Energy optimization scheduling method and power trading method based on eigenspace

Abstract

The present invention discloses an energy optimization scheduling method based on eigenspace, which includes the following steps: S1: constructing a microgrid economy and carbon emission calculation model based on eigenspace; S2: collecting microgrid input variables and input parameters, and configuring them in the microgrid economy and carbon emission calculation model; S3: in the eigenspace, based on multiple specific eigenstates and corresponding input variables, multiple corresponding output functions are obtained, and the net electricity cost of the microgrid and the carbon emissions caused by the microgrid are calculated independently according to the output functions; S4: setting optimization objectives and constraints; S5: using particle swarm algorithm to achieve energy optimization scheduling. The energy optimization scheduling method proposed by the present invention adopts the eigenstate method in the field of linear algebra in the calculation model, which can conveniently decouple multiple key outputs from each other, and then solve the energy optimization scheduling problem within and between energy sources through computer modeling.

Description

Technical Field

[0001] The present invention relates to the technical field of energy utilization, and in particular to an energy optimization scheduling method based on intrinsic space and a power trading method thereof. Background Art

[0002] A microgrid is a small power generation and distribution system that organically integrates distributed power sources, loads, energy storage devices, converters, and monitoring and protection devices. Since traditional microgrids have a common bus and a hierarchical control structure, the structure and capacity expansion of the microgrid are very complex and expensive. A modular microgrid consists of a three-port converter, a battery, a load, a wind and solar power generation unit, and a backup diesel generator. Modular microgrids are easy to expand, and through operation control and energy management, they can realize independent or interconnected operation of modules, reduce the adverse effects of intermittent distributed power sources on the distribution network, maximize the use of renewable energy power output, improve power supply reliability and power quality, and realize the transformation from traditional diesel power generation systems to clean energy power generation systems.

[0003] For the low-carbon operation of microgrids, the existing technology has been studying the optimization control methods of microgrid operation for many years, but the research direction is mainly focused on economic fields such as minimum investment and lowest comprehensive cost. In recent years, as people pay more and more attention to environmental protection, the low-carbon effect has attracted more attention. It is necessary to adopt certain low-carbon operation optimization control for microgrids to meet environmental protection needs. The few existing studies on low-carbon operation of microgrids do not take low carbon as the main optimization goal, but only take it as an additional optimization goal, and do not pay enough attention to it. At the same time, the microgrid carbon emission formula given by the study is too rough, and cannot accurately reflect the impact of energy efficiency and distribution location on carbon emissions, which will produce large errors. Current research has not given a detailed optimization control strategy for the goal of low carbon.

[0004] Chinese patent document CN105552894B discloses an optimization control method for low-carbon operation of a microgrid. The flow chart of the optimization control method is as follows: Figure 1As shown, mathematical programming is used for optimization and solution, but there is a lack of data-based control based on the basic linear algebra framework, and there is basically no priority application, which cannot provide power output, carbon emissions, or economic index / costs and other key variables specified by users. Chinese patent document CN113612219A discloses a carbon reduction microgrid day-ahead energy optimization method based on source-grid-load-storage interaction, which proposes a microgrid economic and carbon emission calculation model. The energy optimization method of this model adopts data-based control based on the basic linear algebra framework, which can provide power output, carbon emissions, or economic index / costs and other key variables specified by users. Priority application, but still cannot achieve power output, carbon emissions, or electricity cost (economic index / cost) and other key variables specified by users are decoupled from each other. The current power generation carbon emissions are basically proportional to the output power used, and the fundamental mathematical framework of smart energy and its dispatching control has not been disclosed. Summary of the invention

[0005] In view of the above-mentioned shortcomings of the prior art, on the one hand, the present invention provides an energy optimization scheduling method based on intrinsic space to solve the problem that the existing microgrid economic and carbon emission calculation models cannot conveniently decouple key outputs such as power output, carbon emissions, or electricity costs, and thus cannot achieve the problem of changing the output power during the energy scheduling process without affecting the carbon emissions.

[0006] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0007] An energy optimization scheduling method based on eigenspace, the energy optimization scheduling method comprising the following steps:

[0008] S1: Construct a microgrid economic and carbon emission calculation model based on intrinsic space;

[0009] S2: collecting microgrid input variables and input parameters, and configuring them in the microgrid economic efficiency and carbon emission calculation model;

[0010] S3: In the eigenspace, a plurality of corresponding output functions are obtained based on a plurality of specific eigenstates and corresponding input variables, and a net electricity cost of the microgrid and carbon emissions caused by the microgrid are calculated independently of each other according to the output functions;

[0011] S4: Set the optimization goal and its constraints;

[0012] S5: Use particle swarm algorithm to achieve energy optimization scheduling.

[0013] Preferably, the microgrid economy and carbon emission calculation model based on eigenspace includes eigenspace of third order or above.

[0014] Preferably, the microgrid economy and carbon emission calculation model based on eigenspace is as shown in Equation 1:

[0015]

[0016] Among them, [PG, ES, GP] are three input variables, representing the power of renewable energy, the power of energy storage system and the power of external large power grid respectively; [Load, Fin, Carbon] are three output functions, representing the power load required in the microgrid, the net electricity cost and the carbon emission respectively; [Kij; 3×3] represents the 3×3 power utility matrix that can be diagonalized in the eigenspace, and the 3×3 power utility matrix is ​​shown in Equation 2,

[0017]

[0018] Preferably, in step S2, the input variables include the corresponding input power generation of renewable energy, energy storage system and external large power grid, and the input parameters include cost per kilowatt-hour, carbon emissions per kilowatt-hour, economic weight coefficient and carbon emission weight coefficient.

[0019] Preferably, in step S3, the number of the specific eigenstates is three or more, and the specific eigenstates can be represented by units of different dimensions.

[0020] Preferably, in step S4, the optimization objective is to minimize carbon emissions or to take economic cost as the optimization objective; the constraint conditions include the power balance of microgrid power supply and demand, the power constraints of each generator set and the capacity constraints of the energy storage system.

[0021] Preferably, in step S5, the particle swarm algorithm comprises the following steps:

[0022] S5-1: Start to randomly initialize each particle;

[0023] S5-2: Evaluate the fitness value of each particle function and obtain the global optimal position;

[0024] S5-3: When the end condition is met, the operation ends, otherwise, steps S5-4 to S5-7 are continued;

[0025] S5-4: otherwise, update the velocity and position of each particle;

[0026] S5-5: re-evaluate the fitness value of each particle function;

[0027] S5-6: Update the historical optimal position of individual particles;

[0028] S5-7: Update the global optimal position of the group and return to step S5-3.

[0029] Preferably, the microgrid economy and carbon emission calculation model based on the intrinsic space is extended to the dual energy storage system, and the charging and discharging tasks of the dual energy storage system are separated and performed separately, so that the energy storage battery can work at the optimal D b working point, which has the advantage of extending the service life of the energy storage system.

[0030] Preferably, the data collection of the microgrid input variables is first performed by collecting meteorological and power load demand forecast information, and then processed through a forecast model to obtain the forecast power of the renewable energy unit and the power load.

[0031] Preferably, the prediction model adopts a neural network prediction method, which can predict the power of renewable energy and loads at multiple time scales (day before, day during, and near real-time stage).

[0032] Preferably, the energy optimization scheduling method further includes:

[0033] Step S6: Formulate the optimal scheduling plan obtained by solving the energy optimization scheduling in step S5, and then send the formulated optimal scheduling plan to the lower-level execution component for execution;

[0034] Step S7: Real-time current and power information is collected through measuring components such as smart meters, and carbon emissions and economic performance indicators after actual execution are calculated and compared with the user's expected needs to meet the user's needs.

[0035] Another aspect of the present invention is to provide an electricity trading method based on blockchain technology, the electricity trading method comprising the following steps:

[0036] S1: According to the above-mentioned energy optimization scheduling method based on eigenspace, by collecting input parameters and predicted input variables, the particle swarm algorithm is used to perform energy optimization scheduling to complete the determination of power generation / consumption and time of each node;

[0037] S2: Each node will quote and report the planned transaction volume to the central matching system, and the matching server will match the order and complete the signing of the smart contract;

[0038] S3: The network security personnel simulate and verify the safety of the power network for the planned electricity transaction plan. After the verification is passed, the signed smart contract is verified and recorded using blockchain technology;

[0039] S4: Carry out actual electricity delivery. Finally, each entity executes the signed smart contract power generation / consumption curve, settles with the actual transaction volume, and records any breach of contract in the transaction. At the same time, the blockchain records the transaction information.

[0040] Beneficial effects of the present invention:

[0041] The energy optimization scheduling method based on eigenspace of the present invention and the microgrid economy and carbon emission calculation model adopt the eigenstate method in the field of linear algebra. By constructing a simplified mathematical architecture based on the eigenstate space, key outputs such as power output, carbon emissions, or electricity costs can be easily decoupled from each other, and then the energy optimization scheduling problems within and between energy sources can be solved through computer modeling, so that the output power can be changed during the energy optimization scheduling process without affecting the carbon emissions.

[0042] The present invention is based on the blockchain technology of the intelligent energy power trading method, through the energy optimization scheduling method based on the eigenspace, for the microgrid with coordinated interaction of source, network, load and storage, its demand information for purchasing and selling electricity can be optimized and decided on the day-ahead scale, and this result is the economic optimum or the minimum carbon emission. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the flow of the energy optimization scheduling method based on the intrinsic space of the present invention.

[0044] Figure 2 This is a schematic diagram of the optimization scheduling process implemented by the particle swarm algorithm in the energy optimization scheduling method of the present invention.

[0045] Figure 3 This is a schematic diagram of the energy storage system and its grid connection point power plan obtained by optimizing the scheduling of the present invention.

[0046] Figure 4 Schematic diagram of the microgrid prosumer architecture based on dual energy storage according to the present invention.

[0047] Figure 5 It is a working schematic diagram of the dual energy storage system of the present invention.

[0048] Figure 6 This is a schematic diagram of a power trading method using an energy optimization scheduling method based on eigenspace in the present invention.

[0049] Figure 7 This is a schematic diagram of the power trading network based on blockchain technology in the present invention.

[0050] Figure 8 Schematic diagram of the microgrid and energy storage system solution of the present invention. DETAILED DESCRIPTION

[0051] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention.

[0052] Example

[0053] like Figure 1 As shown, the energy optimization scheduling method based on the eigenspace of this embodiment includes the following steps:

[0054] S1: Construct a microgrid economic efficiency and carbon emission calculation model based on eigenspace; the microgrid economic efficiency and carbon emission calculation model based on eigenspace includes a third-order or higher eigenspace. The microgrid economic efficiency and carbon emission calculation model including the third-order eigenspace is shown in Equation 1.

[0055]

[0056] Among them, [PG, ES, GP] are three input variables, representing the power of renewable energy, the power of energy storage system and the power of external large power grid respectively; [Load, Fin, Carbon] are three output functions, representing the power load required in the microgrid, the net electricity cost and the carbon emission respectively; [Kij; 3×3] represents the 3×3 power utility matrix that can be diagonalized in the eigenspace, and the 3×3 power utility matrix is ​​shown in Equation 2,

[0057]

[0058] Wherein, λ1, λ2, λ3 represent the eigenvalues ​​after diagonalization of equation 1. The renewable energy referred to in this application includes but is not limited to intermittent energy sources such as wind power and photovoltaics. The energy storage system includes but is not limited to electrochemical energy storage batteries of other materials such as lithium batteries, lead-carbon and sodium flow. According to the calculation model of the present application, those skilled in the art can make simple deformations, such as splitting renewable energy sources such as wind power and photovoltaics into two independent input variables, so that the calculation model is converted into an eigenspace including the fourth order, and then perform corresponding energy optimization scheduling on this basis, which is still within the scope of protection of the present application. Similarly, according to the calculation model of the present application, those skilled in the art can make simple deformations, such as setting the energy storage system as two independent systems as two independent input variables, so that the calculation model is converted into an eigenspace including the fourth order, and then perform corresponding energy optimization scheduling on this basis, which is still within the scope of protection of the present application. Alternatively, renewable energy sources such as wind power and photovoltaic power are split into two independent input variables at the same time, and the energy storage system is set as two independent systems as two independent input variables, so that the calculation model is converted into an eigenspace including fifth order or even higher, and then the corresponding energy optimization scheduling is carried out on this basis, which is still within the scope of protection of this application.

[0059] The microgrid economy and carbon emission calculation model of this embodiment has an initial power utility matrix model as described in the disclosure of the Chinese patent document CN113612219A "A method for day-ahead energy optimization of a carbon-reducing microgrid based on source-grid-load-storage interaction" previously applied by the inventor. Specifically, the initial power utility matrix model can be expressed by the following formula:

[0060]

[0061] The first row represents the power demand of the load in the microgrid, which needs to be met by the renewable energy in the system (including wind power, photovoltaic and other distributed power generation parts), energy storage system and external large power grid; the second row represents the net electricity cost of the microgrid, which consists of two parts, namely, revenue and cost; the third row represents the carbon emissions caused by the microgrid. Δt is the scheduling duration, T is 1 hour. For example, if the scheduling duration is 10 minutes, then τ = 1 / 6. is the load CP power, PG power for the power generation part, is the energy storage system ES power, is the power of the external large power grid GP, the power of the power generation part PG and the load CP are unidirectional; it is positive when the energy storage system ES generates electricity into the microgrid, otherwise it is negative; it is positive when the external large power grid GP generates electricity into the microgrid, otherwise it is negative. is the real-time net electricity cost of the microgrid, K C_PG The cost of generating electricity for PG, K C_ES is the electricity cost of ES charging and discharging, K C_EX is the electricity cost of EX, To consume carbon emissions in real time, K CD_PG is the PG cost of power generation, K CD-ES is the energy storage system ES cost, K CD-GP The initial power utility matrix model cannot well decouple key variables specified by users, such as power output, carbon emissions, or electricity costs (economic index / costs), from each other when the computer program is running.

[0062] S2: Collect microgrid input variables and parameters, and configure them in the microgrid economic and carbon emission calculation model; the input variables include the corresponding input power generation of renewable energy, energy storage system, and external large power grid; the input parameters include cost per kilowatt-hour, carbon emissions per kilowatt-hour, economic weight coefficient, and carbon emission weight coefficient.

[0063] S3: In the eigenspace, multiple corresponding output functions are obtained based on multiple specific eigenstates and corresponding input variables, and the net electricity cost of the microgrid and the carbon emissions caused by the microgrid are calculated independently according to the output functions. The eigenspace of the present invention has multiple eigenstates of the smart energy microgrid system matrix of the smart Internet of Things. In this embodiment, the input variables are set to three, namely, the power generation power (3i) input by the three corresponding inputs of renewable energy, energy storage system, and external large power grid, and the corresponding output functions are also three (3o), corresponding to three specific eigenstates, and the specific eigenstates can be represented by different dimensional units, that is, those skilled in the art can know that only through simple flexible dimensional units, a new representation of the 3×3 power utility matrix is ​​obtained, which is different from the calculation model requested for protection in this application in form, but this type of matrix model obtained by simply converting dimensional units will still fall within the scope of protection of the present invention. The inventor can simplify the new linear algebraic equation constructed in the eigenspace by solving three eigenvalues ​​and deriving three eigenstates in the eigenspace. Linear algebraic equations lead to characteristic (eigenvalue) equations in the so-called eigenspace, which has a diagonalizable matrix and three orthogonal variables. For a 3×3 square matrix that can generate a diagonal matrix, linear algebra can be applied on the matrix to obtain the eigenstates, thereby obtaining the determinant, subterms and cofactors. After obtaining the determinant, subterms and cofactors, the solution of the power utility matrix constructed in this application and its eigenvalues ​​(EV) can be determined mathematically even without analyzing the technical details. In the case of a 3×3 square matrix, there are three EVs. In the eigenspace, the eigenstates should be derived. For the benefit of the user, this application defines three key output functions (3o), namely power generation o1, financial cost o2 and carbon emission data o3 (also known as carbon footprint). The optimal distributed energy is characterized by a set of eigenstates given a power effect matrix (3i3o) in the eigenspace. These eigenstates illustrate the power and eigenstates related to carbon peak and neutrality targets, providing guidance for related design tools. For example, the 3D feature space including o1-o2-o3 can be decoupled into other useful features, where o1-o2-o3 can be one of the key outputs mentioned above. If the other two variables are fixed, o3 can be adjusted to reduce the carbon footprint, that is, to achieve the change of output power during energy optimization scheduling without affecting carbon emissions. When the aforementioned calculation model is converted to an eigenspace including fourth-order, fifth-order or even higher-order eigenstates, the corresponding specific eigenstates also include four, five or even more than five eigenstates, and then the corresponding energy optimization scheduling is carried out on this basis, which is still within the scope of protection of this application.

[0064] S4: Set the optimization goal and its constraints; the optimization goal is to minimize carbon emissions or economic cost; the constraints include the power balance of microgrid power supply and demand, the power constraints of each generator set, and the capacity constraints of the energy storage system. In order to ensure the power quality of the load, the power balance constraint of the microgrid power supply and demand should be met at every moment, that is, As a result, the power demand of the load in the microgrid is met by the distributed power generation part, energy storage system and external large power grid in the system, ensuring stable power consumption. In addition, the power constraints of each generator set and the capacity constraints of the energy storage system must meet the conventional constraints in microgrid scheduling. That is, the power constraints of each generator set, as recorded in the disclosure of the specification of Chinese patent document CN113612219A "A method for day-ahead energy optimization of carbon-reducing microgrids based on source-grid-load-storage interaction", must ensure that the power generation power of wind turbines, photovoltaic generators and grid interconnection lines are scheduled and operated within the power range of their respective upper and lower limits; and the capacity constraints of the energy storage system must be guaranteed to be within the range of the upper and lower limits of the remaining capacity, while ensuring that the charging and discharging power constraints of the energy storage system are limited within the range of the upper and lower limits.

[0065] S5: Use particle swarm algorithm to achieve energy optimization scheduling. Figure 2 As shown, in step S5, the particle swarm algorithm includes the following steps: S5-1: start to randomly initialize each particle;

[0066] S5-2: Evaluate the fitness value of each particle function and obtain the global optimal position;

[0067] S5-3: When the end condition is met, the operation ends, otherwise, steps S5-4 to S5-7 are continued;

[0068] S5-4: otherwise, update the velocity and position of each particle;

[0069] S5-5: Evaluate the fitness value of each particle function again;

[0070] S5-6: Update the historical optimal position of individual particles;

[0071] S5-7: Update the global optimal position of the group and return to step S5-3.

[0072] In one embodiment, a day-ahead energy optimization scheduling method with economic cost as the target is adopted, and the optimization target is to minimize the economic cost of the microgrid within a one-day scheduling cycle, that is, minF(t) = {C ECO}, the economic cost is the result of the optimization objective. Figure 3 As shown in Table 1,

[0073] Table 1: Comparison of economic costs of different solutions

[0074]

[0075]

[0076] As can be seen from Table 1, the technical solution of using a wind-solar hybrid microgrid (equipped with energy storage) saves electricity costs and generates surpluses, with good economic benefits, compared with the technical solution of a traditional wind-solar hybrid microgrid after the energy optimization scheduling method of this application. Figure 3 It can be seen that the energy storage system and its grid connection point power plan obtained by the optimized scheduling of this application can be charged during the valley period when the electricity price is lower and discharged during the peak period, thereby adjusting the power curve of the interaction between the grid connection point and the external power grid, effectively reducing the economic cost of the microgrid.

[0077] In another embodiment, a day-ahead energy optimization scheduling method with carbon emission reduction as the goal is adopted, and the optimization goal is to minimize the carbon emissions of the microgrid within a daily scheduling cycle, that is, minF(t) = {E CD}, the results of the optimization target with the minimum carbon emission are shown in Table 2.

[0078] Table 2: Comparison of carbon emissions of different solutions

[0079] plan Base load Traditional wind-solar hybrid microgrid Wind-solar hybrid microgrid (with energy storage) Carbon emissions (t) 8.5 1.2 0.9 Renewable energy consumption ratio / 87％ 94％

[0080] It can be seen from Table 2 that the technical solution of adopting a wind-solar complementary microgrid (equipped with energy storage) has significantly reduced carbon emissions and significantly improved the proportion of renewable energy consumption compared with the technical solution of a traditional wind-solar complementary microgrid after the energy optimization scheduling method of this application.

[0081] In another embodiment, the microgrid economic efficiency and carbon emission calculation model based on the intrinsic space is extended to the dual energy storage system. The microgrid prosumer architecture based on the dual energy storage is as follows: Figure 4 As shown, the dual energy storage system DBESS includes energy storage system BESS1 and energy storage system BESS2. By separating the charge and discharge of the dual energy storage system, the charge and discharge tasks are performed separately, so that the energy storage battery can work at the optimal charge and discharge depth D as much as possible. b The present invention designs a microgrid architecture in a novel power trading system, and proposes a grid-connected microgrid based on coordinated interaction of source, grid, load and storage, which is composed of renewable energy generation, basic load, energy storage and a large power grid. In particular, the present invention particularly utilizes a dual energy storage switching strategy to reduce the rapid life loss caused by frequent charging and discharging of the energy storage system in the new energy system.

[0082] Different energy storage batteries have optimal charge and discharge cycle depth DOD b, so that the throughput power in the life cycle is maximized. The dual energy storage system used in the present invention separates charging and discharging, and performs charging and discharging tasks separately, so that the energy storage battery works at the optimal D b Working point. Its working diagram is as follows Figure 5 As shown, the specific working mode is as follows: the charging state energy storage battery A or B only accepts the charging task of the energy storage system, and starts charging from the initial charging value until the critical condition is reached for state conversion, that is, from the charging state to the discharging state; the discharging state energy storage battery B or A only accepts the discharging task of the energy storage system, and starts discharging from the initial discharging value until the critical condition is reached for state conversion, and the process is carried out in sequence. The critical condition is that the charging state energy storage battery is charged to the remaining power SOC max (i.e. SOC A-max or SOC B-max ), or the discharged energy storage battery is discharged down to SOC min (i.e. SOC A-min or SOC B-min ).

[0083] In one embodiment, a microgrid and energy storage system solution is as follows: Figure 8As shown, in the microgrid optimization scheduling stage, based on the short-term forecast data of load, renewable energy and electricity price, with the goal of operating economy or environmental protection (such as carbon emissions), the energy flow in the microgrid is optimized to optimize the economic and environmental protection indicators, thereby meeting user needs. In the energy optimization scheduling method of this embodiment, the data collection of the microgrid input variables is first collected by collecting meteorological and power load demand forecast information, and then processed by the forecast model to obtain the forecast power of renewable energy units and power loads; at the same time, the user's preference setting user demand information for economy and environmental protection (such as carbon emissions, etc.) is obtained. The forecast model adopts a neural network forecasting method, which can predict the power of renewable energy and loads at multiple time scales (day before, intraday, near real-time stage). Further, the energy optimization scheduling method related to the optimization scheduling model established by steps S1 to S5 described above and the artificial intelligence optimization algorithm such as particle swarm are solved. The energy optimization scheduling method of this embodiment formulates an optimization scheduling strategy based on the state of the flexible load and the energy storage system, combined with the forecast information, and uses the optimization algorithm to solve and obtain the optimal scheduling plan. Furthermore, the energy optimization scheduling method of this embodiment also includes: step S6: formulate the optimal scheduling plan obtained by solving the energy optimization scheduling in step S5, and then send the formulated optimal scheduling plan to the lower-level execution component for execution; that is, the microgrid hardware and input distributor receive IP instructions such as the optimal scheduling plan calculated by the optimization scheduling model, and send the optimal scheduling plan as an execution plan to the lower-level execution component for execution; step S7: collect real-time current and power information through measuring components such as smart meters, and calculate the carbon emissions and economic performance indicators after actual execution, and compare them with the user's expected needs to meet the user's economic and environmental protection (such as carbon emissions) and other needs.

[0084] Another aspect of the present invention is to provide a method for power trading, such as Figure 6 As shown, it is obtained based on blockchain technology, and the power trading method includes the following steps:

[0085] S1: According to the above-mentioned energy optimization scheduling method based on eigenspace, by collecting input parameters and predicted input variables, the particle swarm algorithm is used to perform energy optimization scheduling to complete the determination of power generation / consumption and time of each node;

[0086] S2: Each node will quote and report the planned transaction volume to the central matching system, and the matching server will match the order and complete the signing of the smart contract;

[0087] S3: The network security personnel simulate and verify the safety of the power network for the planned electricity transaction plan. After the verification is passed, the signed smart contract is verified and recorded using blockchain technology;

[0088] S4: Carry out actual electricity delivery. Finally, each entity executes the signed smart contract power generation / consumption curve, settles with the actual transaction volume, and records any breach of contract in the transaction. At the same time, the blockchain records the transaction information.

[0089] In the novel power trading network of the present embodiment, firstly, according to the above-mentioned energy optimization scheduling method based on intrinsic space, by collecting input parameters and predicted input variables, the particle swarm algorithm is used to perform energy optimization scheduling, and the power generation / consumption and time of each node are determined. The microgrid, power generation companies and their power users determine the power generation and consumption plan in the day before through prediction technology; then each node quotes and reports the proposed trading power plan to the central matching system, and the matching server matches the order to complete the signing of the smart contract; the network security personnel simulates and security checks the power network of the proposed trading power plan, and after the verification is passed, the blockchain technology is used to verify and record the signed contract; in the actual delivery stage, each subject executes the signed contract power generation and consumption curve, and uses the large power grid to play a bottom-up role, and records the breach of contract in the transaction, settles with the actual trading power, and records the breach of contract in the transaction, and the blockchain records the transaction information.

[0090] The power trading network based on blockchain technology proposed in this invention is as follows Figure 7 As shown. Blockchain is a distributed transaction technology with good data tracking characteristics of intelligence, marketization, decentralization and non-tamperability. The application of blockchain technology in distributed energy can give full play to its advantages of security, reliability and transaction transparency. At the same time, it is consistent with the decentralized network structure of distributed energy. Blockchain technology is applied in distributed energy systems, in which electricity transactions are conducted directly between users, and transactions are managed by distributed and cost-effective rather than third-party centers. In decentralized energy systems, energy supply contracts can be communicated between producers and consumers. The emergence of distributed energy enables energy consumers to change their identities to producers and consumers. The addition of blockchain will generate a large number of transaction demands directly between producers and consumers, so that transactions with low marginal costs can be achieved. Based on blockchain technology, the technology will allow transactions of local energy producers and consumers to participate in local energy transactions in a direct transaction manner without the participation of a third-party monitoring platform centered on the power grid company. Due to the use of encryption processes and distributed storage, transactions almost eliminate the possibility of tampering with data. By combining blockchain technology with microgrids, users have the right to return excess photovoltaic power generation to the grid and transfer electricity to other users based on agreements reached with other users.

[0091] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention, and these changes and improvements fall within the scope of the present invention claimed.

Claims

1. An energy optimization scheduling method based on eigenspace, It is characterized in that The energy optimization scheduling method comprises the following steps: S1: constructing a microgrid economic efficiency and carbon emission calculation model based on eigenspace; the microgrid economic efficiency and carbon emission calculation model based on eigenspace includes an eigenspace of third order or higher; S2: Collect microgrid input variables and input parameters, and configure them in the microgrid economic efficiency and carbon emission calculation model; the input variables include the corresponding input power generation of renewable energy, energy storage system, and external large power grid; the input parameters include the cost per kilowatt-hour, carbon emissions per kilowatt-hour, economic weight coefficient, and carbon emission weight coefficient; S3: In the eigenspace, a plurality of corresponding output functions are obtained based on a plurality of specific eigenstates and corresponding input variables, and a net electricity cost of the microgrid and carbon emissions caused by the microgrid are calculated independently of each other according to the output functions; S4: Setting optimization objectives and constraints; the optimization objectives are to minimize carbon emissions or to achieve economic cost; the constraints include power balance of microgrid power supply and demand, power constraints of each generator set, and capacity constraints of the energy storage system; S5: Using particle swarm algorithm to achieve energy optimization scheduling; The microgrid economic efficiency and carbon emission calculation model based on eigenspace is shown in Equation 1: Among them, [PG, ES, GP] are three input variables, representing the power of renewable energy, the power of energy storage system and the power of external large power grid respectively; [Load, Fin, Carbon] are three output functions, representing the power load required in the microgrid, the net electricity cost and the carbon emission respectively; [Kij; 3×3] represents the 3×3 power utility matrix that can be diagonalized in the eigenspace, and the 3×3 power utility matrix is ​​shown in Equation 2, Where λ1, λ2, and λ3 represent the eigenvalues ​​after diagonalization of Equation 1; is the load CP power, PG power for the power generation part, is the energy storage system ES power, is the external large power grid GP power, The net electricity cost generated by the microgrid in real time. To consume carbon emissions in real time.

2. The energy optimization scheduling method based on eigenspace as claimed in claim 1, It is characterized in that In step S3, the number of the specific eigenstates is three or more, and the specific eigenstates can be represented by units of different dimensions.

3. The energy optimization scheduling method based on eigenspace according to claim 1, It is characterized in that In step S5, the particle swarm algorithm includes the following steps: S5-1: Start to randomly initialize each particle; S5-2: Evaluate the fitness value of each particle function and obtain the global optimal position; S5-3: When the end condition is met, the operation ends, otherwise, continue with steps S5-4 to S5-7; S5-4: otherwise, update the velocity and position of each particle; S5-5: Evaluate the fitness value of each particle function again; S5-6: Update the historical optimal position of individual particles; S5-7: Update the global optimal position of the group and return to step S5-3.

4. The energy optimization scheduling method based on eigenspace according to claim 1, It is characterized in that The microgrid economy and carbon emission calculation model based on intrinsic space is extended to the dual energy storage system. By separating the charging and discharging of the dual energy storage system and performing the charging and discharging tasks separately, the energy storage battery can work at the optimal charging and discharging depth Db working point as much as possible, which has the advantage of extending the service life of the energy storage system.

5. The energy optimization scheduling method based on eigenspace as claimed in claim 1, It is characterized in that The energy optimization scheduling method also includes: Step S6: Formulate the optimal scheduling plan obtained by solving the energy optimization scheduling in step S5, and then send the formulated optimal scheduling plan to the lower-level execution component for execution; Step S7: Real-time current and power information is collected through the measuring components, and the carbon emissions and economic performance indicators after actual execution are calculated and compared with the expected needs of the user to meet the needs of the user.

6. A method for electricity trading based on blockchain technology, It is characterized in that The power trading method comprises the following steps: S1: According to the energy optimization scheduling method based on eigenspace according to any one of claims 1 to 5, by collecting input parameters and predicted input variables, using particle swarm algorithm to perform energy optimization scheduling, and completing the determination of power generation / consumption and time of each node; S2: Each node will quote and report the planned transaction volume to the central matching system, and the matching server will match the order and complete the signing of the smart contract; S3: The network security personnel simulate and verify the safety of the power network for the planned electricity transaction plan. After the verification is passed, the signed smart contract is verified and recorded using blockchain technology; S4: Carry out actual electricity delivery. Finally, each entity executes the signed smart contract power generation / consumption curve, settles with the actual transaction volume, and records any breach of contract in the transaction. At the same time, the blockchain records the transaction information.

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