Power system low-carbon optimization method and system based on on-chain and off-chain cooperation

Abstract

The invention discloses a power system low-carbon optimization method and system based on chain-up and chain-down cooperation, and relates to the technical field of power systems, and the method comprises the steps: obtaining the load power data of a power system, and inputting the load power data of the power system into a pre-established power system low-carbon optimization double-layer model; the pre-established power system low-carbon optimization double-layer model carries out power consumption data processing calculation based on an on-chain and off-chain data collaborative interaction process facing the power-carbon service to obtain final power generation and consumption data, and node carbon emission factors and carbon emission are calculated based on the final power generation and consumption data; and low-carbon operation of the power system is comprehensively optimized, and powerful support is provided for low-carbon transformation and sustainable development of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and specifically to a low-carbon optimization method and system for power systems based on on-chain and off-chain collaboration. Background Art

[0002] In recent years, research on low-carbon optimization of power systems has made significant progress. Existing low-carbon optimization models primarily focus on optimizing generator output to reduce carbon emissions. These models typically consider a variety of low-carbon technologies, such as carbon capture, power-to-gas, and renewable energy. However, most existing dispatch models either prioritize economic efficiency and ignore the need for low-carbon emissions, or overemphasize low-carbon performance, resulting in excessively high system operating costs. Demand response, as an effective load management tool, can adjust user electricity consumption to achieve peak load shifting and valley shifting. Furthermore, the introduction of carbon trading markets provides economic incentives for low-carbon operation of power systems. However, how to effectively integrate demand response with carbon trading remains an urgent challenge. Low-carbon demand response mechanisms can actively encourage user participation through dynamic carbon emission factors and carbon market incentive signals, thereby significantly reducing carbon emissions in the power system. Blockchain technology, due to its decentralized, tamper-proof, and transparent characteristics, is increasingly being applied to energy management systems. However, existing research has primarily focused on the management of a single energy chain, lacking in-depth exploration of the coordinated operation of power and carbon trading systems. The utilization of low-carbon energy is a key direction for the low-carbon transformation of power systems. Research indicates that the power sector needs to utilize a very high proportion of renewable energy (primarily wind and solar) to achieve deep emissions reductions. For example, large-scale deployment of wind and solar power could reduce carbon emissions by 80% relative to 1990 levels without increasing the cost of electricity. Furthermore, the combined use of multiple low-carbon technologies, such as nuclear, geothermal, biomass, and fossil fuels with carbon capture and storage (CCS), is also an important path to achieving deep emissions reductions in the power sector. Summary of the Invention

[0003] In order to address the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a low-carbon optimization method and system for power systems based on on-chain and off-chain collaboration.

[0004] In the first aspect, the purpose of the present invention can be achieved through the following technical solution: a low-carbon optimization method for a power system based on on-chain and off-chain collaboration, the method comprising the following steps:

[0005] Obtaining power system load data, and inputting the power system load data into a pre-established power system low-carbon optimization two-layer model;

[0006] The pre-established two-layer model for low-carbon optimization of the power system processes and calculates electricity consumption data based on the collaborative interaction process of on-chain and off-chain data for the electricity-carbon business, and obtains the final power generation and consumption data. Based on the final power generation and consumption data, the node carbon emission factor and carbon emissions are calculated.

[0007] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the pre-established power system low-carbon optimization two-layer model includes an upper model and a lower model.

[0008] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the upper-level model is an electricity-carbon coordinated economic dispatch model, which makes decisions and plans the output of coal-fired units, gas-fired units, and new energy units with the goal of minimizing the total system operating cost and carbon trading cost;

[0009] The lower-level model is a low-carbon demand response model. It aims to minimize carbon trading costs and demand response costs. It adjusts the load power data and transmits it back to the upper-level model. The upper-level model replans the unit output based on the updated load power.

[0010] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the objective function of the electricity-carbon coordinated economic dispatch model includes:

[0011] The goal is to minimize the cost of thermal power generation, renewable energy generation, and carbon trading costs:

[0012] minC total =C elec +C carbon

[0013]

[0014] Where C elec is the power generation cost of the unit; C carbon is the carbon transaction cost on the power generation side of the system; a Gi 、b Gi and c Gi are the power generation cost coefficients of generator i; P Gi,t is the output of generator i at time t; δ i,t is the carbon price of generator i at time t; CE Gi,t is the carbon emissions of generator i at time t; is the free quota of generator i at time t; NG is the number of generator sets; T is the time period;

[0015] Constraints include:

[0016] Unit output constraints:

[0017]

[0018] Where, and P Gi are the maximum and minimum output values of generator i respectively;

[0019] Unit climbing constraints:

[0020]

[0021] Where: P Gi,t-1 is the output of generator i at time t-1; and They are the maximum climbing rate and maximum sliding rate of the generator set respectively;

[0022] Line transmission capacity constraints:

[0023]

[0024] Where, and P line are the upper and lower limits of the transmission power of line l respectively; PTDF is the power transmission distribution factor; P Lk,t is the power demand of load k at time t;

[0025] Power balance constraints:

[0026]

[0027] Where N D is the load quantity;

[0028] Carbon emission constraints:

[0029] CE Gi,t =e Gi P Gi,t

[0030] Where, e Gi is the carbon emission intensity of generator i;

[0031] The upper model calculates the carbon flow based on the unit output and branch flow data, obtains the carbon emission factor of each node and transmits it to the lower layer.

[0032] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the low-carbon demand response model uses the carbon emission factor as an incentive signal to adjust the flexible load power consumption strategy, and the objective function is to minimize the carbon trading cost and the demand response cost;

[0033] minC total =C carbon +C DR

[0034] Where Ccarbon is the carbon trading cost on the demand side of the system, and CDR is the demand response cost;

[0035] The carbon transaction cost on the demand side of the system, i.e.

[0036]

[0037] Demand response cost, i.e.

[0038]

[0039] Where, δ k,t is the carbon price of load k at time t; β DR represents the unit cost of demand response, ΔPLk is the demand response amount of load k at time t;

[0040] Constraints include:

[0041] Demand response constraints:

[0042] -0.25P Lk ≤ΔP Lk,t ≤0.25P Lk

[0043] P' Lk,t =P Lk,t +ΔP Lk,t

[0044]

[0045] Where, 0.25 is the load regulation coefficient; P' Lk,t is the load demand after load k demand response at time t;

[0046] Carbon emission constraints:

[0047] CE Lk,t =E Nk P Lk,t

[0048] Where, E Nk is the carbon emission factor of load k.

[0049] In combination with the first aspect, in certain implementations of the first aspect, the method also includes: the on-chain and off-chain data collaborative interaction process based on the electric carbon business includes an intranet chain and an extranet chain in the on-chain and off-chain collaborative architecture for the electric carbon business, wherein the intranet chain includes a control center node, an information center node, a trading center node and a metering center node; the extranet chain includes an environmental exchange node, a park node, a government node, and a management and control node; the intranet is responsible for the energy management of the power system, including decision-making on generator output and power load demand response; the extranet chain focuses on the management of the carbon trading system, calculates the carbon quota trading volume, and through two-way anchoring technology, the two chains realize cross-blockchain interaction of valuable information, and collects users' electric carbon information through smart terminals off-chain, calculates node carbon emission factors, and calculates users' indirect carbon emissions from electricity and stores them on the chain.

[0050] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: the on-chain and off-chain data collaborative interaction process based on the electric carbon business includes the following steps:

[0051] The power market forms transaction results through centralized matching. The transaction results generate new data blocks in the intranet chain. The data blocks are verified by the digital signatures of all nodes. After verification, the block information is broadcast to the entire network through the intranet chain system.

[0052] Using blockchain two-way anchoring technology, power generation and consumption, as well as power flow information, is sent to the external network. Combined with the node carbon emission factor calculation model, the indirect carbon emissions of power users are calculated. New data and historical data are combined to form the latest block on the external network and broadcast to all nodes in the carbon market, including power generators, power users, and regulatory agencies.

[0053] The carbon emission factor and carbon emission data are anchored back to the intranet chain. Participants in the intranet chain dynamically adjust power generation and consumption plans based on carbon emission information and optimize power trading strategies. The above steps are then repeated until the load demand reaches the convergence condition, and the final power generation and consumption data is obtained.

[0054] Calculate the node carbon emission factor and carbon emissions based on the final power generation and consumption data;

[0055] Calculation of the node carbon emission factor based on the final power generation and consumption data:

[0056] Without considering the network loss, the carbon emission factor E of node j is given based on the method of calculating the carbon emission flow of the power system. Nj for:

[0057]

[0058] Where, ρ kj is the carbon flow density of branch kj; P kjis the branch kj power; P Gj is the generator output of node j; e Gj is the carbon emission intensity of the generator set connected to node j; Uj is the set of upstream nodes of node j, that is, the set of nodes directly connected to node j and with active power flowing to j; the above formula means that for a given node, the node carbon emission factor E Nj .

[0059] In a second aspect, in order to achieve the above-mentioned objectives, the present invention discloses a low-carbon optimization system for power systems based on on-chain and off-chain collaboration, comprising:

[0060] A low-carbon optimization module is used to obtain power system load power data and input the power system load power data into a pre-established power system low-carbon optimization two-layer model;

[0061] The on-chain and off-chain collaborative module is used to process and calculate electricity consumption data based on the pre-established two-layer model of low-carbon optimization of the power system based on the on-chain and off-chain data collaborative interaction process for the electricity-carbon business, and obtain the final power generation and consumption data. Based on the final power generation and consumption data, the node carbon emission factor and carbon emissions are calculated.

[0062] In another aspect of the present invention, in order to achieve the above-mentioned purpose, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, it adopts the above-mentioned low-carbon optimization method for the power system based on on-chain and off-chain collaboration.

[0063] In another aspect of the present invention, in order to achieve the above-mentioned purpose, a computer-readable storage medium is disclosed, in which a computer program is stored. When the computer program is loaded and executed by a processor, the above-mentioned low-carbon optimization method of the power system based on on-chain and off-chain collaboration is adopted.

[0064] Beneficial effects of the present invention:

[0065] This invention improves the accuracy of carbon emission accounting, realizes in-depth interaction between the power generation side and the demand side, and breaks through the information barriers between the power-carbon system by constructing a two-layer optimization architecture for electricity-carbon collaboration and a blockchain on-chain and off-chain collaboration mechanism, thereby comprehensively optimizing the low-carbon economic operation of the power system and providing strong support for the low-carbon transformation and sustainable development of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0067] Figure 1 It is a schematic flow chart of the method of the present invention;

[0068] Figure 2 This is the topological structure diagram of the IEEE 14-node power grid system of the present invention;

[0069] Figure 3 This is a schematic diagram of the carbon emission factor results under different scenarios of the present invention;

[0070] Figure 4 This is a schematic diagram of the carbon emission factor results at different nodes in different time periods under scenario 3 of the present invention;

[0071] Figure 5 This is a schematic diagram of carbon emission results under different scenarios of the present invention;

[0072] Figure 6 Schematic diagram of load demand under scenario 2 and scenario 3 of the present invention;

[0073] Figure 7 This is a schematic diagram of load demand response under different carbon prices in the present invention;

[0074] Figure 8 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0075] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0076] Example 1:

[0077] like Figure 1 As shown, a low-carbon optimization method for power systems based on on-chain and off-chain collaboration includes the following steps:

[0078] S101: Obtaining power system load data, and inputting the power system load data into a pre-established power system low-carbon optimization two-layer model;

[0079] The pre-established power system low-carbon optimization two-layer model includes an upper layer model and a lower layer model.

[0080] The upper-level model is an electricity-carbon coordinated economic dispatch model, which aims to minimize the total system operating cost and carbon trading cost, and plans the output of coal-fired units, gas-fired units, and new energy units.

[0081] The lower-level model is a low-carbon demand response model. It aims to minimize carbon trading costs and demand response costs, adjusts load power data and transmits it back to the upper-level model. The upper-level model replans the unit output based on the updated load power to achieve the goal of low-carbon economic operation of the power system.

[0082] The upper-level electricity-carbon coordinated economic dispatch model is constructed, including:

[0083] Objective function:

[0084] The goal is to minimize the power generation costs of thermal power units, renewable energy power generation costs and carbon trading costs.

[0085] minC total =C elec +C carbon

[0086]

[0087] Where C elec is the power generation cost of the unit; C carbon is the carbon transaction cost on the power generation side of the system; a Gi 、b Gi and c Gi are the power generation cost coefficients of generator i; P Gi,t is the output of generator i at time t; δ i,t is the carbon price of generator i at time t; CE Gi,t is the carbon emissions of generator i at time t; is the free quota of generator i at time t; NG is the number of generator sets; T is the time period.

[0088] The upper-level electricity-carbon coordinated economic dispatch model is constructed, including:

[0089] Unit output constraints:

[0090]

[0091] Where, and P Gi are the maximum and minimum output values of generator i respectively.

[0092] Unit climbing constraints:

[0093]

[0094] Where: P Gi,t-1 is the output of generator i at time t-1; and They are the maximum climbing rate and maximum sliding rate of the generator set respectively.

[0095] Line transmission capacity constraints:

[0096]

[0097] Where, and P line are the upper and lower limits of the transmission power of line l respectively; PTDF is the power transmission distribution factor; P Lk,t is the power demand of load k at time t.

[0098] Power balance constraints:

[0099]

[0100] Where N D is the load quantity.

[0101] Carbon emission constraints:

[0102] CE Gi,t =e Gi P Gi,t

[0103] Where, e Gi is the carbon emission intensity of generator i.

[0104] The upper model calculates the carbon flow based on the unit output and branch flow data, and can obtain the carbon emission factor of each node and transmit it to the lower layer.

[0105] The construction of the lower-level low-carbon demand response model includes:

[0106] The lower layer is a low-carbon demand response model, which uses the carbon emission factor as an incentive signal to adjust the flexible load power consumption strategy. The objective function is to minimize carbon trading costs and demand response costs.

[0107] minC total =C carbon +C DR

[0108] Where Ccarbon is the carbon trading cost on the demand side of the system, and CDR is the demand response cost.

[0109] The carbon transaction cost on the demand side of the system, i.e.

[0110]

[0111] Demand response cost, i.e.

[0112]

[0113] Where, δ k,t is the carbon price of load k at time t; β DR represents the unit cost of demand response, and ΔPLk is the demand response amount of load k at time t.

[0114] The construction of the lower-level low-carbon demand response model includes:

[0115] Demand response constraints:

[0116] -0.25P Lk ≤ΔP Lk,t ≤0.25P Lk

[0117] P' Lk,t =P Lk,t +ΔP Lk,t

[0118]

[0119] Where, 0.25 is the load adjustment coefficient, which is set to 25% in this paper; P' Lk,t is the load demand after load k responds to the demand at time t.

[0120] Carbon emission constraints:

[0121] CE Lk,t =E Nk P Lk,t

[0122] Where, E Nk is the carbon emission factor of load k.

[0123] S102: The pre-established two-layer model for low-carbon optimization of the power system processes and calculates electricity consumption data based on the collaborative interaction process of on-chain and off-chain data for the electricity-carbon business, and obtains the final power generation and consumption data. Based on the final power generation and consumption data, the node carbon emission factor and carbon emissions are calculated.

[0124] Build an on-chain and off-chain collaborative architecture for electric carbon business, including:

[0125] In the on-chain and off-chain collaborative architecture for the electricity and carbon business, the on-chain consists of an intranet and an extranet. The intranet primarily includes nodes from the control center, information center, trading center, and metering center; the extranet primarily includes nodes from the environmental exchange, industrial park, government, and management and control centers. The intranet is primarily responsible for energy management of the power system, including determining generator output and responding to load demand. The extranet focuses on managing the carbon trading system and calculating carbon allowance trading volume. Through two-way anchoring technology, the two chains enable cross-blockchain exchange of valuable information, providing data support for the low-carbon operation of the power system. Off-chain operations primarily collect user electricity and carbon information through smart terminals, calculate node carbon emission factors, and calculate users' indirect carbon emissions from electricity, storing them on-chain.

[0126] The collaborative interaction process of on-chain and off-chain data for the electric carbon business includes:

[0127] 1) The power market forms a transaction result through centralized matching, which generates a new data block in the intranet chain. This block is verified by the digital signature of all nodes. Once verified, the block information is broadcast to the entire intranet chain system.

[0128] 2) Utilizing blockchain bidirectional anchoring technology, power generation and consumption information, as well as power flow information, is sent to the Extranet. Combined with the node carbon emission factor calculation model, this data is used to calculate the indirect carbon emissions of power users. This new data, combined with historical data, forms the latest block on the Extranet and is broadcast to all nodes in the carbon market, including generators, power users, and regulators.

[0129] 3) At the same time, the carbon emission factor and carbon emission data are anchored back to the intranet chain. Intranet chain participants (such as power generators and power users) can dynamically adjust power generation and consumption plans based on carbon emission information, optimize power trading strategies, and then repeat step 1) until load demand reaches convergence conditions.

[0130] Calculation of node carbon emission factor based on final power generation and consumption data:

[0131] Without considering the network loss, the basic method for calculating the carbon emission flow of the power system gives the carbon emission factor E of node j in the system: Nj for:

[0132]

[0133] Where ρkj is the carbon flow density of branch kj; P kj is the branch kj power; P Gj is the generator output of node j; e Gjis the carbon emission intensity of the generator set connected to node j; Uj is the upstream node set of node j, that is, the node set directly connected to node j and with active power flowing to j; the above formula means that for a given node, its node carbon emission factor E Nj , depends only on the total inflow branch carbon emission flow to the node and the inflow carbon emission flow from the generator.

[0134] The present invention is verified by simulation below.

[0135] In this paper, the two-layer model of electricity-carbon coordinated optimization scheduling is applied to the improved 14-node system. Figure 2 As shown in the figure, there are five generator sets in the 14-node system. Node 1 is a coal-fired unit with a carbon emission intensity of 0.875; nodes 2 and 6 are gas-fired units with carbon emission intensities of 0.525 and 0.520, respectively; and nodes 3 and 8 are distributed wind power and distributed photovoltaic generator sets, respectively, with carbon emission intensities set to 0.

[0136] To verify the effectiveness of the electricity-carbon synergy model, this paper sets up three scenarios for comparative analysis.

[0137] Scenario 1: No consideration of low-carbon demand response

[0138] Scenario 2: Considering carbon trading without considering demand response

[0139] Scenario 3: Considering low-carbon demand response, that is, the low-carbon optimization double-layer model of the power system in this invention

[0140] Specifically, the present invention will be further described below through examples:

[0141] Taking node 4 as an example, the carbon emission factors under different scenarios are as follows: Figure 3 As shown in the figure, between 9:00 AM and 4:00 PM, renewable energy output is high, resulting in a lower system carbon emission factor and a cleaner energy mix for users. Comparing the carbon emission factors under the three scenarios reveals that the system carbon emission factor is significantly reduced with the carbon trading and demand response mechanisms. In Scenario 2, which only considers the carbon trading mechanism, the carbon emission factor fluctuates significantly. Scenario 3, which incorporates low-carbon demand response, uses flexible loads to regulate the system carbon emission factor, resulting in a more stable carbon emission factor at different times.

[0142] Figure 4 The carbon emission factors for different nodes in Scenario 3 are given. Since nodes 3 and 8 are equipped with clean energy units and have low carbon emissions, the carbon emission factors for nodes 3, 8, and their nearby nodes are relatively low. The coal-fired unit installed at node 1 has high carbon emissions, so the carbon emission factor for node 1 is relatively high.

[0143] Carbon emissions under different scenarios Figure 5 As shown in the figure, the carbon trading mechanism significantly reduces system carbon emissions. Furthermore, the low-carbon demand response model uses the carbon emission factor as an incentive signal to further reduce system carbon emissions, promoting the low-carbon, green development of the power system.

[0144] Load demand before and after demand response Figure 6 Comparing Scenario 2 and Scenario 3, it can be found that the flexible load increases the load when the carbon emission factor is low, and reduces the load when the carbon emission factor is high, thereby achieving the effect of reducing carbon emissions.

[0145] In scenario 3, the load power adjustment in different periods is observed and analyzed by changing the carbon price. The carbon price is set to 50 yuan / t, 80 yuan / t, 100 yuan / t and 150 yuan / t respectively. The simulation results are as follows: Figure 7 shown.

[0146] When the carbon price is set low, such as 50 yuan / t, node load power remains essentially unchanged, and users do not participate in demand response. Therefore, the carbon price cannot be set too low, as this will fail to incentivize users to participate in demand response and achieve the carbon reduction effect. When the carbon price is greater than 80 yuan / t, as the carbon price increases, users' willingness to participate in demand response and adjust load power becomes more pronounced, achieving carbon reduction effects. From the above analysis, we can see that the appropriate setting of carbon prices is of great significance to promoting the coordinated operation of electricity and carbon.

[0147] Table 1 Comparison of system economy and low carbon performance under different scenarios

[0148]

[0149] It can be found from Table 1 that the power system low-carbon optimization two-layer model proposed in Scenario 3 of the present invention increases the power generation cost by 0.861 and 0.0163 million yuan respectively compared with Scenario 1 and Scenario 2. In terms of carbon trading costs, Scenario 3 reduces 5.3656 and 0.8792 thousand yuan respectively compared with Scenario 1 and Scenario 2. In terms of total carbon emissions, Scenario 3 reduces 225.5392 and 10.9908 tons respectively compared with Scenario 1 and Scenario 2. In terms of total system cost, Scenario 3 increases by 0.3244 million yuan compared with Scenario 1 and reduces by 0.0716 million yuan compared with Scenario 2. In general, the power system low-carbon optimization two-layer model proposed in the present invention can reduce the total carbon emissions of the system while minimizing the total system cost, achieving a balance between the economic benefits and environmental benefits of the system.

[0150] Example 2: The second aspect, as Figure 8 As shown, in order to achieve the above-mentioned purpose, the present invention discloses a low-carbon optimization system for power systems based on on-chain and off-chain collaboration, comprising:

[0151] The low-carbon optimization module 11 is used to obtain power system load power data and input the power system load power data into a pre-established power system low-carbon optimization two-layer model;

[0152] The on-chain and off-chain collaborative module 12 is used to process and calculate electricity consumption data based on the pre-established two-layer model of low-carbon optimization of the power system based on the on-chain and off-chain data collaborative interaction process for the electricity-carbon business, and obtain the final power generation and consumption data, and calculate the node carbon emission factor and carbon emissions based on the final power generation and consumption data.

[0153] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0154] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium having a computer program stored thereon, which executes the above method when executed by a processor. The storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0155] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0156] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present disclosure. Various changes and improvements may be made to the present disclosure without departing from the spirit and scope of the present disclosure, and such changes and improvements shall fall within the scope of the present disclosure.

Claims

1. A low-carbon optimization method for power systems based on on-chain and off-chain collaboration, characterized in that: The method comprises the following steps: Obtaining power system load data, and inputting the power system load data into a pre-established power system low-carbon optimization two-layer model; The pre-established two-layer model for low-carbon optimization of the power system processes and calculates electricity consumption data based on the collaborative interaction process of on-chain and off-chain data for the electricity-carbon business, and obtains the final power generation and consumption data. Based on the final power generation and consumption data, the node carbon emission factor and carbon emissions are calculated.

2. A low-carbon optimization method for power systems based on on-chain and off-chain collaboration according to claim 1, characterized in that: The pre-established power system low-carbon optimization two-layer model includes an upper layer model and a lower layer model.

3. The low-carbon optimization method for power systems based on on-chain and off-chain collaboration according to claim 2 is characterized in that: The upper-level model is an electricity-carbon coordinated economic dispatch model, which aims to minimize the total system operating cost and carbon trading cost, and plans the output of coal-fired units, gas-fired units, and new energy units. The lower-level model is a low-carbon demand response model. It aims to minimize carbon trading costs and demand response costs. It adjusts the load power data and transmits it back to the upper-level model. The upper-level model replans the unit output based on the updated load power.

4. The low-carbon optimization method for power systems based on on-chain and off-chain collaboration according to claim 3 is characterized in that: The objective function of the electricity-carbon coordinated economic dispatch model includes: The goal is to minimize the cost of thermal power generation, renewable energy generation, and carbon trading costs: minC total =C elec +C carbon Where C elec is the power generation cost of the unit; C carbon is the carbon transaction cost on the power generation side of the system; a Gi 、b Gi and c Gi are the power generation cost coefficients of generator i; P Gi,t is the output of generator i at time t; δ i,t is the carbon price of generator i at time t; CE Gi,t is the carbon emissions of generator i at time t; is the free quota of generator i at time t; NG is the number of generator sets; T is the time period; Constraints include: Unit output constraints: Where, and P Gi are the maximum and minimum output values of generator i respectively; Unit climbing constraints: Where: P Gi,t-1 is the output of generator i at time t-1; and They are the maximum climbing rate and maximum sliding rate of the generator set respectively; Line transmission capacity constraints: Where, and P line are the upper and lower limits of the transmission power of line l respectively; PTDF is the power transmission distribution factor; P Lk,t is the power demand of load k at time t; Power balance constraints: Where N D is the load quantity; Carbon emission constraints: WHAT Gi,t =e Gi P Gi,t Where, e Gi is the carbon emission intensity of generator i; The upper model calculates the carbon flow based on the unit output and branch flow data, obtains the carbon emission factor of each node and transmits it to the lower layer.

5. The low-carbon optimization method for power systems based on on-chain and off-chain collaboration according to claim 4 is characterized in that: The low-carbon demand response model uses the carbon emission factor as an incentive signal to adjust the flexible load power consumption strategy, and the objective function is to minimize the carbon trading cost and the demand response cost; minC total =C carbon +C DR Where C carbon is the carbon transaction cost on the demand side of the system, C DR is the demand response cost; The carbon transaction cost on the demand side of the system, i.e. Demand response cost, i.e. Where, δ k,t is the carbon price of load k at time t; β DR Denotes the unit cost of demand response, ΔP Lk,t is the demand response of load k at time t; Constraints include: Demand response constraints: -0.25P Lk ≤ΔP Lk,t ≤0.25P Lk P' Lk,t =P Lk,t +ΔP Lk,t Where, 0.25 is the load regulation coefficient; P' Lk,t is the load demand after load k demand response at time t; Carbon emission constraints: WHAT Lk,t =E Nk P Lk,t Where, E Nk is the carbon emission factor of load k.

6. The low-carbon optimization method for power systems based on on-chain and off-chain collaboration according to claim 1 is characterized in that: The on-chain and off-chain data collaborative interaction process based on the electric carbon business includes an intranet chain and an extranet chain in the on-chain and off-chain collaborative architecture for the electric carbon business, wherein the intranet chain includes a control center node, an information center node, a trading center node and a metering center node; the extranet chain includes an environmental exchange node, a park node, a government node and a management and control node; the intranet is responsible for the energy management of the power system, including decision-making on generator output and power load demand response; the extranet chain focuses on the management of the carbon trading system and calculates the carbon quota trading volume. Through the two-way anchoring technology, the two chains realize cross-blockchain interaction of valuable information. Off-chain, the user's electric carbon information is collected through smart terminals, the node carbon emission factor is calculated, and the user's indirect carbon emissions from electricity are calculated and stored on the chain.

7. A low-carbon optimization method for power systems based on on-chain and off-chain collaboration according to claim 6, characterized in that: The collaborative interaction process of on-chain and off-chain data for the electric carbon business includes the following steps: The power market forms transaction results through centralized matching. The transaction results generate new data blocks in the intranet chain. The data blocks are verified by the digital signatures of all nodes. After verification, the block information is broadcast to the entire network through the intranet chain system. Using blockchain two-way anchoring technology, power generation and consumption, as well as power flow information, is sent to the external network. Combined with the node carbon emission factor calculation model, the indirect carbon emissions of power users are calculated. New data and historical data are combined to form the latest block on the external network and broadcast to all nodes in the carbon market, including power generators, power users, and regulatory agencies. The carbon emission factor and carbon emission data are anchored back to the intranet chain. Participants in the intranet chain dynamically adjust power generation and consumption plans based on carbon emission information and optimize power trading strategies. The above steps are then repeated until the load demand reaches the convergence condition, and the final power generation and consumption data is obtained. Calculate the node carbon emission factor and carbon emissions based on the final power generation and consumption data; Calculation of node carbon emission factor based on final power generation and consumption data: Without considering the network loss, the carbon emission factor E of node j is given based on the method of calculating the carbon emission flow of the power system. Nj for: Where, ρ kj is the carbon flow density of branch kj; P kj is the branch kj power; P Gj is the generator output of node j; e Gj is the carbon emission intensity of the generator set connected to node j; Uj is the set of upstream nodes of node j, that is, the set of nodes directly connected to node j and with active power flowing to j; the above formula means that for a given node, the node carbon emission factor E Nj .

8. A low-carbon optimization system for power systems based on on-chain and off-chain collaboration, characterized in that: include: A low-carbon optimization module is used to obtain power system load power data and input the power system load power data into a pre-established power system low-carbon optimization two-layer model; The on-chain and off-chain collaborative module is used to process and calculate electricity consumption data based on the pre-established two-layer model of low-carbon optimization of the power system based on the on-chain and off-chain data collaborative interaction process for the electricity-carbon business, and obtain the final power generation and consumption data. Based on the final power generation and consumption data, the node carbon emission factor and carbon emissions are calculated.

9. A terminal device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on a processor. When the processor loads and executes the computer program, it adopts a low-carbon optimization method for the power system based on on-chain and off-chain collaboration as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, wherein: When the computer program is loaded and executed by the processor, a low-carbon optimization method for the power system based on on-chain and off-chain collaboration as described in any one of claims 1 to 7 is adopted.

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