Source network load storage decentralized cooperative scheduling system and method

Abstract

The invention is suitable for the technical field of power systems, and relates to a source network load storage decentralized cooperative scheduling system and method, and the system comprises a model prediction control optimization module which is used for solving a preset multi-target optimization model according to system state data and prediction information which are collected in real time, and outputting a cooperative scheduling plan; the block chain collaborative network is used for providing a trusted channel for data interaction and instruction issuing among the main bodies; the intelligent contract module comprises an intelligent contract used for managing the collaborative scheduling process; and the data bridging and execution module is used for converting the collaborative scheduling instruction into a block chain transaction and triggering intelligent contract execution, and issuing a consensus instruction achieved on the chain to a physical device at the same time. According to the method, a centralized advanced optimization algorithm and a distributed trusted execution mechanism are combined, so that the scientific property and the real-time property of a scheduling decision are guaranteed, meanwhile, the problems of trust missing and information asymmetry in multi-interest subject collaboration are solved, and efficient, transparent and automatic decentralized collaborative scheduling is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power systems, and particularly relates to a source-grid-load-storage decentralized collaborative scheduling system and method. BACKGROUND

[0002] With the deepening of energy transformation, the scheduling objects of the power system are increasingly complex, and the traditional "source following load" is changing to "source-grid-load-storage" multi-dimensional interaction. The traditional centralized scheduling mode faces two challenges: one is the technical level, the strong uncertainty of new energy and load requires the scheduling to have real-time optimization and response ability of minutes or even seconds, and advanced algorithms such as model predictive control (MPC) are introduced to cope with this challenge; the second is the mechanism level, the scheduling under the participation of multiple subjects involves complex interest coordination, the centralized scheduling institution is difficult to obtain the private real information of each subject, there is information asymmetry, and the execution of the scheduling instruction depends on the voluntary cooperation of each subject, lacks credible supervision and performance guarantee, and is easy to cause disputes.

[0003] Blockchain technology has great potential in building trust mechanisms due to its decentralized, transparent, credible, tamper-proof and programmable characteristics. In the prior art, some studies propose to use blockchain in the fields of power trading and demand response signing, but its combination with the core real-time scheduling control business of the power system is still in the early stage. Simply putting all scheduling calculations on the chain will bring unbearable performance overhead, and completely relying on off-chain calculation cannot guarantee the public credibility of the process. Patent with publication number CN114926078B provides a power real-time demand response method based on blockchain and related equipment, which obtains the real-time demand response instruction chained by the power grid control center; decomposes the real-time demand response instruction based on the dynamic adjustable information chained by the distributed energy storage power station to obtain the real-time demand response sub-instruction and chains it, and the distributed energy storage power station executes the demand response operation based on the corresponding real-time demand response sub-instruction; after completing the demand response operation, a transaction settlement record is generated and chained, so that the power grid scheduling center, the virtual energy storage power station and the distributed energy storage power station complete the transaction based on the transaction settlement record. In this patent, all scheduling calculations are chained, which has huge performance overhead and the same drawbacks as the prior art.

[0004] Therefore, how to provide a new type of collaborative scheduling system that deeply integrates "off-chain efficient optimization" and "on-chain credible execution", and how to make the scheduling instructions generated by optimization algorithms such as MPC that require centralized calculation be trusted and automatically executed in a decentralized manner, and complete the after-the-fact clearing, are problems that need to be solved by people in this technical field. SUMMARY

[0005] In view of the deficiencies of the prior art, the purpose of the present application is to provide a source network load storage decentralized collaborative scheduling system to solve the problem that the power system scheduling in the prior art cannot integrate off-chain efficient optimization and on-chain trusted execution when applying blockchain technology, resulting in lack of trust and information asymmetry in multi-stakeholder collaboration.

[0006] To solve the above technical problems, the present application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a source network load storage decentralized collaborative scheduling system, comprising:

[0008] A model predictive control optimization module is configured to solve a pre-set multi-objective optimization model in each scheduling control cycle according to real-time collected system state data and prediction information, and output a collaborative scheduling plan containing power source, power grid, load and energy storage control instructions.

[0009] A blockchain collaborative network is a permission-based consortium chain, and its consensus nodes include multiple operating subjects in the power system, which is configured to provide a decentralized and tamper-proof trusted channel for data interaction and instruction issuance between subjects.

[0010] An intelligent contract module is deployed on the blockchain collaborative network and includes at least one intelligent contract for managing the collaborative scheduling process.

[0011] A data bridging and execution module is connected with the model predictive control optimization module, the blockchain collaborative network and the underlying physical devices, used for encapsulating the collaborative scheduling plan as a transaction and submitting it to the blockchain, triggering instruction execution according to the smart contract logic, and distributing the final control instruction confirmed by the blockchain consensus to the corresponding physical device execution unit. The data bridging and execution module is responsible for safe and reliable data conversion and instruction transmission between the physical world and the digital world, centralized optimization and distributed ledger, and ensures the final realization of the control closed loop.

[0012] Further, the rolling solution process of the model predictive control optimization module includes: at the starting moment of each control period, based on the latest system state and future short-time prediction, solving a finite-time domain optimization problem to obtain an optimal control sequence, and taking the control instruction corresponding to the current moment in the sequence as the output.

[0013] Further, the blockchain collaborative network adopts a Byzantine fault-tolerant or variant consensus algorithm, and the consensus nodes include server nodes representing power grid dispatching agencies, new energy power generators, adjustable load aggregators and energy storage operators.

[0014] Further, the smart contract in the smart contract module is specifically used to execute the following logic:

[0015] Verify the compliance of the scheduling plan transaction format submitted by the data bridging and execution module and the validity of the digital signature;

[0016] At a preset time point or when the condition is met, automatically mark the verified scheduling instruction as a to-be-executed state and broadcast it;

[0017] Receive feedback data from each consensus node about the instruction execution result, and check and score according to the preset evaluation rules;

[0018] According to the checking result, automatically calculate the income and payment of each subject, and generate a settlement transaction.

[0019] Further, the data bridging and execution module specifically includes:

[0020] A plan chaining unit is used to receive the scheduling plan from the model predictive control optimization module, add a timestamp and an initiator signature, construct and broadcast a plan submission transaction to the blockchain network;

[0021] An instruction monitoring and analysis unit is used to monitor the instruction execution event issued by the smart contract on the blockchain, and analyze the specific control instruction and target device;

[0022] An instruction issuing unit is configured to issue the parsed control instruction to a corresponding power plant station control system, load control terminal or energy storage management system through a secure communication protocol.

[0023] Further, the system further comprises an on-chain data oracle configured to inject real-time operation data or prediction data outside the blockchain collaborative network into the blockchain collaborative network after multi-node verification, so as to be called by the smart contract or the model predictive control optimization module.

[0024] In a second aspect, the application further provides a method applied to the system, comprising the following steps:

[0025] S10. In each scheduling control cycle, the model predictive control optimization module solves an optimization problem based on real-time system state and prediction data, and generates a collaborative scheduling plan;

[0026] S20. The data bridging and execution module submits the collaborative scheduling plan as a transaction to the blockchain collaborative network;

[0027] S30. The smart contract deployed on the blockchain collaborative network automatically verifies and confirms the transaction, generates an execution event containing control instructions and reaches a global consensus when the execution condition is met;

[0028] S40. The execution node of each participant listens to and captures the execution event, and the data bridging and execution module parses and issues the consensus instruction to the corresponding physical device for execution;

[0029] S50. After the physical device executes, the monitoring node thereof submits the execution result data as a feedback transaction to the blockchain;

[0030] S60. The smart contract automatically executes the assessment and settlement logic according to the plan and feedback data stored on the chain, and completes the closed loop of the scheduling cycle.

[0031] Further, the objective function of the optimization problem in S10 includes minimization of total system operation cost and maximization of new energy consumption rate.

[0032] Further, the constraint condition of the objective function includes power balance, device operation limit value and grid safety constraint.

[0033] Further, the confirmation process of the smart contract in S30 includes checking whether the scheduling plan meets the pre-agreed market rules or safety boundary, and whether the initiator has the corresponding authority.

[0034] The source-grid-load-storage decentralized collaborative scheduling system and method provided by the application have at least the following beneficial effects compared with the prior art:

[0035] Trust built-in, collaboration efficient: Through blockchain and smart contracts, the dispatch is transformed from the "central command-passive execution" mode to the "rule consensus-automatic execution" mode, greatly improving the collaboration trust and collaboration efficiency among multiple subjects, reducing the communication and dispute costs;

[0036] Optimization and trusted integration: Creatively distinguishes the "optimization calculation" and "trusted execution" two levels, MPC ensures the technical optimality of the dispatch, and the blockchain ensures the public trust of the process and results, the two complementary advantages realize the effect of "1+1>2".

[0037] Process transparency, automatic settlement: All dispatch instructions, execution evidence and assessment results are on-chain, publicly available, achieving unprecedented transparency. The automatic settlement driven by smart contracts ensures the instant, accurate and fair distribution of interests.

[0038] Enhance system flexibility: Decentralized architecture makes it easier for new market participants (such as virtual power plants, electric vehicle aggregation platforms) to access and participate in system dispatch, enhancing the overall flexibility and flexibility of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the scheme of the present application, the drawings needed in the following embodiment description will be simply introduced, obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0040] Figure 1 The structural block diagram of a source network load storage decentralized collaborative dispatch system provided by the embodiment of the present application;

[0041] Figure 2 The flowchart of a source network load storage decentralized collaborative dispatch method provided by the embodiment of the present application. DETAILED DESCRIPTION

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting upon the scope of the application. For example, the terms "length", "width", "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like, merely describe the orientation in the drawings on which the application is illustrated and are not intended to be limiting upon the scope of the application. The terms "comprising", "having", "including", and "containing" and any variations thereof in the present specification are intended to cover a non-exclusive inclusion such that a process, method, article, composition or apparatus that comprises, has, includes or contains a list of elements can include additional elements not expressly listed or inherent to such process, method, article, composition or apparatus. The terms "first", "second", and the like, used in the description and in the claims, do not necessarily denote any ordinal, sequential, or temporal significance, but are used to distinguish one element from another. When an element is referred to as being "connected to" or "coupled to" another element, it can be directly or indirectly connected or coupled to the other element or intervening elements can be present. In contrast, when an element is referred to as being "directly connected to" or "directly coupled to" another element, it is meant that no intervening element is present. The terms "comprise", "comprising", "include", "including", "contain", "containing" and any variations thereof are intended to cover a non-exclusive inclusion such that a process, method, article, composition or apparatus that comprises, has, includes or contains a list of elements can include additional elements not expressly listed or inherent to such process, method, article, composition or apparatus. The terms "first", "second", and the like, used in the description and in the claims, do not necessarily denote any ordinal, sequential, or temporal significance, but are used to distinguish one element from another. The terms "fixed", "mounted", "set", or "connected" and any variations thereof, used in the specification and in the claims, mean that an element can be directly or indirectly connected to another element or intervening elements can be present. In contrast, the terms "directly fixed", "directly mounted", "directly set", or "directly connected" and any variations thereof, mean that no intervening element is present. The term "exemplary" is used herein to mean "serving as an example, instance, or illustration". Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations. Unless otherwise indicated, the terms "plurality" and "a plurality" as used herein refer to two or more.

[0043] The application provides a source network load storage decentralized collaborative scheduling system, which is applied to a new power system containing a high proportion of renewable resources and multiple operation subjects. The source network load storage decentralized collaborative scheduling system comprises:

[0044] A model predictive control optimization module is configured to solve a preset multi-objective optimization model in each scheduling control cycle according to real-time collected system state data and prediction information, and output a coordinated scheduling plan including power supply, power grid, load and energy storage control instructions; a blockchain coordination network is a permission-based consortium chain, consensus nodes of which include a plurality of operation subjects in the power system, and is configured to provide a decentralized and tamper-proof trusted channel for data interaction and instruction issuance among the subjects; an intelligent contract module is deployed on the blockchain coordination network, and includes at least one intelligent contract for managing the coordinated scheduling process, the intelligent contract being coded with scheduling instruction validity verification rules, execution result evaluation rules and benefit distribution rules based on the evaluation result; and a data bridging and execution module is connected with the model predictive control optimization module, the blockchain coordination network and underlying physical devices, and is configured to encapsulate the coordinated scheduling plan as a transaction and submit it to the blockchain, trigger instruction execution according to the intelligent contract logic, and distribute the final control instruction confirmed by the blockchain consensus to the corresponding physical device execution unit.

[0045] The present application combines centralized advanced optimization algorithms with distributed trusted execution mechanisms, solves the problems of trust loss and information asymmetry in multi-stakeholder coordination, and realizes efficient, transparent and automated decentralized coordinated scheduling.

[0046] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings.

[0047] The present application provides a source network load storage decentralized coordinated scheduling system, which is applied to a new type of power system including a high proportion of renewable resources and multiple operation subjects, as shown in Figure 1 As shown in the figure, in the embodiment, the source network load storage decentralized coordinated scheduling system includes:

[0048] A model predictive control (MPC) optimization module: the module is deployed on the cloud platform of the regional dispatching center, continuously receives data from the wide area measurement system (WAMS), the new energy power prediction system and the load prediction system, starts rolling optimization once every 5 minutes (one control cycle), the optimization model has a look-ahead period of 4 hours (48 time periods), the target is to minimize the total operation cost of the system (including fuel cost of thermal power, penalty cost of abandoned wind and light, energy storage loss cost, etc.), and to meet the constraints of power balance, unit climbing, line power flow, energy storage SOC, etc. A high-efficiency quadratic programming (QP) solver is used for solving, and the output result is the set value of each controllable resource in the future 5 minutes. For example, A wind farm reduces the output by 50MW, B energy storage power station charges 30MW, and C industrial load aggregator increases the electricity consumption by 20MW.

[0049] Specifically, in this embodiment, a multi-objective optimization scheduling model is established with the minimum system operation cost, the highest new energy consumption rate and the lowest carbon emission as the targets.

[0050] The system operation cost includes new energy generation cost, conventional unit generation cost, energy storage charging and discharging cost, and network loss cost, and the expression is:

[0051]

[0052] Among them, is the new energy generation cost, is the conventional unit generation cost, is the energy storage charging and discharging cost, is the network loss cost.

[0053] The new energy consumption rate target function is:

[0054]

[0055] Among them, is the new energy consumption, is the total new energy generation.

[0056] The carbon emission target function is:

[0057]

[0058] Among them, represents the marginal carbon emission coefficient of the conventional unit closely related to the power generation, that is, the carbon emission generated by the additional unit power generation; is the fixed carbon emission coefficient of the conventional unit, which covers the basic carbon emission generated by the generator set during operation, even if the power generation is zero, for example, the carbon emission caused by the preheating of the equipment, the operation of the auxiliary system, etc. is the power generation of the conventional unit.

[0059] The constraint conditions include:

[0060] Power balance constraint: the total power generation of the system is equal to the total load power plus the network loss power.

[0061]

[0062] Among them, is the conventional unit power generation, is the new energy power generation, is the energy storage discharging power, is the load power, is the network loss power.

[0063] Unit output constraint: the power generation of conventional units is between its minimum and maximum output.

[0064]

[0065] Energy storage constraint: the charge and discharge power and SOC (State of Charge) of energy storage device are within a reasonable range.

[0066]

[0067]

[0068]

[0069] wherein, is the charge and discharge power of energy storage, and SOC is the state of charge of energy storage.

[0070] Grid safety constraint: including line flow constraint, node voltage constraint, etc.

[0071]

[0072]

[0073] wherein, is the flow of the kth line, is the voltage of the lth node.

[0074] Further, in this embodiment, since new energy generation and load have uncertainty, the traditional static scheduling method is difficult to meet the demand of real-time operation of the system. The model predictive control (MPC) method is adopted, and through rolling optimization and feedback correction, the real-time optimal scheduling of source-grid-load-storage is realized.

[0075] The basic principle of MPC is: in each control period, according to the current system state and the prediction information of a future period of time, an optimization problem in a finite time domain is solved to obtain the control quantity at the current time; in the next control period, the system state is updated according to the new measurement information, and the above process is repeated.

[0076] The specific steps are as follows:

[0077] Prediction model establishment: a prediction model of new energy power generation and load demand is established to predict the new energy power generation and load demand at N future time points.

[0078] Optimization problem construction: taking the minimum system operation cost as the target, considering the constraint conditions of the system, an optimization problem in a finite time domain is constructed.

[0079] Optimization solution: Use intelligent optimization algorithms such as particle swarm optimization algorithm and genetic algorithm to solve optimization problems and obtain the source-grid-load-storage operation scheme at the next N time points.

[0080] Control quantity implementation: Apply the control quantity at the current time to the system and collect the actual operation state of the system.

[0081] Feedback correction: According to the deviation between the actual operation state and the predicted state, the prediction model of the next control cycle is corrected to improve the prediction accuracy.

[0082] Further, in this embodiment, in order to solve the problems of information asymmetry and lack of trust among the interest subjects of source-grid-load-storage, blockchain technology is introduced to build a decentralized collaborative mechanism.

[0083] Blockchain has characteristics such as decentralization, non-tamperability, transparency and traceability, and can realize information sharing and trusted interaction among various interest subjects. The specific implementation is as follows:

[0084] 1) Alliance chain construction: The interest subjects such as power grid companies, new energy power generation enterprises, load aggregators, and energy storage operators jointly participate in the construction of the source-grid-load-storage collaborative operation alliance chain.

[0085] 2) Smart contract design: According to the rights and obligations of each interest subject, design a smart contract to clearly define the rules and benefit distribution mechanism of collaborative operation. When the preset conditions are met, the smart contract is automatically executed to realize the automation and intelligentization of collaborative operation.

[0086] 3) Information on-chain storage: Store key data such as new energy generation plan, load prediction data, energy storage charging and discharging information, and transaction information on the chain to ensure the authenticity and non-tamperability of the information.

[0087] 4) Consensus mechanism selection: Use a consensus mechanism suitable for alliance chains such as practical Byzantine fault tolerance algorithm to ensure that all nodes reach an agreement on the information on the blockchain.

[0088] Blockchain collaborative network: Use alliance chain architecture and build based on Hyperledger Fabric framework. There are four organizations (Organization) in the network, representing regional power grid companies (Org1), new energy power generation companies (Org2), load aggregators (Org3), and energy storage operators (Org4). Each organization maintains at least two peer nodes (Peer), which together form a channel (Channel) named "SGLS-Dispatch". The network uses Kafka ordering service and the consensus mechanism is the endorsement strategy of each organization in the channel.

[0089] Smart contract module: Chaincode (smart contract) named dispatch_cc, written in Go language, its main function logic includes:

[0090] SubmitPlan: Invoked by MPC optimization module (through its agent node) to submit the dispatch plan. The contract will verify whether the submitter's certificate belongs to the authorized list, and check whether the plan timestamp is in the future period.

[0091] VerifyAndCommit: Triggered by a time oracle or a specific transaction 1 minute before the plan execution time, the contract will set the verified plan state to "confirmed", and generate a DispatchOrder event containing detailed instructions.

[0092] SubmitFeedback: Invoked by each execution subject to submit its actual execution data (such as actual output, actual power consumption). The contract will compare the feedback data with the original plan.

[0093] CalculateSettlement: Automatically executed at the end of each settlement period (such as daily), according to the comparison result, the pre-set reward and punishment formula (such as 200 yuan reward for each additional 1MWh of actual consumption of new energy, and 300 yuan penalty for each 1MW of under-executed load adjustment) is used to calculate the settlement amount of each subject, and the on-chain account balance is updated.

[0094] Data bridging and execution module includes:

[0095] Plan on-chain unit: As an adapter between MPC module and blockchain network. When the MPC module generates a new plan, this unit serializes it into JSON format, calls the SDK of the organization (Org1) to which the MPC module belongs, initiates a SubmitPlan transaction, and waits for the endorsement and ordering confirmation of the blockchain network.

[0096] Instruction listening and parsing unit: Running on the application node of each participating organization. This unit subscribes to the DispatchOrder event on the blockchain through an event listener (Event Listener). Once the event is captured, the instructions that the organization needs to execute are parsed.

[0097] Instruction issuing unit: According to the parsing result, through a secure gateway and a power-specific communication protocol (such as IEC 104, Modbus TCP or MQTT), control instructions are issued to specific station energy management systems (EMS), load control terminals or energy storage converters (PCS).

[0098] The embodiment of the application also provides a source network load storage decentralized collaborative dispatching method, which is applied to the source network load storage decentralized collaborative dispatching system described in the above embodiment.Figure 2 As shown, in this embodiment, the source network load decentralized collaborative scheduling method (taking a typical scheduling period from time T to time T+5mins as an example) includes the following steps:

[0099] Step A (time T): the MPC optimization module is started, based on the system measured state at time T (obtained from an off-chain database or through an on-chain oracle), as well as future wind speed, illumination, load prediction, a rolling optimization problem is solved to generate a scheduling plan Plan_T from time T to time T+5min.

[0100] Step B (T+0.5min): the plan on-chain unit in the data bridge and execution module packs Plan_T, attaches the timestamp at time T and the digital signature of the scheduling center, constructs a transaction Tx_Plan, and submits it to the blockchain collaborative network. The endorsement nodes of each organization in the network execute the SubmitPlan function in the smart contract dispatch_cc according to the endorsement strategy (for example, requiring Org1 and at least one other Org to endorse) in the smart contract, verify the signature and time validity, and the transaction that passes the verification is sorted, packaged into a block and chained. At this time, Plan_T is in the state of "submitted" on the chain.

[0101] Step C (T+4.5min, i.e. 1 minute before execution): a chain timing task triggers the VerifyAndCommit function, which checks the current time and confirms that Plan_T is approaching the execution window and no higher priority instruction will override it, and then updates its state to "confirmed". Subsequently, the smart contract automatically issues a DispatchOrder event, which is written into a block, indicating that the instruction has reached a global consensus and has the force of execution.

[0102] Step D (T+4.5min to T+5min): the instruction listening and analysis unit of each participating organization listens to the DispatchOrder event. For example, the node of Org2 parses "require wind farm Alpha to set the output to 80MW at time T+5min", and the instruction issuing unit immediately issues this set value instruction to the local controller of wind farm Alpha through a secure channel.

[0103] Step E (T+5min to T+10min): physical devices execute instructions. During the execution period from T+5min to T+10min, the monitoring system of each subject records the actual execution curve.

[0104] Step F (T+10min): Each principal calls the SubmitFeedback function to chain the actual execution data (authenticated by a local metering device) in the period from T+5min to T+10min. For example, wind farm Alpha actually uploads "average output 79.8MW".

[0105] Step G (0 o'clock every day): The smart contract automatically executes the CalculateSettlement function, traverses all executed plans and feedbacks of the past day, compares and calculates the deviation, generates a final settlement list according to the deviation and the preset reward and punishment unit price, and updates the on-chain account. All organizations can query the unalterable settlement result.

[0106] Through the above process, the embodiment of the application realizes the full-process decentralization, automation and trust of scheduling from optimization decision, instruction issuance, execution to settlement, and provides core technical support for building an open and shared new power system operation ecosystem.

[0107] Taking a new power system in a certain region as an example, the key technologies of source-grid-load-storage collaborative operation and planning proposed in this paper are verified. The new energy in this region mainly includes wind power and photovoltaic power, and the installed capacity is 1000MW and 800MW respectively; the load mainly includes industrial load, commercial load and residential load, and the maximum load is 1500MW; the energy storage system adopts lithium battery energy storage, and the planned installed capacity is 500MW / 1000MWh.

[0108] The source-grid-load-storage decentralized collaborative scheduling method provided by the embodiment of the application is used to control the collaborative operation of the source-grid-load-storage system in the region. The operation results are shown in Table 1.

[0109] Table 1

[0110] Index Traditional operation mode Method of the embodiment of the application New energy consumption rate (%) 82.5 95.3 System operation cost (ten thousand yuan / day) 520 450 Wind curtailment rate (%) 10.2 3.1 Light curtailment rate (%) 8.5 2.7

[0111] As can be seen from Table 1, compared with the traditional operation mode, after using the method of the embodiment of the application, the new energy consumption rate is increased by 12.8%, the system operation cost is reduced by 13.5%, and the wind power abandonment rate and the light power abandonment rate are reduced by 7.1% and 5.8% respectively, which indicates that the source-grid-load-storage decentralized collaborative scheduling method proposed in the embodiment of the application can effectively improve the economy and new energy consumption capacity of the system.

[0112] Compared with the prior art, the source network load decentralized collaborative scheduling system and method described in the above embodiments cannot integrate off-chain efficient optimization and on-chain reliable execution when the blockchain technology is applied in the prior art power system scheduling, resulting in lack of trust and information asymmetry in multi-stakeholder collaboration. The present application changes the scheduling mode from "central command-passive execution" to "rule consensus-automatic execution" through blockchain and smart contract, greatly improving the collaboration trust and collaboration efficiency among multiple subjects, reducing the communication and dispute costs; the present application creatively distinguishes the two levels of "optimization calculation" and "reliable execution", MPC ensures the technical optimality of scheduling, and blockchain ensures the public trust of the process and results, the two complement each other, achieving the effect of "1+1>2". All scheduling instructions, execution evidence and evaluation results in the present application are chained, publicly checked, achieving unprecedented transparency. The automatic settlement driven by the smart contract ensures the instant, accurate and fair distribution of benefits. The decentralized architecture of the present application makes it more convenient for new market subjects (such as virtual power plants and electric vehicle aggregation platforms) to access and participate in system scheduling, enhancing the overall flexibility and elasticity of the power system.

[0113] Obviously, the above-described embodiments are only the preferred embodiments of the present application, not all the embodiments, and the preferred embodiments of the present application are given in the drawings, but do not limit the patent scope of the present application. The present application can be realized in many different forms, and on the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing specific embodiments, or make equivalent replacements to some technical features. Any equivalent structure made by using the content of the present application specification and drawings, directly or indirectly applied in other related technical fields, is also within the patent protection scope of the present application.

Claims

1. A decentralized collaborative scheduling system for source-grid-load-storage, characterized in that, include: The model predictive control optimization module is used to solve the preset multi-objective optimization model in each scheduling control cycle based on the real-time collected system status data and prediction information, and output a coordinated scheduling plan that includes control commands for power supply, power grid, load and energy storage. The blockchain collaborative network is a permissioned consortium blockchain whose consensus nodes include multiple operating entities in the power system, providing a decentralized, tamper-proof, and trusted channel for data interaction and instruction issuance among these entities. The smart contract module, deployed on the blockchain collaborative network, includes at least one smart contract for managing the collaborative scheduling process. The smart contract is encoded with scheduling instruction validity verification rules, execution result assessment rules, and profit distribution rules based on the assessment results. The data bridging and execution module is connected to the model prediction control optimization module, the blockchain collaborative network, and the underlying physical devices, respectively. It is used to encapsulate the collaborative scheduling plan into a transaction and submit it to the blockchain, trigger the execution of instructions according to the smart contract logic, and distribute the final control instructions confirmed by the blockchain consensus to the corresponding physical device execution units.

2. The decentralized collaborative scheduling system for source-grid-load-storage as described in claim 1, characterized in that, The rolling solution process of the model predictive control optimization module includes: at the beginning of each control cycle, based on the latest system state and future short-term predictions, solving a finite-time domain optimization problem to obtain the optimal control sequence, and outputting the control command corresponding to the current moment in the sequence.

3. The decentralized collaborative scheduling system for source-grid-load-storage according to claim 1, characterized in that, The blockchain collaborative network adopts a Byzantine fault-tolerant consensus algorithm or a variant thereof, and the consensus nodes include server nodes representing power grid dispatching agencies, renewable energy generators, adjustable load aggregators, and energy storage operators.

4. The decentralized collaborative scheduling system for source-grid-load-storage as described in claim 1, characterized in that, The smart contracts in the smart contract module are specifically used to execute the following logic: Verify the compliance of the scheduling plan transaction format and the validity of the digital signature submitted by the data bridging and execution module; When a preset time point or condition is met, the verified scheduling instruction will be automatically marked as pending execution and broadcast. It receives feedback data from each consensus node regarding the results of instruction execution and performs verification and scoring according to preset assessment rules; Based on the verification results, the system automatically calculates the revenue that each entity should receive or should pay and generates settlement transactions.

5. The decentralized collaborative scheduling system for source-grid-load-storage according to claim 1, characterized in that, The data bridging and execution module specifically includes: The on-chain planning unit is used to receive the scheduling plan from the model prediction control optimization module, attach a timestamp and the initiator's signature, construct and broadcast a plan submission transaction to the blockchain network; The instruction monitoring and parsing unit is used to monitor instruction execution events issued by the smart contract on the blockchain and parse out the specific control instructions and target devices. The command issuing unit is used to send the parsed control commands to the corresponding power plant control system, load control terminal or energy storage management system through a secure communication protocol.

6. The decentralized collaborative scheduling system for source-grid-load-storage according to claim 1, characterized in that, It also includes an on-chain data oracle, which is used to inject trusted real-time operational data or prediction data from outside the blockchain collaborative network into the blockchain collaborative network after verification by multiple nodes, so that the smart contract or the model prediction control optimization module can call it.

7. A method applied to the system according to any one of claims 1 to 6, characterized in that, Includes the following steps: S10. In each scheduling control cycle, the model predictive control optimization module solves the optimization problem on a rolling basis based on the real-time system status and prediction data, and generates a collaborative scheduling plan. S20. The data bridging and execution module submits the collaborative scheduling plan as a transaction to the blockchain collaborative network; S30. The smart contract deployed on the blockchain collaborative network automatically verifies and confirms the transaction. When the execution conditions are met, it generates an execution event containing control instructions and achieves network-wide consensus. S40. The execution nodes of each participating entity listen to and capture the execution events, and the data bridging and execution module parses the consensus instructions and sends them to the corresponding physical devices for execution. S50. After the physical device executes the command, its monitoring node submits the execution result data as a feedback transaction to the blockchain. S60. The smart contract automatically executes the assessment and settlement logic based on the plan and feedback data stored on the chain, completing the closed loop of the scheduling cycle.

8. The method according to claim 7, characterized in that, The objective function of the optimization problem in S10 includes minimizing the total operating cost of the system and maximizing the renewable energy absorption rate.

9. The method according to claim 8, characterized in that, The constraints of the objective function include power balance, equipment operating limits, and power grid security constraints.

10. The method according to claim 7, characterized in that, The confirmation process of the smart contract in S30 includes checking whether the scheduling plan complies with the pre-agreed market rules or security boundaries, and whether the initiator has the corresponding permissions.

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