Power demand response financial incentive method

Abstract

The invention relates to the technical field of power demand response and financial incentive, in particular to a power demand response financial incentive method. The method comprises the steps that information is collected from power demand response signals and user response data, an incentive distribution list is generated through a multi-dimensional contribution degree calculation model, and a final incentive scheme structure is generated through further optimization. Then, a token reward is generated according to the list, and transparent issuing of the reward and non-tampering of data are ensured through intelligent contract triggering and a block chain technology; and then, recording and verifying the reward data through the block chain, generating a user feedback report, and summarizing the user feedback report into a complete financial incentive report. According to the method, the fairness and transparency of incentive distribution are effectively improved, automation and real-time performance of reward distribution in the power demand response process are achieved, human errors are reduced, user trust is enhanced, and the method is suitable for complex power grid loads and diversified response scenes.

Description

Technical Field

[0001] This invention relates to the field of electricity demand response and financial incentive technology, and in particular to a financial incentive method for electricity demand response. Background Technology

[0002] In the field of electricity demand response, existing solutions typically collect electricity load data and user response information, and then allocate rewards using a fixed incentive model. However, these traditional methods suffer from problems such as unfair incentive allocation, low transparency of incentive mechanisms, and long settlement cycles. Existing systems often allocate incentives based on a single load reduction amount or response time, making it difficult to accurately reflect users' actual contributions in different scenarios, resulting in inaccurate reward allocation. Furthermore, since data recording and incentive distribution are often handled by centralized systems, the lack of transparency and traceability can easily lead to user doubts about the fairness of reward allocation, thereby affecting their participation.

[0003] Traditional electricity demand response systems typically rely on manual or semi-automated settlement processes, making it difficult to distribute and adjust incentives in real time. This results in low system efficiency and an inability to respond promptly to changes in grid load. Furthermore, existing methods often lack dynamic contribution calculation models, failing to adjust incentive allocation strategies based on changes in electricity demand, and do not utilize advanced technologies such as blockchain for incentive recording and transparent management.

[0004] Regarding how to achieve a fair, transparent, and automated incentive mechanism in electricity demand response, existing technologies generally have shortcomings in combining multi-dimensional contribution calculation with automated incentive distribution. This makes it difficult to form a continuous closed loop of data collection, calculation, distribution, verification, and settlement in electricity demand response scenarios, thereby affecting the overall performance of the system and user experience. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a financial incentive method for electricity demand response, comprising:

[0006] Acquire power demand response signals and user response data, perform data acquisition and processing, including using data cleaning techniques to remove noise data and identify outliers, extracting contribution calculations of key indicators of response timeliness and response stability, dynamically adjusting parameters and considering external factors through a multi-dimensional contribution calculation model, dynamically adjusting the incentive allocation ratio of each user, and generating the final incentive scheme structure.

[0007] Obtain the incentive allocation list, process token reward generation, execute the following steps: calculate and dynamically adjust the token amount according to the preset token conversion rules, trigger smart contracts, automatically verify the token amount and contribution and dynamically adjust the reward standard, process blockchain writing, record transaction data using blockchain technology, and generate on-chain transaction records.

[0008] Perform reward data verification and auditing, including checking whether the reward data complies with the contract rules and identifying abnormal data, extracting the reward amount and distribution rules for each user, performing user feedback generation and processing, generating feedback content including response behavior, contribution level and reward amount, and obtaining a report delivery structure.

[0009] The report generation and aggregation process combines feedback data from each user into a complete report framework, extracts each user's incentive status, performs traceability verification, verifies blockchain records, and generates a complete demand response financial incentive report.

[0010] Furthermore, electricity demand response signals and user response data include:

[0011] The power demand response signal includes the predicted and actual values ​​of the total grid load, frequency adjustment instructions, and specific demand response requests sent to the user side and their real-time change information; the user response data includes the actual reduction in power consumption by the user, the response time from receiving the signal to starting the response, the duration of a single response, and the response stability used to measure the consistency and continuity of the response behavior.

[0012] Furthermore, the incentive allocation list includes:

[0013] The incentive allocation list includes each participating user's unique identification information, a contribution value derived from a quantitative assessment of their response behavior, and an incentive allocation ratio determined based on that contribution value and system rules.

[0014] Furthermore, the process of extracting the contribution of key indicators of response timeliness and response stability also includes:

[0015] Key indicators for response timeliness and response stability are extracted. Response timeliness is the response delay after a user receives a demand response signal, and response stability is the stability of load changes during the response period. The response contribution of each user is evaluated through a contribution calculation model. Through dynamic adjustment of multi-dimensional parameters, the response effect, continuity, and load reduction of each user in different time periods are considered, along with fluctuations in power system load and external factors affecting the response capability of user equipment. The response of each user is quantitatively evaluated through preset weights and multi-level optimization strategies to generate a contribution value for each user.

[0016] Furthermore, the process of dynamically adjusting the incentive allocation ratio for each user also includes:

[0017] Based on the real-time load demand of the power system and the actual response of users, the incentive allocation ratio of each user is dynamically adjusted; for users with poor response timeliness, their incentive ratio is appropriately reduced, while for users with high response stability and large load reduction, the incentive ratio is increased; the optimization process balances the adjustability of the overall system load and the fairness of the response; the optimization methods include incentive adjustment based on priority and weighted correction of response effect.

[0018] Furthermore, the token reward generation process also includes:

[0019] The incentive allocation ratio and corresponding contribution value for each user are obtained from the incentive allocation list. Based on each user's contribution and response, the number of tokens that the user should receive is calculated according to the preset token conversion rules, which comprehensively consider multiple factors such as response intensity, stability, and the magnitude of load reduction. The process involves a dynamic adjustment mechanism to adapt to external fluctuations that occur during the power demand response process.

[0020] Furthermore, the process of triggering smart contracts, automatically verifying token quantities and contributions, and dynamically adjusting reward standards also includes:

[0021] Extract the token amount for each user from the list of tokens to be issued; based on the token reward information in the list, the system automatically generates token reward distribution information; the smart contract automatically executes preset rules to verify the number of tokens for each user and their corresponding contribution; and dynamically adjusts the reward standard according to real-time grid load conditions and external changes.

[0022] Furthermore, the blockchain write process also includes:

[0023] The token reward distribution information is written and processed using blockchain technology; each token distribution transaction is recorded to form a reward distribution transaction record for each user; each transaction record includes token reward distribution details, timestamp, user information, and related smart contract execution data; and data security and privacy are ensured through encryption.

[0024] Furthermore, the process of generating user feedback also includes:

[0025] Extract the reward amount and allocation rules for each user from the reward data report; generate detailed feedback content for each user, listing the user's response behavior, contribution, reward amount, and incentive allocation rules; the feedback generation process considers factors such as the timeliness, stability, and load reduction of the response; user feedback also includes each user's reward distribution history.

[0026] Furthermore, the traceability verification process also includes:

[0027] Extract each user's incentive details from the user feedback summary, including the actual reward amount, the execution status of the response behavior, and the reward distribution history; verify that each user's reward information is consistent with the data recorded on the blockchain; and conduct a final review of each user's feedback to ensure that all data is processed within a compliant framework.

[0028] The key innovations of this invention include:

[0029] (1) This invention adopts a multi-dimensional contribution calculation model, which not only considers user response time and load reduction, but also combines multiple factors such as response stability to calculate the user's contribution. Traditional systems usually only rely on load reduction or response time, which cannot fully reflect the user's contribution. This innovation accurately quantifies the user's actual contribution to electricity demand response by integrating multi-dimensional data.

[0030] (2) In traditional methods, contribution calculation is usually static and does not consider the real-time changes in power system load fluctuations and user responses. In contrast, the contribution adjustment mechanism of this invention can dynamically adjust the contribution calculation results of each user based on external factors such as changes in power system load and fluctuations in user behavior. This mechanism ensures the fairness and accuracy of reward allocation under different power grid load scenarios.

[0031] (3) This invention records token reward distribution information using blockchain technology and uses smart contracts for automated processing to ensure the transparency and immutability of reward distribution. Unlike traditional reward distribution methods that rely on centralized databases, blockchain provides higher transparency and data security, and smart contracts enable the reward distribution process to be fully automated without human intervention.

[0032] The following are its main beneficial effects:

[0033] (1) By combining multiple factors (such as response timeliness, response stability, load reduction magnitude, etc.), this invention can comprehensively and accurately reflect the actual contribution of users in demand response, avoiding the limitations of traditional methods that rely too much on a single indicator. This mechanism improves the fairness and rationality of reward allocation, is applicable to various power grid load change scenarios, and helps to improve user participation and response enthusiasm.

[0034] (2) By dynamically adjusting the contribution weight in real time, this invention can flexibly adjust the reward allocation strategy according to the actual load changes of the power system and the response behavior of users. Compared with the static model, this mechanism ensures the fairness and accuracy of incentive allocation under different load scenarios, and can better adapt to various complex operating environments and emergencies in power demand response.

[0035] (3) By recording reward distribution information on the blockchain, all data can be publicly queried and is tamper-proof, which improves the transparency of the system and user trust. The introduction of smart contracts automates reward distribution, which not only improves efficiency but also reduces human error, ensuring the fairness and accuracy of the reward distribution process. It is suitable for power demand response systems that require efficient, transparent, and decentralized incentive distribution. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating a financial incentive method for electricity demand response provided in an embodiment of this application. Detailed Implementation

[0037] Example 1: Refer to Figure 1 This is a flowchart illustrating a financial incentive method for electricity demand response provided in an embodiment of the present invention. The process may include at least steps S100-S400:

[0038] S100: Acquire power demand response signals and user response data, perform data acquisition and processing, including using data cleaning technology to remove noise data and identify outliers, extracting contribution calculation processing of key indicators of response timeliness and response stability, dynamically adjusting parameters and considering external factors through a multi-dimensional contribution calculation model, dynamically adjusting the incentive allocation ratio of each user, and generating the final incentive scheme structure.

[0039] S200: Obtain the incentive allocation list, process token reward generation, execute the token amount extraction that is dynamically adjusted according to the preset token conversion rules, trigger smart contracts, automatically verify the token amount and contribution and dynamically adjust the reward standard, and process blockchain writing, using blockchain technology to record transaction data and generate on-chain transaction records.

[0040] S300 performs reward data verification and auditing, including checking whether the reward data complies with the contract rules and identifying abnormal data, extracting the reward amount and distribution rules for each user, performing user feedback generation processing, generating feedback content including response behavior, contribution level and reward amount, and obtaining a report delivery structure.

[0041] S400 performs report generation and aggregation processing, combining feedback data from each user to form a complete report framework, extracting each user's incentive status, performing traceability verification, checking blockchain records, and generating a complete demand response financial incentive report.

[0042] Step S100 includes at least steps S110-S130:

[0043] S110. Acquire power demand response signals and user response data, perform data acquisition and processing, and obtain user response dataset;

[0044] The electricity demand response signal specifically includes instructions and information issued by the power system's load forecasting model or dispatching system, reflecting the real-time operating status of the power grid. This mainly includes the predicted and actual values ​​of the total grid load, frequency adjustment instructions required to maintain grid stability, and specific demand response requests issued to users and their real-time changes. The user response data specifically includes response behavior records collected from each end-user terminal through smart sensors and data acquisition systems after receiving the demand response signal. This data mainly includes the actual reduction in electricity consumption by the user, the response time from signal reception to response initiation, the duration of a single response, and multi-dimensional information such as response stability used to measure the consistency and sustainability of response behavior. This specific data constitutes the basic input for data acquisition and processing, ensuring the accuracy and reliability of subsequent contribution calculations.

[0045] Specifically, real-time power demand signals and user response data are acquired from the power demand response system. Power demand response signals typically originate from the power system's load forecasting model or dispatching system, and include real-time changes in grid load, frequency adjustments, and demand response requests. User response data comes from the response status of various end-user terminals, including information such as the reduction in power consumption, response duration, and response timeliness when users participate in demand response. Through interfacing with smart sensors and data acquisition systems, this data is transmitted to a data processing platform. During data acquisition, real-time monitoring methods are used to continuously collect power demand signals and user responses, and data cleaning techniques are employed to remove noise data, ensuring the accuracy and reliability of the collected data. In this process, various outliers are also identified and labeled to prevent invalid data from affecting subsequent analysis. After the above processing, the resulting user response dataset includes information such as the time of each user's participation in demand response, response intensity, and the stability of their response behavior. This dataset will serve as the basic input for subsequent contribution calculations and incentive allocation.

[0046] S120. Extract response timeliness and response stability from the user response dataset, perform contribution calculation processing, and generate an incentive allocation list.

[0047] Furthermore, key indicators such as response timeliness and response stability are extracted from the user response dataset for contribution calculation. Response timeliness refers to the delay time in a user's response after receiving a demand response signal, while response stability refers to the stability of load changes during the response period. By analyzing these factors and combining them with the real-time load situation of the power system, a contribution calculation model is used to evaluate the response contribution of each user. This model dynamically adjusts multi-dimensional parameters, specifically considering the response effect, response duration, and load reduction magnitude of each user at different time periods. To ensure fairness and accuracy, external factors, such as fluctuations in power system load and the response capabilities of user equipment, are also considered during the contribution calculation process. The calculation quantifies and evaluates each user's response through preset weights and multi-level optimization strategies, thereby generating a contribution value for each user. After this processing, the generated incentive allocation list includes each user's contribution value and the corresponding incentive allocation ratio. This list will serve as the basis for smart contract incentive distribution.

[0048] S130. Optimize the incentive allocation list to generate the final incentive scheme structure;

[0049] Understandably, after the incentive allocation list is generated, it undergoes further optimization. This optimization process dynamically adjusts the incentive allocation ratio for each user based on the real-time load demand of the power system and the actual response of users, ensuring a more reasonable and fair incentive allocation. For example, the incentive ratio may be appropriately reduced for users with poor response timeliness, while the incentive ratio may be increased for users with high response stability and large load reduction. Furthermore, the optimization process also needs to balance the adjustability of the overall system load and the fairness of the response, ensuring the efficient operation of the power demand response system while protecting the incentive benefits of users. Optimization methods include priority-based incentive adjustments and weighted corrections based on response effectiveness. Through these optimization methods, the final incentive scheme structure accurately reflects the actual contributions of users and provides reasonable incentive rewards. The output of this step is the final incentive scheme structure, which will be input into the subsequent token reward generation stage as the basis for token reward distribution.

[0050] In one embodiment, electricity demand response signals and user response data are acquired, and data acquisition and processing and contribution calculation processing are performed to generate an incentive allocation list; the incentive allocation list is further optimized to generate the final incentive scheme structure.

[0051] Specifically, real-time power demand signals and user response data are acquired from the power demand response system. The power demand signals originate from the power system's load forecasting model or dispatching system, including predicted and actual total grid load values, frequency adjustment commands, and real-time changes in demand response requests. The user response data originates from response behavior records collected by each end-user terminal through intelligent sensors and data acquisition systems, including the reduction in power consumption when users participate in demand response, the response time from receiving the signal to initiating the response, the duration of a single response, and the load change sequence during the response period. During data acquisition, real-time monitoring methods are used to continuously collect power demand signals and user responses, and data cleaning techniques are employed to remove noise data. Simultaneously, various outliers are identified and labeled to ensure data accuracy and reliability. After the above processing, the resulting user response dataset includes information such as the time of each user's participation in demand response, response intensity, and stability of response behavior. To further quantify user response characteristics, key indicators need to be extracted from the raw data for subsequent contribution calculation. Formula ① is used to calculate the response timeliness index. :

[0052]

[0053] in: The response delay time is derived from the demand response request timestamp from data source A. The user's response start timestamp from data source B The difference, that is ;

[0054] This is a scaling factor, set based on historical system data, used to normalize latency.

[0055] The response timeliness index has a value range of (0,1], where a larger value indicates better timeliness.

[0056] The data source is mapped so that the request timestamp is extracted from data source A and the start response timestamp is extracted from data source B, and the latency is calculated. This forms the formula ① This metric is one of the fields in the user response dataset. Next, formula ② is used to calculate the response stability metric. :

[0057]

[0058] in: The mean of the load reduction sequence during the user response period is derived from the power usage reduction sequence in the user response data of data source B;

[0059] This represents the standard deviation of the sequence, which originates from the same data source;

[0060] In response to the stability index, the value range is (0,1], and the larger the value, the better the stability.

[0061] The data source is mapped to extracting the load reduction sequence from data source B and calculating the mean. and standard deviation This forms the formula ②. This metric serves as another field in the user response dataset. Furthermore, Formula ③ is used to calculate the load reduction metric. :

[0062]

[0063] in: The total load reduction for this user response is derived from the power usage reduction in the user response data from data source B.

[0064] The maximum reference load reduction is set based on user historical data or system parameters;

[0065] This is a load reduction index with a value range of [0,1]. The larger the value, the greater the reduction.

[0066] The data source is mapped to extract the load reduction from data source B. Obtained from system parameters Calculate the formula in ③ This metric is also stored in the user response dataset. This step outputs the user response dataset, including... , , These fields are used by S120 to extract response timeliness and response stability inputs from the user response dataset.

[0067] Building upon the aforementioned user response dataset, response timeliness metrics are extracted from it. Response stability index and load reduction indicators The process involves calculating the contribution of each user. This calculation uses a multi-dimensional contribution model to dynamically evaluate each user's response contribution. The model considers the user's response effectiveness, duration, and load reduction magnitude over different time periods, and dynamically adjusts for external factors such as fluctuations in power system load. Formula ④ is used to calculate the user's contribution. :

[0068]

[0069] in: , , For dynamic weights, satisfying ; This represents the user's contribution value, with a range of [0,1].

[0070] Data sources are mapped to indicators obtained from formulas ①, ②, and ③. , , As input, the system load is extracted from data source A. Calculate the weights to form formula ④. Formula ④ directly uses the conclusion indicators of Formulas ①, ②, and ③, and calculates multi-dimensional contribution through a weighted fusion tool. Its technical goal is to transform user responses into quantifiable contribution values. This step outputs an incentive allocation list, including the contribution value for each user. and the corresponding incentive allocation ratio (in (Or based on a preset mapping), which is used by S130 to optimize the input of the stimulus allocation list.

[0071] Following the aforementioned incentive allocation list, obtain the contribution value for each user. and basic incentive allocation ratio The process involves optimization. This optimization dynamically adjusts the incentive allocation ratio for each user based on the real-time load demand of the power system and the actual response status of users, ensuring a more reasonable and fair incentive allocation. Optimization methods include priority-based incentive adjustments and weighted corrections based on response performance. For example, the incentive ratio is reduced for users with poor response timeliness and increased for users with high response stability. Formula ⑤ is used to calculate the adjusted incentive ratio. :

[0072]

[0073] in: The basic incentive allocation ratio is derived from the contribution value in the incentive allocation list. Or direct mapping;

[0074] This is a system load indicator, derived from data source A and normalized to [0,1].

[0075] , , To optimize the weight parameters, they are set according to the requirements of system fairness and adjustability;

[0076] This is the adjusted incentive ratio, with a value range of [0,1].

[0077] Data source mapping is obtained from the incentive allocation list. The index is obtained from formulas ① and ②. , Extracted from data source A This forms the formula in ⑤. Formula ⑤ directly uses the indicators from Formulas ① and ② and the basic ratio from Formula ④, achieving dynamic optimization through a linear weighting tool. Its technical objective is to balance system load and user response fairness. This step outputs the final incentive scheme structure, including the adjusted incentive ratio for each user. The contribution record is used as input for the incentive allocation list in the subsequent token reward generation process.

[0078] The technical effects of this section can be summarized as follows: a standardized dataset is formed through data collection and indicator calculation; the contribution model dynamically integrates multi-dimensional response features; optimized processing ensures that incentive allocation is adaptively adjusted according to system needs; and the entire closed-loop process is verifiable.

[0079] Step S200 includes at least steps S210-S230:

[0080] S210. Obtain the incentive allocation list, perform token reward generation processing, and obtain the list of tokens to be distributed;

[0081] The incentive allocation list specifically includes core data items generated after contribution calculation processing, used for subsequent smart contract incentive distribution. These mainly include each participating user's unique identifier, a contribution value derived from a quantitative assessment of their response behavior, and an incentive allocation ratio determined based on this contribution value and system rules. The contribution value is a quantitative result derived by comprehensively considering multiple key indicators extracted from the user response dataset. These indicators include at least the timeliness of the user response (i.e., response latency) and response stability (i.e., the stability of load changes during the response period), and further incorporate external factors such as the user's response effectiveness at different time periods, the continuity of the response, the magnitude of load reduction, and power system load fluctuations. This list is dynamically calculated using preset weights and multi-level optimization strategies. As a crucial data carrier bridging the preceding and following steps, this list is both the output of the previous contribution calculation processing and the direct basis for subsequent list optimization to generate the final incentive scheme structure and for token reward conversion.

[0082] Specifically, the incentive allocation ratio and corresponding contribution value for each user are obtained from the incentive allocation list to generate token rewards. In this process, the number of tokens each user should receive is first calculated based on their contribution and response status using a preset token conversion rule. This rule, based on the contribution data in the incentive allocation list, comprehensively considers multiple factors such as response intensity, stability, and the magnitude of load reduction to generate the corresponding token reward amount for each user. During this process, the token reward generation not only relies on contribution calculation but also involves a dynamic adjustment mechanism to adapt to external fluctuations that may occur during the power demand response process, such as changes in grid load and system optimization adjustments. The generated token list to be issued contains the token reward amount and reward type for each user. This list will serve as the basic input for subsequent token issuance.

[0083] S220: Extract the token amount for each user from the list of tokens to be issued, trigger the smart contract, and generate token reward distribution information;

[0084] Furthermore, the token amount for each user is extracted from the list of tokens to be distributed, and a smart contract is triggered. The triggering condition is based on the token reward information in the list, and the system automatically generates token reward distribution information. The smart contract automatically executes preset rules to verify the token quantity and corresponding contribution of each user, ensuring accurate reward distribution. In this process, the smart contract not only triggers token distribution but also dynamically adjusts the reward standard based on real-time grid load conditions and possible external changes. For example, if the system load is too high or some users do not respond in time, the smart contract can dynamically adjust the token reward amount for that user according to preset rules. The token reward distribution information includes token distribution details for each user and contract execution status, serving as the basis for the next step of processing.

[0085] S230: Perform blockchain writing processing on token reward distribution information to generate on-chain transaction records;

[0086] Understandably, after the token reward distribution information is generated, it will be written into the blockchain to ensure the transparency and immutability of all distribution data. Specifically, the blockchain will record each token distribution transaction, forming a reward distribution transaction record for each user. Each transaction record includes details of the token reward distribution, a timestamp, user information, and related smart contract execution data. This writing operation ensures the traceability and fairness of the system because all data is immutable and can be queried at any time. The use of blockchain also provides a reliable source of basic data for the subsequent generation of incentive reports. All transaction records will be stored according to the blockchain protocol, and data security and privacy will be ensured through encryption. Ultimately, the generated on-chain transaction records will serve as the basis for subsequent user feedback and report generation.

[0087] This step ensures the transparency, immutability, and traceability of incentive distribution by writing token reward distribution information into the blockchain, providing reliable data support for subsequent user feedback and report generation.

[0088] In one embodiment, S200, the incentive allocation list is obtained, token reward generation processing, smart contract triggering, blockchain writing processing is performed, and on-chain transaction records are generated.

[0089] Specifically, the incentive allocation ratio and contribution value for each user are obtained from the incentive allocation list, and token reward generation is performed. The incentive allocation list originates from the output of step S100 and includes the user's unique identifier and contribution value. and incentive allocation ratio During processing, the base token quantity is first calculated using a preset token conversion rule. This rule is based on a direct mapping of the incentive allocation ratio and takes into account the size of the system's token pool. Formula ⑦ is used to calculate the base token quantity. :

[0090]

[0091] in:

[0092] The incentive allocation ratio, in physical terms, represents the user's optimized incentive share. The source data is a field in the incentive allocation list, and the value range is [0,1].

[0093] This is the base exchange rate for the tokens. Physically, it represents the number of tokens corresponding to a unit incentive ratio. The source data is the token exchange parameters set by the system, and the value range is positive real numbers.

[0094] This represents the base quantity of tokens, and its physical meaning is the token reward amount before adjustment.

[0095] Data source mapping is extracted from the incentive allocation list. And recorded as , and system parameters Forming formula ⑦ This indicator serves as an intermediate quantity for generating token rewards. Next, to adapt to external fluctuations such as changes in grid load, the token quantity needs to be dynamically adjusted. Formula ⑧ is used to calculate the adjusted token quantity. :

[0096]

[0097] in: This is a system load index, which physically represents the real-time load level of the power grid. The source data is the actual total load value of the power grid from data source A, normalized to the [0,1] interval, and the value range is [0,1].

[0098] For adjustment functions, the physical meaning represents a dynamic scaling factor based on the load;

[0099] This represents the adjusted number of tokens.

[0100] The data source is mapped as obtained by formula ⑦. Extracted from data source A calculate This forms the formula in ⑧. Formula ⑧ directly uses the conclusion indicators from Formula ⑦, achieving dynamic adjustment through a linear weighting tool. Its technical goal is to adapt token rewards to the real-time needs of the power grid. This step outputs a list of tokens to be distributed, including the token amount for each user. The reward type is used by S220 to extract the token amount for each user from the list of tokens to be issued.

[0101] Based on the aforementioned list of tokens to be issued, the token amount for each user is extracted from it. The smart contract is then triggered. The smart contract automatically executes preset rules to verify the matching of token quantity and contribution level, and dynamically adjusts based on real-time grid load. Formula 9 is used for the consistency score during the smart contract verification process. calculate:

[0102]

[0103] in: For consistency scores, the physical meaning represents the ratio of token amount to contribution.

[0104] : Base exchange rate of the token;

[0105] Contribution value.

[0106] The data source is mapped as obtained by formula ⑧. Extracted from the incentive allocation list , and system parameters Forming formula ⑨ This score is used to detect abnormal allocations. If the value deviates from the threshold range, the smart contract triggers an adjustment mechanism. Formula 9 directly uses the conclusion metric from Formula 8, verified through a ratio tool, with the technical goal of ensuring fairness in reward distribution. Based on the consistency score and real-time load, Formula 10 is used to calculate the final token quantity. :

[0107]

[0108] in:

[0109] and To adjust the parameters, the physical meaning controls the sensitivity of consistency deviation and load impact, respectively. The source data is the smart contract's preset rules, and the value range is positive real numbers.

[0110] It is an exponential function;

[0111] This represents the final number of tokens.

[0112] The data source is mapped to the values ​​obtained from formulas ⑧ and ⑨. and Extracted from data source A This forms the formula in ⑩. Formula 10 directly uses the conclusion indicators of Formulas 8 and 9, and achieves dynamic optimization through an exponential weighting tool. Its technical goal is to further refine the reward amount based on verification. This step outputs token reward distribution information, including token distribution details for each user. The contract execution status is used by S230 to process the token reward distribution information on the blockchain.

[0113] Following the aforementioned token reward distribution information, the amount of tokens extracted from it is... The user details are then written to the blockchain. This process uses blockchain technology to ensure data transparency and immutability. Formula 11 is used to generate transaction records. hash value :

[0114]

[0115] Transaction records Includes the following field: User ID Token Amount timestamp and smart contract execution status .

[0116] in: It serves as a unique identifier for users, physically representing user identity, and its source data is the user details in the token reward distribution information;

[0117] This represents the final number of tokens obtained from formula ⑩.

[0118] This is a timestamp, which physically represents the transaction time, and the source data is the current system time;

[0119] The contract execution status, in physical terms, represents the smart contract verification result;

[0120] For cryptographic hash functions, the physical meaning is to map data of arbitrary length to a hash value of fixed length;

[0121] This is the hash value of the transaction record.

[0122] Data source mapping is extracted from token reward distribution information. , , Combined with system time , forming transaction records Then, through hash calculation, we obtain the formula in formula 11. This hash value is used for transaction integrity verification. Formula 12 is used to construct blockchain blocks. Merkelgen :

[0123]

[0124] in: For the first The hash value of a transaction has the same physical meaning as in formula 11. The source data is a set of hash values ​​from multiple transactions within the same batch;

[0125] For transaction index, values ​​range from 1 to... ;

[0126] This represents the total number of transactions within the batch.

[0127] The Merkle root calculation function, a standard blockchain function, physically means recursively hashing multiple hash values ​​into a single root value;

[0128] For Merkelgen, the physical meaning represents a summary of the integrity of this batch of transactions.

[0129] The data source is mapped to the hash value of formula ⑪ for all transactions in this batch. Form a set and calculate the formula (12). Formula 12 directly uses the conclusion indicator of Formula 11, and achieves efficient batch transaction verification using the Merkle tree tool. Its technical goal is to ensure the immutability of multiple transaction data. This step outputs on-chain transaction records, including the transaction hash of each user. Merkelgen The timestamp and related metadata are used by the subsequent S300 steps to retrieve the already written token reward distribution information from the blockchain system.

[0130] The technical effects of this section can be summarized as follows: token generation adapts to system fluctuations through dynamic conversion, smart contract verification ensures distribution consistency, blockchain writing uses hashing and Merkle root to ensure data immutability, and the entire chain is auditable.

[0131] Step S300 includes at least steps S310-S330:

[0132] S310. Obtain blockchain transaction records, verify and audit reward data, and generate a reward data report;

[0133] Specifically, the system retrieves the written token reward distribution information from the blockchain system. This information includes each user's reward data, token amount, reward distribution time, etc. The retrieved data will serve as input for further reward data verification and auditing. During data verification, the system first checks whether all reward data conforms to the preset contract rules and allocation standards, ensuring that all token rewards are distributed according to the rules in the incentive allocation list, and that each user's reward amount matches their actual contribution. During the auditing process, abnormal data identification and processing are also required. For example, if some users' reward data is inconsistent with the contract rules, the system will mark this data as abnormal and notify the relevant personnel. The processed data will be recorded as the final reward data report, ensuring that every reward distribution can be tracked and verified. The reward data report will serve as the basis for subsequent user feedback generation and processing, and will provide the smart contract system with the necessary information for further incentive settlement.

[0134] S320. Extract the reward amount and allocation rules from the reward data report, perform user feedback generation processing, and generate a user feedback report.

[0135] Furthermore, the reward amount and allocation rules for each user are extracted from the reward data report, and user feedback is generated. The extracted reward amount is checked against the user's response contribution and incentive allocation list to ensure that each user's feedback data reflects their actual contribution. The feedback generation process includes generating detailed feedback content for each user, clearly listing the user's response behavior, contribution level, reward amount, and corresponding incentive allocation rules. In this process, the system will consider all factors related to reward distribution, including the timeliness, stability, and load reduction of the response, to ensure the accuracy and completeness of the feedback information. In addition, user feedback will also include each user's reward distribution history, ensuring that they can clearly see their specific contribution to the demand response and the rewards they received. The final generated user feedback report will serve as the basis for the report delivery structure for use in subsequent steps.

[0136] S330. Perform final confirmation processing on the user feedback report and generate a report delivery structure;

[0137] Understandably, the user feedback report undergoes a final confirmation process after generation. During this process, the system reviews the content of the user feedback report to ensure the accuracy and completeness of all data, particularly key indicators such as reward amounts, response behaviors, and contribution levels. The confirmation process includes reviewing and verifying each piece of user feedback to ensure that no information is missing or errors occur in the report. Once confirmed, the system generates a report delivery structure according to the report delivery standards. This structure includes the report format, user details, incentive distribution records, and other relevant data, ensuring the report can be clearly presented to the user and meets relevant compliance requirements. The final generated report delivery structure will serve as input for subsequent steps, laying the foundation for summarizing user feedback and generating the final report.

[0138] This step, through the final confirmation of user feedback reports, ensures the accuracy and completeness of the report content and provides reliable data support for subsequent report generation and user feedback summarization, further improving the system's transparency and trustworthiness.

[0139] Step S400 includes at least steps S410-S430:

[0140] S410. Obtain the report delivery structure, perform report generation and summary processing, and generate a summary of end-user feedback.

[0141] Specifically, the system retrieves user incentive distribution information and related feedback data from the report delivery structure. This structure includes detailed feedback information for each user, such as incentive distribution records, reward amounts, contribution scores, and specific response behaviors. After acquiring this data, the system generates and summarizes it according to a predetermined report format. During generation, the system combines each user's feedback data to summarize all users' incentive situations into a complete report framework. This framework includes individual user feedback and overall system data, and through automated processing, ensures the accuracy and consistency of the report content. During the summary processing, all reward information, participation data, and user feedback are integrated into a single format and adjusted and organized according to user requirements or the needs of the power demand response system. The final user feedback summary generated after summarization serves as the basic input for subsequent system feedback data, used for further verification and traceability checks in subsequent steps.

[0142] S420. Extract user incentive information from the end-user feedback summary, conduct traceability verification, and generate system feedback data;

[0143] Furthermore, the incentive details for each user are extracted from the aggregated user feedback and verified for traceability. The extracted user incentive details include the user's actual reward amount, the execution of the response behavior, and the historical record of reward distribution. This information will be used as input for traceability verification to ensure that all user incentive data complies with system regulations and that there is no data tampering or error. During the verification process, the system will check whether each user's reward information is consistent with the data recorded on the blockchain, ensuring the transparency and fairness of reward distribution. In addition, the system will conduct a final review of each user's feedback to ensure that all data is processed within a compliant framework. The final generated system feedback data will provide the basis for the final confirmation process of the report and prepare for the subsequent generation of a complete report.

[0144] S430. Perform final confirmation and processing of the system feedback data to generate a complete demand response financial incentive report;

[0145] Understandably, after final confirmation and processing, the system feedback data generates a complete demand response financial incentive report. During this process, the system first reviews and verifies all feedback data to ensure that all information included in the report is accurate, complete, and meets the requirements of the electricity demand response system. The system then makes final adjustments and optimizations to the report content based on user incentive details, reward records, and feedback information from the system feedback data. The report will include each user's incentive distribution, a detailed description of their participation in electricity demand response activities, the exact amount of rewards distributed, and a detailed record of their response behavior. All data will be recorded in the report and provided to relevant parties or users in an appropriate format. The generated final report will serve as the complete output of this electricity demand response financial incentive process, available for users or managers to view and further analyze.

[0146] This step, through the final confirmation and processing of system feedback data, ensures the accuracy and completeness of the report content, ultimately generating a complete demand response financial incentive report that can be used by users and managers, guaranteeing the transparency, traceability, and efficiency of the incentive process.

Claims

1. A financial incentive method for electricity demand response, characterized in that, include: Acquire power demand response signals and user response data, perform data acquisition and processing, including using data cleaning techniques to remove noise data and identify outliers, extracting contribution calculations of key indicators of response timeliness and response stability, dynamically adjusting parameters and considering external factors through a multi-dimensional contribution calculation model, dynamically adjusting the incentive allocation ratio of each user, and generating the final incentive scheme structure. Obtain the incentive allocation list, process token reward generation, execute the following steps: calculate and dynamically adjust the token amount according to the preset token conversion rules, trigger smart contracts, automatically verify the token amount and contribution and dynamically adjust the reward standard, process blockchain writing, record transaction data using blockchain technology, and generate on-chain transaction records. Perform reward data verification and auditing, including checking whether the reward data complies with the contract rules and identifying abnormal data, extracting the reward amount and distribution rules for each user, performing user feedback generation and processing, generating feedback content including response behavior, contribution level and reward amount, and obtaining a report delivery structure. The report generation and aggregation process combines feedback data from each user into a complete report framework, extracts each user's incentive status, performs traceability verification, verifies blockchain records, and generates a complete demand response financial incentive report.

2. The method according to claim 1, characterized in that, Electricity demand response signals and user response data include: The power demand response signal includes the predicted and actual values ​​of the total grid load, frequency adjustment instructions, and specific demand response requests sent to the user side and their real-time change information; the user response data includes the actual reduction in power consumption by the user, the response time from receiving the signal to starting the response, the duration of a single response, and the response stability used to measure the consistency and continuity of the response behavior.

3. The method according to claim 1, characterized in that, The incentive allocation list includes: The incentive allocation list includes each participating user's unique identification information, a contribution value derived from a quantitative assessment of their response behavior, and an incentive allocation ratio determined based on that contribution value and system rules.

4. The method according to claim 1, characterized in that, The process of extracting the contribution of key indicators of response timeliness and response stability also includes: Key indicators for response timeliness and response stability are extracted. Response timeliness is the response delay after a user receives a demand response signal, and response stability is the stability of load changes during the response period. The response contribution of each user is evaluated through a contribution calculation model. Through dynamic adjustment of multi-dimensional parameters, the response effect, continuity, and load reduction of each user in different time periods are considered, along with fluctuations in power system load and external factors affecting the response capability of user equipment. The response of each user is quantitatively evaluated through preset weights and multi-level optimization strategies to generate a contribution value for each user.

5. The method according to claim 1, characterized in that, The process of dynamically adjusting the incentive allocation ratio for each user also includes: Based on the real-time load demand of the power system and the actual response of users, the incentive allocation ratio of each user is dynamically adjusted; for users with poor response timeliness, their incentive ratio is appropriately reduced, while for users with high response stability and large load reduction, the incentive ratio is increased; the optimization process balances the adjustability of the overall system load and the fairness of the response; the optimization methods include incentive adjustment based on priority and weighted correction of response effect.

6. The method according to claim 1, characterized in that, The process of generating token rewards also includes: The incentive allocation ratio and corresponding contribution value for each user are obtained from the incentive allocation list. Based on each user's contribution and response, the number of tokens that the user should receive is calculated according to the preset token conversion rules, which comprehensively consider multiple factors such as response intensity, stability, and the magnitude of load reduction. The process involves a dynamic adjustment mechanism to adapt to external fluctuations that occur during the power demand response process.

7. The method according to claim 1, characterized in that, The process of triggering smart contracts, automatically verifying token quantities and contributions, and dynamically adjusting reward standards also includes: Extract the token amount for each user from the list of tokens to be issued; based on the token reward information in the list, the system automatically generates token reward distribution information; the smart contract automatically executes preset rules to verify the number of tokens for each user and their corresponding contribution; and dynamically adjusts the reward standard according to real-time grid load conditions and external changes.

8. The method according to claim 1, characterized in that, The blockchain write process also includes: The token reward distribution information is written and processed using blockchain technology; each token distribution transaction is recorded to form a reward distribution transaction record for each user; each transaction record includes token reward distribution details, timestamp, user information, and related smart contract execution data; and data security and privacy are ensured through encryption.

9. The method according to claim 1, characterized in that, The process of generating user feedback also includes: Extract the reward amount and allocation rules for each user from the reward data report; generate detailed feedback content for each user, listing the user's response behavior, contribution, reward amount, and incentive allocation rules; the feedback generation process considers factors such as the timeliness, stability, and load reduction of the response; user feedback also includes each user's reward distribution history.

10. The method according to claim 1, characterized in that, The traceability verification process also includes: Extract each user's incentive details from the user feedback summary, including the actual reward amount, the execution status of the response behavior, and the reward distribution history; verify that each user's reward information is consistent with the data recorded on the blockchain; and conduct a final review of each user's feedback to ensure that all data is processed within a compliant framework.

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