Blockchain-based regional integrated energy multi-energy flow collaborative regulation method

Abstract

This invention discloses a regional integrated energy multi-energy flow collaborative regulation method based on blockchain, belonging to the field of multi-energy flow collaborative regulation technology. Specific steps include: Step 1: Determining the region to be regulated, encoding geographical and energy network parameter data and writing it into the genesis block, defining three types of nodes and generating unique IDs, completing spatial benchmarks and parameter confirmation; Step 2: Verifying node coordinates to achieve spatial anchoring, allocating capacity vectors and credit values, aligning data timestamps, and calculating the unit loss coefficient of equipment and the unit distance maintenance cost; Step 3: Determining the cost coefficient by combining the unit loss coefficient of equipment and the unit distance maintenance cost of equipment, constructing an optimization model that integrates spatial constraints, and determining the global optimal solution based on multi-dimensional constraints. This invention achieves controllable and reliable node behavior and collaboration by standardizing node management, constructing precise cost coefficients and optimization models, and improving the economic efficiency of regulation schemes.

Description

Technical Field

[0001] This invention belongs to the field of multi-energy flow coordinated regulation technology, specifically involving a regional integrated energy multi-energy flow coordinated regulation method based on blockchain. Background Technology

[0002] As the global energy structure shifts towards cleaner and distributed energy, regional integrated energy systems (such as industrial parks and new urban energy networks) have become core carriers. These systems encompass multiple energy flow forms, including electricity (including photovoltaics and energy storage), gas, and heat. They require coordinated regulation to achieve energy supply and demand balance, efficiency improvement, and cost optimization. However, their complex "multi-energy coupling + multi-entity participation" characteristics present numerous bottlenecks for existing technologies. Existing regional integrated energy regulation systems often lack clear node management systems, leading to ambiguous authority allocation. Some solutions fail to clearly define nodes' data collection rights, contract call rights (such as modifying electricity price parameters), and decision-making voting rights. This results in the risk of exceeding authority in core operations (such as adjusting the heat pipeline's transmission capacity), or ordinary users being unable to participate in load regulation proposals. Existing technologies lack dynamic credit constraints, making it difficult to trace and punish node violations (such as data upload timeouts and falsification of equipment operating parameters). Relying solely on manual inspection and supervision is inefficient and costly.

[0003] Therefore, there is an urgent need for a blockchain-based regional integrated energy multi-energy flow coordinated regulation method to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide a regional integrated energy multi-energy flow coordinated regulation method based on blockchain, which can solve the technical problems of ambiguous permission allocation and lack of dynamic credit constraints in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A blockchain-based regional integrated energy multi-energy flow coordinated regulation method includes:

[0007] Step 1: Determine the area to be regulated, encode the geographic and energy network parameter data and write it into the genesis block, define three types of nodes and generate unique IDs, and complete the spatial benchmark and parameter rights confirmation.

[0008] Step 2: Verify node coordinates to achieve spatial anchoring, assign capability vectors and credit values, align data timestamps, and calculate the unit loss coefficient and unit distance maintenance cost of the equipment.

[0009] Step 3: Combine the unit loss coefficient of equipment with the unit distance maintenance cost of equipment to determine the cost coefficient, construct an optimization model that integrates spatial constraints, and determine the global optimal solution based on multi-dimensional constraints.

[0010] Furthermore, the encoded geographic and energy network parameter data is written into the genesis block, specifically as follows:

[0011] The area to be regulated is determined, the legal boundaries are clarified through cadastral survey data, and the coordinate point set of physical isolation facilities is obtained by combining on-site surveying and mapping. The coordinate point set is encoded into polygon coordinate strings in WKT format to form a complete dataset of the root data layer. The coordinate strings are written into the genesis block after being confirmed by the regional energy operator and the surveying and mapping institute.

[0012] Energy network parameter data is written to the device layer. This energy network parameter data includes power distribution ring network data, gas pipeline network data, heating pipeline network data, and photovoltaic data.

[0013] Furthermore, three types of nodes are defined and unique IDs are generated to complete the spatial benchmark and parameter weighting. The specific method is as follows:

[0014] The digital mapping of physical and logical entities participating in the coordinated regulation of regional integrated energy systems defines equipment nodes, main nodes, and edge computing nodes.

[0015] Among them, device nodes refer to physical devices that are directly connected to the energy network, and each device node corresponds to a unique digital identity;

[0016] The main node refers to the organization or institution that participates in energy regulation;

[0017] Edge computing nodes refer to localized computing power units deployed within a region, serving as communication gateways between device nodes and the blockchain mainnet.

[0018] Furthermore, the unit loss coefficient of the equipment is calculated using the following method:

[0019] Collect the actual energy transmission loss during equipment operation, the corresponding energy transmission power of the equipment, and the energy transmission distance covered by the equipment;

[0020] Let C be the total loss cost of all equipment in the region during historical operation and maintenance, P be the power of a single equipment, and L be the transmission distance of a single equipment. The unit loss coefficient of each equipment within the coverage area is determined by summing the losses of all equipment in the coverage area. =C / ∑(P×L).

[0021] Furthermore, the unit distance maintenance cost is calculated using the following method:

[0022] The initial value is determined by the regional energy operator, with year T as the unit, covering all aspects of the operation and maintenance expenditures of all energy equipment in the region. Through annual operation and maintenance expense details, cost reimbursement records of the operation and maintenance department, and third-party service contracts, costs unrelated to distance are eliminated, retaining only distance-related costs, and the total annual operation and maintenance cost W is obtained. Through the inspection records of the operation and maintenance management platform, each operation and maintenance record is statistically analyzed according to the actual service distance, and the distance of all operation and maintenance activities within the year is summarized to obtain the total operation and maintenance distance I. The initial value is equal to W / I, and it is updated every T years. The smart contract automatically updates the on-chain storage. The new value will take effect from the agreed date, and the cost calculation for all equipment will be updated accordingly. .

[0023] Furthermore, the cost coefficient is determined by combining the unit loss coefficient of the equipment with the unit distance maintenance cost of the equipment. The specific method is as follows:

[0024] Using formula Cost coefficient, where This is the unit loss coefficient for the equipment. Let j be the energy transmission loss rate of device j. The unit distance operation and maintenance cost of the equipment is updated by the regional energy operator through smart contract proposals. The operating life of equipment j is taken from the difference between the equipment's manufacturing date and the current time.

[0025] Furthermore, an optimization model incorporating spatial constraints is constructed, specifically through the following method:

[0026] Combining the energy network parameters of the genesis block, using the formula Let represent an optimization model that incorporates spatial constraints, where t represents the t-th time period, i represents the energy type index, and T represents the total time period. Let j represent the price of energy i during time period t, and j represent the equipment number. This represents the cost coefficient of equipment j. This represents the straight-line distance from device j to the load center, calculated based on the device coordinates and load center coordinates of the genesis block. The amount of photovoltaic subsidies during time period t. Let t represent the photovoltaic power generation during the period t.

[0027] Furthermore, the global optimal solution is determined based on multi-dimensional constraints, specifically through the following method:

[0028] Define optimization variables that are strongly correlated with each energy flow and equipment status to ensure that the model can be implemented in practice. Specifically, this includes:

[0029] Energy allocation variables , representing the amount of energy transmitted by device j with respect to energy i during time period t;

[0030] Equipment operating status variables , indicating the operating mode of device j during time period t;

[0031] Load response variables , representing the adjustable load of energy i during time period t;

[0032] Multi-dimensional constraints are set, and the optimization model must meet energy power balance constraints, equipment capacity constraints, spatial constraints, and node permission constraints. Edge computing nodes collect real-time operating data from each device j. , , After filtering and encryption, the data is uploaded to the blockchain. Core nodes retrieve energy network parameters from the genesis block and use particle swarm optimization to solve the optimization model. The process involves distributed computing by edge computing nodes, with core nodes aggregating the local optimal solutions from each edge node. A global optimal solution, i.e., the optimal transmission volume for each device during time period t, is then determined through multi-node consensus. Optimal operating state With optimal load adjustment .

[0033] Furthermore, multi-dimensional constraints are set, and the optimization model must satisfy energy power balance constraints, equipment capacity constraints, spatial constraints, and node permission constraints. The specific method is as follows:

[0034] The energy power balance constraint requires that the total supply of multiple energy flows in each time period must match the total demand, i.e. To ensure that supply and demand are not out of sync;

[0035] The equipment capacity constraint means that the energy transmission capacity of equipment j must not exceed its rated capacity;

[0036] The spatial constraints are based on the WKT boundary and device coordinates of the genesis block, which limit the energy transmission range. The transmission distance from device j to the load center must be less than or equal to the maximum effective transmission radius of this type of device.

[0037] Node permission constraints refer to the fact that only core nodes with contract call permissions can modify and optimize the boundary conditions of variables, and the modifications must be confirmed by multi-signature and stored on the chain.

[0038] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0039] 1. This invention fits differentiated unit loss coefficients according to equipment type, combines equipment transmission loss rate, accurately quantifies equipment loss cost, dynamically adjusts unit distance operation and maintenance cost through smart contracts, eliminates irrelevant costs and updates based on annual operation and maintenance data, ensuring that cost parameters match actual expenditures, integrates equipment loss, operation and maintenance distance, and operating years to set cost coefficients, comprehensively reflects equipment operating status, provides accurate cost input for optimization model, and makes control schemes more economical and feasible;

[0040] 2. This invention writes the geographic data and energy network parameters of the area to be regulated into the genesis block after being confirmed by the regional energy operator and the surveying and mapping institute. This generates a state root hash composed of the root data layer and the device layer sub-hashes. This not only clarifies the digital benchmark of the regional spatial boundary, but also completes the on-chain confirmation of the key physical parameters of the energy network. This ensures that the parameter source is transparent and tamper-proof, providing a reliable and traceable digital foundation for subsequent multi-energy flow coordinated regulation and avoiding regulation deviations caused by parameter distortion and boundary ambiguity in traditional technologies.

[0041] 3. This invention constructs an efficient, transparent, and practical energy system optimization framework by integrating spatial constraints, multi-energy flow collaborative optimization, and blockchain technology. It eliminates the sampling frequency differences of energy networks such as electricity, gas, and heat, ensures strict synchronization of data in the time dimension, and avoids supply and demand matching errors caused by time misalignment. By accurately calculating the distance between equipment coordinates and load centers, spatial transmission losses are transformed into optimizable cost coefficients, making the model closer to the actual physical scenario. Parameters such as electricity prices, gas prices, and photovoltaic subsidies are stored on the blockchain through smart contracts and updated in real time. Combined with dynamic data such as equipment operating years and loss rates, the model can adaptively adjust to market fluctuations and equipment aging. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 The diagram illustrates the steps of the regional integrated energy multi-energy flow coordinated regulation method based on blockchain according to the present invention.

[0044] Figure 2 The flowchart of the regional integrated energy multi-energy flow coordinated regulation method based on blockchain of the present invention is shown.

[0045] Figure 3 The diagram illustrates the steps of calculating the unit loss coefficient of the equipment according to the present invention.

[0046] Figure 4 The diagram illustrates the steps of the method for calculating the unit distance maintenance cost according to the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] like Figure 1 , Figure 2 , Figure 3 and Figure 4 The blockchain-based regional integrated energy multi-energy flow coordinated regulation method shown includes the following steps:

[0049] Step 1: Determine the area to be regulated, encode the geographic and energy network parameter data and write it into the genesis block, define three types of nodes and generate unique IDs, and complete the spatial benchmark and parameter rights confirmation.

[0050] The system identifies the areas to be regulated (such as administrative regions or energy network coverage areas), clarifies legal boundaries (such as the red line range defined by state-owned land use rights certificates) through cadastral survey data, and obtains coordinate point sets of physical isolation facilities such as walls and fences through on-site surveying. The coordinate point sets are then encoded into polygon coordinate strings using WKT format and simultaneously incorporated into the main road network data within the region (such as the centerline coordinates of main roads) to form a complete dataset at the root data layer. The coordinate strings are written into the genesis block after being confirmed by multiple signatures from regional energy operators and surveying institutes, providing a reliable, transparent, and traceable digital foundation for the regional energy system. Energy network parameter data is then written into the equipment layer, including data from power distribution ring networks, gas pipeline networks, heating pipeline networks, and photovoltaic data.

[0051] For power distribution ring network data, ring network cabinets and branch boxes are used as data collection points. The point attributes and edge attributes of the data collection points are obtained by combining export from the GIS system with on-site verification.

[0052] Point attributes include data acquisition point number (e.g., ring main unit number HX-001), installation location coordinates, current transmission capacity and real-time electricity price; edge attributes include the actual measured length between adjacent data acquisition points and cable laying depth. The edge-point topology model is used to store the distribution ring network data. All data is grouped according to distribution intervals, and each group of data generates an independent hash value and writes it to the device layer.

[0053] Gas pipeline network data includes the latitude and longitude coordinates of pressure regulating stations, gas transmission volume, and real-time gas price. All gas pipeline network data is clustered by gas supply zone, and after generating a gas topology hash, it is written to the equipment layer.

[0054] Heat exchanger network data is used to determine the heat exchanger station spacing threshold (e.g., 800 meters according to urban heating network design specifications, set by the heating company through smart contract proposals). Heat exchanger station coordinates are collected, the spacing between adjacent heat exchanger stations is calculated, an upper triangular matrix is ​​generated to record the spacing, and spacing data greater than or equal to the heat exchanger station spacing threshold are removed before generating a heat topology hash and writing it to the equipment layer.

[0055] Photovoltaic data includes the device number, latitude and longitude coordinates, photovoltaic power generation and energy storage capacity of photovoltaic inverters and energy storage batteries;

[0056] The state root hash of the genesis block is generated by concatenating the root data layer hash and the sub-hashes of the device layer. The genesis block not only establishes the spatial benchmark of the area to be regulated, but also completes the on-chain confirmation of key physical parameters of the energy network.

[0057] The digital mapping of physical and logical entities participating in the coordinated regulation of regional integrated energy systems includes equipment nodes, main nodes, and edge computing nodes;

[0058] Device nodes refer to physical devices that are directly connected to the energy network, including distribution ring network cabinets, branch boxes, gas pressure regulating stations, heat exchange stations, photovoltaic inverters and energy storage battery packs. Each device node has a unique digital identity (bound to the topological coordinates in the genesis block).

[0059] Main nodes refer to organizations or institutions that participate in energy regulation, such as regional energy operators, power grid companies, gas companies, industrial users, and commercial users. Each main node needs to submit proof of legal person status (such as a business license hash) to complete on-chain registration.

[0060] Edge computing nodes refer to localized computing units deployed within a region. They are responsible for collecting data from device nodes, performing basic data processing (such as filtering and encryption), and acting as communication gateways between device nodes and the blockchain mainnet. Examples include microgrid control cabinets in industrial parks and energy management servers in commercial complexes.

[0061] All nodes must pass the spatial boundary verification of the genesis block (falling within the WKT-encoded area) and generate a unique identifier (node ​​ID) on the chain, in the format of "type-number" (e.g., device node "D-HX001", main node "S-Power Grid Company").

[0062] A unique data index is constructed using a regional code (2 bytes, adopting the GB / T2260 administrative division code, such as 110101 for Dongcheng District, Beijing) + device ID + timestamp, and stored in a Merkle-Patricia tree. This allows for querying historical control data of any device through the index. The construction process of the Merkle-Patricia tree is as follows: all data indexes are sorted in lexicographical order, and then hash values ​​are calculated in pairs to serve as parent nodes. This process is repeated upwards until the root hash (multi-energy flow state root) is generated. During a query, the hash of the child nodes is verified layer by layer according to the index starting from the root node until the corresponding leaf node is found, and the data storage address is obtained.

[0063] Step 2: Verify node coordinates to achieve spatial anchoring, assign capability vectors and credit values, align data timestamps, and calculate the unit loss coefficient and unit distance maintenance cost of the equipment.

[0064] Based on the boundary coordinates encoded in the genesis block in step one, participating nodes need to submit the coordinates of the device access point (such as the latitude and longitude of the grid connection point of a photovoltaic power station). The smart contract verifies whether the coordinates fall within the boundary of the genesis block WKT through the SpatialVerify function and match the energy network topology data (such as the 1km radiation range of the distribution network node HX-001) to achieve spatial anchoring.

[0065] A capability vector V=(v1, v2, v3) is assigned to each node, where v1 is the data collection permission (1=collectible, 0=non-collectible), v2 is the contract call permission (1=price parameter can be modified, 0=price parameter cannot be modified), and v3 is the decision voting weight (core nodes v3=3, participating nodes v3=1, where the core nodes are predefined by the genesis block as an initial core node list, such as power grid companies, gas companies, and regional energy operators). The vector value is written to the blockchain account after the core node multi-signs the node qualification certificate (such as power installation license).

[0066] Set a uniform initial credit value for each node (e.g., initial value equal to 100). Add 1 point for each compliant operation (e.g., data is uploaded to the chain on time), deduct 5 points for each non-compliant operation (e.g., data upload timeout), and trigger permission freeze when the credit value is less than 60 to achieve behavior traceability.

[0067] Equipment unit loss coefficient It is a loss cost parameter that is strongly tied to the equipment type. Data features are extracted from the historical operation and maintenance data of the equipment, and the cost quantification value per unit loss is determined through mathematical fitting.

[0068] Collect long-term operation and maintenance data of target equipment (such as distribution ring network cabinets and photovoltaic inverters). The core data dimensions include the actual energy transmission loss during equipment operation (such as kW-level loss, which can be collected through equipment sensors and energy monitoring systems), the corresponding energy transmission power of the equipment (kW, taken from equipment operation logs or real-time monitoring data), and the energy transmission distance covered by the equipment (km, such as the cable laying length of distribution ring network cabinets and the distance from photovoltaic inverters to grid connection points).

[0069] Let C be the total loss cost of each device (such as a power distribution ring main unit) in the region during historical operation and maintenance, and let ∑(P×L) be the total transmission power × distance (where P is the power of a single device and L is the transmission distance of a single device, and the summation covers all sample devices). Through fitting, we obtain C = ×∑(P×L), therefore =C / ∑(P×L), since the loss mechanisms and maintenance costs of different devices vary significantly, it is necessary to perform fitting calculations separately for each device type. The fitted result is... Uploaded to the blockchain system for evidence storage.

[0070] Unit distance maintenance cost It is a unified operation and maintenance cost parameter that does not differentiate between equipment types. It is a regionally unified benchmark value that is dynamically adjusted through a smart contract mechanism. The initial value is determined by the regional energy operator (such as a local power grid company or integrated energy service enterprise), using year T as the unit (such as a calendar year or fiscal year). It covers all aspects of the operation and maintenance expenditures of all energy equipment (power distribution, photovoltaic, gas) within the region. Through the annual operation and maintenance cost details, the cost reimbursement records of the operation and maintenance department, and third-party service contracts (such as outsourced operation and maintenance, vehicle rental), costs unrelated to distance (such as indoor equipment commissioning fees and operation and maintenance personnel training fees) are eliminated, and only distance-related costs (such as inspection personnel salaries, inspection vehicle costs, and inspection route planning service fees) are retained. The total annual operation and maintenance cost W is obtained by summing these costs. Through the inspection records of the operation and maintenance management platform, for each operation and maintenance record, the actual service distance is calculated (such as the one-way distance of the inspection vehicle from the operation and maintenance station to the equipment point). The total distance of all operation and maintenance activities within the year is summed to obtain the total operation and maintenance distance I. The initial value is equal to W / I, and it is updated every T years. Regional energy operators can submit data to the smart contract based on the latest operation and maintenance cost data (such as the average operation and maintenance cost change rate over the past year). Update proposals (such as applications to change) The cost will be adjusted from 0.5 yuan / km to 0.55 yuan / km, and supporting documentation for the cost change (such as financial statements and operation and maintenance records) will be attached. The smart contract will automatically notify relevant entities within the region (such as power grid companies, photovoltaic owners, and energy storage operators) to participate in the verification to ensure that the supporting documentation is accurate. If the verification is successful, the smart contract will automatically update the data stored on the blockchain. The new value will take effect from the agreed date, and the cost calculation for all equipment will be updated accordingly. .

[0071] Step 3: Combine the unit loss coefficient of equipment with the unit distance maintenance cost of equipment to determine the cost coefficient, construct an optimization model that integrates spatial constraints, and determine the global optimal solution based on multi-dimensional constraints.

[0072] Divide the data into time periods with fixed durations and analyze the energy network data for each time period;

[0073] Based on the energy network parameters of the genesis block, an optimization model incorporating spatial constraints is constructed, with the specific formula shown below:

[0074] ;

[0075] Where t represents the t-th time period, i represents the energy type index (e.g., electricity, gas), and T represents the total time period. The price of energy i during time period t is represented by the electricity price, which is taken from the time-of-use electricity price file of the power grid company (stored on-chain). The gas price is updated daily by the gas company through a smart contract. j represents the equipment number (such as a distribution ring network cabinet or a photovoltaic inverter). This represents the cost coefficient of device j, used to quantify the impact of spatial transmission loss on energy efficiency. This represents the straight-line distance from device j to the load center, calculated based on the device coordinates and load center coordinates (such as the production workshop cluster center in an industrial park) within the genesis block. The amount of photovoltaic subsidy for time period t (e.g., 0.3 yuan / kWh, taken from local new energy policy documents and stored on the blockchain). The photovoltaic power generation during time period t is taken from standardized data uploaded by the photovoltaic inverter. If there is energy storage battery, the photovoltaic power consumed by energy storage charging needs to be deducted.

[0076] The specific formula for the cost coefficient is shown below:

[0077] ;

[0078] in, Unit loss coefficient of equipment (power distribution ring main unit) =0.02 yuan / (kW・km), photovoltaic inverter =0.01 yuan / (kW・km), generated based on historical equipment operation and maintenance data and stored on the blockchain. The energy transmission loss rate of device j is calculated by dividing the difference between the energy input to the device and the energy output before the device stops working by the energy input. The unit distance operation and maintenance cost of the equipment is updated by the regional energy operator through smart contract proposals. The service life of equipment j is taken from the difference between the equipment's manufacturing date and the current time.

[0079] To achieve multi-energy flow coordination, optimization variables that are strongly correlated with the states of each energy flow and equipment need to be defined to ensure that the model can be implemented. Specifically, this includes:

[0080] Energy allocation variables , representing the amount of energy transmitted by device j with respect to energy i during time period t;

[0081] Equipment operating status variables This indicates the operating mode of device j during time period t. It takes values ​​in the range [0, 1]. =1 indicates full load operation. A value less than 1 indicates reduced operating rate. =0 indicates shutdown. Specifically, this applies to photovoltaic inverters with added energy storage charging / discharging modes, specifically during charging mode. Values ​​within the range [0, 1], discharge mode It takes values ​​in the range [-1, 0].

[0082] Load response variables , represents the adjustable load of energy i during time period t (such as the off-peak load of industrial users and the air conditioning load adjustment of commercial users), which is submitted by the main node (user) through smart contract, and the model optimizes and calls within the constraints.

[0083] Set multi-dimensional constraints, and the optimization model must meet energy power balance constraints, equipment capacity constraints, spatial constraints and node permission constraints.

[0084] Among them, the energy power balance constraint is that the total supply of multiple energy flows in each time period must match the total demand, that is... To ensure that supply and demand are not out of sync;

[0085] The equipment capacity constraint means that the energy transmission capacity of equipment j must not exceed its rated capacity;

[0086] The spatial constraints are based on the WKT boundary and device coordinates of the genesis block, which limit the energy transmission range. The transmission distance from device j to the load center must be less than or equal to the maximum effective transmission radius of this type of device.

[0087] Node permission constraints refer to core nodes that only have contract call permissions (v2=1), can modify the boundary conditions of optimization variables (such as the rated capacity of the device, the adjustable load range), and the modifications must be confirmed by multi-signature and stored on the chain.

[0088] Edge computing nodes collect real-time operational data from each device j (real-time) , , After filtering (removing outliers) and encryption, the data is uploaded to the blockchain. Core nodes retrieve energy network parameters from the genesis block and use particle swarm optimization to solve the optimization model. The process involves distributed computing by edge computing nodes (computing units are divided according to energy type). The core node aggregates the local optimal solutions from each edge node, and the global optimal solution, i.e., the optimal transmission volume of each device in time period t, is determined through multi-node consensus. Optimal operating state With optimal load adjustment .

[0089] The specific steps for solving the optimization model using the particle swarm optimization algorithm include:

[0090] The core node retrieves energy network parameters (such as equipment rated capacity, WKT area boundary, equipment coordinates and distance from the load center) from the genesis block, and sets particle swarm algorithm parameters: number of particles (divided by energy type, such as 20-30 particles each for power distribution, gas, and heat), maximum number of iterations (set to 50-100 times based on real-time control requirements), and inertia weight (initially 0.8, linearly decreasing to 0.2 during iteration). At the same time, it imports multi-dimensional constraints (energy power balance constraints, equipment capacity constraints, spatial constraints, and node permission constraints) as boundary conditions for particle position updates.

[0091] The particle swarm is assigned to corresponding edge computing nodes according to energy type (e.g., power distribution particles are assigned to power distribution network edge nodes, and gas-related particles are assigned to gas pipeline network edge nodes). Each edge node calculates the fitness value of each particle—that is, the combination of variables corresponding to the particle is substituted into the algorithm. The calculation results are used to verify whether the particles meet the constraints, remove particles that do not meet the constraints, and retain the fitness values ​​of the effective particles and their corresponding variable combinations.

[0092] Within each edge node, the current fitness value of each particle is compared with its own historical best fitness value. If the current fitness value is better, the individual's best position (corresponding to the variable combination) is updated. Then, the particles are clustered by energy type, the average fitness value of the same type of particles is calculated, the local best fitness value and the corresponding local best solution of the particle are selected, and they are uploaded to the blockchain mainnet simultaneously.

[0093] Core nodes (such as regional energy operators and power grid companies) call the local optimal solutions of each edge node from the blockchain, and based on the global update rules of the particle swarm algorithm (combining inertial weights, individual cognitive factors, and social cognitive factors to adjust particle positions), they initially screen out candidate global optimal solutions, and then confirm the final global optimal solution through a multi-node consensus mechanism (allocating voting weights according to the node capability vector v3, with a vote rate ≥ 67%).

[0094] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

[0095] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A regional integrated energy multi-energy flow coordinated regulation method based on blockchain, characterized in that, include: Step 1: Determine the area to be regulated, encode the geographic and energy network parameter data and write it into the genesis block, define three types of nodes and generate unique IDs, and complete the spatial benchmark and parameter rights confirmation. Define device nodes consisting of physical devices, main nodes consisting of organizations / institutions, and edge computing nodes based on localized computing power units. All nodes must fall within the WKT encoding range and generate a unique ID in type-number format. Step 2: Verify node coordinates to achieve spatial anchoring, assign capability vectors and credit values, align data timestamps, and calculate the unit loss coefficient and unit distance maintenance cost of the equipment. Invalid nodes outside the filtering area are filtered by spatial anchoring verification, limiting the effective participation scope of collaborative regulation, clarifying the node permission level by allocating capability vectors, and setting a dynamic credit value mechanism to constrain the trustworthy behavior of nodes. Step 3: Combine the unit loss coefficient of equipment with the unit distance maintenance cost of equipment to determine the cost coefficient, construct an optimization model that integrates spatial constraints, and determine the global optimal solution based on multi-dimensional constraints.

2. The regional integrated energy multi-energy flow coordinated regulation method based on blockchain according to claim 1, characterized in that, The coded geographic and energy network parameter data is written into the genesis block, specifically as follows: The area to be regulated is determined, the legal boundaries are clarified through cadastral survey data, and the coordinate point set of physical isolation facilities is obtained by combining on-site surveying and mapping. The coordinate point set is encoded into polygon coordinate strings in WKT format to form a complete dataset of the root data layer. The coordinate strings are written into the genesis block after being confirmed by the regional energy operator and the surveying and mapping institute. Energy network parameter data is written to the device layer. This energy network parameter data includes power distribution ring network data, gas pipeline network data, heating pipeline network data, and photovoltaic data.

3. The regional integrated energy multi-energy flow coordinated regulation method based on blockchain according to claim 1, characterized in that, Define three types of nodes and generate unique IDs to complete the spatial benchmark and parameter weighting. The specific method is as follows: The digital mapping of physical and logical entities participating in the coordinated regulation of regional integrated energy systems defines equipment nodes, main nodes, and edge computing nodes. Among them, device nodes refer to physical devices that are directly connected to the energy network, and each device node corresponds to a unique digital identity; The main node refers to the organization or institution that participates in energy regulation; Edge computing nodes refer to localized computing power units deployed within a region, serving as communication gateways between device nodes and the blockchain mainnet.

4. The regional integrated energy multi-energy flow coordinated regulation method based on blockchain according to claim 1, characterized in that, The specific method for calculating the unit loss coefficient of equipment is as follows: Collect the actual energy transmission loss during equipment operation, the corresponding energy transmission power of the equipment, and the energy transmission distance covered by the equipment; Let C be the total loss cost of all devices in the region during historical operation and maintenance, P be the power of a single device, and L be the transmission distance of a single device. Summing up the losses of all devices within the coverage area will determine the unit loss coefficient for each device. =C / ∑(P×L).

5. The regional integrated energy multi-energy flow coordinated regulation method based on blockchain according to claim 1, characterized in that, The specific method for calculating the unit distance maintenance cost is as follows: The initial value is determined by the regional energy operator, with year T as the unit, covering all aspects of the operation and maintenance expenditures of all energy equipment in the region. Through annual operation and maintenance expense details, cost reimbursement records of the operation and maintenance department, and third-party service contracts, costs unrelated to distance are eliminated, retaining only distance-related costs, and the total annual operation and maintenance cost W is obtained. Through the inspection records of the operation and maintenance management platform, each operation and maintenance record is statistically analyzed according to the actual service distance, and the distance of all operation and maintenance activities within the year is summarized to obtain the total operation and maintenance distance I. The initial value is equal to W / I, and it is updated every T years. The smart contract automatically updates the on-chain storage. The new value will take effect from the agreed date, and the cost calculation for all equipment will be updated accordingly. .

6. The regional integrated energy multi-energy flow coordinated regulation method based on blockchain according to claim 1, characterized in that, The cost coefficient is determined by combining the unit loss coefficient of the equipment with the unit distance maintenance cost of the equipment. The specific method is as follows: Using formula Cost coefficient, where This is the unit loss coefficient for the equipment. Let j be the energy transmission loss rate of device j. The unit distance operation and maintenance cost of the equipment is updated by the regional energy operator through smart contract proposals. The operating life of equipment j is taken from the difference between the equipment's manufacturing date and the current time.

7. The regional integrated energy multi-energy flow coordinated regulation method based on blockchain according to claim 1, characterized in that, The optimization model that incorporates spatial constraints is constructed using the following method: Combining the energy network parameters of the genesis block, using the formula Let represent an optimization model that incorporates spatial constraints, where t represents the t-th time period, i represents the energy type index, and T represents the total time period. Let j represent the price of energy i during time period t, and j represent the equipment number. This represents the cost coefficient of equipment j. This represents the straight-line distance from device j to the load center, calculated based on the device coordinates and load center coordinates of the genesis block. The amount of photovoltaic subsidies during time period t. Let t represent the photovoltaic power generation during the period t.

8. The regional integrated energy multi-energy flow coordinated regulation method based on blockchain according to claim 1, characterized in that, The method for determining the global optimal solution based on multi-dimensional constraints is as follows: Define optimization variables that are strongly correlated with each energy flow and equipment status to ensure that the model can be implemented in practice. Specifically, this includes: Energy allocation variables , representing the amount of energy transmitted by device j with respect to energy i during time period t; Equipment operating status variables , indicating the operating mode of device j during time period t; Load response variables , representing the adjustable load of energy i during time period t; Multi-dimensional constraints are set, and the optimization model must meet energy power balance constraints, equipment capacity constraints, spatial constraints, and node permission constraints. Edge computing nodes collect real-time operating data from each device j. , , After filtering and encryption, the data is uploaded to the blockchain. Core nodes retrieve energy network parameters from the genesis block and use particle swarm optimization to solve the optimization model. The process involves distributed computing by edge computing nodes, with core nodes aggregating the local optimal solutions from each edge node. A global optimal solution, i.e., the optimal transmission volume for each device during time period t, is then determined through multi-node consensus. Optimal operating state With optimal load adjustment .

9. The regional integrated energy multi-energy flow coordinated regulation method based on blockchain according to claim 8, characterized in that, To set multi-dimensional constraints, the optimization model must satisfy energy power balance constraints, equipment capacity constraints, spatial constraints, and node permission constraints. The specific method is as follows: The energy power balance constraint requires that the total supply of multiple energy flows in each time period must match the total demand, i.e. To ensure that supply and demand are not out of sync; The equipment capacity constraint means that the energy transmission capacity of equipment j must not exceed its rated capacity; The spatial constraints are based on the WKT boundary and device coordinates of the genesis block, which limit the energy transmission range. The transmission distance from device j to the load center must be less than or equal to the maximum effective transmission radius of this type of device. Node permission constraints refer to the fact that only core nodes with contract call permissions can modify and optimize the boundary conditions of variables, and the modifications must be confirmed by multi-signature and stored on the chain.

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