CN113554511A - Active power distribution network power trading method based on block chain and particle swarm optimization - Google Patents
Active power distribution network power trading method based on block chain and particle swarm optimization Download PDFInfo
- Publication number
- CN113554511A CN113554511A CN202110699042.9A CN202110699042A CN113554511A CN 113554511 A CN113554511 A CN 113554511A CN 202110699042 A CN202110699042 A CN 202110699042A CN 113554511 A CN113554511 A CN 113554511A
- Authority
- CN
- China
- Prior art keywords
- transaction
- power
- trading
- energy
- node
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000009826 distribution Methods 0.000 title claims abstract description 26
- 239000002245 particle Substances 0.000 title claims abstract description 26
- 238000005457 optimization Methods 0.000 title claims abstract description 14
- 238000003860 storage Methods 0.000 claims abstract description 38
- 230000005611 electricity Effects 0.000 claims abstract description 25
- 230000007246 mechanism Effects 0.000 claims abstract description 12
- 230000008569 process Effects 0.000 claims description 18
- 238000004146 energy storage Methods 0.000 claims description 15
- 230000005540 biological transmission Effects 0.000 claims description 12
- 238000010248 power generation Methods 0.000 claims description 8
- 238000010845 search algorithm Methods 0.000 claims description 7
- 238000004891 communication Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 3
- 238000003306 harvesting Methods 0.000 claims description 3
- 238000013178 mathematical model Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 230000008447 perception Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 239000002028 Biomass Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000005612 types of electricity Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Economics (AREA)
- General Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Software Systems (AREA)
- Finance (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Accounting & Taxation (AREA)
- Bioethics (AREA)
- Tourism & Hospitality (AREA)
- Computer Hardware Design (AREA)
- Computer Security & Cryptography (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Human Resources & Organizations (AREA)
- Primary Health Care (AREA)
- Technology Law (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Mathematical Physics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an active power distribution network electric power transaction method based on block chain and particle swarm optimization, which is characterized in that transaction information is stored in a block mode, an energy request mechanism is constructed according to transaction requirements of supply and demand parties in an active power distribution network, the energy request mechanism comprises a storage sensing request and a cost sensing request, the storage sensing request is stored so as to ensure that the storage limit of a user is not exceeded, and the cost sensing request enables the electricity purchasing cost of the user to be lowest; and performing multi-target optimal search by using a particle swarm algorithm according to two factors of cost and storage to obtain an optimal electric power transaction scheme. The invention can realize safe and efficient electric power transaction, reduce the electricity purchasing cost of users and improve the utilization rate of renewable energy sources.
Description
Technical Field
The invention relates to a power distribution network scheduling method, in particular to an active power distribution network power trading method based on block chain and particle swarm optimization.
Background
With the wide production and application of new energy such as photovoltaic energy, wind power energy, biomass energy and the like, the new energy is connected into a power distribution network in a micro-grid mode, the permeability is high, a large amount of scattered new energy is used as an energy supplier to be connected into the power distribution network to form an active power distribution network (ADN), the ADN is a public power distribution network with a flexible topological structure and adopting an active management distributed power supply, energy storage equipment and a client bidirectional load mode. In ADN, an efficient and reliable energy trading and management and control mechanism is urgently needed to improve the utilization rate of distributed energy. Most of the current electric power transactions are conducted in a centralized transaction center and are dominated by qualified organizations, which ensures the safety and reliability of the electric power transactions to a certain extent, but the centralized transaction center has the problems of transaction congestion and the like and needs further optimization.
In recent years, a great deal of research results about solving the energy transaction problem in the ADN system, such as a multi-microgrid electric energy transaction nano-hybrid bargaining method, a double-layer distributed electric power transaction system based on electric power data sharing and the like, although safe and reliable electric power transaction can be realized, the randomness of new energy output is not considered, the penetration rate of uncertain factors is increased along with the access of a great amount of random electric power to the ADN system, and the existing method cannot meet the requirement of optimizing and matching electric power transaction and ensure the safety and high efficiency of the transaction process.
The blockchain is used as a new transaction mode, information encryption is performed by adopting a modern cryptography algorithm in the transaction process, the completeness of data is guaranteed, and the data is not tampered privately, and the problems of more transaction main bodies and lower safety in the ADN can be well solved by utilizing the characteristic of the blockchain.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a transaction method capable of ensuring the safety when a plurality of transaction subjects exist in a power distribution network.
The technical scheme is as follows: the active power distribution network power transaction method based on block chain and particle swarm optimization comprises the following steps:
(1) establishing a blockchain of ADN power transaction, wherein transaction nodes in the blockchain comprise a supplier and a requester, and redundant energy generated by the supplier uses the blockchain to perform power transaction; transaction information is stored in transaction nodes in a block mode, and all transaction nodes monitor block chain information of an ADN communication network at all times;
(2) a requester with power transaction requirements encrypts power transaction requirement data generated through a Hash algorithm by using a private key of the requester, sends the encrypted data into a block chain, and broadcasts block chain information containing the power transaction requirements to all transaction nodes in an ADN through an ADN communication network;
(3) each supplier receives blockchain information including the latest transaction intention, decrypts the encrypted hash data by combining with a node public key for publishing transaction requirements, and compares the encrypted hash data with the received transaction requirement hash value to judge whether the content of the blockchain is the real power transaction requirement of the publishing node;
(4) the trading node judges whether the trading node can internally meet the power trading requirement, if the trading node can meet the power trading requirement, the trading node is regarded as having self-sufficient capacity, and energy is not required to other nodes; if the situation can not be met, requesting additional energy from other nodes in the AND, solving an optimal power trading scheme by utilizing a particle swarm algorithm according to an energy request mechanism AND request cost, accepting the power trading intention if each node meets the condition, or negotiating with an issuing party on the basis of the power trading intention, proposing a new power trading intention, AND encrypting the block chain information issued with the trading intention by using a private key of each node; if the condition is not met, ignoring the transaction;
(5) the power transaction supply and demand parties who preliminarily reach the power transaction intention record the whole power transaction completion process in a block chain mode.
With ElRepresenting the upper limit of the energy storage capacity of each group of transaction nodes, using decision variables in the step (4)
Mapping transaction node id in expression block chain network in time interval te ∈ TState to generate electricity (0) or request (1):
the available energy storage E in one generation cyclesThe change is as follows:
wherein the content of the first and second substances,
indicating that the trading node may produce a rate equal to the generation rate GrIf the additional energy does not exceed the storage limit ElThen add it to the energy storage unit EsOtherwise, the memory cell will be filled;
when the decision variable is
The request is randomly generated;
if the internal memory cell ElIf the random request of the W storage unit cannot be satisfied, the network request of the rest storage units needs to be sent through the network, which is expressed as follows:
in the transaction process, the energy request is randomly initiated by the transaction node, and if the transaction node can internally meet the requirements of the transaction node, the transaction node can be regarded as having self-sufficient capacity; if the current energy storage cannot meet the energy demand, the transaction node may request additional energy from the ADN, where the energy request mechanism includes cost-aware and storage-aware requests.
The cost-aware request scheme allows a trading node to request energy supply from the nearest available trading node in the network, the trading node attempts to increase the amount of energy generated for its storage unit, replies to the energy request using its internal storage, and if unsuccessful, will exhaust its current storage and harvest the remaining energy from the network of neighboring trading nodes.
The storage aware request scheme sends energy requests to transaction nodes stored close to it and locates a storage unit on the ADN with sufficient stored energy to ensure that the generated energy is fully utilized and places the request in a task waiting list for other transaction nodes to validate.
Assuming that the distance matrix Dis is the distance between the storage unit and the transaction node, the cost for acquiring the request, the relatively small transmission cost per unit distance C based on the distance between the transaction nodestThe request cost C of n units of energy added to the base charge from transaction node e to transaction node f is calculated as:
C＝Cb(n)+Ct(Dis[e][f])
wherein, CbIs a basic cost. In the process of electric power transaction, the electricity price is the basic fee, and the power transmission and distribution fee is the transmission cost.
In the step (4), optimizing the electric power transaction scheme in the real-time electricity price transaction process of the electric power transaction node through a particle swarm multi-target search algorithm, and when n types of electric power suppliers compete with the transaction of electricity purchasing of users, establishing a mathematical model of the transaction process by using the particle swarm multi-target search algorithm as follows:
wherein psieTrading the electricity prices of the power plants for energy; emSatisfying the electric quantity value of opening transaction for the energy transaction power plant;
the cost of power transmission and distribution; m represents the selected power plant and X is the constrained average electricity price for the project selection.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: the safety of electric power transaction in the ADN with a plurality of transaction subjects can be ensured, the transaction process does not need to depend on a third party, and compared with a centralized transaction mode, the efficiency is high and the energy consumption is low; an electric power transaction optimization model is established through a particle swarm algorithm and multi-objective search is carried out, and the finally obtained electric power transaction scheme can ensure that the electricity purchasing cost of a user is minimum, increase the consumption of new energy, reduce the amount of abandoned wind and abandoned light, improve the utilization rate of the new energy and be beneficial to energy conservation.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is an overall architecture diagram of the ADN system;
FIG. 3 is a flow diagram of a cost-aware request;
FIG. 4 is a flow diagram of a storage aware request.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
As shown in fig. 1, the invention provides an active power distribution network power transaction method based on block chain AND particle swarm optimization, which is used for power transaction in AND, each transaction node in the AND area is associated with new energy such as solar energy, hydroenergy or wind energy generation, AND the like, AND by using the characteristic of block chain decentralization, a block chain structure of ADN power transaction is designed, AND transaction information is stored in a block manner; in order to reasonably process the energy requests among the transaction nodes, an energy request mechanism is constructed according to the transaction requirements of users and power suppliers in the ADN; in the energy request mechanism, the sensing request is stored to ensure that the storage limit of the user is not exceeded, while the cost sensing request minimizes the electricity purchase cost of the user; and performing multi-target optimal search by using a particle swarm algorithm according to two factors of cost and storage to obtain an optimal electric power transaction scheme. The method comprises the following steps:
(1) establishing a blockchain of ADN power transactions, wherein a transaction node comprises a supplier and a requester, and the supplier provides energy to the requester; transaction information is stored in transaction nodes in a block mode, and all transaction nodes monitor block chain information of an ADN communication network at all times; surplus energy generated by a supplier uses a blockchain to conduct electric power transaction;
(2) a requester with power transaction requirements encrypts power transaction requirement data generated through a Hash algorithm by using a private key of the requester, sends the encrypted data into a block chain, and broadcasts block chain information containing the power transaction requirements to all transaction nodes in an ADN through an ADN communication network;
(3) each supplier receives blockchain information including the latest transaction intention, decrypts the encrypted hash data by combining with a node public key for publishing transaction requirements, and compares the encrypted hash data with the received transaction requirement hash value to judge whether the content of the blockchain is the real power transaction requirement of the publishing node;
(4) the trading node judges whether the trading node can internally meet the power trading requirement, if the trading node can meet the power trading requirement, the trading node is regarded as having self-sufficient capacity, and energy is not required to other nodes; if the situation can not be met, requesting additional energy from other nodes in the AND, combining an energy request mechanism AND request cost, solving an optimal power trading scheme by utilizing a particle swarm algorithm, if the conditions are met, accepting the power trading intention by each node, or negotiating with an issuing party on the basis of the power trading intention, proposing a new power trading intention, AND encrypting the block chain information issued with the trading intention by using a private key of each node; if the condition is not met, ignoring the transaction;
(5) the power transaction supply and demand parties who preliminarily reach the power transaction intention record the whole power transaction completion process in a block chain mode.
In the AND system, assuming each set of trading nodes has an expendable remaining energy AND energy storage capacity associated with it, the energy storage ceiling is ElAvailable energy storage as Es(ii) a Generating a transaction nodeThe block chain is used for reliable power trading, and the trading nodes have different energy requirements for a period of time.
Defining a decision variable
The state for a transaction node id in a blockchain network to map to a power generation (0) or request (1) within a time interval te ∈ T is expressed as follows:
the available energy storage E in one generation cyclesThe change is as follows:
wherein the content of the first and second substances,
indicating that it is possible to produce a power generation rate G equal torThe energy of (a). If the additional energy does not exceed the storage limit ElThen add it to the energy storage unit EsIn (1). Otherwise, the memory cell will fill. When the decision variable is
The request is randomly generated.
If the internal memory cell ElIf the random request of the W storage unit cannot be satisfied, the network request of the rest storage units needs to be sent through the network, which is expressed as follows:
for the power supplier in the transaction chain, under the constraint of price and the constraint of transaction priority, the optimal electricity selling is realized through a multi-choice electricity selling scheme in the intelligent contract, and the reliability of the transaction is guaranteed.
Assuming that the distance matrix Dis is the distance between the storage unit and the transaction node, the cost for acquiring the request, the relatively small transmission cost per unit distance C based on the distance between the transaction nodestThe request cost C of n units of energy added to the base charge from transaction node e to transaction node f is calculated as:
C＝Cb(n)+Ct(Dis[e][f])
wherein, CbIs a basic cost. In the process of electric power transaction, the electricity price is the basic fee, and the power transmission and distribution fee is the transmission cost.
The energy request mechanism in the step (4) comprises a cost perception request and a storage perception request; the cost-aware request scheme allows a trading node to request energy supply from the nearest available trading node in the network, the trading node attempts to increase the amount of energy generated for its storage unit, replies to the energy request using its internal storage, if unsuccessful, will exhaust its current storage, and will harvest the remaining energy from the network of neighboring trading nodes; the cost-aware request flow is illustrated in fig. 3, which examines the nearest transaction node in the ADN that can provide energy to the requester until the energy demand can be met. Each iteration collects the most recent awarded transaction node N with no empty energy storageCDetailed information of (a). If N is presentCAnd if the energy requirement can be met, W is the value of R. Otherwise, the available energy on the transaction Node is exhausted, so Node [ N ] is fetchedC][1]The value of (c). The request is placed on a task waiting list for selection by other transaction nodes on the ADN.
The storage aware request scheme sends energy requests to transaction nodes stored close to it and locates a storage unit on the ADN with sufficient stored energy to ensure that the generated energy is fully utilized, while placing the request in a task waiting list for other transaction nodes to validate; storage aware request flow as shown in fig. 4, first, a request for energy is sent to the transaction node near its storage and a storage unit with sufficient storage energy is located on the ADN. The request is then placed in a task pending list for verification by other transaction nodes.
Based on two energy request mechanisms of a cost perception request and a storage perception request and request cost, in the real-time electricity price trading process of an electricity trading node, optimal search is carried out on an electricity trading scheme through a particle swarm multi-target search algorithm, wherein the optimal search comprises screening of non-inferior solutions and updating of non-inferior solution sets, and under the assumption that 5 types of electricity supply parties (thermal power generation, photovoltaic power generation, hydroelectric power generation, wind power generation and nuclear power generation) participate in the trading competition of electricity purchasing of users, the trading price value of released trading electricity, the on-grid electricity price and the power transmission and distribution expense which are met by trading are different; establishing a mathematical model of the transaction process according to a particle swarm multi-target search algorithm, expressing the power price on the internet of each energy power plant by psi, and expressing the electric quantity value of each energy power plant meeting the release transaction by E, so as to
The average power transmission and distribution cost of each energy power plant is shown, and psi, E,
The functional relationship between the three variables is as follows:
wherein psieTrading the electricity prices of the power plants for energy; emSatisfying the electric quantity value of opening transaction for the energy transaction power plant;
the cost of power transmission and distribution; m represents the selected power plant, and the constrained average electricity price for the project selection is 0.5 yuan/(kWh). Based on the model, one particle is selected as the optimal solution of the group, and the power trading scheme is optimized in the real-time power price trading process of the power trading node through the particle swarm multi-target search algorithm.
Claims (6)
1. An active power distribution network power transaction method based on block chain and particle swarm optimization is characterized by comprising the following steps:
(1) establishing a blockchain of ADN power transaction, wherein transaction nodes in the blockchain comprise a supplier and a requester, and redundant energy generated by the supplier uses the blockchain to perform power transaction; transaction information is stored in transaction nodes in a block mode, and all transaction nodes monitor block chain information of an ADN communication network at all times;
(2) a requester with power transaction requirements encrypts power transaction requirement data generated through a Hash algorithm by using a private key of the requester, sends the encrypted data into a block chain, and broadcasts block chain information containing the power transaction requirements to all transaction nodes in an ADN through an ADN communication network;
(3) each supplier receives blockchain information including the latest transaction intention, decrypts the encrypted hash data by combining with a node public key for publishing transaction requirements, and compares the encrypted hash data with the received transaction requirement hash value to judge whether the content of the blockchain is the real power transaction requirement of the publishing node;
(4) the trading node judges whether the trading node can internally meet the power trading requirement, if the trading node can meet the power trading requirement, the trading node is regarded as having self-sufficient capacity, and energy is not required to other nodes; if the situation can not be met, requesting additional energy from other nodes in the AND, solving an optimal power trading scheme by utilizing a particle swarm algorithm according to an energy request mechanism AND request cost, accepting the power trading intention if each node meets the condition, or negotiating with an issuing party on the basis of the power trading intention, proposing a new power trading intention, AND encrypting the block chain information issued with the trading intention by using a private key of each node; if the condition is not met, ignoring the transaction;
(5) the power transaction supply and demand parties who preliminarily reach the power transaction intention record the whole power transaction completion process in a block chain mode.
2. The active power distribution network power trading method based on blockchain and particle swarm optimization according to claim 1, wherein E is used as the referencelRepresenting the energy storage capacity upper limit of each group of transaction nodes, wherein the decision variable is used in the step (4)
The state of a transaction node id in the expression blockchain network is mapped to a power generation (0) or request (1) within a time interval te ∈ T:
the available energy storage E in one generation cyclesThe change is as follows:
wherein the content of the first and second substances,
indicating that the trading node may produce a rate equal to the generation rate GrIf the additional energy does not exceed the storage limit ElThen add it to the energy storage unit EsOtherwise, the memory cell will be filled;
when the decision variable is
The request is randomly generated;
if the internal memory cell ElFailing to satisfy the random request of the W memory cell, the remaining memory cells need to be sent over the networkThe network request of (2), expressed as follows:
in the transaction process, the energy request is randomly initiated by the transaction node, and if the transaction node can internally meet the requirements of the transaction node, the transaction node can be regarded as having self-sufficient capacity; if the current energy storage cannot meet the energy demand, the transaction node may request additional energy from the ADN, where the energy request mechanism includes cost-aware and storage-aware requests.
3. The blockchain and particle swarm optimization based active power distribution network power trading method of claim 2, wherein the cost-aware request scheme allows trading nodes to request energy supply from the nearest available trading node in the network, trading nodes attempt to increase the amount of energy generated for their storage units, reply to energy requests using their internal storage, if unsuccessful, will exhaust their current storage, and harvest the remaining energy from the network of neighboring trading nodes.
4. The blockchain and particle swarm optimization based active power distribution network power trading method of claim 2, wherein the storage aware request scheme sends energy requests to trading nodes stored close to it and locates storage units with sufficient stored energy on the ADN to ensure that the generated energy is fully utilized and places the request in a task waiting list for other trading nodes to validate.
5. The active power distribution network power trading method based on blockchain and particle swarm optimization according to claim 2, characterized in that in the step (4), falseLet the distance matrix Dis be the distance between the storage unit and the transaction node, the cost for obtaining the request, the relatively small transmission cost per unit distance C based on the distance between the transaction nodestThe request cost C of n units of energy added to the base charge from transaction node e to transaction node f is calculated as:
C＝Cb(n)+Ct(Dis[e][f])
wherein, CbIs a basic cost.
6. The active power distribution network power trading method based on block chain and particle swarm optimization according to claim 2, wherein in the step (4), the power trading scheme is optimized in the real-time electricity price trading process of the power trading node through a particle swarm multi-objective search algorithm, and when n types of power suppliers compete with the trading of electricity purchasing of users, a mathematical model of the trading process is established by the particle swarm multi-objective search algorithm as follows:
wherein psieTrading the electricity prices of the power plants for energy; emSatisfying the electric quantity value of opening transaction for the energy transaction power plant;
the cost of power transmission and distribution; m represents the selected power plant and X is the constrained average electricity price for the project selection.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110699042.9A CN113554511B (en) | 2021-06-23 | 2021-06-23 | Active power distribution network power transaction method based on blockchain and particle swarm optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110699042.9A CN113554511B (en) | 2021-06-23 | 2021-06-23 | Active power distribution network power transaction method based on blockchain and particle swarm optimization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113554511A true CN113554511A (en) | 2021-10-26 |
| CN113554511B CN113554511B (en) | 2023-06-27 |
Family
ID=78130852
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110699042.9A Active CN113554511B (en) | 2021-06-23 | 2021-06-23 | Active power distribution network power transaction method based on blockchain and particle swarm optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113554511B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115622774A (en) * | 2022-08-08 | 2023-01-17 | 付舒丛 | Data encryption transmission electronic commerce transaction system based on improved particle swarm optimization support vector machine |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110738375A (en) * | 2019-10-16 | 2020-01-31 | 国网湖北省电力有限公司电力科学研究院 | Active power distribution network power transaction main body optimization decision method based on alliance chain framework |
| CN112636327A (en) * | 2020-11-25 | 2021-04-09 | 宁波大学 | Block chain transaction and energy storage system based solution |
| CN112785429A (en) * | 2021-01-08 | 2021-05-11 | 西北工业大学 | Local area multi-microgrid power transaction pairing method based on block chain technology |
-
2021
- 2021-06-23 CN CN202110699042.9A patent/CN113554511B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110738375A (en) * | 2019-10-16 | 2020-01-31 | 国网湖北省电力有限公司电力科学研究院 | Active power distribution network power transaction main body optimization decision method based on alliance chain framework |
| CN112636327A (en) * | 2020-11-25 | 2021-04-09 | 宁波大学 | Block chain transaction and energy storage system based solution |
| CN112785429A (en) * | 2021-01-08 | 2021-05-11 | 西北工业大学 | Local area multi-microgrid power transaction pairing method based on block chain technology |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115622774A (en) * | 2022-08-08 | 2023-01-17 | 付舒丛 | Data encryption transmission electronic commerce transaction system based on improved particle swarm optimization support vector machine |
| CN115622774B (en) * | 2022-08-08 | 2024-01-12 | 付舒丛 | Electronic commerce transaction system based on improved particle swarm optimization and supporting data encryption transmission of vector machine |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113554511B (en) | 2023-06-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108599373B (en) | Cascade analysis method for transmission and distribution coordination scheduling target of high-proportion renewable energy power system | |
| Wang et al. | AEBIS: AI-enabled blockchain-based electric vehicle integration system for power management in smart grid platform | |
| CN107480847B (en) | Energy source block chain network and virtual power plant operation and scheduling method based on network | |
| CN107681675A (en) | Block chain electricity transaction peak-frequency regulation system based on distributed electric power storage facility | |
| Chen et al. | Optimally coordinated dispatch of combined‐heat‐and‐electrical network with demand response | |
| Jia et al. | Joint operation and transaction mode of rural multi microgrid and distribution network | |
| Yang et al. | Research on heat and electricity coordinated dispatch model for better integration of wind power based on electric boiler with thermal storage | |
| Ghaemi et al. | Risk aversion energy management in the networked microgrids with presence of renewable generation using decentralised optimisation approach | |
| Satuyeva et al. | Energy 4.0: towards IoT applications in Kazakhstan | |
| CN110348643A (en) | A kind of distributed power transaction contract common recognition method based on energy block chain | |
| CN111461890A (en) | Distributed power generation transaction system and method based on multiple prediction and block chain technologies | |
| Rajani et al. | A hybrid optimization based energy management between electric vehicle and electricity distribution system | |
| CN108335209A (en) | A kind of distributed energy resource system including feed end index block chain | |
| CN112419055A (en) | Day-ahead energy scheduling platform established by market operation with participation of multiple producers and consumers | |
| Tan et al. | Bidding strategy of virtual power plant with energy storage power station and photovoltaic and wind power | |
| Liu et al. | Microgrid trading game model based on blockchain technology and optimized particle swarm algorithm | |
| CN115587668A (en) | Distributed collaborative optimization scheduling method for multi-park integrated energy system | |
| Wang et al. | Optimal dispatch of integrated energy system considering integrated demand response resource trading | |
| Tao et al. | Research on multi-microgrids scheduling strategy considering dynamic electricity price based on blockchain | |
| CN113554511B (en) | Active power distribution network power transaction method based on blockchain and particle swarm optimization | |
| CN110189223B (en) | Transaction method and system for users based on energy storage system users | |
| CN116862144A (en) | Multi-park low-carbon scheduling method and system based on double games | |
| Wu et al. | Research on optimal storage capacity of DC micro‐grid system in PV station | |
| CN113870030A (en) | Multi-microgrid energy transaction mechanism design method based on improved Nash bargaining method | |
| Song | Blockchain-based power trading process |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |