deviate from actual operating scenarios due to deviations in loss calculation. Based on the total transmission loss calculation model, the determined energy allocation and trading price, power routing optimization is performed. The determined energy allocation clarifies the supply and demand matching relationship between energy sellers and buyers, and the trading price is related to the economic cost of energy transmission. In the optimization process, the path can be planned in the direction of reducing energy transmission loss based on accurate total loss calculation, while taking into account the supply and demand matching requirements and economic cost constraints. This avoids the problem of pursuing low loss at the expense of supply and demand allocation requirements, or focusing only on economic cost while ignoring a large amount of energy loss. As a result, the obtained power routing scheme not only conforms to the actual supply and demand of the wide area system, but also improves the energy transmission efficiency.

[0095] In one optional embodiment of this application, the ohmic loss of the line is calculated based on line current, resistance, length, conductor cross-sectional area and resistivity parameters; and / or, the corona loss of the line is calculated based on system frequency, air density factor, conductor radius, conductor spacing, operating voltage and corona initiation critical voltage parameters.

[0096] Thus, ohmic loss is directly related to the resistance of a conductor when current flows through it. The magnitude of the resistance is determined by the conductor's inherent properties (resistivity) and physical structure (length, cross-sectional area), while the line current directly affects the basic scale of the loss. These parameters comprehensively cover the core physical factors affecting ohmic loss, preventing deviations between calculated ohmic loss and actual conditions due to the omission of key parameters. Corona loss originates from the ionization of the surrounding air when the line operating voltage exceeds the corona initiation critical voltage. The system frequency affects the rate of change of the electric field, the air density factor relates to the air insulation strength, and the conductor radius and spacing alter the electric field distribution. These parameters comprehensively reflect the key conditions for corona loss generation, avoiding inaccurate corona loss calculations due to incomplete parameter consideration. The two types of losses are calculated based on their respective key parameters, which makes the total transmission loss calculation model more closely reflect the actual transmission scenarios of wide-area distributed energy systems, improves the reliability of the total transmission loss calculation results, and provides accurate loss data support for subsequent power routing optimization based on the total transmission loss calculation model. This reduces the problem that routing schemes are unable to effectively reduce actual transmission losses due to loss calculation errors.

[0097] In an optional embodiment of this application, power routing optimization includes: taking the total loss obtained from the total transmission loss calculation model as the objective, taking the surplus energy of all energy sellers as the supply constraint, the energy demand of all energy buyers as the demand constraint, and the tradable relationship or priority between energy sellers and energy buyers defined by the determined energy allocation scheme as the matching constraint, and establishing a transportation problem model; and using the Vogel approximation method to solve the transportation problem model to obtain a power routing scheme.

[0098] In this way, the total loss fully includes the ohmic and corona losses of the line, accurately reflecting the energy consumption in wide-area power transmission. Using this as a target, power routing can be directly guided towards reducing actual energy losses, avoiding the problem of traditional routing focusing only on supply and demand matching while ignoring losses. Using the surplus energy of all energy sellers as a supply constraint ensures that the energy transmission of energy sellers in the routing scheme will not exceed their actual available surplus. Using the energy demand of all energy buyers as a demand constraint ensures that the routing scheme will not fall below the basic energy needs of energy buyers. Furthermore, using the tradable relationship or priority between energy sellers and buyers defined in the determined energy allocation scheme as a matching constraint ensures that power routing remains consistent with the previously determined energy allocation logic, preventing the routing direction from deviating from the allocation result. Integrating these objectives and constraints to establish a transportation problem model can transform the complex problem of wide-area power routing into a structured mathematical problem, providing a clear framework for subsequent solutions. The Vogel approximation method is used to solve this transportation problem model because it has the characteristics of high solution efficiency and quick acquisition of optimal solutions when dealing with transportation-related problems. It can be adapted to the scenario of multiple energy sellers and multiple energy buyers in wide-area distributed energy systems. It can output feasible power routing schemes without complex calculations, and ultimately the obtained power routing schemes not only conform to the actual supply and demand and match the energy allocation rules, but also effectively reduce the total transmission loss.

[0099] Based on the above structure, such as Figure 2 As shown in the embodiment of this application, another energy trading method for a wide-area distributed energy system is provided, including:

[0100] S01, obtain the grid availability status, energy demand and comfort index of multiple energy purchasers, and surplus energy and risk type of multiple energy sellers.

[0101] S02, determine the transaction price for each energy seller based on the grid availability status and the risk type of the energy seller.

[0102] S03 When the grid availability status is unavailable and there is insufficient energy supply, the energy allocation among the energy purchasers is determined based on the energy purchaser's comfort index, the energy purchaser's energy demand, and the energy seller's surplus energy.

[0103] S04. Based on the determined energy allocation and trading price, power routing optimization is performed with the goal of minimizing wide-area transmission losses to determine the actual transmitted energy of each energy purchaser in each energy sales direction.

[0104] S05, calculate the bill for each energy purchaser and the revenue for each energy seller based on the actual transmitted energy and the corresponding transaction price.

[0105] S21. After calculating the bill for each energy purchaser, compare the actual energy consumption pattern of the energy purchaser with the energy consumption pattern predicted based on its declared comfort index.

[0106] S22. If the deviation exceeds the preset threshold, it is determined to be an inflated declaration, and a penalty fee is added to the energy purchaser's bill.

[0107] The energy trading method for wide-area distributed energy systems provided in this application uses a declared comfort index as the core basis for determining the energy demand of energy purchasers and carrying out energy allocation. The energy consumption pattern predicted based on this index represents the reasonable energy consumption state that energy purchasers should exhibit to meet their declared comfort requirements. By comparing the two, it is possible to intuitively determine whether the comfort index declared by the energy purchaser matches their actual energy needs, avoiding the lack of supervision on the authenticity of declarations caused by relying solely on declared data while ignoring actual energy consumption behavior. If the deviation exceeds a preset threshold, it is determined to be an inflated declaration, and a penalty fee is added to the energy purchaser's bill. The penalty fee will cause the energy purchaser with an inflated declaration to face additional economic costs. This cost constraint can effectively reduce the behavior of energy purchasers deliberately inflating their comfort index to obtain more energy allocation, and prevent the energy resources of energy purchasers with genuine high comfort needs from being squeezed out by some energy purchasers with inflated declarations, thereby maintaining the fairness of energy allocation and ensuring the orderly development of energy trading and allocation in wide-area distributed energy systems.

[0108] In this embodiment of the application, an energy trading system for a wide-area distributed energy system includes a system body and an energy trading device installed on the system body. Specifically, the system includes a market coordination module, an energy management module for the energy purchaser side, an energy management module for the energy seller side, a grid interface module, and a wide-area communication and metering module.

[0109] The market coordination module, as a unit with data processing and instruction execution functions, receives the comfort index and comprehensive energy demand from energy buyers, as well as the surplus energy, risk type, and pricing information from energy sellers. This module is further used to perform differentiated electricity price determination based on the energy seller's risk type and grid availability status, solve energy allocation schemes based on the energy buyer's comfort index, and perform power routing optimization calculations aimed at minimizing wide-area transmission losses.

[0110] The energy management module on the energy purchaser's side, deployed on the user's side, is used to collect the energy purchaser's household structure information, economic conditions data, and energy-consuming equipment information. This module is further used to calculate the energy purchaser's comfort index based on the household structure information and economic conditions data, classify the load in the energy-consuming equipment information into movable loads and non-movable loads, and form the energy purchaser's energy demand declaration based on comfort index constraints and the goal of minimizing energy costs.

[0111] The energy management module on the energy seller's side, deployed at distributed energy sites, is used to collect output data from distributed energy sources and the energy seller's own energy demand data. This module further calculates the energy seller's surplus energy based on the output data and self-consumption demand data, and records the risk type and pricing information determined by the energy seller based on historical transaction decisions or pre-set questionnaire feedback.

[0112] The grid interface module is used to realize energy exchange and information interaction between the energy trading system and the main grid. Specifically, this module is used to obtain the grid export price and grid availability status from the main grid, and to exchange any surplus or shortage of energy after the trading within the system with the main grid.

[0113] The wide-area communication and metering module connects multiple energy purchasers and sellers within the system via wired or wireless communication networks. This module further utilizes smart metering devices deployed at each node to collect real-time energy consumption and generation data from both purchasers and sellers, and to meter the actual energy transmitted during point-to-point transactions.

[0114] The aforementioned market coordination module, energy management module for energy purchasers, energy management module for energy sellers, grid interface module, and wide-area communication and metering module work together to execute energy trading methods and realize point-to-point energy trading in the wide-area distributed energy system.

[0115] In this embodiment, the basic parameters required for energy trading in a wide-area distributed energy system are first obtained to construct the basic data framework for subsequent trading decisions. The set of energy purchasers is denoted as EB, which includes all entities with energy demand, mathematically expressed as EB = {eb1, eb2, ..., eb...} M}, M is the quantity of the purchasing party, eb j Let represent the j-th energy purchaser. Energy purchasers can be residential users, commercial users, or other entities with energy needs. Each energy purchaser corresponds to an energy demand and a comfort index. The set of energy sellers is denoted as ES. This set includes all entities with distributed energy resources and surplus energy available for sale. Mathematically, ES = {es1, es2, ..., es} N}, where N is the number of sellers, and es iLet represent the i-th energy seller. Energy sellers can be entities with distributed energy resources, such as residential solar PV systems or industrial microgrids. Each energy seller has surplus energy. Based on the above set of energy buyers, the total energy demand of all energy buyers is denoted as E. dem Through formula Calculations show that E dem,j Let E be the energy demand of the j-th energy purchaser, which can be converted into electrical energy units (kWh). Based on the above set of energy sellers, the total surplus energy of all energy sellers is denoted as E. sur Through formula Calculations show that E sur,i Let represent the surplus energy of the i-th energy seller. Simultaneously, to adapt to the operational scenarios of wide-area distributed energy systems, it is also necessary to determine the grid availability status and time-related parameters: the grid availability status is denoted as γ, used to characterize the grid's operational status. When γ = 1, the grid is available; when γ = 0, the grid is unavailable (e.g., in power outage or power rationing scenarios). A time set is set as T, where t is the time index and t ∈ T. The length of each time interval is denoted as Δt, and the time interval can be set to 30 minutes, serving as the statistical period for energy demand and surplus energy.

[0116] In this embodiment, the comprehensive energy consumption behavior and comfort modeling of energy purchasers is based on a set of energy purchasers. It combines relevant household parameters of the energy purchasers to construct the correlation between comprehensive energy demand and comfort index, classifies load types, determines comprehensive demand, and simultaneously constructs the energy purchaser objective function and satisfaction index. The specific process is as follows:

[0117] In constructing the correlation between energy purchasers' comprehensive energy consumption behavior and comfort, the comprehensive energy demand E of energy purchasers is... dem Comfort Index This is related to the energy purchaser's family structure parameters and family economic conditions parameters. The family structure parameters include the number of family members, the number of children, and the age structure. The family economic conditions parameter is the family's economic situation: the number of family members is denoted as N. family Comprehensive energy demand E dem With the number of family members N family They are positively correlated, mathematically expressed as E dem ∝N family The more family members there are, the higher the overall energy consumption for lighting, air conditioning, hot water, etc.; the family's economic conditions are denoted as F. eco Comfort Index With family economic conditions F eco They are positively correlated, which can be expressed mathematically as follows: The more a family's financial situation, the higher its requirements for comfort, and the higher its comfort index. The larger the number of children, the greater the number of children. child Comprehensive energy demand E demComfort Index With the number of children N child They are all positively correlated, which can be expressed mathematically as follows: The more children there are, the greater the demand for dedicated lighting, air conditioning, etc., and the higher the requirements for environmental stability; the age structure is denoted as Age, and the comprehensive energy demand is E. dem Comfort Index It is positively correlated with the age structure (Age), which can be expressed mathematically as follows: For example, families with a high proportion of elderly people have a stronger demand for heating and cooling.

[0118] In load type classification and comprehensive demand determination, the energy purchaser's load types include movable loads and non-movable loads. Movable loads are loads that can be moved over time (such as washing machines and dryers), while non-movable loads are loads that must be supplied with energy for a specific period of time (such as basic lighting and refrigerators). (Energy purchaser eb) i The set of movable loads is denoted as MD m,n = [1,...,N], where the energy requirement of a single portable device is denoted as . Total relocatable requirements are denoted as Mathematical expression Where N is the number of portable devices, or more specifically, the sum of the energy requirements for each portable device; energy purchaser eb i The set of non-movable loads is denoted as NM.D. m,n = [1,...,N], where the energy requirement of a single non-movable device is denoted as . Total non-movable requirements are denoted as Mathematical expression Where N is the number of non-movable devices; energy purchaser eb i Comprehensive energy demand E dem,i The sum of total movable demand and total immovable demand can be mathematically expressed as:

[0119] In constructing the goal of minimizing the energy purchaser's bill and the comfort constraints, the total energy purchaser's bill within the time set T is denoted as E. billi The goal of the energy purchaser is to minimize the total bill while ensuring that the comfort level is not lower than the declared value, which can be mathematically expressed as:

[0120]

[0121] Among them, E billi,t T represents the actual energy (in kWh) settled by energy purchaser i at time t; p,t Δt represents the transaction price of electricity at time t (unit: yuan / kWh); Δt represents the time interval length. The comfort index submitted by the energy purchaser.

[0122] In the definition of the energy purchaser satisfaction index, energy purchaser satisfaction SL i The ratio of the actual energy allocated to the total energy demand is mathematically expressed as:

[0123]

[0124] Among them, E alloc,i The energy actually allocated to energy purchaser i after system allocation and routing, when SL i When SL = 1, the energy demand of the energy purchaser is fully met; when SL = 1, the energy demand of the energy purchaser is fully met. i When the value is less than 1, there is a gap in the energy demand of the energy purchaser.

[0125] As a numerical example, the combined energy demand vector (unit: kWh) of the 11 energy purchasers at a certain moment is denoted as...

[0126] E dem (kWh)=[2.9076∈eb1,0.80∈eb4,2.3670∈eb9,1.1260∈eb7,1.4587∈eb6,1.2143∈eb2,2.4954∈eb3,2.9283∈eb8,1.0848∈eb5,0.4760∈eb 10 ,0.5350∈eb 11 ]

[0127] This vector is used to demonstrate subsequent energy allocation and settlement steps.

[0128] Based on the above modeling of the energy purchaser's comprehensive energy consumption behavior and comfort, the comprehensive energy demand E of the energy purchaser is obtained. dem,i Comfort Index Total Bill E billi Satisfaction SL i This is the basic input for subsequent energy allocation game steps.

[0129] In this embodiment of the application, obtaining the surplus energy and risk type of each energy seller specifically includes the following process:

[0130] First, calculate the surplus energy of the energy sellers. For each energy seller in the set, based on the power generation data of its power generation facilities and its own energy demand, determine its surplus energy available for external trading. Surplus energy E sur,i Calculated using the following formula:

[0131] E sur,i =E gen,i -E dem,i

[0132] Wherein, parameter E gen,iThis represents the total energy generated by energy seller i's power generation facilities (e.g., photovoltaic power generation systems, wind power generation systems) within a given dispatch cycle, expressed in kilowatt-hours (kWh). Parameter E dem,i This represents the total energy demand of energy seller i within the same cycle, also expressed in kilowatt-hours (kWh). The sum of the surplus energy of all energy sellers constitutes the total supply available for peer-to-peer transactions.

[0133] Secondly, the risk type of the energy seller needs to be determined. Risk type is a classification label used to distinguish the different decision-making preferences of energy sellers when facing uncertainty about future transaction returns. Obtaining the energy seller's risk type involves constructing a risk perception behavior model for the energy seller, based on prospect theory and incorporating a value function and a probability weighting function. This risk perception behavior model aims to characterize the psychological and behavioral features of energy sellers when making decisions in uncertain environments.

[0134] Specifically, risk factors are introduced. Risk factors are a quantitative representation of risk types. The value space of the risk factor contains two discrete values, used to correspond to different risk attitude categories: defining the risk factor when... When, it corresponds to a risk-averse energy seller; when risk factors At that time, the corresponding type of energy seller is the risk-seeking type.

[0135] The risk perception behavior model of the energy seller consists of a value function v(·) and a probability weighting function π(·). The value function v(·) measures the subjective value perceived by the energy seller for different amounts of gain or loss, and its typical characteristic is that it is more sensitive to losses than to gains of the same scale. The probability weighting function π(·) describes the energy seller's subjective distortion of objective probability, such as tending to amplify the weight of low-probability events while compressing the weight of high-probability events.

[0136] The subjective utility U of the energy seller in a transaction scenario with uncertain returns. seller It can be calculated by probabilistically weighted summation of the subjective values ​​of its various possible outcomes. Its utility function is constructed based on prospect theory and mathematically expressed as:

[0137]

[0138] Where v(·) is the value function, which is usually more sensitive to loss; π(·) is the probability weighting function, which amplifies the weight of low-probability events; p k Let be the probability of the k-th event occurring.

[0139] In a scenario containing only two payoff outcomes x and y and their corresponding probabilities p and q, the utility function of the energy seller can be specifically expressed as:

[0140] U seller = π(p)v(x) + π(q)v(y)

[0141] When p + q = 1 and x > y > 0 or x < y < 0, the utility function can be written as:

[0142] U seller = v(y) + π(p)[v(x) - v(y)]

[0143] Meanwhile, the utility function satisfies the following relationships that reflect behavioral characteristics such as loss aversion:

[0144] v(y) + v( - y) > v(x) + v( - x), v( - y) + v( - x) > v(x) + v( - x)

[0145] Further satisfying:

[0146] π(p)v(x) + π(pq)v(y) = π(pq)v(y)

[0147] π(pr)v(x) ≤ π(pqr)v(y)

[0148]

[0149] where p, q, r, pq, pr, pqr are the probabilities of different event combinations occurring.

[0150] In practical applications, based on the historical decision - making data of the energy seller's past participation in market transactions, or by analyzing the feedback information of the energy seller on a preset risk - preference questionnaire, and inputting it into the above - constructed risk - perception behavior model, the specific risk type of the energy seller (i.e., the value of its risk factor ) can be inferred and determined.

[0151] As a specific numerical illustration of the above modeling process, at a certain scheduling moment, the surplus energy of multiple energy sellers in the system can be exemplified in vector form as:

[0152]

[0153] At the same moment, the corresponding risk types of these energy sellers (represented by risk - factor vectors) can be exemplified as:

[0154]

[0155] In addition, the risk type of the energy seller may change over time or in different situations. For example, at another moment (t = 83), the risk - factor vector may become:

[0156]

[0157] At time t=91, the risk factor vector may become:

[0158]

[0159] This reflects the differences in the risk attitude of energy sellers at different times and in different situations.

[0160] Through the above steps, the surplus energy E of each energy seller was obtained. sur,i and its risk type (by risk factors) The identifier provides the necessary input for subsequent steps to determine the electricity price based on differences in risk type.

[0161] In this embodiment of the application, the transaction price for each energy seller is determined based on the grid availability status and the risk type of the energy seller, including the following process:

[0162] A Bayesian game model is established between energy sellers and grid companies to form differentiated electricity prices in scenarios where information on grid availability and energy seller risk types is incomplete.

[0163] Specifically, a Bayesian game model with incomplete information is constructed, mathematically expressed as:

[0164]

[0165] Here, parameter N represents the set of game participants, including all energy sellers and the power grid company. This represents the type space, that is, the set of possible values ​​for all risk types of energy sellers. Parameter A i This represents the set of strategies for participant i, such as the pricing strategy of an energy seller or the pricing strategy of a power grid company. Parameter U i Let T represent the utility function of participant i. For the energy seller, its utility function is constructed based on prospect theory; for the grid company, its utility function can be characterized as a profit function. i This represents the type space of participant i, containing its private information. Parameter τ i This represents the specific type implementation of participant i. The parameter p(·) represents the prior probability distribution of the participant type.

[0166] The grid availability status and the risk type of the energy seller are used as inputs to this game theory model. Based on this, differentiated pricing rules are set for energy sellers with different risk types.

[0167] For risk-averse energy sellers, i.e., when risk factors To ensure the certainty of its revenue, the export electricity price set by the power grid company is directly adopted as its transaction price. Mathematically, this is expressed as:

[0168]

[0169] in, This represents the export price of electricity from the power grid at time t, which is determined by the power grid company based on the real-time electricity market, operating costs, and policy constraints. ES represents the set of energy sellers.

[0170] For risk-seeking energy sellers, i.e., when risk factors... At that time, the transaction price of electricity is further differentiated based on the grid availability status: when the grid availability status is characterized as unavailable (i.e., γ=0), risk-seeking energy sellers can quote prices based on their own judgment of the market, using their own quoted price P. off,i As the electricity price for trading. Mathematically expressed as:

[0171] T p,i =P off,i ,γ=0

[0172] When the grid availability status is characterized as available (i.e., γ = 1), to prevent risk-seeking energy sellers from excessively influencing the grid company's revenue, a punitive pricing mechanism is applied, causing their transaction price to be lower than the grid's export price. Mathematically, this is expressed as:

[0173]

[0174] in, With punitive electricity price T p The difference can serve as a source of profit for the power grid company participating in transaction coordination.

[0175] Solve the above Bayesian game model to determine the final electricity transaction price. The solution process must satisfy the Bayesian Nash equilibrium condition. Given the type configuration τ, what is the optimal strategy a for any participant i? i (τ i The following conditions must be met:

[0176]

[0177] Where, τ -i a represents the combination of types of all other participants except participant i. -i (τ -i () represents the strategy combinations of other participants under the corresponding type combination. By solving this optimization problem, the Bayesian Nash equilibrium solution of the game is obtained, which is the optimal strategy combination of all participants, including the final transaction electricity price T determined for each energy seller. p or T p,i .

[0178] As a numerical example of the pricing process described above, the energy purchaser's bill can be calculated after the electricity price is determined. Energy purchaser eb jThe total bill is the sum of the costs of purchasing energy from each energy supplier, expressed mathematically as:

[0179]

[0180] Where, θ j,i Indicates the energy seller es i Sold to the energy purchaser eb j Energy, TP i This represents the applicable electricity price for energy seller i (determined by the rules mentioned above based on its risk type and grid status). Specific calculation examples include: In the simplified case of a single energy seller and a single electricity price, the bill calculation example is: Ebill j =2.9076 × 7.20 = 20.9347c; In complex scenarios with multiple time periods and multiple prices, the bill calculation example is: Ebill j =(2.9076×7.20)+(1×2.9076)=23.8423c.

[0181] The resulting transaction price Tp, obtained through the Bayesian game-based electricity pricing mechanism, will be used in subsequent comfort-driven energy allocation games, wide-area transmission route routing, and settlement steps.

[0182] In this embodiment of the application, the energy trading method for a wide-area distributed energy system determines the energy allocation among energy buyers when the grid availability status is unavailable and there is insufficient energy supply, based on the energy buyer's comfort index, the energy buyer's energy demand, and the energy seller's surplus energy. The specific process is as follows:

[0183] The energy allocation process between the energy purchaser and the energy seller is modeled as a non-cooperative game. This non-cooperative game can be formally represented as:

[0184] Φ=<N,A,U>

[0185] Here, parameter N represents the set of game participants, which includes all energy buyers and all energy sellers. Parameter A represents the set of strategies for all participants; for energy buyers, their strategies include their declared energy demand E. dem,i and comfort index For energy sellers, their strategies include their risk type and pricing information. Parameter U represents the set of utility functions for all participants, including the utility functions of both energy buyers and sellers.

[0186] When the grid availability is unavailable (i.e., γ = 0) and the total surplus energy available for trading from energy sellers is less than the total energy demand from energy buyers, the optimization objective of the system is to maximize the overall utility of all energy buyers under limited energy supply. This objective and related allocation constraints can be expressed as:

[0187]

[0188] Among them, U i (E dem,i E is the utility function of energy purchaser i. alloc,i It is the energy actually allocated to energy purchaser i, SL i The upper limit of the satisfaction constraint is determined based on factors such as the energy purchaser's comfort index.

[0189] To prove that the above non-cooperative game has a stable solution, namely a Nash equilibrium, we need to prove the existence of the equilibrium. Define the gain function G. i (ξ,a) is as follows:

[0190] G i (ξ,a)=max{0,u i (a,ξ -i )-u i (ξ i ,ξ -i )}

[0191] Where, parameter i is the participant index; parameter ξ = (ξ1,…,ξ) n ) represents the current combination of mixed strategies; parameter ξ -i This represents the strategy combination of all participants except participant i; parameter a∈A i It is an optional pure strategy for participant i; parameter u i (·) is the utility function of participant i.

[0192] Based on the gain function, define the auxiliary function g. i (ξ)(a):

[0193] g i (ξ)(a)=ξ i (a)+G i (ξ,a)

[0194] Where, ξ i (a) indicates the hybrid strategy ξ i The probability of choosing pure strategy a.

[0195] Furthermore, a normalized mapping f is introduced. i (ξ)(a), converting the result of the auxiliary function into a probability distribution:

[0196]

[0197] Let f = (f1, ..., f n ) represents a mapping over the entire policy space. According to the fixed-point theorem, there exists a fixed point ξ in the policy space Δ. * ,satisfy:

[0198] ξ * =f(ξ) * )

[0199] At this fixed point, we can deduce that:

[0200]

[0201] This means that in the strategy combination ξ * Under these circumstances, no participant can improve their utility by unilaterally changing their strategy; therefore, ξ * This is a Nash equilibrium point in the non-cooperative game. By solving for this equilibrium, a stable energy allocation scheme can be obtained.

[0202] In practice, the energy allocation scheme is determined based on the comfort index of the energy purchasers. The system meets the energy needs of energy purchasers in descending order of their comfort index. When the total energy supply is less than the total energy demand, the energy allocated to energy purchasers with lower comfort indices is reduced proportionally.

[0203] As a numerical example, at a certain moment (e.g., t=13), the comfort indices of multiple energy purchasers can form a vector, for example:

[0204]

[0205] The system will prioritize the needs of energy purchasers with higher comfort levels (e.g., indices of 0.99 or 0.94), and when resources are insufficient, it will proportionally reduce the needs of energy purchasers with lower comfort levels (e.g., indices of 0.10 or 0.20) to optimize overall satisfaction.

[0206] Through the above process, the energy allocation scheme based on the comfort index for ranking and proportional reduction is determined, and input is provided for subsequent power routing optimization steps.

[0207] In this embodiment, based on the determined energy allocation and trading price, power routing optimization is performed with the goal of minimizing wide-area transmission losses. The actual transmitted energy of each energy purchaser in each energy sales direction is determined, and the bill for each energy purchaser and the revenue for each energy seller are calculated based on the actual transmitted energy and the corresponding trading price. The specific process is as follows:

[0208] First, a model for calculating total transmission loss is constructed. Wide-area transmission loss includes ohmic loss and corona loss of the line. The ohmic loss per unit length of the line, L... Ω The calculation formula is:

[0209] L Ω =I 2 •R (unit: kW / km)

[0210] Where, parameter I is the line current, measured in amperes (A); parameter R is the resistance per unit length of the line, measured in ohms per kilometer (Ω / km). The corona loss L of the line... c The calculation formula is:

[0211]

[0212] Wherein, parameter f is the system frequency, in Hertz (Hz); parameter δ is the air density factor; parameter r is the conductor radius, in meters (m); parameter d is the conductor spacing, in meters (m); parameter v is the line operating voltage, in kilovolts (kV); parameter v c The corona initiation threshold voltage is expressed in kilovolts (kV). The total line loss TLoss is the sum of ohmic loss and corona loss, i.e., TLoss = L Ω +L c Consider the relationship between conductor resistance and length and cross-sectional area. The total transmission loss calculation model can be further expressed as:

[0213]

[0214] Wherein, parameter ρ is the conductor resistivity; parameter L is the line length in kilometers (km); and parameter A is the conductor cross-sectional area.

[0215] Secondly, with the goal of minimizing total transmission loss, and considering the determined energy allocation and trading price, power routing optimization is performed. Let θ j,i For energy sellers es i To purchase energy eb j The actual energy delivered is measured in kilowatt-hours (kWh). Energy purchaser eb j Ebill j and sales energy es i Erev's earnings i Calculated using the following formulas respectively:

[0216]

[0217] Among them, TP i It is the energy seller es i The applicable electricity trading price.

[0218] Power routing optimization must satisfy the hard constraint of supply and demand balance, that is, the total amount sold by all energy sellers equals their surplus energy, and the total amount purchased by all energy buyers equals their energy demand. Mathematically, this can be expressed as:

[0219]

[0220] Among them, Esur,i It is the surplus energy of the energy seller i, E dem,j This refers to the energy demand of the energy purchaser.

[0221] The optimization objective of power routing is to find a set of energy distribution schemes {θ}. j,i This ensures that, under the aforementioned supply and demand constraints, the overall transmission loss TLoss of the entire wide area network is minimized. Let α i,j Let β(θ) be the weight of the unit power loss from energy seller i to energy buyer j. j,1 If is the corresponding loss function, then the optimization objective can be formally described as:

[0222]

[0223] Combining the total loss calculation model with supply and demand constraints, the complete optimization problem is formulated as follows:

[0224]

[0225] This is a typical transportation problem. By solving the transportation problem model using the Vogel approximation method, the power routing scheme with the minimum total transmission loss can be obtained, which determines the optimal actual transmitted energy θ between each pair of energy sellers and buyers. j,i .

[0226] Before optimizing the routing, it is necessary to determine the system's scenario based on the relationship between total demand and total supply: if total demand exceeds total supply, then the system satisfies the condition. This indicates an energy shortage. In this case, the power routing scheme must strictly adhere to the priority allocation results previously determined based on the comfort index ranking. If total demand is less than total supply, then the energy supply is satisfied. This results in an energy surplus. In this scenario, the surplus energy can be fed back to the main grid or disposed of as curtailed electricity, depending on the electricity trading price mechanism and the risk appetite of the energy seller.

[0227] Finally, as a numerical example, the key parameter vectors are shown at multiple time points. At time t=13, the energy purchaser comfort index vector is:

[0228]

[0229] The energy demand vector of the energy purchaser is:

[0230]

[0231] The surplus energy vector of the energy seller is:

[0232]

[0233] The risk factor vector for the energy seller is:

[0234]

[0235] At time t=83, the risk factor vector becomes:

[0236]

[0237] At time t=91, the risk factor vector becomes:

[0238]

[0239] These examples demonstrate that, with demand and surplus remaining relatively stable, changes in the risk factors of energy sellers alone can affect the final power allocation path and settlement results through the electricity trading mechanism and power routing optimization, reflecting the sensitivity of this method to differences in energy seller behavior.

[0240] The optimal actual transport energy θ obtained by solving the Vogel approximation method j,i and the corresponding transaction price (TP) for each energy seller. i By substituting the formulas for the energy purchaser's bill and the energy seller's revenue into the formulas, the final settlement for all market participants can be completed.

[0241] Combination Figure 3 As shown, this application embodiment provides an energy trading device 800 for a wide-area distributed energy system, including a processor 801 and a memory 802. Optionally, the device may further include a communication interface 803 and a bus 804. The processor 801, communication interface 803, and memory 802 can communicate with each other via the bus 804. The communication interface 803 can be used for information transmission. The processor 801 can call logical instructions in the memory 802 to execute the energy trading method for a wide-area distributed energy system described in the above embodiment.

[0242] Furthermore, the logic instructions in the aforementioned memory 802 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0243] The memory 802, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this application. The processor 801 executes functional applications and data processing by running the program instructions / modules stored in the memory 802, thereby implementing the energy trading method for wide-area distributed energy systems described in the above embodiments.

[0244] The memory 802 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 802 may include high-speed random access memory and may also include non-volatile memory.

[0245] This application provides a system comprising: a system body and the aforementioned energy trading device 800 for a wide-area distributed energy system. The energy trading device 800 for the wide-area distributed energy system is installed on the system body. The installation relationship described herein is not limited to placement within the system, but also includes installation connections with other components of the system, including but not limited to physical connections, electrical connections, or signal transmission connections. Those skilled in the art will understand that the energy trading device 800 for the wide-area distributed energy system can be adapted to feasible system bodies to achieve other feasible embodiments.

[0246] This application provides a computer-readable storage medium storing computer-executable instructions configured to execute the above-described energy trading method for a wide-area distributed energy system.

[0247] The technical solutions of this application embodiment can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this application embodiment. The aforementioned storage medium can be a non-transitory storage medium, including: USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, and other media capable of storing program code.

[0248] The technical solutions of this application embodiment can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this application embodiment. The aforementioned storage medium can be a non-transitory storage medium, including: USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, and other media capable of storing program code.

[0249] The foregoing description and accompanying drawings fully illustrate embodiments of this application to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or,” as used herein, means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., of the embodiments claimed, if they correspond to the method section of the embodiments claimed, then the relevant parts can be referred to the description of the method section.

[0250] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments claimed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0251] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. 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 the units can be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of this application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0252] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. An energy trading method for a wide-area distributed energy system, characterized in that, include: Obtain the grid availability status, energy demand and comfort index of multiple energy purchasers, and surplus energy and risk type of multiple energy sellers; The transaction price for each energy seller is determined based on the grid availability status and the risk type of the energy seller. When the grid availability status is unavailable and there is insufficient energy supply, the energy allocation among energy purchasers is determined based on the energy purchaser's comfort index, energy purchaser's energy demand, and energy seller's surplus energy. Based on the determined energy allocation and electricity trading price, power routing optimization is performed with the goal of minimizing wide-area transmission loss to determine the actual transmitted energy of each energy purchaser in each energy sales direction. Based on the actual transmitted energy and the corresponding transaction price, calculate the bill for each energy purchaser and the revenue for each energy seller.

2. The method according to claim 1, characterized in that, Based on the grid availability status and the risk type of the energy seller, the transaction price for each energy seller is determined, including: Establish a Bayesian game model between energy sellers and power grid companies; The grid availability status and the risk type of the energy seller are used as inputs to the game model; Solve the Bayesian game model and output differentiated trading prices based on different combinations of the energy seller's risk type and the grid availability status.

3. The method according to claim 2, characterized in that, The rules for outputting differentiated electricity trading prices in the Bayesian game model include: For risk-averse energy sellers, the export price of the power grid is used as the transaction price. For risk-seeking energy sellers, when the grid availability status is unavailable, the energy seller's own quotation is output as the transaction price; For risk-seeking energy sellers, when the grid availability status is available, a penalty price lower than the grid export price is output as the transaction price.

4. The method according to any one of claims 1 to 3, characterized in that, Based on the energy purchaser's comfort index, energy demand, and surplus energy of the energy seller, the energy allocation among energy purchasers is determined, including: Establish a non-cooperative game model between energy purchasers and energy sellers; The comfort index of the energy purchaser, the energy demand of the energy purchaser, and the surplus energy of the energy seller are used as inputs to the game model; Solve the non-cooperative game model and output an energy allocation scheme based on the comfort index ranking of energy purchasers.

5. The method according to claim 4, characterized in that, Output an energy allocation scheme based on the energy purchaser's comfort index, including: The energy needs of energy purchasers will be met in descending order of their comfort index. When the total surplus energy of energy sellers is less than the total energy demand of energy buyers, the energy allocated to energy buyers with lower comfort indices will be reduced proportionally.

6. The method according to any one of claims 1 to 3, characterized in that, Obtain the energy demand and comfort index of each energy purchaser, including: Based on the family structure information and economic conditions declared by the energy purchaser, the comfort index of the energy purchaser is calculated. Collect information on the energy-consuming equipment of the energy purchaser and classify its load into movable load and non-movable load; With the goal of minimizing energy costs and the calculated comfort index as a constraint, the energy demand of the energy purchaser is obtained.

7. The method according to claim 6, characterized in that, The comfort index is calculated based on the family structure and economic conditions declared by the energy purchaser, including: Based on data on the number of family members, number of children, age structure, and family economic conditions, calculations are performed using linear functions, piecewise linear functions, or machine learning models.

8. The method according to claim 6, characterized in that, Portable loads include energy-consuming equipment such as washing machines, dryers, dishwashers, and energy storage devices whose operating times can be adjusted over time; and / or, Non-movable loads include lighting, refrigerators, and medical equipment that must be powered at specific times.

9. The method according to any one of claims 1 to 3, characterized in that, Obtain the risk type for each energy seller, including: Based on prospect theory, a risk perception behavior model for energy sellers is constructed, which includes a value function and a probability weighting function. Based on the seller's historical transaction decision data or pre-set questionnaire feedback, the risk type is determined through a risk perception behavior model.

10. The method according to any one of claims 1 to 3, characterized in that, Power routing optimization with the goal of minimizing wide-area transmission losses includes: Construct a model for calculating total transmission loss; where total transmission loss includes both ohmic loss and corona loss of the line. Based on the total transmission loss calculation model and the determined energy allocation and trading price, power routing optimization is performed.

11. The method according to claim 10, characterized in that, The ohmic loss of the line is calculated based on parameters such as line current, resistance, length, conductor cross-sectional area, and resistivity; and / or, The corona loss of the line is calculated based on system frequency, air density factor, conductor radius, conductor spacing, operating voltage, and corona initiation critical voltage parameters.

12. The method according to claim 10, characterized in that, Power routing optimization includes: The transportation problem model is established with the total loss obtained from the total transmission loss calculation model as the objective, the surplus energy of all energy sellers as the supply constraint, the energy demand of all energy buyers as the demand constraint, and the tradable relationship or priority between energy sellers and energy buyers defined by the determined energy allocation scheme as the matching constraint. The Vogel approximation method is used to solve the transportation problem model, and a power routing scheme is obtained.

13. The method according to any one of claims 1 to 3, characterized in that, After calculating the bills for each energy purchaser, the following is also included: The actual energy consumption patterns of the energy purchasers are compared with the predicted energy consumption patterns based on their declared comfort index. If the deviation exceeds the preset threshold, it will be judged as an inflated declaration, and a penalty fee will be added to the energy purchaser's bill.

14. An energy trading device for a wide-area distributed energy system, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to execute, when running the program instructions, the energy trading method for a wide-area distributed energy system as described in any one of claims 1 to 13.

15. An energy trading system for a wide-area distributed energy system, characterized in that, include: System body; as well as, The energy trading device for a wide-area distributed energy system as described in claim 14 is installed on the system body.

16. A computer-readable storage medium storing program instructions, characterized in that, When the program instructions are executed, they cause the computer to perform the energy trading method for a wide-area distributed energy system as described in any one of claims 1 to 13.

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