Centralized clearing method for power transactions in limited area and related device

Abstract

The invention belongs to the field of electric power automation, and discloses a centralized clearing method for electric power transactions in a limited area and a related device, and the method comprises the steps: obtaining time-phased energy block declaration data of each electric power transaction subject in the limited area; and according to the time-sharing energy block declaration data of each power transaction subject, adopting a clearing method based on a multi-target genetic algorithm, and taking maximization of new energy consumption and minimization of total power purchase cost as targets, performing centralized bidding clearing of each power transaction subject to obtain a clearing result of each power transaction subject. Through adoption of time-phased energy block declaration data, power generation and utilization behaviors of each adjustable resource in a limited area can be effectively adjusted, in-situ balance is promoted, a complementary relationship of distributed resource power generation and utilization characteristics can be intelligently matched, and consumption of distributed new energy is promoted. The method supports continuous and autonomous distributed power transaction, reduces transaction cost, improves transaction efficiency, is suitable for small and micro resources to directly participate in transaction, can also participate in transaction by an aggregator agent, and is good in flexibility.

Description

Technical Field

[0001] The present invention belongs to the field of electric power automation and relates to a centralized clearing method for electric power transactions within a limited area and related devices. Background Art

[0002] Distributed renewable energy, a key component of clean energy, is being integrated into the power system at an unprecedented rate. In recent years, with the rapid growth of distributed photovoltaic installed capacity, the traditional distribution network is gradually evolving towards an active distribution network. This transition has led to increasingly common issues with reverse transmission and reverse overloads at substations, exacerbating the challenges of accommodating distributed renewable energy and becoming a key constraint on efficient energy utilization and stable power system operation.

[0003] On the demand side, while a vast array of small and micro resources, such as distributed photovoltaics, decentralized wind power, energy storage, electric vehicle charging stations, and adjustable loads, possess enormous regulatory potential, most lack the qualifications of independent market entities and struggle to participate directly in electricity market transactions. To address this issue, aggregators such as virtual power plants, electricity retail companies, and load aggregators have emerged. By aggregating these small and micro resources, they enable them to participate in wholesale markets as agents, thereby achieving, to a certain extent, optimal resource allocation.

[0004] However, under the current electricity market mechanism, existing clearing methods, particularly for the absorption of distributed renewable energy, suffer from numerous shortcomings, making it difficult to effectively and efficiently absorb clean energy. First, electricity price signals from higher-level wholesale markets lack effectiveness in alleviating local balancing issues in terminal distribution networks. During certain holidays and seasonal periods, distributed renewable energy generation surges, potentially leading to reverse overloads and severe supply-demand tensions in some areas, even forcing the curtailment of wind and solar power. Second, while aggregators provide a way for distributed renewable energy resources to participate in the spot market, they suffer from high costs and limited efficiency and flexibility. These factors result in inefficient aggregator participation in the spot market, making it difficult for them to quickly respond to fluctuations in distributed renewable energy output and market demand, hindering their full regulatory potential and thus hindering the absorption of clean energy. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method and related device for centralized clearing of electricity transactions within a limited area.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The first aspect of the present application provides a centralized clearing method for power transactions in a defined area, comprising: obtaining time-of-use energy block declaration data of each power transaction subject in the defined area; and using a clearing method based on a multi-objective genetic algorithm to maximize new energy consumption and minimize total power purchase cost, and performing centralized bidding clearing of each power transaction subject to obtain clearing results of each power transaction subject.

[0008] Optionally, the time-of-use energy block declaration data of each power transaction subject includes: when the power transaction subject is a fixed resource, the time-of-use energy block declaration data of the power transaction subject includes power, price and negotiation space of each time period of the power transaction subject; when the power transaction subject is an adjustable resource, the time-of-use energy block declaration data of the power transaction subject includes power, price, negotiation space and adjustment period of each time period of the power transaction subject; and when the power transaction subject is energy storage, the time-of-use energy block declaration data of the power transaction subject further includes a bidding mode and an operation mode.

[0009] Optionally, the method further comprises: initializing a population; wherein the population includes a plurality of individuals, each individual includes two segments, the first segment is a power generation type power transaction subject, and the second segment is a load type power transaction subject; a calculation step: constructing a fitness function with the objectives of maximizing new energy consumption and minimizing total power purchase cost, and calculating the fitness function value of each individual based on the time-of-use energy block declaration data of each power transaction subject and the sequential transaction of the first segment power generation type power transaction subject and the second segment load type power transaction subject; an updating step: obtaining the Pareto optimal solution individual in the current population by a fast non-dominated sorting algorithm according to the fitness function value of each individual, and obtaining a plurality of new individuals by population crossover and population mutation, and combining the Pareto optimal solution individual and the new individual to obtain a new population; repeating the calculation step and the updating step until a preset stop condition is met, obtaining the optimal solution individual in the current population, and obtaining the clearing results of each power transaction subject based on the sequential transaction of the first segment power generation type power transaction subject and the second segment load type power transaction subject of the optimal solution individual.

[0010] Optionally, the population crossover and population mutation to obtain several new individuals include: for each individual in the current population: generating two random numbers according to the total number of power generation trading entities, and performing a crossover process on the first fragment of the current individual based on the two random numbers by a sequential crossover method to obtain a first crossover fragment; generating two random numbers according to the total number of power load trading entities, and performing a crossover process on the second fragment of the current individual based on the two random numbers by a sequential crossover method to obtain a second crossover fragment; combining the first crossover fragment and the second crossover fragment to obtain a new individual based on population crossover; for each individual in the current population: when the mutation probability of the current individual meets the preset conditions, the new individual is obtained based on the power generation trading entities. Two random numbers are generated according to the total number of load power trading entities, and the power generation trading entities at the positions of the two random numbers in the first segment of the current individual are crossed, and when the time-sharing energy block declaration data of the crossed power generation trading entities include the adjustment period, the adjustment period is randomly selected to obtain the first variant segment; two random numbers are generated according to the total number of load power trading entities, and the load power trading entities at the positions of the two random numbers in the second segment of the current individual are crossed, and when the time-sharing energy block declaration data of the crossed load power trading entities include the adjustment period, the adjustment period is randomly selected to obtain the second variant segment; the first variant segment and the second variant segment are combined to obtain a new individual based on population variation.

[0011] Optionally, obtaining the optimal solution individual in the current population includes: obtaining the weighted transaction electricity and weighted electricity purchase cost of each individual in the current population; obtaining the individual whose weighted electricity purchase cost is λ% lower than the maximum weighted electricity purchase cost and whose weighted transaction electricity is the largest, as the optimal solution individual in the current population; wherein λ is a preset proportion parameter.

[0012] Optionally, it also includes: obtaining the surplus electricity of each power trading entity based on the clearing results of each power trading entity; based on the surplus electricity of each power trading entity, adopting a clearing method based on a multi-objective genetic algorithm, with the goal of maximizing the new energy consumption and minimizing the total electricity purchase cost, conducting centralized bargaining and clearing of each power trading entity, and obtaining the bargaining and clearing results of each power trading entity.

[0013] Optionally, when the centralized bargaining and clearing of each power trading subject is carried out: when both the power generation trading subject and the load trading subject can bargain, if the power price P of the power generation trading subject is G The electricity price P with the load-type electricity trading entity L The difference is greater than the bargaining space M of the power generation trading entity G Bargaining space M with load-type power trading entities L If the sum of G Less than fair concession price Mfair The transaction price is P G -M G ; When M L Less than M fair The transaction price is P L +M L Otherwise, the transaction price is (P G +P L ) / 2; where M fair =(P G -P L ) / 2.

[0014] Optionally, it also includes: writing the clearing results of each power trading entity in an encrypted form into the blockchain distributed ledger of the blockchain system by calling the blockchain smart contract interface of the blockchain system; obtaining the time period energy block declaration data of each power trading entity in a limited area includes: obtaining the time period energy block declaration data of each power trading entity in a limited area written in an encrypted form into the blockchain distributed ledger of the blockchain system by calling the blockchain smart contract interface of the blockchain system.

[0015] In a second aspect, the present invention provides a centralized clearing system for power transactions within a limited area, comprising: a data acquisition module for acquiring time-sharing energy block declaration data of each power trading entity within the limited area; a power clearing module for conducting centralized bidding and clearing of each power trading entity based on the time-sharing energy block declaration data of each power trading entity, adopting a clearing method based on a multi-objective genetic algorithm, with the goal of maximizing the amount of new energy consumption and minimizing the total power purchase cost, and obtaining the clearing results of each power trading entity.

[0016] Optionally, the time-based energy block declaration data of each power trading entity include: when the power trading entity is a fixed resource, the time-based energy block declaration data of the power trading entity include the power quantity, electricity price and bargaining space of the power trading entity in each time period; when the power trading entity is an adjustable resource, the time-based energy block declaration data of the power trading entity include the power quantity, electricity price, bargaining space and adjustment period of the power trading entity in each time period; among them, when the power trading entity is energy storage, the time-based energy block declaration data of the power trading entity also includes the quotation method and operation mode.

[0017] Optionally, the power clearing module is specifically used to: initialize a population; wherein the population includes several individuals, each individual includes two fragments, the first fragment is a power generation type power trading subject, and the second fragment is a load type power trading subject; a calculation step: constructing a fitness function with the goal of maximizing the amount of new energy consumption and minimizing the total power purchase cost, and calculating the fitness function value of each individual based on the time-sharing energy block declaration data of each power trading subject and the sequential trading method of the power generation type power trading subject in the first fragment and the load type power trading subject in the second fragment according to the time-sharing energy block declaration data of each power trading subject; an updating step: according to the fitness function value of each individual, obtaining the Pareto optimal solution individual in the current population through a fast non-dominated sorting algorithm, and performing population crossover and population mutation to obtain several new individuals, and combining the Pareto optimal solution individual and the new individual to obtain a new population; repeating the calculation step and the updating step until the preset stop condition is met, obtaining the optimal solution individual in the current population, and obtaining the clearing result of each power trading subject based on the sequential trading method of the power generation type power trading subject in the first fragment and the load type power trading subject in the second fragment of the optimal solution individual.

[0018] Optionally, it also includes a bargaining and clearing module; the bargaining and clearing module is used to: obtain the remaining electricity of each power trading entity based on the clearing results of each power trading entity; based on the remaining electricity of each power trading entity, adopt a clearing method based on a multi-objective genetic algorithm, with the goal of maximizing the amount of new energy consumption and minimizing the total electricity purchase cost, conduct centralized bargaining and clearing of each power trading entity, and obtain the bargaining and clearing results of each power trading entity.

[0019] According to a third aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for centralized clearing of electricity transactions within a limited area when executing the computer program.

[0020] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for centralized clearing of electricity transactions within a limited area are implemented.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] The present invention provides a method for centralized clearing of power transactions within a defined area. Based on the time-divided energy block declaration data of each power transaction subject within the defined area, a clearing method based on a multi-objective genetic algorithm is used to conduct centralized bidding and clearing of each power transaction subject with the goal of maximizing the amount of new energy consumed and minimizing the total cost of electricity purchase, thereby obtaining the clearing results of each power transaction subject. By adopting flexible time-divided energy block declaration data, the power generation and consumption behavior of each adjustable resource within the defined area can be effectively adjusted to promote local balance. At the same time, the clearing method based on the multi-objective genetic algorithm is used to clear power with the goal of maximizing the amount of new energy consumed and minimizing the total cost of electricity purchase, which can intelligently match the complementary relationship between the power generation and consumption characteristics of distributed resources and effectively promote the consumption of distributed new energy. In addition, it supports continuous and autonomous distributed power transactions within a defined area, which can reduce transaction costs and improve transaction efficiency. It is suitable for small and micro resources to directly participate in transactions, and can also be used by aggregators to participate in transactions, with good flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a method for centralized clearing of power transactions within a limited area according to an embodiment of the present invention.

[0024] Figure 2 Schematic diagram of an application scenario of the method for centralized clearing of power transactions within a limited area according to an embodiment of the present invention.

[0025] Figure 3 This is a structural block diagram of a centralized clearing system for power transactions within a limited area according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] The present invention is described in further detail below with reference to the accompanying drawings:

[0029] See also Figure 1 In one embodiment of the present invention, a method for centralized clearing of power transactions within a limited area is provided, which can intelligently match the power generation and consumption characteristics of demand-side resources, effectively promote the consumption of distributed new energy, alleviate the power balance tension in the distribution network within the limited area, and promote local balance.

[0030] Specifically, the centralized clearing method for power transactions within a limited area of ​​the present invention includes the following steps:

[0031] S1: Obtain the time-based energy block declaration data of each power trading entity in the limited area;

[0032] S2: Based on the time-sharing energy block declaration data of each power trading entity, a clearing method based on a multi-objective genetic algorithm is adopted to conduct centralized bidding clearing of each power trading entity with the goal of maximizing the new energy consumption and minimizing the total electricity purchase cost, and obtain the clearing results of each power trading entity.

[0033] The present invention provides a method for centralized clearing of power transactions within a defined area. Based on the time-divided energy block declaration data of each power transaction subject within the defined area, a clearing method based on a multi-objective genetic algorithm is used to conduct centralized bidding and clearing of each power transaction subject with the goal of maximizing the amount of new energy consumed and minimizing the total cost of electricity purchase, thereby obtaining the clearing results of each power transaction subject. By adopting flexible time-divided energy block declaration data, the power generation and consumption behavior of each adjustable resource within the defined area can be effectively adjusted to promote local balance. At the same time, the clearing method based on the multi-objective genetic algorithm is used to clear power with the goal of maximizing the amount of new energy consumed and minimizing the total cost of electricity purchase, which can intelligently match the complementary relationship between the power generation and consumption characteristics of distributed resources and effectively promote the consumption of distributed new energy. In addition, it supports continuous and autonomous distributed power transactions within a defined area, which can reduce transaction costs and improve transaction efficiency. It is suitable for small and micro resources to directly participate in transactions, and can also be used by aggregators to participate in transactions, with good flexibility.

[0034] For explanation, in this embodiment, the limited area mainly refers to the 110kV or 220kV node area, which is used to realize local micro-market transactions within the limited area.

[0035] In one possible embodiment, the time-based energy block declaration data of each power trading entity include: when the power trading entity is a fixed resource, the time-based energy block declaration data of the power trading entity include the power quantity, electricity price and bargaining space of the power trading entity in each time period; when the power trading entity is an adjustable resource, the time-based energy block declaration data of the power trading entity include the power quantity, electricity price, bargaining space and adjustment period of the power trading entity in each time period; among them, when the power trading entity is energy storage, the time-based energy block declaration data of the power trading entity also includes the quotation method and operation mode.

[0036] For explanation, the power trading entities in this embodiment include power generation-type power trading entities and power load-type power trading entities. For power generation-type power trading entities, the amount of electricity in each time period can be understood as the output in each time period. For power load-type power trading entities, the amount of electricity in each time period can be understood as the load in each time period.

[0037] For example, the time-based energy block declaration data can declare the electricity consumption and electricity price for 24 time periods according to the household number. It is generally divided into fixed time-based energy blocks and movable time-based energy blocks. Fixed time-based energy blocks are suitable for electricity trading entities with fixed and unadjustable electricity consumption, such as distributed photovoltaics; movable time-based energy blocks are suitable for resources with adjustable power generation and consumption time, such as energy storage and adjustable loads.

[0038] For example, distributed photovoltaic power trading entities can report their daily electricity consumption and price in fixed time-of-day energy blocks, or they can report their total electricity generation. Then, based on local conditions such as sunlight and generating hours, a normalized time-of-day output curve is generated, automatically breaking down the total electricity generation into daily time periods. Furthermore, the time-of-day energy block data reported for distributed photovoltaic power generation also includes the bargaining margin, which is the maximum amount of price concessions that can be made, for example, 10 yuan / MWh.

[0039] For distributed wind power among electricity trading entities, it is the same as distributed photovoltaic.

[0040] For energy storage trading entities, the electricity consumption and 24-hour electricity price for charging and discharging periods are reported separately using a time-of-use energy block format that can be shifted. The electricity consumption during non-charging and discharging periods is reported as zero. In addition, the time-of-use energy block data reported for energy storage also includes the quotation method, operating model, adjustment period, and bargaining space. The quotation method is divided into quotation by volume and quotation by volume without quotation. The quotation by volume without quotation model allows only the electricity consumption to be reported and the counterparty's quotation to be accepted. The operating model is divided into price difference model and profit model. The price difference model requires the minimum charge-discharge price difference, for example, 0.7 yuan / kWh; the profit model means that the discharge revenue exceeds the charging cost. The adjustment period indicates the period during which the overall charge-discharge curve can be shifted, for example, 4 hours or 8 hours. This indicates that the charge-discharge curve can be shifted by 4 or 8 hours. During this shift, only the energy block electricity consumption is moved, and the time-of-use electricity price remains unchanged. The bargaining space is similar to that of distributed photovoltaic power generation, namely the extent to which the electricity price for energy storage charging can be adjusted upward or downward. Energy storage charging is a load-type power trading entity, with positive power; energy storage discharging is a generation-type power trading entity, with negative power. Energy storage charging and discharging are reported together when submitting declarations. When submitting time-based energy block data, submit separate time-based energy block data for each role, namely, energy storage charging and discharging.

[0041] For electric vehicle charging piles, electricity trading entities must submit separate declarations for charging and discharging periods, along with 24-hour electricity prices, using a portable time-based energy block format. Furthermore, the time-based energy block declaration data for electric vehicle charging piles also includes adjustment periods and negotiation space. These adjustment periods and negotiation space are similar to those for energy storage. Unlike energy storage, electric vehicle charging piles can charge and discharge simultaneously. When charging, electric vehicle charging piles are considered load-type electricity trading entities, with positive electricity consumption; discharging, electric vehicle charging piles are considered generation-type electricity trading entities, with negative electricity consumption. When submitting declarations, the time-based energy block declaration data for charging and discharging is submitted separately, based on the charging and generating roles of the electric vehicle charging piles.

[0042] For the adjustable load in the power transaction subject, the power consumption period and the 24-period electricity price are declared in a translatable time period energy block manner. In addition to this, the time period energy block declaration data of the adjustable load also includes the adjustment period and the negotiation space. Among them, the adjustment period is similar to the energy storage, and the negotiation space is similar to the energy storage charging role.

[0043] In a possible implementation, the clearing method based on the multi-objective genetic algorithm is adopted according to the time-of-use energy block declaration data of each power transaction subject, to maximize the new energy consumption and minimize the total power purchase cost, to carry out centralized bidding and clearing of each power transaction subject, and to obtain the clearing result of each power transaction subject, including: initializing a population; wherein the population includes a plurality of individuals, each individual includes two segments, the first segment is a power generation type power transaction subject, and the second segment is a load type power transaction subject; a calculation step: constructing a fitness function to maximize the new energy consumption and minimize the total power purchase cost, and calculating the fitness function value of each individual according to the time-of-use energy block declaration data of each power transaction subject, based on the sequential transaction of the first segment power generation type power transaction subject and the second segment load type power transaction subject; an updating step: obtaining the Pareto optimal solution individual in the current population by the fast non-dominated sorting algorithm according to the fitness function value of each individual, and obtaining a plurality of new individuals by population crossover and population mutation, and combining the Pareto optimal solution individual and the new individual to obtain a new population; repeating the calculation step and the updating step until a preset stop condition is met, obtaining the optimal solution individual in the current population, and obtaining the clearing result of each power transaction subject based on the sequential transaction of the first segment power generation type power transaction subject and the second segment load type power transaction subject of the optimal solution individual.

[0044] Explanatorily, when initializing the population, the individuals in the population are randomly generated in a random combination manner based on a preset number of individuals, one individual as a chromosome represents a complete clearing solution. Two segments are designed in the composition of the individual, the first segment is a power generation type power transaction subject, and the second segment is a load type power transaction subject. Illustratively, the power generation type power transaction subject and the load type power transaction subject adopt the form of gene coding, specifically, the form of 32-bit integer coding can be adopted, wherein the high 8 bits represent the adjustment period, and the low 24 bits represent the power generation type power transaction subject serial number or the load type power transaction subject serial number.

[0045] Explanatory, when setting the fitness function of the multi-objective genetic algorithm, it is constructed with the goals of maximizing the amount of new energy consumption and minimizing the total electricity purchase cost, forming two optimization objective functions, and designing corresponding constraints, such as energy storage discharge cannot sell electricity to energy storage charging, storage storage cannot be charged and discharged at the same time, electric vehicle charging pile discharge cannot sell electricity to energy storage charging, energy storage charging and discharging price difference or profit conforms to the operating model, electricity price constraints and remaining power constraints, etc. When calculating the fitness function value of each individual, the following steps are included:

[0046] Step 1: Analyze the current individual to obtain the corresponding order of power generation and load power trading entities, and obtain the time-sharing energy block declaration data of each power trading entity.

[0047] Step 2: Calculate the cleared electricity quantity and electricity price data of power generation trading entities and load trading entities in sequence. The electricity quantity is the smaller of the two, and the electricity price is the average of the two.

[0048] Step 3: Calculate the weighted transaction volume and weighted electricity purchase cost taking into account network losses and transmission and distribution prices based on the transmission and distribution prices of the voltage levels of power generation and load trading entities.

[0049] Step 4: Repeat steps 2 and 3 until all transactions are cleared, and add up the total weighted transaction electricity and weighted electricity purchase cost to obtain the current individual fitness function value.

[0050] Explanatory, the rolling centralized clearing method can form a one-to-one, clear point-to-point purchase and sales relationship, adapt to the green electricity traceability requirements of green electricity direct supply and green electricity direct connection scenarios such as smart microgrids, source-grid-load-storage integrated projects, and low-carbon parks, and can provide credentials for green electricity traceability.

[0051] In a possible embodiment, the population crossover and population mutation to obtain several new individuals include: for each individual in the current population: generating two random numbers according to the total number of power generation trading entities, and based on the two random numbers, performing a crossover process on the first fragment of the current individual in a sequential crossover method to obtain a first crossover fragment; generating two random numbers according to the total number of load power trading entities, and based on the two random numbers, performing a crossover process on the second fragment of the current individual in a sequential crossover method to obtain a second crossover fragment; combining the first crossover fragment and the second crossover fragment to obtain a new individual based on population crossover; for each individual in the current population: when the mutation probability of the current individual meets the preset conditions, performing a crossover process on the first ..., combining the first crossover fragment and the second crossover fragment; combining the first crossover fragment and the second crossover fragment, combining the first crossover fragment and the second crossover fragment; combining the first crossover fragment and the second crossover fragment, combining the first crossover fragment and the second crossover fragment; combining the first crossover fragment and the second crossover fragment, combining the first crossover fragment and the second Two random numbers are generated based on the total number of trading entities, and the power generation trading entities at the positions of the two random numbers in the first segment of the current individual are crossed, and when the time-sharing energy block declaration data of the crossed power generation trading entities include the adjustment period, the adjustment period is randomly selected to obtain the first variant segment; two random numbers are generated based on the total number of load trading entities, and the load trading entities at the positions of the two random numbers in the second segment of the current individual are crossed, and when the time-sharing energy block declaration data of the crossed load trading entities include the adjustment period, the adjustment period is randomly selected to obtain the second variant segment; the first variant segment and the second variant segment are combined to obtain a new individual based on population variation.

[0052] Interpretatively, by performing crossover and mutation on power generation and load trading entities, we can specifically explore the solution space for different resource types, enhancing the diversity of the genetic algorithm. Furthermore, processing and adjusting time period data makes the algorithm more realistic and helps find more optimal solutions.

[0053] In one possible implementation, obtaining the optimal solution individual in the current population includes: obtaining the weighted transaction electricity and weighted electricity purchase cost of each individual in the current population; obtaining the individual whose weighted electricity purchase cost is λ% lower than the maximum weighted electricity purchase cost and whose weighted transaction electricity is the largest, as the optimal solution individual in the current population; wherein λ is a preset proportion parameter.

[0054] Explanatory, in order to quickly obtain the optimal solution individual, the individuals in the current population can be sorted in order of weighted transaction electricity from large to small and weighted electricity purchase cost from large to small. Then, based on the sorting results, the individual whose weighted electricity purchase cost is λ% lower than the maximum weighted electricity purchase cost and whose weighted transaction electricity is the largest can be quickly found as the optimal solution individual in the current population.

[0055] In a possible implementation, the method for centralized clearing of power transactions within a limited area further includes: obtaining the remaining power of each power trading entity based on the clearing results of each power trading entity; based on the remaining power of each power trading entity, adopting a clearing method based on a multi-objective genetic algorithm, with the goal of maximizing the amount of new energy consumption and minimizing the total power purchase cost, conducting centralized bargaining and clearing of each power trading entity, and obtaining the bargaining and clearing results of each power trading entity.

[0056] Explanatory note: a two-round clearing method that supports bargaining is adopted. The first round of clearing is based on actual bids, and the second round is for the remaining electricity. The second round of clearing is similar to the first round, except that during the second round, the electricity prices of power trading entities can be compromised based on the bargaining space. By adopting a two-round bargaining trading method, the maximum number of transactions can be achieved, thereby fully realizing the consumption of new energy.

[0057] In a possible implementation, when the centralized bargaining and clearing of each power trading subject is carried out: when both the power generation trading subject and the load trading subject can bargain, if the power price P of the power generation trading subject is G The electricity price P with the load-type electricity trading entity L The difference is greater than the bargaining space M of the power generation trading entity G Bargaining space M with load-type power trading entities L If the sum of G Less than fair concession price M fair The electricity price at that time is P G -M G ; When M L Less than M fair The transaction price is P L +M L Otherwise, the transaction price is (P G +P L ) / 2; where M fair =(P G -P L ) / 2.

[0058] Explanatory, when conducting centralized bargaining and clearing, there are three situations for the electricity price after negotiation. 1) Power generation power trading entities do not bargain, while load power trading entities can bargain. If the difference Pd between the electricity price of the power generation power trading entity and the electricity price of the load power trading entity is greater than the bargaining space of the load power trading entity, the transaction will not be completed; otherwise, the transaction will be completed, and the bargained electricity price of the load power trading entity is calculated as Pd. 2) Power generation power trading entities can bargain, while load power trading entities do not bargain. If the difference Pd between the electricity price of the power generation power trading entity and the electricity price of the load power trading entity is greater than the bargaining space of the power generation power trading entity, the transaction will not be completed; otherwise, the transaction will be completed, and the bargained electricity price of the load power trading entity is calculated as Pd. 3) Power generation power trading entities can bargain, and load power trading entities can also bargain. Assume that the electricity price of the power generation power trading entity is P G , the electricity price of the load-type power trading entity is P L , the bargaining space of power generation trading entities is M G , the bargaining space of load-type power trading entities is M L If the difference between the electricity price of the power generation trading subject and the electricity price of the load trading subject (P G -P L ) is greater than the sum of the bargaining space of power generation trading entities and the bargaining space of power load trading entities (M G +M L ), then the transaction is not completed; otherwise, the transaction is completed. First, the fair concession price is calculated: M fair =(P G -P L ) / 2; if the bargaining space M of power generation trading entities is G Less than fair concession price M fair , then the transaction price is P G -M G , the negotiated electricity price of power generation trading entities is M G , the negotiated electricity price of load-type power trading entities is P G -P L -M G ; If the bargaining space M of load-type power trading entities L Less than fair concession price M fair , then the transaction price is P L +M L , the bargaining price of power generation trading entities is P G -P L +M L , the negotiated electricity price of load-type power trading entities is M LOtherwise, the transaction price is the average of the electricity price of the power generation trading entity and the electricity price of the load trading entity (P G +P L ) / 2, the negotiated electricity price of power generation trading entities and load trading entities is M fair .

[0059] In one possible implementation, the method for centralized clearing of electricity transactions within a limited area is characterized in that it also includes: writing the clearing results of each electricity trading entity into the blockchain distributed ledger of the blockchain system in an encrypted form by calling the blockchain smart contract interface of the blockchain system.

[0060] The obtaining of the time-based energy block declaration data of each power trading entity in the limited area includes: obtaining the time-based energy block declaration data of each power trading entity in the limited area written in an encrypted form in the blockchain distributed ledger of the blockchain system by calling the blockchain smart contract interface of the blockchain system.

[0061] Explanatory, the introduction of blockchain technology can effectively protect private information such as quotations.

[0062] For example, the time-based energy block declaration data of each power trading entity within a defined region is written into the blockchain system's distributed ledger using a pre-defined declaration data format. Optionally, the time-based energy block declaration data can be encrypted using the national secret SM2 asymmetric encryption method, and then re-encrypted using the national secret SM4 symmetric encryption method when stored on the blockchain to ensure data security.

[0063] Exemplarily, the declaration data format includes market entity hash (string), cycle type (enumerated integer, monthly or intra-month), start and end time (integer), role (enumerated integer, including distributed photovoltaic, distributed wind power, energy storage charging, energy storage discharging, electric vehicle charging pile charging, electric vehicle charging pile discharging, adjustable load, aggregator, etc.), power supply area (string), voltage level (string), aggregator hash (string), energy block type (enumerated integer, including fixed time-sharing energy block or shiftable time-sharing energy block), total declared electricity (floating point number), quotation method (enumerated integer, including quotation for quantity and quotation for quantity without quotation), operation mode (enumerated integer, including not applicable, price difference mode, profit model), adjustment period (integer array), bargaining space (floating point number), 24-period declared electricity and electricity price.

[0064] Exemplarily, the clearing results of each power trading entity are written into the blockchain distributed ledger of the blockchain system in a pre-defined clearing data format. The clearing data format includes the entity hash, counterparty hash, period type, start and end time, seller quote hash, buyer quote hash, seller concession price, buyer concession price, seller shift period, buyer shift period, total traded electricity, time-based cleared electricity, and electricity price.

[0065] For example, the settlement data after the clearing results are applied can also be written into the blockchain distributed ledger of the blockchain system using a preset settlement data format. The settlement data format includes the subject hash, counterparty hash, period type, start and end time, clearing hash, total settled electricity volume, total settled electricity fee, time-based settled electricity volume, electricity price, and electricity fee.

[0066] For example, in the above data, the unit of electricity is megawatt-hour, and the unit of electricity price is yuan / megawatt-hour.

[0067] In another embodiment of the present invention, see Figure 2 This demonstrates an application scenario for the centralized clearing method for power transactions within a defined region. Within the defined region, power trading entities, such as distributed photovoltaic (PV) providers, energy storage systems, electric vehicle charging stations, and adjustable loads, can participate in the micro-market by submitting their time-sharing energy block data independently or through an aggregator. Data generated during the application of this centralized clearing method for power transactions within a defined region can be stored in a blockchain system.

[0068] In another embodiment of the present invention, the centralized clearing method for power transactions within a defined region is illustrated using the example of power clearing within a defined region. On April 20, 2025, a region launches micro-market trading, builds a blockchain system for trading, demarcates N 110kV node areas, and opens n (n≤N) micro-markets.

[0069] Distributed photovoltaic entity A logs into the trading system to participate in the monthly trading in May, submitting data with a negotiating margin of 15 yuan / MWh. The trading system automatically breaks down the data into time-based energy blocks based on local sunlight conditions and daytime power generation periods. Energy storage entity B logs into the trading system to participate in the monthly trading in May, quoting volume and using a price difference model (0.7 yuan / MWh). The time period can be adjusted from 1 to 8 hours, with a negotiating margin of 5 yuan / MWh. It also submits a 24-hour electricity price, as well as a time-of-use charging curve for the peak midday photovoltaic power generation period (10:00 AM to 2:00 PM) and a time-of-use discharging curve for the 4:00 PM to 8:00 PM period. Adjustable load entity C logs into the trading system to participate in the monthly trading in May, with adjustable time periods of 1 and 8 hours, a negotiating margin of 20 yuan / MWh, and a 24-hour electricity price and a time-of-use electricity consumption curve for the 6:00 AM to 8:00 PM period.

[0070] The distributed photovoltaic subject A, the energy storage B and the adjustable load C belong to the same 110kV node, that is, the transaction can be reached within the same micro-market range. They fill in the transaction data on the transaction system, submit after confirmation, and the transaction system generates the distributed photovoltaic subject A fixed time block transaction declaration data, the energy storage B chargeable time block transaction declaration data, the energy storage B dischargeable time block transaction declaration data, and the adjustable load C time block transaction declaration data, calls the blockchain smart contract writing storage interface, and writes the above data into the blockchain distributed ledger by using the national SM2 asymmetric encryption algorithm and the SM4 symmetric encryption algorithm. At 13:00 on the same day, the transaction system batch queries all transaction declaration storage data of the May monthly transaction, assuming that there are 10000 transactions, then calls the limited regional power transaction centralized clearing method, intelligently matches the power generation and consumption characteristics curve of the distributed resources by shifting the energy storage charge and discharge and the adjustable load power consumption period, iteratively calculates the optimal solution, then calculates the clearing data of the optimal solution, assuming that 2000 transactions are reached, calls the blockchain smart contract writing storage interface, and writes the 2000 clearing data into the blockchain distributed ledger by using the national SM2 asymmetric encryption algorithm and the SM4 symmetric encryption algorithm. On June 1, the unified settlement module batch queries all transaction declaration and market clearing storage data of the May monthly transaction, obtains 10000 transaction declaration data and 2000 market clearing data, then obtains settlement power data through other interfaces, performs settlement calculation, calculates settlement power, settlement price, settlement electricity fee and total settlement fee for each transaction, a total of 2000, then calls the blockchain smart contract writing storage interface, and writes the 2000 settlement data into the blockchain distributed ledger by using the national SM2 asymmetric encryption algorithm and the SM4 symmetric encryption algorithm.

[0071] The distributed photovoltaic subject A, the energy storage B and the adjustable load C can query the declaration, clearing and settlement storage data belonging to themselves at any time and anywhere through the blockchain system called by the transaction system.

[0072] The following is an apparatus embodiment of the present application, which can be used to execute the method embodiment of the present application. For details not disclosed in the apparatus embodiment, please refer to the method embodiment of the present application.

[0073] Referring to Figure 3 In another embodiment of the present application, a limited regional power transaction centralized clearing system is provided, which can be used to implement the above-mentioned limited regional power transaction centralized clearing method. Specifically, the limited regional power transaction centralized clearing system comprises a data acquisition module and a power clearing module.

[0074] The data acquisition module is configured to acquire time-of-use energy block declaration data of each power transaction subject in a defined area; and the power clearing module is configured to perform centralized bidding and clearing of the power transaction subjects by using a clearing method based on a multi-objective genetic algorithm, so as to maximize new energy consumption and minimize total power purchase cost, and to obtain clearing results of the power transaction subjects according to the time-of-use energy block declaration data of the power transaction subjects.

[0075] In a possible implementation, the time-of-use energy block declaration data of each power transaction subject includes: when the power transaction subject is a fixed resource, the time-of-use energy block declaration data of the power transaction subject includes power, price and negotiation space of the power transaction subject in each time period; when the power transaction subject is an adjustable resource, the time-of-use energy block declaration data of the power transaction subject includes power, price, negotiation space and adjustment time period of the power transaction subject in each time period; and when the power transaction subject is a storage resource, the time-of-use energy block declaration data of the power transaction subject further includes a bidding mode and an operation mode.

[0076] In a possible implementation, the power clearing module is specifically configured to: initialize a population; wherein the population includes a plurality of individuals, and each individual includes two segments, a first segment being a power generation type power transaction subject and a second segment being a load type power transaction subject; calculate a step: construct a fitness function with the aim of maximizing new energy consumption and minimizing total power purchase cost, and calculate a fitness function value of each individual based on a sequential transaction mode of the first segment power generation type power transaction subject and the second segment load type power transaction subject according to the time-of-use energy block declaration data of the power transaction subjects; an update step: obtain a Pareto optimal solution individual in the current population by using a fast non-dominated sorting algorithm according to the fitness function value of each individual, and obtain a plurality of new individuals by performing population crossover and population mutation, and combine the Pareto optimal solution individual and the new individuals to obtain a new population; repeat the calculation step and the update step until a preset stop condition is met, obtain an optimal solution individual in the current population, and obtain the clearing results of the power transaction subjects based on the sequential transaction mode of the first segment power generation type power transaction subject and the second segment load type power transaction subject of the optimal solution individual.

[0077] In a possible embodiment, the population crossover and population mutation to obtain several new individuals include: for each individual in the current population: generating two random numbers according to the total number of power generation trading entities, and based on the two random numbers, performing a crossover process on the first fragment of the current individual in a sequential crossover method to obtain a first crossover fragment; generating two random numbers according to the total number of load power trading entities, and based on the two random numbers, performing a crossover process on the second fragment of the current individual in a sequential crossover method to obtain a second crossover fragment; combining the first crossover fragment and the second crossover fragment to obtain a new individual based on population crossover; for each individual in the current population: when the mutation probability of the current individual meets the preset conditions, performing a crossover process on the first ..., combining the first crossover fragment and the second crossover fragment; combining the first crossover fragment and the second crossover fragment, combining the first crossover fragment and the second crossover fragment; combining the first crossover fragment and the second crossover fragment, combining the first crossover fragment and the second crossover fragment; combining the first crossover fragment and the second crossover fragment, combining the first crossover fragment and the second Two random numbers are generated based on the total number of trading entities, and the power generation trading entities at the positions of the two random numbers in the first segment of the current individual are crossed, and when the time-sharing energy block declaration data of the crossed power generation trading entities include the adjustment period, the adjustment period is randomly selected to obtain the first variant segment; two random numbers are generated based on the total number of load trading entities, and the load trading entities at the positions of the two random numbers in the second segment of the current individual are crossed, and when the time-sharing energy block declaration data of the crossed load trading entities include the adjustment period, the adjustment period is randomly selected to obtain the second variant segment; the first variant segment and the second variant segment are combined to obtain a new individual based on population variation.

[0078] In one possible implementation, obtaining the optimal solution individual in the current population includes: obtaining the weighted transaction electricity and weighted electricity purchase cost of each individual in the current population; obtaining the individual whose weighted electricity purchase cost is λ% lower than the maximum weighted electricity purchase cost and whose weighted transaction electricity is the largest, as the optimal solution individual in the current population; wherein λ is a preset proportion parameter.

[0079] In a possible implementation, it also includes a bargaining and clearing module, which is used to: obtain the remaining electricity of each power trading entity based on the clearing results of each power trading entity; based on the remaining electricity of each power trading entity, adopt a clearing method based on a multi-objective genetic algorithm, with the goal of maximizing the amount of new energy consumption and minimizing the total electricity purchase cost, to conduct centralized bargaining and clearing of each power trading entity, and obtain the bargaining and clearing results of each power trading entity.

[0080] In a possible implementation, when the centralized bargaining and clearing of each power trading subject is carried out: when both the power generation trading subject and the load trading subject can bargain, if the power price P of the power generation trading subject is G The electricity price P with the load-type electricity trading entity L The difference is greater than the bargaining space M of the power generation trading entity G Bargaining space M with load-type power trading entitiesL If the sum of G Less than fair concession price M fair The transaction price is P G -M G ; When M L Less than M fair The transaction price is P L +M L Otherwise, the transaction price is (P G +P L ) / 2; where M fair =(P G -P L ) / 2.

[0081] In a possible implementation, it further includes: writing the clearing results of each power trading entity into the blockchain distributed ledger of the blockchain system in an encrypted form by calling the blockchain smart contract interface of the blockchain system; obtaining the time-based energy block declaration data of each power trading entity in the limited area includes: obtaining the time-based energy block declaration data of each power trading entity in the limited area written in the blockchain distributed ledger of the blockchain system in an encrypted form by calling the blockchain smart contract interface of the blockchain system.

[0082] All relevant contents of each step involved in the embodiment of the aforementioned method for centralized clearing of power transactions within a limited area can be referred to the functional description of the functional modules corresponding to the centralized clearing system for power transactions within a limited area in the embodiment of the present invention, and will not be repeated here.

[0083] The module division in the embodiments of the present invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in various embodiments of the present invention may be integrated into a single processor, exist physically as separate modules, or two or more modules may be integrated into a single module. The integrated modules may be implemented in either hardware or software functional modules.

[0084] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory, wherein the memory is used to store a computer program, wherein the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used to operate the method for centralized clearing of power transactions within a limited area.

[0085] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for centralized clearing of power transactions within a limited area in the above embodiment.

[0086] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for centralized clearing of power transactions within a limited area, characterized in that: include: Obtain the time-based energy block declaration data of each power trading entity in the limited area; According to the time-sharing energy block declaration data of each power trading entity, a clearing method based on a multi-objective genetic algorithm is adopted to conduct centralized bidding clearing of each power trading entity with the goal of maximizing the new energy consumption and minimizing the total electricity purchase cost, and the clearing results of each power trading entity are obtained.

2. The centralized clearing method for power transactions within a limited area according to claim 1, characterized in that: The time-based energy block declaration data of each power trading entity includes: When the power trading subject is a fixed resource, the time-based energy block declaration data of the power trading subject includes the power quantity, power price and bargaining space of the power trading subject in each time period; When the power trading subject is an adjustable resource, the time-based energy block declaration data of the power trading subject includes the power quantity, electricity price, bargaining space and adjustment period of the power trading subject in each time period; Among them, when the power trading subject is energy storage, the time-based energy block declaration data of the power trading subject also includes the quotation method and operation mode.

3. The centralized clearing method for power transactions within a limited area according to claim 1, characterized in that: According to the time-sharing energy block declaration data of each power trading entity, a clearing method based on a multi-objective genetic algorithm is adopted to maximize the amount of new energy consumption and minimize the total power purchase cost. The centralized bidding clearing of each power trading entity is carried out, and the clearing results of each power trading entity include: Initialize a population; wherein the population includes a number of individuals, each of which includes two segments, the first segment being a power generation trading entity, and the second segment being a power load trading entity; Calculation steps: A fitness function is constructed with the goal of maximizing renewable energy consumption and minimizing total electricity purchase costs. Based on the time-sharing energy block declaration data of each power trading entity, and based on the sequential trading method of the first segment power generation power trading entity and the second segment load power trading entity, the fitness function value of each entity is calculated; Update step: According to the fitness function value of each individual, the fast non-dominated sorting algorithm is used to obtain the Pareto optimal solution individuals in the current population, and then the population crossover and population mutation are performed to obtain several new individuals, and the Pareto optimal solution individuals and the new individuals are combined to obtain a new population; Repeat the calculation steps and the update steps until the preset stop condition is met to obtain the optimal solution individual in the current population. Based on the sequential trading method of the first segment power generation type power trading subject and the second segment load type power trading subject of the optimal solution individual, the clearing results of each power trading subject are obtained.

4. The method for centralized clearing of power transactions within a limited area according to claim 3, characterized in that: The method of performing population crossover and population mutation to obtain a number of new individuals includes: For each individual in the current population: two random numbers are generated according to the total number of power generation trading entities, and based on these two random numbers, the first segment of the current individual is cross-processed using a sequential cross-processing method to obtain a first cross-segment; two random numbers are generated according to the total number of power load trading entities, and based on these two random numbers, the second segment of the current individual is cross-processed using a sequential cross-processing method to obtain a second cross-segment; the first cross-segment and the second cross-segment are combined to obtain a new individual based on population cross-processing; For each individual in the current population: when the mutation probability of the current individual meets the preset conditions, two random numbers are generated according to the total number of power generation trading entities, and the power generation trading entities at the positions of the two random numbers in the first segment of the current individual are crossed, and when the time-sharing energy block declaration data of the crossed power generation trading entities include the adjustment period, the adjustment period is randomly selected to obtain the first mutation segment; two random numbers are generated according to the total number of load power trading entities, and the load power trading entities at the positions of the two random numbers in the second segment of the current individual are crossed, and when the time-sharing energy block declaration data of the crossed load power trading entities include the adjustment period, the adjustment period is randomly selected to obtain the second mutation segment; the first mutation segment and the second mutation segment are combined to obtain a new individual based on population mutation.

5. The centralized clearing method for power transactions within a limited area according to claim 3, characterized in that: The obtaining of the optimal solution individual in the current population includes: Obtain the weighted transaction power and weighted electricity purchase cost of each individual in the current population; The individual whose weighted electricity purchase cost is λ% lower than the maximum weighted electricity purchase cost and whose weighted transaction volume is the largest is obtained as the optimal solution individual in the current population; where λ is a preset proportion parameter.

6. The centralized clearing method for power transactions within a limited area according to claim 1, characterized in that: Also includes: Obtain the remaining power of each power trading entity based on the clearing results of each power trading entity; According to the surplus electricity of each power trading entity, a clearing method based on a multi-objective genetic algorithm is adopted to carry out centralized bargaining and clearing of each power trading entity with the goal of maximizing the consumption of new energy and minimizing the total electricity purchase cost, and the bargaining and clearing results of each power trading entity are obtained.

7. The method for centralized clearing of power transactions within a limited area according to claim 6, characterized in that: When conducting centralized bargaining and clearing of power trading entities: When both power generation trading entities and load trading entities are negotiable, if the power price P of power generation trading entities is G The electricity price P with the load-type electricity trading entity L The difference is greater than the bargaining space M of the power generation trading entity G Bargaining space M with load-type power trading entities L If the sum of G Less than fair concession price M fair The transaction price is P G -M G ; When M L Less than M fair The transaction price is P L +M L Otherwise, the transaction price is (P G +P L ) / 2; where M fair =(P G -P L ) / 2.

8. The centralized clearing method for power transactions within a limited area according to claim 1, characterized in that: Also includes: By calling the blockchain smart contract interface of the blockchain system, the clearing results of each power trading entity are written into the blockchain distributed ledger of the blockchain system in an encrypted form; The obtaining of the time-based energy block declaration data of each power trading entity in the limited area includes: obtaining the time-based energy block declaration data of each power trading entity in the limited area written in an encrypted form in the blockchain distributed ledger of the blockchain system by calling the blockchain smart contract interface of the blockchain system.

9. A centralized clearing system for power transactions within a limited area, characterized by: include: The data acquisition module is used to obtain the time-based energy block declaration data of each power trading entity in a limited area; The power clearing module is used to conduct centralized bidding and clearing of each power trading entity based on the time-sharing energy block declaration data of each power trading entity, using a clearing method based on a multi-objective genetic algorithm, with the goal of maximizing the amount of new energy consumption and minimizing the total electricity purchase cost, and obtain the clearing results of each power trading entity.

10. The centralized clearing system for power transactions within a limited area according to claim 9, characterized in that: The time-based energy block declaration data of each power trading entity includes: When the power trading subject is a fixed resource, the time-based energy block declaration data of the power trading subject includes the power quantity, power price and bargaining space of the power trading subject in each time period; When the power trading subject is an adjustable resource, the time-based energy block declaration data of the power trading subject includes the power quantity, electricity price, bargaining space and adjustment period of the power trading subject in each time period; Among them, when the power trading subject is energy storage, the time-based energy block declaration data of the power trading subject also includes the quotation method and operation mode.

11. The centralized clearing system for power transactions within a limited area according to claim 9, characterized in that: The power clearing module is specifically used for: Initialize a population; wherein the population includes a number of individuals, each of which includes two segments, the first segment being a power generation trading entity, and the second segment being a power load trading entity; Calculation steps: A fitness function is constructed with the goal of maximizing renewable energy consumption and minimizing total electricity purchase costs. Based on the time-sharing energy block declaration data of each power trading entity, and based on the sequential trading method of the first segment power generation power trading entity and the second segment load power trading entity, the fitness function value of each entity is calculated; Update step: According to the fitness function value of each individual, the fast non-dominated sorting algorithm is used to obtain the Pareto optimal solution individuals in the current population, and then the population crossover and population mutation are performed to obtain several new individuals, and the Pareto optimal solution individuals and the new individuals are combined to obtain a new population; Repeat the calculation steps and the update steps until the preset stop condition is met to obtain the optimal solution individual in the current population. Based on the sequential trading method of the first segment power generation type power trading subject and the second segment load type power trading subject of the optimal solution individual, the clearing results of each power trading subject are obtained.

12. The centralized clearing system for power transactions within a limited area according to claim 9, characterized in that: It also includes a bargaining and clearing module; the bargaining and clearing module is used to: obtain the remaining electricity of each power trading entity based on the clearing results of each power trading entity; based on the remaining electricity of each power trading entity, adopt a clearing method based on a multi-objective genetic algorithm, with the goal of maximizing the amount of new energy consumption and minimizing the total electricity purchase cost, conduct centralized bargaining and clearing of each power trading entity, and obtain the bargaining and clearing results of each power trading entity.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for centralized clearing of electricity transactions within a limited area as claimed in any one of claims 1 to 8 are implemented.

14. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for centralized clearing of electricity transactions within a limited area as claimed in any one of claims 1 to 8 are implemented.

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